Better description of white noise.

01-g-h-filter.ipynb

@@ -329,7 +329,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x43406a0>"
+       "<matplotlib.figure.Figure at 0x40ef6a0>"
       ]
      },
      "metadata": {},
@@ -365,7 +365,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x4340668>"
+       "<matplotlib.figure.Figure at 0x40ef668>"
       ]
      },
      "metadata": {},
@@ -397,7 +397,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x718e470>"
+       "<matplotlib.figure.Figure at 0x708d208>"
       ]
      },
      "metadata": {},
@@ -433,7 +433,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 165.0006\n"
+      "Average of measurements is 165.0192\n"
      ]
     }
    ],
@@ -464,7 +464,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 164.9314\n"
+      "Average of measurements is 165.0368\n"
      ]
     }
    ],
@@ -497,7 +497,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x4340358>"
+       "<matplotlib.figure.Figure at 0x40ef5c0>"
       ]
      },
      "metadata": {},
@@ -535,7 +535,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x4340160>"
+       "<matplotlib.figure.Figure at 0x40ef3c8>"
       ]
      },
      "metadata": {},
@@ -564,7 +564,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7354080>"
+       "<matplotlib.figure.Figure at 0x72071d0>"
       ]
      },
      "metadata": {},
@@ -595,7 +595,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x72f7898>"
+       "<matplotlib.figure.Figure at 0x72bc438>"
       ]
      },
      "metadata": {},
@@ -627,7 +627,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x743d320>"
+       "<matplotlib.figure.Figure at 0x7266518>"
       ]
      },
      "metadata": {},
@@ -662,7 +662,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x73b61d0>"
+       "<matplotlib.figure.Figure at 0x7272550>"
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      },
      "metadata": {},
@@ -691,7 +691,7 @@
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7201f60>"
+       "<matplotlib.figure.Figure at 0x72d5278>"
       ]
      },
      "metadata": {},
@@ -733,7 +733,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7387198>"
+       "<matplotlib.figure.Figure at 0x714f4a8>"
       ]
      },
      "metadata": {},
@@ -794,7 +794,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6be4cc0>"
+       "<matplotlib.figure.Figure at 0x70cf4e0>"
       ]
      },
      "metadata": {},
@@ -871,7 +871,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6sAAAEmCAYAAACEUwaqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x720ccc0>"
+       "<matplotlib.figure.Figure at 0x711c518>"
       ]
      },
      "metadata": {},
@@ -913,7 +913,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6kAAAEmCAYAAACAptaXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8596550>"
+       "<matplotlib.figure.Figure at 0x8481048>"
       ]
      },
      "metadata": {},
@@ -954,7 +954,7 @@
     "book_plots.plot_track([0, n], [160, 160+n], c='k', label='Actual weight')\n",
     "book_plots.plot_track(range(1, n+1), predictions, c='r', label='Predictions')\n",
     "book_plots.show_legend()\n",
-    "book_plots.set_labels(x='day', y='weight (lbs)')"
+    "book_plots.set_labels(x='day', y='weight (lbs)');"
    ]
   },
   {
@@ -1015,7 +1015,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x726bba8>"
+       "<matplotlib.figure.Figure at 0x711c9b0>"
       ]
      },
      "metadata": {},
@@ -1078,9 +1078,7 @@
     "\n",
     "$$\\mathbf{x} = \\begin{bmatrix}62 \\\\ 0.3\\end{bmatrix}$$\n",
     "\n",
-    "Finally, a hat '$\\hat{}$' indicates an *estimate*. So the output of the predict step time $k$ at is the *estimate* of our state, which we denote as $\\mathbf{\\hat{x}_k}$. \n",
-    "\n",
-    "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 $\\mathbf{\\hat{x}_k}$."
+    "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 $\\mathbf{x}_k$."
    ]
   },
   {
@@ -1423,7 +1421,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x718f208>"
+       "<matplotlib.figure.Figure at 0x8498278>"
       ]
      },
      "metadata": {},
@@ -1484,7 +1482,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now let's write a function that generates noisy data for us. Recall from chapter 0 (**author's note**: I have not yet written chapter 0!) that we model a noisy signal as the signal plus white noise generated by `numpy.random.randn()`.  We want a function that we call with the starting value, the amount of change per step, the number of steps, and the amount of noise we want to add. It should return a list of the data. Test it by creating 30 points, filtering it with `g_h_filter()`, and plot the results with `plot_g_h_results()`."
+    "Now let's write a function that generates noisy data for us. In this book I model a noisy signal as the signal plus [white noise](https://en.wikipedia.org/wiki/White_noise). We've not yet covered the statistics to fully understand the definition of white noise. In essence, think of it as data that randomly varies higher and lower than the signal with no pattern. We say that it is a serially uncorrelated random variable with zero mean and finite variance. If you don't follow that, you will by the end of the *Gaussians* chapter. You may not be successful at this exercise if you have no knowledge of statistics. If so, just read the solution and discussion.\n",
+    "\n",
+    "White noise can be generated by `numpy.random.randn()`. We want a function that we call with the starting value, the amount of change per step, the number of steps, and the amount of noise we want to add. It should return a list of the data. Test it by creating 30 points, filtering it with `g_h_filter()`, and plot the results with `plot_g_h_results()`."
    ]
   },
   {
@@ -1509,14 +1509,15 @@
    "cell_type": "code",
    "execution_count": 30,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5EAAAEPCAYAAADbIUClAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x857dac8>"
+       "<matplotlib.figure.Figure at 0x847da58>"
       ]
      },
      "metadata": {},
@@ -1533,6 +1534,47 @@
     "plot_g_h_results(measurements, data)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Discussion\n",
+    "\n",
+    "`randn()` returns random numbers centered around 0 - it is just as likely to be greater than zero as under zero. It varies by *one standard deviation* - don't worry if you don't know what that means. I've plotted 3000 calls to `randn()` - you can see that the values are centered around zero and mostly range from a bit under -1 to a bit more than +1, though occasionally that get much larger."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x72ca940>]"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuYAAAEWCAYAAAAq41LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x71918d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot([random.randn() for _ in range(3000)], lw=1)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1549,7 +1591,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {
     "collapsed": false
    },
@@ -1567,16 +1609,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "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 0x8580cf8>"
+       "<matplotlib.figure.Figure at 0x709a748>"
       ]
      },
      "metadata": {},
@@ -1612,7 +1654,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "metadata": {
     "collapsed": false
    },
@@ -1630,16 +1672,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6UAAAEPCAYAAAC6OWLnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x734a208>"
+       "<matplotlib.figure.Figure at 0x7313eb8>"
       ]
      },
      "metadata": {},
@@ -1677,7 +1719,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "metadata": {
     "collapsed": false
    },
@@ -1695,7 +1737,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "metadata": {
     "collapsed": false
    },
@@ -1704,7 +1746,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x74401d0>"
+       "<matplotlib.figure.Figure at 0x85fc9b0>"
       ]
      },
      "metadata": {},
@@ -1754,7 +1796,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 38,
    "metadata": {
     "collapsed": false
    },
@@ -1772,7 +1814,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 39,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -1782,7 +1824,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x86fb0b8>"
+       "<matplotlib.figure.Figure at 0x8664d68>"
       ]
      },
      "metadata": {},
@@ -1814,7 +1856,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 40,
    "metadata": {
     "collapsed": false
    },
@@ -1823,7 +1865,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x87706d8>"
+       "<matplotlib.figure.Figure at 0x86ab080>"
       ]
      },
      "metadata": {},
@@ -1859,7 +1901,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 41,
    "metadata": {
     "collapsed": false
    },
@@ -1868,7 +1910,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8777470>"
+       "<matplotlib.figure.Figure at 0x85ec358>"
       ]
      },
      "metadata": {},
@@ -1906,7 +1948,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 42,
    "metadata": {
     "collapsed": false
    },
@@ -1915,7 +1957,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x86f1ac8>"
+       "<matplotlib.figure.Figure at 0x8498278>"
       ]
      },
      "metadata": {},
@@ -1955,7 +1997,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 43,
    "metadata": {
     "collapsed": false
    },
@@ -1964,7 +2006,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6c7ff60>"
+       "<matplotlib.figure.Figure at 0x7140be0>"
       ]
      },
      "metadata": {},
@@ -2001,7 +2043,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 44,
    "metadata": {
     "collapsed": false
    },
@@ -2010,7 +2052,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x87abfd0>"
+       "<matplotlib.figure.Figure at 0x8663a20>"
       ]
      },
      "metadata": {},
@@ -2037,7 +2079,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 45,
    "metadata": {
     "collapsed": false
    },
@@ -2046,7 +2088,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x71e0e10>"
+       "<matplotlib.figure.Figure at 0x6b7d0f0>"
       ]
      },
      "metadata": {},
@@ -2100,7 +2142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 46,
    "metadata": {
     "collapsed": false
    },
@@ -2109,7 +2151,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x87b8908>"
+       "<matplotlib.figure.Figure at 0x7352eb8>"
       ]
      },
      "metadata": {},
@@ -2151,7 +2193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 47,
    "metadata": {
     "collapsed": false
    },
@@ -2160,7 +2202,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8719d30>"
+       "<matplotlib.figure.Figure at 0x8604438>"
       ]
      },
      "metadata": {},
@@ -2182,7 +2224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 48,
    "metadata": {
     "collapsed": false
    },
@@ -2191,7 +2233,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x747cd30>"
+       "<matplotlib.figure.Figure at 0x73868d0>"
       ]
      },
      "metadata": {},
@@ -2215,7 +2257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 49,
    "metadata": {
     "collapsed": false
    },
@@ -2224,7 +2266,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x87816d8>"
+       "<matplotlib.figure.Figure at 0x73e2dd8>"
       ]
      },
      "metadata": {},
@@ -2248,7 +2290,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 50,
    "metadata": {
     "collapsed": false
    },
@@ -2265,7 +2307,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 51,
    "metadata": {
     "collapsed": false
    },
@@ -2274,7 +2316,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x71956d8>"
+       "<matplotlib.figure.Figure at 0x73c0d68>"
       ]
      },
      "metadata": {},
@@ -2296,7 +2338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 52,
    "metadata": {
     "collapsed": false
    },
@@ -2305,7 +2347,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8564c50>"
+       "<matplotlib.figure.Figure at 0x711cf98>"
       ]
      },
      "metadata": {},
@@ -2351,7 +2393,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 53,
    "metadata": {
     "collapsed": true
    },
@@ -2372,7 +2414,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 54,
    "metadata": {
     "collapsed": false
    },
@@ -2383,7 +2425,7 @@
        "(0.96, 0.24)"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2401,7 +2443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 55,
    "metadata": {
     "collapsed": false
    },
@@ -2427,7 +2469,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 56,
    "metadata": {
     "collapsed": false
    },
@@ -2438,7 +2480,7 @@
        "(1.965, 0.375)"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2456,7 +2498,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 57,
    "metadata": {
     "collapsed": false
    },
@@ -2470,7 +2512,7 @@
        "       [ 4.9014,  0.7306]])"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2488,7 +2530,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 58,
    "metadata": {
     "collapsed": false
    },
@@ -2499,7 +2541,7 @@
        "(array([   3.8 ,   13.2 ,  101.64]), array([ 8.2 ,  9.8 ,  0.56]))"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }