Fixed off by one error.

01_g-h_filter.ipynb

@@ -324,7 +324,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6e97400>"
+       "<matplotlib.figure.Figure at 0x7037470>"
       ]
      },
      "metadata": {},
@@ -359,7 +359,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6ee2860>"
+       "<matplotlib.figure.Figure at 0x7037390>"
       ]
      },
      "metadata": {},
@@ -391,7 +391,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x76b0cf8>"
+       "<matplotlib.figure.Figure at 0x783fef0>"
       ]
      },
      "metadata": {},
@@ -427,7 +427,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 164.9647\n"
+      "Average of measurements is 165.0096\n"
      ]
     }
    ],
@@ -458,7 +458,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 165.0147\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 0x502b390>"
+       "<matplotlib.figure.Figure at 0x51ab4a8>"
       ]
      },
      "metadata": {},
@@ -529,7 +529,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7772198>"
+       "<matplotlib.figure.Figure at 0x78fcf98>"
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      "metadata": {},
@@ -558,7 +558,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x784dd68>"
+       "<matplotlib.figure.Figure at 0x79e57f0>"
       ]
      },
      "metadata": {},
@@ -589,7 +589,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x77f4550>"
+       "<matplotlib.figure.Figure at 0x7a63358>"
       ]
      },
      "metadata": {},
@@ -621,7 +621,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x784a400>"
+       "<matplotlib.figure.Figure at 0x79e54a8>"
       ]
      },
      "metadata": {},
@@ -656,7 +656,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6f5b828>"
+       "<matplotlib.figure.Figure at 0x7835710>"
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      },
      "metadata": {},
@@ -685,7 +685,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x76e7240>"
+       "<matplotlib.figure.Figure at 0x784a438>"
       ]
      },
      "metadata": {},
@@ -727,7 +727,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x770e1d0>"
+       "<matplotlib.figure.Figure at 0x78a66d8>"
       ]
      },
      "metadata": {},
@@ -751,14 +751,14 @@
     "\n",
     "Let's just code that up and see the result when we test it against the series of weights from above. We have to take into account one other factor. Weight gain has units of lbs/time, so to be general we will need to add a time step $t$, which we will set to 1 (day). \n",
     "\n",
-    "I hand generated the weight data to correspond to a true starting weight of 160 lbs, and a weight gain of 1 lb per day. In other words one day one the true weight is 160lbs, on the second day the true weight is 161 lbs, and so on. \n",
+    "I hand generated the weight data to correspond to a true starting weight of 160 lbs, and a weight gain of 1 lb per day. In other words on the first day (day zero) the true weight is 160lbs, on the second day (day one, the first day of weighing) the true weight is 161 lbs, and so on. \n",
     "\n",
     "We need to make a guess for the initial weight to feed into the filter. It is too early to talk about strategies for making that initial guess/estimate, so for now I will just assume 159 lbs."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 24,
    "metadata": {
     "collapsed": false
    },
@@ -783,9 +783,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6sAAAEmCAYAAACEUwaqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7b0c9b0>"
+       "<matplotlib.figure.Figure at 0x7cc3828>"
       ]
      },
      "metadata": {},
@@ -828,7 +828,7 @@
     "    xs = list(range(n+1))\n",
     "    book_plots.plot_filter(xs, estimates, marker='o')\n",
     "    book_plots.plot_measurements(xs[1:], weights, c='b', label='Scale')\n",
-    "    book_plots.plot_track([1, n], [160, 160+n], c='k', label='Actual Weight')\n",
+    "    book_plots.plot_track([0, n], [160, 160+n], c='k', label='Actual Weight')\n",
     "    book_plots.plot_track(xs[1:], predictions, c='r', label='Predictions', marker='v')\n",
     "    book_plots.show_legend()\n",
     "    plt.xlabel('day')\n",
@@ -853,16 +853,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 21,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6sAAAEmCAYAAACEUwaqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6sAAAEmCAYAAACEUwaqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7783470>"
+       "<matplotlib.figure.Figure at 0x7d27550>"
       ]
      },
      "metadata": {},
@@ -895,16 +895,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 25,
    "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 0x6f314a8>"
+       "<matplotlib.figure.Figure at 0x8d765f8>"
       ]
      },
      "metadata": {},
@@ -942,7 +942,7 @@
     "days = np.linspace(1, n, n)\n",
     "book_plots.plot_filter(estimates)\n",
     "book_plots.plot_measurements(days, weights, c='b', label='Scale')\n",
-    "book_plots.plot_track([1, n], [160, 160+n], c='k', label='Actual weight')\n",
+    "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",
     "plt.xlabel('day')\n",