Fixed bug in code.

Plotting 2 data sets, but only scaled one of them by 1000.

01-g-h-filter.ipynb

@@ -329,7 +329,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x5a630f0>"
+       "<matplotlib.figure.Figure at 0x4d7a198>"
       ]
      },
      "metadata": {},
@@ -364,7 +364,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x78ee710>"
+       "<matplotlib.figure.Figure at 0x6ca35c0>"
       ]
      },
      "metadata": {},
@@ -396,7 +396,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x80574e0>"
+       "<matplotlib.figure.Figure at 0x73def98>"
       ]
      },
      "metadata": {},
@@ -432,7 +432,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 165.0055\n"
+      "Average of measurements is 165.0121\n"
      ]
     }
    ],
@@ -463,7 +463,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 164.9421\n"
+      "Average of measurements is 165.0270\n"
      ]
     }
    ],
@@ -496,7 +496,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x5a4bef0>"
+       "<matplotlib.figure.Figure at 0x7429048>"
       ]
      },
      "metadata": {},
@@ -534,7 +534,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x5a4b8d0>"
+       "<matplotlib.figure.Figure at 0x7427ef0>"
       ]
      },
      "metadata": {},
@@ -563,7 +563,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x824aeb8>"
+       "<matplotlib.figure.Figure at 0x7590400>"
       ]
      },
      "metadata": {},
@@ -594,7 +594,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x82c7cf8>"
+       "<matplotlib.figure.Figure at 0x7623470>"
       ]
      },
      "metadata": {},
@@ -626,7 +626,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x81f1d30>"
+       "<matplotlib.figure.Figure at 0x75cb588>"
       ]
      },
      "metadata": {},
@@ -661,7 +661,7 @@
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8137b70>"
+       "<matplotlib.figure.Figure at 0x6c6d128>"
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      },
      "metadata": {},
@@ -690,7 +690,7 @@
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x81a4400>"
+       "<matplotlib.figure.Figure at 0x75e8ba8>"
       ]
      },
      "metadata": {},
@@ -732,7 +732,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x80e6588>"
+       "<matplotlib.figure.Figure at 0x74383c8>"
       ]
      },
      "metadata": {},
@@ -790,7 +790,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x83149e8>"
+       "<matplotlib.figure.Figure at 0x78da080>"
       ]
      },
      "metadata": {},
@@ -867,7 +867,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x856e160>"
+       "<matplotlib.figure.Figure at 0x7914160>"
       ]
      },
      "metadata": {},
@@ -909,7 +909,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7972160>"
+       "<matplotlib.figure.Figure at 0x757c3c8>"
       ]
      },
      "metadata": {},
@@ -1010,7 +1010,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x82fbeb8>"
+       "<matplotlib.figure.Figure at 0x766e390>"
       ]
      },
      "metadata": {},
@@ -1417,7 +1417,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5kAAAEPCAYAAADI9gBRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x81cac88>"
+       "<matplotlib.figure.Figure at 0x73dea58>"
       ]
      },
      "metadata": {},
@@ -1508,9 +1508,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x806f390>"
+       "<matplotlib.figure.Figure at 0x74b6710>"
       ]
      },
      "metadata": {},
@@ -1568,9 +1568,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8230550>"
+       "<matplotlib.figure.Figure at 0x7589e10>"
       ]
      },
      "metadata": {},
@@ -1631,9 +1631,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9739a58>"
+       "<matplotlib.figure.Figure at 0x77b3828>"
       ]
      },
      "metadata": {},
@@ -1698,7 +1698,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x974bac8>"
+       "<matplotlib.figure.Figure at 0x77d0550>"
       ]
      },
      "metadata": {},
@@ -1776,7 +1776,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x97c4358>"
+       "<matplotlib.figure.Figure at 0x7837780>"
       ]
      },
      "metadata": {},
@@ -1817,7 +1817,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9dd1400>"
+       "<matplotlib.figure.Figure at 0x7843dd8>"
       ]
      },
      "metadata": {},
@@ -1862,7 +1862,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x95ce2b0>"
+       "<matplotlib.figure.Figure at 0x759d518>"
       ]
      },
      "metadata": {},
@@ -1909,7 +1909,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x81c0390>"
+       "<matplotlib.figure.Figure at 0x74b2668>"
       ]
      },
      "metadata": {},
@@ -1958,7 +1958,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x80c9d30>"
+       "<matplotlib.figure.Figure at 0x74069e8>"
       ]
      },
      "metadata": {},
@@ -2004,7 +2004,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x97c6588>"
+       "<matplotlib.figure.Figure at 0x9145cc0>"
       ]
      },
      "metadata": {},
@@ -2024,9 +2024,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "I brilliantly extracted a straight line out of very noisy data! Maybe I shouldn't try to collect my Fields Medal in mathematics just yet. I did this by setting both $g$ and $h$ to 0. What does this do? It makes the filter ignore the measurements, and so for each update it computes the new position as $x + \\Delta x \\Delta t$. Of course the result is a straight line if we ignore the measurements. But a filter that ignores the measurements is useless.\n",
+    "I brilliantly extracted a straight line out of very noisy data! Maybe I shouldn't try to collect my Fields Medal in mathematics just yet. I did this by setting both $g$ and $h$ to 0. What does this do? It makes the filter ignore the measurements, and so for each update it computes the new position as $x + \\Delta x \\Delta t$. Of course the result is a straight line if we ignore the measurements. \n",
     "\n",
-    "You would never set $g$ and $h$ to zero as that takes a special kind of brilliance that only I possess, but I promise that if you are not careful you will set them lower than they should be. You can always make great looking results from test data. When you try your filter on different data you will be disappointed in the results because you finely tuned the constants for a specific data set. $g$ and $h$ must reflect the real world behavior of the system you are filtering, not the behavior of one specific data set. In later chapters we will learn a lot about how to do that. For now I can only say be careful, or you will be getting perfect results with your test data, but results like this once you switch to real data:"
+    "A filter that ignores measurements is useless. I know you would never set both $g$ and $h$ to zero as that takes a special kind of genius that only I possess, but I promise that if you are not careful you will set them lower than they should be. You can always make great looking results from test data. When you try your filter on different data you will be disappointed in the results because you finely tuned the constants for a specific data set. $g$ and $h$ must reflect the real world behavior of the system you are filtering, not the behavior of one specific data set. In later chapters we will learn a lot about how to do that. For now I can only say be careful, or you will be getting perfect results with your test data, but results like this once you switch to real data:"
    ]
   },
   {
@@ -2040,7 +2040,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x831d1d0>"
+       "<matplotlib.figure.Figure at 0x7661be0>"
       ]
      },
      "metadata": {},
@@ -2100,7 +2100,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8100f98>"
+       "<matplotlib.figure.Figure at 0x7911080>"
       ]
      },
      "metadata": {},
@@ -2149,9 +2149,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6sAAAEjCAYAAADUnpcZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA54AAAEjCAYAAABaRN5lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x97c84e0>"
+       "<matplotlib.figure.Figure at 0x73edd30>"
       ]
      },
      "metadata": {},
@@ -2161,7 +2161,7 @@
    "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, data/1000., 'g=0.01, h=0.0001', z_label='Measurements')"
+    "plot_g_h_results(zs/1000, data/1000., 'g=0.01, h=0.0001', z_label='Measurements')"
    ]
   },
   {
@@ -2182,7 +2182,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA54AAAEjCAYAAABaRN5lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x806b780>"
+       "<matplotlib.figure.Figure at 0x77e8c18>"
       ]
      },
      "metadata": {},
@@ -2215,7 +2215,7 @@
      "data": {
       "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x97660f0>"
+       "<matplotlib.figure.Figure at 0x8a74208>"
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@@ -2265,7 +2265,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x976e128>"
+       "<matplotlib.figure.Figure at 0x8a8dda0>"
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      },
      "metadata": {},
@@ -2296,7 +2296,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x80f7d68>"
+       "<matplotlib.figure.Figure at 0x8adb828>"
       ]
      },
      "metadata": {},