Merge branch 'master' of https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python

00-Preface.ipynb

@@ -27,6 +27,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from __future__ import division, print_function\n",
     "%matplotlib inline"
    ]
   },
@@ -514,7 +515,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "abddition:\n",
+      "addition:\n",
       " [[2. 4.]\n",
       " [6. 8.]]\n",
       "\n",
@@ -535,7 +536,7 @@
    "source": [
     "x = np.array([[1., 2.],\n",
     "              [3., 4.]])\n",
-    "print('abddition:\\n', x + x)\n",
+    "print('addition:\\n', x + x)\n",
     "print('\\nelement-wise multiplication\\n', x * x)\n",
     "print('\\nmultiplication\\n', np.dot(x, x))\n",
     "print('\\ndot is also a member of np.array\\n', x.dot(x))"
@@ -714,12 +715,14 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x22b94689be0>]"
+       "[<matplotlib.lines.Line2D at 0x24ec4f535c0>]"
       ]
      },
      "execution_count": 18,
@@ -972,6 +975,57 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.6.10"
+  },
+  "nbdime-conflicts": {
+   "local_diff": [
+    {
+     "diff": [
+      {
+       "diff": [
+        {
+         "diff": [
+          {
+           "key": 4,
+           "op": "addrange",
+           "valuelist": "10"
+          },
+          {
+           "key": 4,
+           "length": 1,
+           "op": "removerange"
+          }
+         ],
+         "key": 0,
+         "op": "patch"
+        }
+       ],
+       "key": "version",
+       "op": "patch"
+      }
+     ],
+     "key": "language_info",
+     "op": "patch"
+    }
+   ],
+   "remote_diff": [
+    {
+     "diff": [
+      {
+       "diff": [
+        {
+         "key": 0,
+         "length": 1,
+         "op": "removerange"
+        }
+       ],
+       "key": "version",
+       "op": "patch"
+      }
+     ],
+     "key": "language_info",
+     "op": "patch"
+    }
+   ]
   }
  },
  "nbformat": 4,

01-g-h-filter.ipynb

@@ -148,7 +148,7 @@
    "source": [
     "A word on how I generated this plot. I import code from the module book_plots in the `kf_book` subdirectory. Generating this plot takes a lot of boilerplate Python that isn't interesting to read. I take this tack often in the book. When the cell is run `plot_errorbars()` gets called and the plot is inserted into the book.\n",
     "\n",
-    "If this is your first time using [Jupyter Notebook](http://jupyter.org/), the code above is in a *cell*. The text \"In [2]:\" labels this as a cell where you can enter input, and the number in the bracket denotes that this cell was run second. To run the cell, click on it with your mouse so that it has focus, then press CTRL+ENTER on the keyboard. As we continue you will be able to alter the code inside the cells and rerun them. Try changing the values \"160\", \"170\", and \"10000\" to some other value and run the cell. The printed output should change depending on what you entered.\n",
+    "If this is your first time using [Jupyter Notebook](http://jupyter.org/), the code above is in a *cell*. The text \"In [2]:\" labels this as a cell where you can enter input, and the number in the bracket denotes that this cell was run second. To run the cell, click on it with your mouse so that it has focus, then press CTRL+ENTER on the keyboard. As we continue you will be able to alter the code inside the cells and rerun them. Try changing the values \"160\", \"170\", and \"8\" to some other value and run the cell. The printed output should change depending on what you entered.\n",
     "\n",
     "If you want to view the code for plot_errorbars, either open it in an editor, or create a new cell and type the function name followed by two question marks. Press Ctrl+Enter, and your browser will open a window displaying the source code. This is a feature of Jupyter Notebooks. If you want to just view the documentation for the function, do the same but with one question mark.\n",
     "\n",
@@ -245,7 +245,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 165.0223\n"
+      "Average of measurements is 164.9852\n"
      ]
     }
    ],
@@ -276,7 +276,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 165.0166\n"
+      "Average of measurements is 165.0120\n"
      ]
     }
    ],
@@ -583,18 +583,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "previous: 160.00, prediction: 161.00 estimate 159.80\n",
-      "previous: 159.80, prediction: 160.80 estimate 162.16\n",
-      "previous: 162.16, prediction: 163.16 estimate 162.02\n",
-      "previous: 162.02, prediction: 163.02 estimate 161.77\n",
-      "previous: 161.77, prediction: 162.77 estimate 162.50\n",
-      "previous: 162.50, prediction: 163.50 estimate 163.94\n",
-      "previous: 163.94, prediction: 164.94 estimate 166.80\n",
-      "previous: 166.80, prediction: 167.80 estimate 167.64\n",
-      "previous: 167.64, prediction: 168.64 estimate 167.75\n",
-      "previous: 167.75, prediction: 168.75 estimate 169.65\n",
-      "previous: 169.65, prediction: 170.65 estimate 170.87\n",
-      "previous: 170.87, prediction: 171.87 estimate 172.16\n"
+      "previous estimate: 160.00, prediction: 161.00, estimate 159.80\n",
+      "previous estimate: 159.80, prediction: 160.80, estimate 162.16\n",
+      "previous estimate: 162.16, prediction: 163.16, estimate 162.02\n",
+      "previous estimate: 162.02, prediction: 163.02, estimate 161.77\n",
+      "previous estimate: 161.77, prediction: 162.77, estimate 162.50\n",
+      "previous estimate: 162.50, prediction: 163.50, estimate 163.94\n",
+      "previous estimate: 163.94, prediction: 164.94, estimate 166.80\n",
+      "previous estimate: 166.80, prediction: 167.80, estimate 167.64\n",
+      "previous estimate: 167.64, prediction: 168.64, estimate 167.75\n",
+      "previous estimate: 167.75, prediction: 168.75, estimate 169.65\n",
+      "previous estimate: 169.65, prediction: 170.65, estimate 170.87\n",
+      "previous estimate: 170.87, prediction: 171.87, estimate 172.16\n"
      ]
     }
    ],
@@ -608,30 +608,29 @@
     "time_step = 1.0  # day\n",
     "scale_factor = 4.0/10\n",
     "\n",
-    "def predict_using_gain_guess(weight, gain_rate, do_print=False):     \n",
-    "    # store the filtered results\n",
-    "    estimates, predictions = [weight], []\n",
+    "def predict_using_gain_guess(estimated_weight, gain_rate, do_print=False):     \n",
+    "    # storage for the filtered results\n",
+    "    estimates, predictions = [estimated_weight], []\n",
     "\n",
     "    # most filter literature uses 'z' for measurements\n",
     "    for z in weights: \n",
     "        # predict new position\n",
-    "        prediction = weight + gain_rate * time_step\n",
+    "        predicted_weight = estimated_weight + gain_rate * time_step\n",
     "\n",
     "        # update filter \n",
-    "        weight = prediction + scale_factor * (z - prediction)\n",
+    "        estimated_weight = predicted_weight + scale_factor * (z - predicted_weight)\n",
     "\n",
-    "        # save\n",
-    "        estimates.append(weight)\n",
-    "        predictions.append(prediction)\n",
+    "        # save and log\n",
+    "        estimates.append(estimated_weight)\n",
+    "        predictions.append(predicted_weight)\n",
     "        if do_print:\n",
-    "            gh.print_results(estimates, prediction, weight)\n",
+    "            gh.print_results(estimates, predicted_weight, estimated_weight)\n",
     "\n",
     "    return estimates, predictions\n",
     "\n",
-    "initial_guess = 160.\n",
-    "\n",
+    "initial_estimate = 160.\n",
     "estimates, predictions = predict_using_gain_guess(\n",
-    "    weight=initial_guess, gain_rate=1, do_print=True)     "
+    "    estimated_weight=initial_estimate, gain_rate=1, do_print=True)     "
    ]
   },
   {
@@ -643,7 +642,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 1000x400 with 1 Axes>"
       ]
@@ -655,7 +654,7 @@
    "source": [
     "# plot results\n",
     "book_plots.set_figsize(10)\n",
-    "gh.plot_gh_results(weights, estimates, predictions)"
+    "gh.plot_gh_results(weights, estimates, predictions, [160, 173])"
    ]
   },
   {
@@ -680,7 +679,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 1000x400 with 1 Axes>"
       ]
@@ -690,8 +689,8 @@
     }
    ],
    "source": [
-    "e, p = predict_using_gain_guess(initial_guess, -1.)\n",
-    "gh.plot_gh_results(weights, e, p)"
+    "e, p = predict_using_gain_guess(initial_estimate, -1.)\n",
+    "gh.plot_gh_results(weights, e, p, [160, 173])"
    ]
   },
   {
@@ -725,7 +724,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 1000x400 with 1 Axes>"
       ]
@@ -758,14 +757,14 @@
     "  \n",
     "    estimates.append(weight)\n",
     "\n",
-    "gh.plot_gh_results(weights, estimates, predictions)"
+    "gh.plot_gh_results(weights, estimates, predictions, [160, 173])"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "I think this is starting to look really good. Because of the poor initial guess of the weight gain being -1 it takes the filter several days to accurately predict the weight, but once it does that it starts to accurately track the weight. We used no methodology for choosing our scaling factors of $\\frac{4}{10}$ and $\\frac{1}{3}$ (actually, they are poor choices for this problem), but otherwise all of the math followed from very reasonable assumptions. Recall that you can change the value of the parameter `time_step` to a larger value and re-run the cell if you want to see the plot drawn step-by-step.\n",
+    "I think this is starting to look really good. Because of the poor initial guess of the weight gain being 1 it takes the filter several days to accurately predict the weight, but once it does that it starts to accurately track the weight. We used no methodology for choosing our scaling factors of $\\frac{4}{10}$ and $\\frac{1}{3}$ (actually, they are poor choices for this problem), but otherwise all of the math followed from very reasonable assumptions. Recall that you can change the value of the parameter `time_step` to a larger value and re-run the cell if you want to see the plot drawn step-by-step.\n",
     "\n",
     "One final point before we go on. In the prediction step I wrote the line\n",
     "```python\n",
@@ -830,7 +829,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let me introduce some more formal terminology. The *system* is the object that we want to estimate. In this chapter the system is whatever we are trying to weigh. Some texts call this the *plant*. That terminology comes from control system theory. https://en.wikipedia.org/wiki/Plant_(control_theory%29\n",
+    "Let me introduce some more formal terminology. The *system* is the object that we want to estimate. In this chapter the system is whatever we are trying to weigh. Some texts call this the *plant*. That terminology comes from control system theory. https://en.wikipedia.org/wiki/Plant_(control_theory)\n",
     "\n",
     "The *state* of the system is the current configuration or values of that system that is of interest to us. We are interested only in the weight reading. If I put a 100 kg weight on the scale, the state is 100kg. We define the state based on what is relevant to us. The color of the scale is irrelevant to us so we do not include those values in the state. A QA engineer for the manufacturer might include color in the state so that she can track and control the manufacturing process. \n",
     "\n",
@@ -932,6 +931,7 @@
    },
    "outputs": [],
    "source": [
+    "from kf_book.gh_internal import plot_g_h_results\n",
     "def g_h_filter(data, x0, dx, g, h, dt):\n",
     "    pass #  your solution here\n",
     "\n",
@@ -967,7 +967,6 @@
     }
    ],
    "source": [
-    "from kf_book.gh_internal import plot_g_h_results\n",
     "import matplotlib.pylab as pylab\n",
     "\n",
     "def g_h_filter(data, x0, dx, g, h, dt=1.):\n",
@@ -1058,7 +1057,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 1000x400 with 1 Axes>"
       ]
@@ -1095,7 +1094,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 1000x400 with 1 Axes>"
       ]
@@ -1149,7 +1148,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 1000x400 with 1 Axes>"
       ]
@@ -1212,7 +1211,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 1000x400 with 1 Axes>"
       ]
@@ -1533,13 +1532,9 @@
    "outputs": [
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a4cddaa19a974159882e8eafaa6975fe",
-       "version_major": 2,
-       "version_minor": 0
-      },
+      "image/png": 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+       "align_self": null,
+       "border": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "overflow_x": null,
+       "overflow_y": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
       }
      }
     },

03-Gaussians.ipynb

@@ -313,7 +313,7 @@
     "\n",
     "If $x$ is continuous we substitute the sum for an integral, like so\n",
     "\n",
-    "$$\\mathbb E[X] = \\int_{a}^b\\, f(x) \\,dx$$\n",
+    "$$\\mathbb E[X] = \\int_{a}^b\\, xf(x) \\,dx$$\n",
     "\n",
     "where $f(x)$ is the probability distribution function of $x$. We won't be using this equation yet, but we will be using it in the next chapter.\n",
     "\n",
@@ -328,7 +328,7 @@
     {
      "data": {
       "text/plain": [
-       "1.501364"
+       "1.49924"
       ]
      },
      "execution_count": 7,
@@ -375,7 +375,7 @@
     "\n",
     "$$f(x) = \\frac{1}{b - a}$$\n",
     "\n",
-    "compute the expected value for $a=0$ and $B=20$.\n",
+    "compute the expected value for $a=0$ and $b=20$.\n",
     "\n",
     "### Solution\n",
     "$$\\begin{aligned}\n",
@@ -572,7 +572,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -584,8 +584,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "mean = 1.802\n",
-      "std  = 0.148\n"
+      "mean = 1.796\n",
+      "std  = 0.143\n"
      ]
     }
    ],
@@ -614,7 +614,7 @@
     {
      "data": {
       "text/plain": [
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@@ -782,7 +782,7 @@
    "source": [
     "Is this \"correct\"? You tell me. Without the outlier of 100 we get $\\sigma^2=2.03$, which accurately reflects how $X$ is varying absent the outlier. The one outlier swamps the variance computation. Do we want to swamp the computation so we know there is an outlier, or robustly incorporate the outlier and still provide an estimate close to the value absent the outlier? Again, you tell me. Obviously it depends on your problem.\n",
     "\n",
-    "I will not continue down this path; if you are interested you might want to look at the work that James Berger has done on this problem, in a field called *Bayesian robustness*, or the excellent publications on *robust statistics* by Peter J. Huber [3]. In this book we will always use variance and standard deviation as defined by Gauss.\n",
+    "I will not continue down this path; if you are interested you might want to look at the work that James Berger has done on this problem, in a field called *Bayesian robustness*, or the excellent publications on *robust statistics* by Peter J. Huber [4]. In this book we will always use variance and standard deviation as defined by Gauss.\n",
     "\n",
     "The point to gather from this is that these *summary* statistics always tell an incomplete story about our data. In this example variance as defined by Gauss does not tell us we have a single large outlier. However, it is a powerful tool, as we can concisely describe a large data set with a few numbers. If we had 1 billion data points we would not want to inspect plots by eye or look at lists of numbers; summary statistics give us a way to describe the shape of the data in a useful way."
    ]
@@ -826,7 +826,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This curve is a [*probability density function*](https://en.wikipedia.org/wiki/Probability_density_function) or *pdf* for short. It shows the relative likelihood  for the random variable to take on a value. We can tell from the chart student is somewhat more likely to have a height near 1.8 m than 1.7 m, and far more likely to have a height of 1.9 m vs 1.4 m. Put another way, many students will have a height near 1.8 m, and very few students will have a height of 1.4 m or 1.9 meaters. Finally, notice that the curve is centered over the mean of 1.8 m.\n",
+    "This curve is a [*probability density function*](https://en.wikipedia.org/wiki/Probability_density_function) or *pdf* for short. It shows the relative likelihood  for the random variable to take on a value. We can tell from the chart student is somewhat more likely to have a height near 1.8 m than 1.7 m, and far more likely to have a height of 1.9 m vs 1.4 m. Put another way, many students will have a height near 1.8 m, and very few students will have a height of 1.4 m or 2.2 meters. Finally, notice that the curve is centered over the mean of 1.8 m.\n",
     "\n",
     "> I explain how to plot Gaussians, and much more, in the Notebook *Computing_and_Plotting_PDFs* in the \n",
     "Supporting_Notebooks folder. You can read it online [here](https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/Supporting_Notebooks/Computing_and_plotting_PDFs.ipynb) [1].\n",
@@ -1011,9 +1011,9 @@
     "\n",
     "$$M = \\iiint_R p(x,y,z)\\, dV$$\n",
     "\n",
-    "We do the same with *probability density*. If you want to know the temperature being between 20°C and 21°C kph you would integrate the curve above from 20 to 21. As you know the integral of a curve gives you the area under the curve. Since this is a curve of the probability density, the integral of the density is the probability. \n",
+    "We do the same with *probability density*. If you want to know the temperature being between 20°C and 21°C you would integrate the curve above from 20 to 21. As you know the integral of a curve gives you the area under the curve. Since this is a curve of the probability density, the integral of the density is the probability. \n",
     "\n",
-    "What is the probability of a the temperature being exactly 22°C? Intuitively, 0. These are real numbers, and the odds of 22°C vs, say, 22.00000000000017°C is infinitesimal. Mathematically, what would we get if we integrate from 22 to 22? Zero. \n",
+    "What is the probability of the temperature being exactly 22°C? Intuitively, 0. These are real numbers, and the odds of 22°C vs, say, 22.00000000000017°C is infinitesimal. Mathematically, what would we get if we integrate from 22 to 22? Zero. \n",
     "\n",
     "Thinking back to the rock, what is the weight of an single point on the rock? An infinitesimal point must have no weight. It makes no sense to ask the weight of a single point, and it makes no sense to ask about the probability of a continuous distribution having a single value. The answer for both is obviously zero.\n",
     "\n",
@@ -1062,7 +1062,7 @@
     "\n",
     "$$\\text{temp} \\sim \\mathcal{N}(22,4)$$\n",
     "\n",
-    "This is an extremely important result. Gaussians allow me to capture an infinite number of possible values with only two numbers! With the values $\\mu=22$ and $\\sigma^2=4$ I can compute the distribution of measurements for over any range.\n",
+    "This is an extremely important result. Gaussians allow me to capture an infinite number of possible values with only two numbers! With the values $\\mu=22$ and $\\sigma^2=4$ I can compute the distribution of measurements over any range.\n",
     "\n",
     "Some sources use $\\mathcal N (\\mu, \\sigma)$ instead of $\\mathcal N (\\mu, \\sigma^2)$. Either is fine, they are both conventions. You need to keep in mind which form is being used if you see a term such as $\\mathcal{N}(22,4)$. In this book I always use $\\mathcal N (\\mu, \\sigma^2)$, so $\\sigma=2$, $\\sigma^2=4$ for this example."
    ]
@@ -1243,13 +1243,9 @@
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       "text/plain": [
-       "interactive(children=(FloatSlider(value=5.0, description='mu', max=7.0, min=3.0), FloatSlider(value=0.03, desc…"
+       "<Figure size 900x400 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1378,7 +1374,7 @@
    "source": [
     "## Putting it all Together\n",
     "\n",
-    "Now we are ready to talk about Gaussians can be used in filtering. In the next chapter we will implement a filter using Gaussins. Here I will explain why we would want to use Gaussians.\n",
+    "Now we are ready to talk about how Gaussians can be used in filtering. In the next chapter we will implement a filter using Gaussins. Here I will explain why we would want to use Gaussians.\n",
     "\n",
     "In the previous chapter we represented probability distributions with an array. We performed the update computation by computing the element-wise product of that distribution with another distribution representing the likelihood of the measurement at each point, like so:"
    ]
@@ -1514,13 +1510,13 @@
     "\n",
     "$$p(A \\mid B) = \\frac{p(B \\mid A)\\, p(A)}{p(B)}$$\n",
     "\n",
-    "In the equation above $B$ is the *evidence*, $p(A)$ is the *prior*, $p(B \\mid A)$ is the *likelihood*, and $p(A \\mid B)$ is the *posterior*. By substituting the mathematical terms with the corresponding words  you can see that Bayes theorem matches out update equation. Let's rewrite the equation in terms of our problem. We will use $x_i$ for the position at *i*, and $z$ for the measurement. Hence, we want to know $P(x_i \\mid z)$, that is, the probability of the dog being at $x_i$ given the measurement $z$. \n",
+    "In the equation above $B$ is the *evidence*, $p(A)$ is the *prior*, $p(B \\mid A)$ is the *likelihood*, and $p(A \\mid B)$ is the *posterior*. By substituting the mathematical terms with the corresponding words you can see that Bayes theorem matches our update equation. Let's rewrite the equation in terms of our problem. We will use $x_i$ for the position at *i*, and $z$ for the measurement. Hence, we want to know $P(x_i \\mid z)$, that is, the probability of the dog being at $x_i$ given the measurement $z$. \n",
     "\n",
     "So, let's plug that into the equation and solve it.\n",
     "\n",
     "$$p(x_i \\mid z) = \\frac{p(z \\mid x_i) p(x_i)}{p(z)}$$\n",
     "\n",
-    "That looks ugly, but it is actually quite simple. Let's figure out what each term on the right means. First is $p(z \\mid x_i)$. This is the the likelihood, or the probability for the measurement at every cell $x_i$. $p(x_i)$ is the *prior* - our belief before incorporating the measurements. We multiply those together. This is just the unnormalized multiplication in the `update()` function:\n",
+    "That looks ugly, but it is actually quite simple. Let's figure out what each term on the right means. First is $p(z \\mid x_i)$. This is the likelihood, or the probability for the measurement at every cell $x_i$. $p(x_i)$ is the *prior* - our belief before incorporating the measurements. We multiply those together. This is just the unnormalized multiplication in the `update()` function:\n",
     "\n",
     "```python\n",
     "def update(likelihood, prior):\n",
@@ -1646,9 +1642,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 3.375  4.544  4.565  1.669 -2.095  8.907  2.539\n",
-      "  7.548  4.165  4.512 -1.016 -8.215  5.464  3.347\n",
-      " -0.49 ]\n"
+      "[-0.08   2.024  1.4    3.024  5.799  0.989  2.083\n",
+      "  0.978  7.542 -2.22   4.984  0.626  4.387  3.676\n",
+      " -0.12 ]\n"
      ]
     }
    ],
@@ -1789,7 +1785,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1807,7 +1803,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "That looks like I would expect. The signal is centered around 10. A standard deviation of 2 means that 68% of the measurements will be within $\\pm$ 2 of 10, and 99% will be within $\\pm$ 6 of 10, and that looks like what is happening. \n",
+    "That looks like what I would expect. The signal is centered around 10. A standard deviation of 2 means that 68% of the measurements will be within $\\pm$ 2 of 10, and 99% will be within $\\pm$ 6 of 10, and that looks like what is happening. \n",
     "\n",
     "Now let's look at distribution generated with the Student's $t$-distribution. I will not go into the math, but just give you the source code for it and then plot a distribution using it."
    ]
@@ -1838,7 +1834,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1882,7 +1878,7 @@
     {
      "data": {
       "text/plain": [
-       "DescribeResult(nobs=5000, minmax=(-1.5547859124655847, 21.06432417714437), mean=9.974744793102513, variance=2.8078700924700306, skewness=0.1306700644140642, kurtosis=2.428508424535787)"
+       "DescribeResult(nobs=5000, minmax=(-0.0070992153027660265, 18.502660722626167), mean=9.967146957530227, variance=2.8318603858557094, skewness=-0.1075382179126775, kurtosis=1.846893680503677)"
       ]
      },
      "execution_count": 46,
@@ -1913,9 +1909,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "DescribeResult(nobs=10, minmax=(-1.6030954078612931, 1.0480604500520796), mean=-0.4332351768805024, variance=0.6450239085832324, skewness=0.2468032200106635, kurtosis=-0.7351757738397682)\n",
+      "DescribeResult(nobs=10, minmax=(-1.8106190910322406, 1.7202801709655346), mean=0.03998695860303425, variance=1.2099810612140205, skewness=0.054824114606583485, kurtosis=-0.8322079773586668)\n",
       "\n",
-      "DescribeResult(nobs=300000, minmax=(-5.097163110733026, 4.584973329295028), mean=0.002353555555551788, variance=0.9990956267399185, skewness=0.00044006797029653776, kurtosis=0.0020992123028142906)\n"
+      "DescribeResult(nobs=300000, minmax=(-5.136201903633123, 4.498934900223554), mean=0.0016752908705450242, variance=1.0019122279656631, skewness=0.002460339180965745, kurtosis=-0.0022807108788165387)\n"
      ]
     }
    ],
@@ -1961,7 +1957,7 @@
     "$$\\begin{aligned}\n",
     "p(x \\mid z) &\\propto \\exp \\Big[-\\frac{(z-x)^2}{2\\sigma_z^2}\\Big]\\exp \\Big[-\\frac{(x-\\bar\\mu)^2}{2\\bar\\sigma^2}\\Big]\\\\\n",
     "&\\propto \\exp \\Big[-\\frac{(z-x)^2}{2\\sigma_z^2}-\\frac{(x-\\bar\\mu)^2}{2\\bar\\sigma^2}\\Big] \\\\\n",
-    "&\\propto \\exp \\Big[-\\frac{1}{2\\sigma_z^2\\bar\\sigma^2}[\\bar\\sigma^2(z-x)^2-\\sigma_z^2(x-\\bar\\mu)^2]\\Big]\n",
+    "&\\propto \\exp \\Big[-\\frac{1}{2\\sigma_z^2\\bar\\sigma^2}[\\bar\\sigma^2(z-x)^2+\\sigma_z^2(x-\\bar\\mu)^2]\\Big]\n",
     "\\end{aligned}$$\n",
     "\n",
     "Now we multiply out the squared terms and group in terms of the posterior $x$.\n",
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+       "grid_template_columns": null,
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+       "object_fit": null,
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+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
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+       "_model_module_version": "1.2.0",
+       "_model_name": "LayoutModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "1.2.0",
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
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+       "grid_auto_flow": null,
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+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "SliderStyleModel",
+      "state": {
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+       "_model_module_version": "1.5.0",
+       "_model_name": "SliderStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "1.2.0",
+       "_view_name": "StyleView",
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+       "handle_color": null
+      }
+     },
+     "e8b579851c8f49d08780096612ef9e1b": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "FloatSliderModel",
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+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
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+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "1.5.0",
+       "_view_name": "FloatSliderView",
+       "continuous_update": true,
+       "description": "variance",
+       "description_tooltip": null,
+       "disabled": false,
+       "layout": "IPY_MODEL_5e75c0320ed8462c9929a00efdfd1c08",
+       "max": 1.0,
+       "min": 0.01,
+       "orientation": "horizontal",
+       "readout": true,
+       "readout_format": ".2f",
+       "step": 0.1,
+       "style": "IPY_MODEL_15ffa33092a04aa69b58934228279827",
+       "value": 0.03
+      }
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     },
-    "version_major": 4,
-    "version_minor": 2
+    "version_major": 2,
+    "version_minor": 0
    }
   }
  },

05-Multivariate-Gaussians.ipynb

@@ -397,7 +397,7 @@
    "outputs": [
     {
      "data": {
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truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -409,8 +409,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[11.01   3.079]\n",
-      " [ 3.079  2.311]]\n"
+      "[[10.691  3.098]\n",
+      " [ 3.098  2.392]]\n"
      ]
     }
    ],
@@ -438,7 +438,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -450,8 +450,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[1.011 0.004]\n",
-      " [0.004 1.011]]\n"
+      "[[1.004 0.001]\n",
+      " [0.001 1.004]]\n"
      ]
     }
    ],
@@ -680,7 +680,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1039,13 +1039,9 @@
    "outputs": [
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f226b8d49bb84d3a89d984dcedcdf9d9",
-       "version_major": 2,
-       "version_minor": 0
-      },
+      "image/png": "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 truncated
       "text/plain": [
-       "interactive(children=(FloatSlider(value=5.0, continuous_update=False, description='var_x', max=20.0), FloatSli…"
+       "<Figure size 400x400 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1817,7 +1813,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1886,274 +1882,498 @@
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    "version": "3.6.10"
   },
+  "nbdime-conflicts": {
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+     "diff": [
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+           "valuelist": "10"
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+           "op": "removerange"
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+       "key": "version",
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+      }
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+     "key": "language_info",
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+   "remote_diff": [
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07-Kalman-Filter-Math.ipynb

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+      }
+     ],
+     "key": "language_info",
+     "op": "patch"
+    }
+   ]
   }
  },
  "nbformat": 4,

08-Designing-Kalman-Filters.ipynb

@@ -134,7 +134,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -426,7 +426,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -3984,6 +3984,57 @@
    "pygments_lexer": "ipython3",
    "version": "3.6.10"
   },
+  "nbdime-conflicts": {
+   "local_diff": [
+    {
+     "diff": [
+      {
+       "diff": [
+        {
+         "diff": [
+          {
+           "key": 4,
+           "op": "addrange",
+           "valuelist": "10"
+          },
+          {
+           "key": 4,
+           "length": 1,
+           "op": "removerange"
+          }
+         ],
+         "key": 0,
+         "op": "patch"
+        }
+       ],
+       "key": "version",
+       "op": "patch"
+      }
+     ],
+     "key": "language_info",
+     "op": "patch"
+    }
+   ],
+   "remote_diff": [
+    {
+     "diff": [
+      {
+       "diff": [
+        {
+         "key": 0,
+         "length": 1,
+         "op": "removerange"
+        }
+       ],
+       "key": "version",
+       "op": "patch"
+      }
+     ],
+     "key": "language_info",
+     "op": "patch"
+    }
+   ]
+  },
   "widgets": {
    "state": {},
    "version": "1.1.2"

09-Nonlinear-Filtering.ipynb

@@ -142,7 +142,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -194,7 +194,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -226,7 +226,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 3 Axes>"
       ]
@@ -264,7 +264,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 3 Axes>"
       ]
@@ -303,7 +303,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -338,7 +338,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 3 Axes>"
       ]
@@ -368,8 +368,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "input  mean, variance: -0.0017, 1.0007\n",
-      "output mean, variance: -0.1220, 2.4068\n"
+      "input  mean, variance: -0.0014, 1.0015\n",
+      "output mean, variance: -0.1223, 2.4074\n"
      ]
     }
    ],
@@ -396,7 +396,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 3 Axes>"
       ]
@@ -408,7 +408,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "output mean, variance: 0.0025, 2.2515\n"
+      "output mean, variance: 0.0020, 2.2533\n"
      ]
     }
    ],
@@ -438,8 +438,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "linear    output mean, variance: 0.1435, 7486.7614\n",
-      "nonlinear output mean, variance: -8.8823, 30537.1407\n"
+      "linear    output mean, variance: 0.1173, 7492.8388\n",
+      "nonlinear output mean, variance: -8.9527, 30531.3374\n"
      ]
     }
    ],
@@ -474,7 +474,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 3 Axes>"
       ]
@@ -707,6 +707,57 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.6.10"
+  },
+  "nbdime-conflicts": {
+   "local_diff": [
+    {
+     "diff": [
+      {
+       "diff": [
+        {
+         "diff": [
+          {
+           "key": 4,
+           "op": "addrange",
+           "valuelist": "10"
+          },
+          {
+           "key": 4,
+           "length": 1,
+           "op": "removerange"
+          }
+         ],
+         "key": 0,
+         "op": "patch"
+        }
+       ],
+       "key": "version",
+       "op": "patch"
+      }
+     ],
+     "key": "language_info",
+     "op": "patch"
+    }
+   ],
+   "remote_diff": [
+    {
+     "diff": [
+      {
+       "diff": [
+        {
+         "key": 0,
+         "length": 1,
+         "op": "removerange"
+        }
+       ],
+       "key": "version",
+       "op": "patch"
+      }
+     ],
+     "key": "language_info",
+     "op": "patch"
+    }
+   ]
   }
  },
  "nbformat": 4,

10-Unscented-Kalman-Filter.ipynb

@@ -82,7 +82,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 900x400 with 3 Axes>"
       ]
@@ -124,7 +124,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -224,12 +224,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Difference in mean x=0.033, y=42.345\n"
+      "Difference in mean x=-0.037, y=43.103\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -356,7 +356,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -3311,6 +3311,57 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.6.10"
+  },
+  "nbdime-conflicts": {
+   "local_diff": [
+    {
+     "diff": [
+      {
+       "diff": [
+        {
+         "diff": [
+          {
+           "key": 4,
+           "op": "addrange",
+           "valuelist": "10"
+          },
+          {
+           "key": 4,
+           "length": 1,
+           "op": "removerange"
+          }
+         ],
+         "key": 0,
+         "op": "patch"
+        }
+       ],
+       "key": "version",
+       "op": "patch"
+      }
+     ],
+     "key": "language_info",
+     "op": "patch"
+    }
+   ],
+   "remote_diff": [
+    {
+     "diff": [
+      {
+       "diff": [
+        {
+         "key": 0,
+         "length": 1,
+         "op": "removerange"
+        }
+       ],
+       "key": "version",
+       "op": "patch"
+      }
+     ],
+     "key": "language_info",
+     "op": "patch"
+    }
+   ]
   }
  },
  "nbformat": 4,

11-Extended-Kalman-Filters.ipynb

@@ -101,7 +101,7 @@
     "\n",
     "The EKF does not alter the Kalman filter's linear equations. Instead, it *linearizes* the nonlinear equations at the point of the current estimate, and uses this linearization in the linear Kalman filter. \n",
     "\n",
-    "*Linearize* means what it sounds like. We find a line that most closely matches the curve at a defined point. The graph below linearizes the parabola $f(x)=x^2−2x$ at $x=1.5$."
+    "*Linearize* means what it sounds like. We find a line that most closely matches the curve at a defined point. The graph below linearizes the parabola $f(x)=x^2-2x$ at $x=1.5$."
    ]
   },
   {
@@ -497,7 +497,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -507,7 +507,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -517,7 +517,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1303,7 +1303,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1315,7 +1315,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Final P: [0.024 0.044 0.002]\n"
+      "Final P: [0.026 0.041 0.002]\n"
      ]
     }
    ],
@@ -1348,7 +1348,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1360,7 +1360,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Final P: [0.019 0.022 0.002]\n"
+      "Final P: [0.021 0.02  0.002]\n"
      ]
     }
    ],
@@ -1390,7 +1390,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1402,7 +1402,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Final P: [0.02  0.045 0.   ]\n"
+      "Final P: [0.021 0.045 0.   ]\n"
      ]
     }
    ],
@@ -1429,7 +1429,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1441,7 +1441,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Final P: [0.267 0.754 0.003]\n"
+      "Final P: [0.285 0.778 0.004]\n"
      ]
     }
    ],
@@ -1468,7 +1468,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1530,7 +1530,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1542,7 +1542,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "actual mean=1.29, std=1.12\n",
+      "actual mean=1.30, std=1.14\n",
       "EKF    mean=1.00, std=0.95\n"
      ]
     }
@@ -1566,7 +1566,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1613,6 +1613,57 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.6.10"
+  },
+  "nbdime-conflicts": {
+   "local_diff": [
+    {
+     "diff": [
+      {
+       "diff": [
+        {
+         "diff": [
+          {
+           "key": 4,
+           "op": "addrange",
+           "valuelist": "10"
+          },
+          {
+           "key": 4,
+           "length": 1,
+           "op": "removerange"
+          }
+         ],
+         "key": 0,
+         "op": "patch"
+        }
+       ],
+       "key": "version",
+       "op": "patch"
+      }
+     ],
+     "key": "language_info",
+     "op": "patch"
+    }
+   ],
+   "remote_diff": [
+    {
+     "diff": [
+      {
+       "diff": [
+        {
+         "key": 0,
+         "length": 1,
+         "op": "removerange"
+        }
+       ],
+       "key": "version",
+       "op": "patch"
+      }
+     ],
+     "key": "language_info",
+     "op": "patch"
+    }
+   ]
   }
  },
  "nbformat": 4,

12-Particle-Filters.ipynb

@@ -111,7 +111,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -545,7 +545,6 @@
    "outputs": [],
    "source": [
     "def update(particles, weights, z, R, landmarks):\n",
-    "    weights.fill(1.)\n",
     "    for i, landmark in enumerate(landmarks):\n",
     "        distance = np.linalg.norm(particles[:, 0:2] - landmark, axis=1)\n",
     "        weights *= scipy.stats.norm(distance, R).pdf(z[i])\n",
@@ -757,7 +756,7 @@
     "            mean=initial_x, std=(5, 5, np.pi/4), N=N)\n",
     "    else:\n",
     "        particles = create_uniform_particles((0,20), (0,20), (0, 6.28), N)\n",
-    "    weights = np.zeros(N)\n",
+    "    weights = np.ones(N) / N\n",
     "\n",
     "    if plot_particles:\n",
     "        alpha = .20\n",
@@ -786,7 +785,7 @@
     "        if neff(weights) < N/2:\n",
     "            indexes = systematic_resample(weights)\n",
     "            resample_from_index(particles, weights, indexes)\n",
-    "\n",
+    "            assert np.allclose(weights, 1/N)\n",
     "        mu, var = estimate(particles, weights)\n",
     "        xs.append(mu)\n",
     "\n",
@@ -1070,7 +1069,6 @@
     "\n",
     "```python\n",
     "def update(particles, weights, z, R, landmarks):\n",
-    "    weights.fill(1.)\n",
     "    for i, landmark in enumerate(landmarks):\n",
     "        dist = np.linalg.norm(particles[:, 0:2] - landmark, axis=1)\n",
     "        weights *= scipy.stats.norm(dist, R).pdf(z[i])\n",
@@ -1078,8 +1076,8 @@
     "    weights += 1.e-300      # avoid round-off to zero\n",
     "    weights /= sum(weights) # normalize\n",
     "```\n",
-    "    \n",
-    "The reason for `self.weights.fill(1.)` might have confused you. In all the Bayesian filters up to this chapter we started with the probability distribution created by the `predict` step, and this appears to discard that information by setting all of the weights to 1. Well, we are discarding the weights, but we do not discard the particles. That is a direct result of applying importance sampling - we draw from the known distribution, but weight by the unknown distribution. In this case our known distribution is the uniform distribution - all are weighted equally.\n",
+    "   \n",
+    "Here we compute the weight as the based on the Bayesian computation $\\| \\text{likelihood} \\times \\text{prior}\\|$\n",
     "\n",
     "Of course if you can compute the posterior probability distribution from the prior you should do so. If you cannot, then importance sampling gives you a way to solve this problem. In practice, computing the posterior is incredibly difficult. The Kalman filter became a spectacular success because it took advantage of the properties of Gaussians to find an analytic solution. Once we relax the conditions required by the Kalman filter (Markov property, Gaussian measurements and process) importance sampling and monte carlo methods make the problem tractable."
    ]
@@ -1570,6 +1568,57 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.6.10"
+  },
+  "nbdime-conflicts": {
+   "local_diff": [
+    {
+     "diff": [
+      {
+       "diff": [
+        {
+         "diff": [
+          {
+           "key": 4,
+           "op": "addrange",
+           "valuelist": "10"
+          },
+          {
+           "key": 4,
+           "length": 1,
+           "op": "removerange"
+          }
+         ],
+         "key": 0,
+         "op": "patch"
+        }
+       ],
+       "key": "version",
+       "op": "patch"
+      }
+     ],
+     "key": "language_info",
+     "op": "patch"
+    }
+   ],
+   "remote_diff": [
+    {
+     "diff": [
+      {
+       "diff": [
+        {
+         "key": 0,
+         "length": 1,
+         "op": "removerange"
+        }
+       ],
+       "key": "version",
+       "op": "patch"
+      }
+     ],
+     "key": "language_info",
+     "op": "patch"
+    }
+   ]
   }
  },
  "nbformat": 4,

13-Smoothing.ipynb

@@ -611,6 +611,57 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.6.10"
+  },
+  "nbdime-conflicts": {
+   "local_diff": [
+    {
+     "diff": [
+      {
+       "diff": [
+        {
+         "diff": [
+          {
+           "key": 4,
+           "op": "addrange",
+           "valuelist": "10"
+          },
+          {
+           "key": 4,
+           "length": 1,
+           "op": "removerange"
+          }
+         ],
+         "key": 0,
+         "op": "patch"
+        }
+       ],
+       "key": "version",
+       "op": "patch"
+      }
+     ],
+     "key": "language_info",
+     "op": "patch"
+    }
+   ],
+   "remote_diff": [
+    {
+     "diff": [
+      {
+       "diff": [
+        {
+         "key": 0,
+         "length": 1,
+         "op": "removerange"
+        }
+       ],
+       "key": "version",
+       "op": "patch"
+      }
+     ],
+     "key": "language_info",
+     "op": "patch"
+    }
+   ]
   }
  },
  "nbformat": 4,

14-Adaptive-Filtering.ipynb

@@ -194,7 +194,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -297,7 +297,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -343,7 +343,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -384,7 +384,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -422,7 +422,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -485,7 +485,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -554,7 +554,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -653,7 +653,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -703,7 +703,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -784,7 +784,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -879,7 +879,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -906,7 +906,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -933,7 +933,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -943,7 +943,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -975,7 +975,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -985,7 +985,7 @@
     },
     {
      "data": {
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       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -995,7 +995,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -1005,7 +1005,7 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -1015,7 +1015,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -1086,7 +1086,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 4 Axes>"
       ]
@@ -1214,7 +1214,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -1245,7 +1245,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -1300,7 +1300,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -1350,7 +1350,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -1426,7 +1426,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1802,7 +1802,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -1894,7 +1894,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -2042,6 +2042,57 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.6.10"
+  },
+  "nbdime-conflicts": {
+   "local_diff": [
+    {
+     "diff": [
+      {
+       "diff": [
+        {
+         "diff": [
+          {
+           "key": 4,
+           "op": "addrange",
+           "valuelist": "10"
+          },
+          {
+           "key": 4,
+           "length": 1,
+           "op": "removerange"
+          }
+         ],
+         "key": 0,
+         "op": "patch"
+        }
+       ],
+       "key": "version",
+       "op": "patch"
+      }
+     ],
+     "key": "language_info",
+     "op": "patch"
+    }
+   ],
+   "remote_diff": [
+    {
+     "diff": [
+      {
+       "diff": [
+        {
+         "key": 0,
+         "length": 1,
+         "op": "removerange"
+        }
+       ],
+       "key": "version",
+       "op": "patch"
+      }
+     ],
+     "key": "language_info",
+     "op": "patch"
+    }
+   ]
   }
  },
  "nbformat": 4,

Appendix-A-Installation.ipynb

@@ -202,7 +202,7 @@
     {
      "data": {
       "text/latex": [
-       "$$\\phi$$"
+       "$\\displaystyle \\phi$"
       ],
       "text/plain": [
        "\\phi"
@@ -236,7 +236,7 @@
     {
      "data": {
       "text/latex": [
-       "$$\\frac{1}{2 \\sqrt{\\phi}}$$"
+       "$\\displaystyle \\frac{1}{2 \\sqrt{\\phi}}$"
       ],
       "text/plain": [
        " 1  \n",
@@ -268,7 +268,7 @@
     {
      "data": {
       "text/latex": [
-       "$$\\left(\\phi - 1\\right) \\left(\\phi^{2} + 1\\right)$$"
+       "$\\displaystyle \\left(\\phi - 1\\right) \\left(\\phi^{2} + 1\\right)$"
       ],
       "text/plain": [
        "           ⎛    2    ⎞\n",
@@ -299,7 +299,7 @@
     {
      "data": {
       "text/latex": [
-       "$$\\phi^{2} - 3 \\phi - 4$$"
+       "$\\displaystyle \\phi^{2} - 3 \\phi - 4$"
       ],
       "text/plain": [
        "    2             \n",
@@ -357,7 +357,7 @@
     {
      "data": {
       "text/latex": [
-       "$$2 t + 2$$"
+       "$\\displaystyle 2 t + 2$"
       ],
       "text/plain": [
        "2⋅t + 2"
@@ -392,7 +392,7 @@
     {
      "data": {
       "text/latex": [
-       "$$\\left[\\begin{matrix}\\frac{x}{\\sqrt{x^{2} + z^{2}}} & 0 & \\frac{z}{\\sqrt{x^{2} + z^{2}}}\\end{matrix}\\right]$$"
+       "$\\displaystyle \\left[\\begin{matrix}\\frac{x}{\\sqrt{x^{2} + z^{2}}} & 0 & \\frac{z}{\\sqrt{x^{2} + z^{2}}}\\end{matrix}\\right]$"
       ],
       "text/plain": [
        "⎡     x                z      ⎤\n",
@@ -439,7 +439,7 @@
     {
      "data": {
       "text/latex": [
-       "$$\\left[\\begin{matrix}\\frac{\\Delta{t}^{5}}{20} & \\frac{\\Delta{t}^{4}}{8} & \\frac{\\Delta{t}^{3}}{6}\\\\\\frac{\\Delta{t}^{4}}{8} & \\frac{\\Delta{t}^{3}}{3} & \\frac{\\Delta{t}^{2}}{2}\\\\\\frac{\\Delta{t}^{3}}{6} & \\frac{\\Delta{t}^{2}}{2} & \\Delta{t}\\end{matrix}\\right]$$"
+       "$\\displaystyle \\left[\\begin{matrix}\\frac{\\Delta{t}^{5}}{20} & \\frac{\\Delta{t}^{4}}{8} & \\frac{\\Delta{t}^{3}}{6}\\\\\\frac{\\Delta{t}^{4}}{8} & \\frac{\\Delta{t}^{3}}{3} & \\frac{\\Delta{t}^{2}}{2}\\\\\\frac{\\Delta{t}^{3}}{6} & \\frac{\\Delta{t}^{2}}{2} & \\Delta{t}\\end{matrix}\\right]$"
       ],
       "text/plain": [
        "⎡         5           4           3⎤\n",

Appendix-B-Symbols-and-Notations.ipynb

@@ -110,7 +110,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,

Appendix-D-HInfinity-Filters.ipynb

@@ -153,6 +153,57 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.6.10"
+  },
+  "nbdime-conflicts": {
+   "local_diff": [
+    {
+     "diff": [
+      {
+       "diff": [
+        {
+         "diff": [
+          {
+           "key": 4,
+           "op": "addrange",
+           "valuelist": "10"
+          },
+          {
+           "key": 4,
+           "length": 1,
+           "op": "removerange"
+          }
+         ],
+         "key": 0,
+         "op": "patch"
+        }
+       ],
+       "key": "version",
+       "op": "patch"
+      }
+     ],
+     "key": "language_info",
+     "op": "patch"
+    }
+   ],
+   "remote_diff": [
+    {
+     "diff": [
+      {
+       "diff": [
+        {
+         "key": 0,
+         "length": 1,
+         "op": "removerange"
+        }
+       ],
+       "key": "version",
+       "op": "patch"
+      }
+     ],
+     "key": "language_info",
+     "op": "patch"
+    }
+   ]
   }
  },
  "nbformat": 4,

Appendix-E-Ensemble-Kalman-Filters.ipynb

@@ -85,7 +85,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -537,6 +537,57 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.6.10"
+  },
+  "nbdime-conflicts": {
+   "local_diff": [
+    {
+     "diff": [
+      {
+       "diff": [
+        {
+         "diff": [
+          {
+           "key": 4,
+           "op": "addrange",
+           "valuelist": "10"
+          },
+          {
+           "key": 4,
+           "length": 1,
+           "op": "removerange"
+          }
+         ],
+         "key": 0,
+         "op": "patch"
+        }
+       ],
+       "key": "version",
+       "op": "patch"
+      }
+     ],
+     "key": "language_info",
+     "op": "patch"
+    }
+   ],
+   "remote_diff": [
+    {
+     "diff": [
+      {
+       "diff": [
+        {
+         "key": 0,
+         "length": 1,
+         "op": "removerange"
+        }
+       ],
+       "key": "version",
+       "op": "patch"
+      }
+     ],
+     "key": "language_info",
+     "op": "patch"
+    }
+   ]
   }
  },
  "nbformat": 4,

Appendix-G-Designing-Nonlinear-Kalman-Filters.ipynb

@@ -283,13 +283,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\phi$"
+      ],
+      "text/plain": [
+       "\\phi"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import sympy\n",
     "from sympy import init_printing\n",
-    "init_printing(use_latex='png')\n",
+    "init_printing(use_latex='mathjax')\n",
     "\n",
     "phi, x = sympy.symbols('\\phi, x')\n",
     "phi"
@@ -304,9 +318,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\frac{1}{2 \\sqrt{\\phi}}$"
+      ],
+      "text/plain": [
+       " 1  \n",
+       "────\n",
+       "2⋅√φ"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "sympy.diff('sqrt(phi)')"
    ]
@@ -320,9 +350,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\left(\\phi - 1\\right) \\left(\\phi^{2} + 1\\right)$"
+      ],
+      "text/plain": [
+       "        ⎛ 2    ⎞\n",
+       "(φ - 1)⋅⎝φ  + 1⎠"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "sympy.factor('phi**3 -phi**2 + phi - 1')"
    ]
@@ -336,9 +381,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\left[\\begin{matrix}\\frac{x - x_{a}}{\\sqrt{\\left(x - x_{a}\\right)^{2} + \\left(y - y_{a}\\right)^{2}}} & 0 & \\frac{y - y_{a}}{\\sqrt{\\left(x - x_{a}\\right)^{2} + \\left(y - y_{a}\\right)^{2}}} & 0\\\\\\frac{x - x_{b}}{\\sqrt{\\left(x - x_{b}\\right)^{2} + \\left(y - y_{b}\\right)^{2}}} & 0 & \\frac{y - y_{b}}{\\sqrt{\\left(x - x_{b}\\right)^{2} + \\left(y - y_{b}\\right)^{2}}} & 0\\end{matrix}\\right]$"
+      ],
+      "text/plain": [
+       "⎡           x - xₐ                           y - yₐ              ⎤\n",
+       "⎢ ──────────────────────────   0   ──────────────────────────   0⎥\n",
+       "⎢    _______________________          _______________________    ⎥\n",
+       "⎢   ╱         2           2          ╱         2           2     ⎥\n",
+       "⎢ ╲╱  (x - xₐ)  + (y - yₐ)         ╲╱  (x - xₐ)  + (y - yₐ)      ⎥\n",
+       "⎢                                                                ⎥\n",
+       "⎢          x - x_b                          y - y_b              ⎥\n",
+       "⎢────────────────────────────  0  ────────────────────────────  0⎥\n",
+       "⎢   _________________________        _________________________   ⎥\n",
+       "⎢  ╱          2            2        ╱          2            2    ⎥\n",
+       "⎣╲╱  (x - x_b)  + (y - y_b)       ╲╱  (x - x_b)  + (y - y_b)     ⎦"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from sympy import symbols, Matrix\n",
     "phi = symbols('\\phi')\n",
@@ -402,7 +471,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -430,7 +499,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -463,9 +532,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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 truncated
+      "text/plain": [
+       "<Figure size 900x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import kf_book.book_plots as bp\n",
     "from filterpy.kalman import KalmanFilter\n",
@@ -563,9 +643,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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 truncated
+      "text/plain": [
+       "<Figure size 900x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import kf_book.ekf_internal as ekf\n",
     "ekf.plot_ball()"
@@ -639,7 +730,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -698,6 +789,57 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.6.10"
+  },
+  "nbdime-conflicts": {
+   "local_diff": [
+    {
+     "diff": [
+      {
+       "diff": [
+        {
+         "diff": [
+          {
+           "key": 4,
+           "op": "addrange",
+           "valuelist": "10"
+          },
+          {
+           "key": 4,
+           "length": 1,
+           "op": "removerange"
+          }
+         ],
+         "key": 0,
+         "op": "patch"
+        }
+       ],
+       "key": "version",
+       "op": "patch"
+      }
+     ],
+     "key": "language_info",
+     "op": "patch"
+    }
+   ],
+   "remote_diff": [
+    {
+     "diff": [
+      {
+       "diff": [
+        {
+         "key": 0,
+         "length": 1,
+         "op": "removerange"
+        }
+       ],
+       "key": "version",
+       "op": "patch"
+      }
+     ],
+     "key": "language_info",
+     "op": "patch"
+    }
+   ]
   }
  },
  "nbformat": 4,

Appendix-H-Least-Squares-Filters.ipynb

@@ -109,7 +109,58 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.10"
+   "version": "3.7.13.6.5"
+  },
+  "nbdime-conflicts": {
+   "local_diff": [
+    {
+     "diff": [
+      {
+       "diff": [
+        {
+         "diff": [
+          {
+           "key": 4,
+           "op": "addrange",
+           "valuelist": "10"
+          },
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+           "op": "removerange"
+          }
+         ],
+         "key": 0,
+         "op": "patch"
+        }
+       ],
+       "key": "version",
+       "op": "patch"
+      }
+     ],
+     "key": "language_info",
+     "op": "patch"
+    }
+   ],
+   "remote_diff": [
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+       ],
+       "key": "version",
+       "op": "patch"
+      }
+     ],
+     "key": "language_info",
+     "op": "patch"
+    }
+   ]
   }
  },
  "nbformat": 4,

Appendix-I-Analytic-Evaluation-of-Performance.ipynb

@@ -39,6 +39,57 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.6.10"
+  },
+  "nbdime-conflicts": {
+   "local_diff": [
+    {
+     "diff": [
+      {
+       "diff": [
+        {
+         "diff": [
+          {
+           "key": 4,
+           "op": "addrange",
+           "valuelist": "10"
+          },
+          {
+           "key": 4,
+           "length": 1,
+           "op": "removerange"
+          }
+         ],
+         "key": 0,
+         "op": "patch"
+        }
+       ],
+       "key": "version",
+       "op": "patch"
+      }
+     ],
+     "key": "language_info",
+     "op": "patch"
+    }
+   ],
+   "remote_diff": [
+    {
+     "diff": [
+      {
+       "diff": [
+        {
+         "key": 0,
+         "length": 1,
+         "op": "removerange"
+        }
+       ],
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+       "op": "patch"
+      }
+     ],
+     "key": "language_info",
+     "op": "patch"
+    }
+   ]
   }
  },
  "nbformat": 4,

README.md

@@ -125,7 +125,7 @@ If you do not have pip, you may follow the instructions here: https://pip.pypa.i
 All of the filters used in this book as well as others not in this book are implemented in my Python library FilterPy, available [here](https://github.com/rlabbe/filterpy). You do not need to download or install this to read the book, but you will likely want to use this library to write your own filters. It includes Kalman filters, Fading Memory filters, H infinity filters, Extended and Unscented filters, least square filters, and many more.  It also includes helper routines that simplify the designing the matrices used by some of the filters, and other code such as Kalman based smoothers.
 
 
-FilterPy is hosted github at (https://github.com/rlabbe/filterpy).  If you want the bleading edge release you will want to grab a copy from github, and follow your Python installation's instructions for adding it to the Python search path. This might expose you to some instability since you might not get a tested release, but as a benefit you will also get all of the test scripts used to test the library. You can examine these scripts to see many examples of writing and running filters while not in the Jupyter Notebook environment.
+FilterPy is hosted on github at (https://github.com/rlabbe/filterpy).  If you want the bleeding edge release you will want to grab a copy from github, and follow your Python installation's instructions for adding it to the Python search path. This might expose you to some instability since you might not get a tested release, but as a benefit you will also get all of the test scripts used to test the library. You can examine these scripts to see many examples of writing and running filters while not in the Jupyter Notebook environment.
 
 Alternative Way of Running the Book in Conda environment
 ----
@@ -135,11 +135,11 @@ If you have conda or miniconda installed, you can create environment by
 
 and use
 
-    source activate kf_bf
+    conda activate kf_bf
 
 and
 
-    source deactivate kf_bf
+    conda deactivate kf_bf
 
 to activate and deactivate the environment.
 

Supporting_Notebooks/Computing_and_plotting_PDFs.ipynb

@@ -2,256 +2,42 @@
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   {
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-       "                 \"fast-preview\": {\n",
-       "                    \"disabled\": false\n",
-       "                }\n",
-       "        });\n",
-       "</script>\n"
+       "        <style>\n",
+       "        .output_wrapper, .output {\n",
+       "            height:auto !important;\n",
+       "            max-height:100000px; \n",
+       "        }\n",
+       "        .output_scroll {\n",
+       "            box-shadow:none !important;\n",
+       "            webkit-box-shadow:none !important;\n",
+       "        }\n",
+       "        </style>\n",
+       "    "
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "#format the book\n",
-    "%matplotlib inline\n",
     "from __future__ import division, print_function\n",
-    "import sys;sys.path.insert(0,'..')\n",
-    "from  book_format import load_style;load_style('..')"
+    "%matplotlib inline\n",
+    "import sys\n",
+    "sys.path.insert(0, '..')\n",
+    "import book_format\n",
+    "book_format.set_style()"
    ]
   },
   {
@@ -273,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -281,8 +67,8 @@
      "output_type": "stream",
      "text": [
       "50000\n",
-      "3.00067325106\n",
-      "2.00433280075\n"
+      "2.9947741815932556\n",
+      "2.0063665533914694\n"
      ]
     }
    ],
@@ -310,16 +96,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 16,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c191ba47b8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -350,16 +136,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 17,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c1911d7400>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -375,19 +161,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "that is not very useful to us - we want the PDF, not bin counts. Fortunately `hist` includes a `normed` parameter which will plot the PDF for us."
+    "that is not very useful to us - we want the PDF, not bin counts. Fortunately `hist` includes a `density` parameter which will plot the PDF for us."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c190171390>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -395,7 +181,7 @@
     }
    ],
    "source": [
-    "plt.hist(data, bins=200, normed=True)\n",
+    "plt.hist(data, bins=200, density=True)\n",
     "plt.show()"
    ]
   },
@@ -408,14 +194,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c19202c668>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -423,7 +209,7 @@
     }
    ],
    "source": [
-    "plt.hist(data, bins=200, normed=True, histtype='step', lw=2)\n",
+    "plt.hist(data, bins=200, density=True, histtype='step', lw=2)\n",
     "plt.show()"
    ]
   },
@@ -436,14 +222,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c1937a32b0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -451,7 +237,7 @@
     }
    ],
    "source": [
-    "plt.hist(data, bins=200, normed=True, histtype='step', lw=2)\n",
+    "plt.hist(data, bins=200, density=True, histtype='step', lw=2)\n",
     "norm = stats.norm(mean, std)\n",
     "plt.plot(xs, norm.pdf(xs), color='k', lw=2)\n",
     "plt.show()"
@@ -466,14 +252,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c193822e80>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -502,14 +288,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c19381c1d0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -519,7 +305,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "mean = 13.99\n",
+      "mean = 14.00\n",
       "std  = 2.80\n"
      ]
     }
@@ -534,8 +320,8 @@
     "\n",
     "d_t = f(data) # transform data through f(x)\n",
     "\n",
-    "plt.hist(data, bins=200, normed=True, histtype='step', lw=2)\n",
-    "plt.hist(d_t, bins=200, normed=True, histtype='step', lw=2)\n",
+    "plt.hist(data, bins=200, density=True, histtype='step', lw=2)\n",
+    "plt.hist(d_t, bins=200, density=True, histtype='step', lw=2)\n",
     "\n",
     "plt.ylim(0, .35)\n",
     "plt.show()\n",
@@ -564,14 +350,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c1938f79e8>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -582,7 +368,7 @@
      "output_type": "stream",
      "text": [
       "mean = -1.59\n",
-      "std  = 2.10\n"
+      "std  = 2.09\n"
      ]
     }
    ],
@@ -592,7 +378,7 @@
     "\n",
     "d_t = f2(data)\n",
     "plt.subplot(121)\n",
-    "plt.hist(d_t, bins=200, normed=True, histtype='step', lw=2)\n",
+    "plt.hist(d_t, bins=200, density=True, histtype='step', lw=2)\n",
     "\n",
     "plt.subplot(122)\n",
     "kde = stats.gaussian_kde(d_t)\n",
@@ -617,7 +403,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -626,7 +412,7 @@
        "-1.5*sin(x/3)*sin(1.5*x + 2.1) + cos(x/3)*cos(1.5*x + 2.1)/3 - 1.6"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -648,7 +434,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -657,7 +443,7 @@
        "-1.66528051815545"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -676,14 +462,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c19419d5c0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -691,7 +477,7 @@
     }
    ],
    "source": [
-    "plt.hist(d_t, bins=200, normed=True, histtype='step', lw=2)\n",
+    "plt.hist(d_t, bins=200, density=True, histtype='step', lw=2)\n",
     "plot_normal(xs, f2(mean), abs(float(m)*std), color='k', lw=3, label='EKF')\n",
     "plot_normal(xs, d_t.mean(), d_t.std(), color='r', lw=3, label='MC')\n",
     "plt.legend()\n",
@@ -722,7 +508,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.7.1"
   }
  },
  "nbformat": 4,

Supporting_Notebooks/Converting-Multivariate-Equations-to-Univariate.ipynb

@@ -3,277 +3,23 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>\n",
-       "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
-       "@import url('http://fonts.googleapis.com/css?family=Lora');\n",
        "\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Open+Sans');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Karla');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Poppins');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Roboto');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Lato');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Domine');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Chivo');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Cardo');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Arvo');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Crimson+Text');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Ubuntu');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Fontin');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Raleway');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Merriweather');\n",
-       "\n",
-       "\n",
-       ".CodeMirror pre {\n",
-       "    font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
-       "}\n",
-       "    div.cell{\n",
-       "        width: 850px;\n",
-       "        margin-left: 0% !important;\n",
-       "        margin-right: auto;\n",
-       "    }\n",
-       "    div.text_cell_render{\n",
-       "        font-family: 'Lora';\n",
-       "        //font-family: 'Open Sans';\n",
-       "        //font-family: 'Karla',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Roboto',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Lato',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Domine',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Chivo',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Cardo',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Arvo',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Poppins',verdana,arial,sans-serif;   \n",
-       "        //font-family: 'Ubuntu',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Fontin',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Raleway',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Merriweather',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Crimson Text', verdana,arial,sans-serif;\n",
-       "        //font-family: verdana,arial,sans-serif;\n",
-       "        //font-family: arial,sans-serif;\n",
-       "        line-height: 125%;\n",
-       "        font-size: 130%;\n",
-       "        text-align: justify;\n",
-       "        text-justify:inter-word;\n",
-       "    }\n",
-       "    div.text_cell code {\n",
-       "        background: transparent;\n",
-       "        color: #000000;\n",
-       "        font-weight: 400;\n",
-       "        font-size: 12pt;\n",
-       "        //font-style: bold;\n",
-       "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
-       "   }\n",
-       "    h1 {\n",
-       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
-       "\t}\n",
-       "\t\n",
-       "    div.input_area {\n",
-       "        background: #F6F6F9;\n",
-       "        border: 1px solid #586e75;      \n",
-       "    }\n",
-       "\n",
-       "    .text_cell_render h1 {\n",
-       "        font-weight: 200;\n",
-       "        font-size: 30pt;\n",
-       "        line-height: 100%;\n",
-       "        color:#c76c0c;\n",
-       "        margin-bottom: 0.5em;\n",
-       "        margin-top: 1em;\n",
-       "        display: block;\n",
-       "        white-space: wrap;\n",
-       "        text-align: left;\n",
-       "    } \n",
-       "    h2 {\n",
-       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "    .text_cell_render h2 {\n",
-       "        font-weight: 200;\n",
-       "        font-size: 16pt;\n",
-       "        font-style: italic;\n",
-       "        line-height: 100%;\n",
-       "        color:#c76c0c;\n",
-       "        margin-bottom: 0.5em;\n",
-       "        margin-top: 1.5em;\n",
-       "        display: block;\n",
-       "        white-space: wrap;\n",
-       "        text-align: left;\n",
-       "    } \n",
-       "    h3 {\n",
-       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
-       "    }\n",
-       "    .text_cell_render h3 {\n",
-       "        font-weight: 200;\n",
-       "        font-size: 14pt;\n",
-       "        line-height: 100%;\n",
-       "        color:#d77c0c;\n",
-       "        margin-bottom: 0.5em;\n",
-       "        margin-top: 2em;\n",
-       "        display: block;\n",
-       "        white-space: wrap;\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "    h4 {\n",
-       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
-       "    }\n",
-       "    .text_cell_render h4 {\n",
-       "        font-weight: 100;\n",
-       "        font-size: 14pt;\n",
-       "        color:#d77c0c;\n",
-       "        margin-bottom: 0.5em;\n",
-       "        margin-top: 0.5em;\n",
-       "        display: block;\n",
-       "        white-space: nowrap;\n",
-       "    }\n",
-       "    h5 {\n",
-       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
-       "    }\n",
-       "\n",
-       "    .text_cell_render h5 {\n",
-       "        font-weight: 200;\n",
-       "        font-style: normal;\n",
-       "        color: #1d3b84;\n",
-       "        font-size: 16pt;\n",
-       "        margin-bottom: 0em;\n",
-       "        margin-top: 0.5em;\n",
-       "        display: block;\n",
-       "        white-space: nowrap;\n",
-       "    }\n",
-       "    div.output_subarea.output_text.output_pyout {\n",
-       "        overflow-x: auto;\n",
-       "        overflow-y: scroll;\n",
-       "        max-height: 50000px;\n",
-       "    }\n",
-       "    div.output_subarea.output_stream.output_stdout.output_text {\n",
-       "        overflow-x: auto;\n",
-       "        overflow-y: scroll;\n",
-       "        max-height: 50000px;\n",
-       "    }\n",
-       "    div.output_wrapper{\n",
-       "        margin-top:0.2em;\n",
-       "        margin-bottom:0.2em;\n",
-       "}\n",
-       "\n",
-       "    code{\n",
-       "        font-size: 6pt;\n",
-       "\n",
-       "    }\n",
-       "    .rendered_html code{\n",
-       "    background-color: transparent;\n",
-       "    }\n",
-       "    ul{\n",
-       "        margin: 2em;\n",
-       "    }\n",
-       "    ul li{\n",
-       "        padding-left: 0.5em; \n",
-       "        margin-bottom: 0.5em; \n",
-       "        margin-top: 0.5em; \n",
-       "    }\n",
-       "    ul li li{\n",
-       "        padding-left: 0.2em; \n",
-       "        margin-bottom: 0.2em; \n",
-       "        margin-top: 0.2em; \n",
-       "    }\n",
-       "    ol{\n",
-       "        margin: 2em;\n",
-       "    }\n",
-       "    ol li{\n",
-       "        padding-left: 0.5em; \n",
-       "        margin-bottom: 0.5em; \n",
-       "        margin-top: 0.5em; \n",
-       "    }\n",
-       "    ul li{\n",
-       "        padding-left: 0.5em; \n",
-       "        margin-bottom: 0.5em; \n",
-       "        margin-top: 0.2em; \n",
-       "    }\n",
-       "    a:link{\n",
-       "       font-weight: bold;\n",
-       "       color:#447adb;\n",
-       "    }\n",
-       "    a:visited{\n",
-       "       font-weight: bold;\n",
-       "       color: #1d3b84;\n",
-       "    }\n",
-       "    a:hover{\n",
-       "       font-weight: bold;\n",
-       "       color: #1d3b84;\n",
-       "    }\n",
-       "    a:focus{\n",
-       "       font-weight: bold;\n",
-       "       color:#447adb;\n",
-       "    }\n",
-       "    a:active{\n",
-       "       font-weight: bold;\n",
-       "       color:#447adb;\n",
-       "    }\n",
-       "    .rendered_html :link {\n",
-       "       text-decoration: underline; \n",
-       "    }\n",
-       "    .rendered_html :hover {\n",
-       "       text-decoration: none; \n",
-       "    }\n",
-       "    .rendered_html :visited {\n",
-       "      text-decoration: none;\n",
-       "    }\n",
-       "    .rendered_html :focus {\n",
-       "      text-decoration: none;\n",
-       "    }\n",
-       "    .rendered_html :active {\n",
-       "      text-decoration: none;\n",
-       "    }\n",
-       "    .warning{\n",
-       "        color: rgb( 240, 20, 20 )\n",
-       "    } \n",
-       "    hr {\n",
-       "      color: #f3f3f3;\n",
-       "      background-color: #f3f3f3;\n",
-       "      height: 1px;\n",
-       "    }\n",
-       "    blockquote{\n",
-       "      display:block;\n",
-       "      background: #fcfcfc;\n",
-       "      border-left: 5px solid #c76c0c;\n",
-       "      font-family: 'Open sans',verdana,arial,sans-serif;\n",
-       "      width:680px;\n",
-       "      padding: 10px 10px 10px 10px;\n",
-       "      text-align:justify;\n",
-       "      text-justify:inter-word;\n",
-       "      }\n",
-       "      blockquote p {\n",
-       "        margin-bottom: 0;\n",
-       "        line-height: 125%;\n",
-       "        font-size: 100%;\n",
-       "      }\n",
-       "</style>\n",
-       "<script>\n",
-       "    MathJax.Hub.Config({\n",
-       "                        TeX: {\n",
-       "                           extensions: [\"AMSmath.js\"],\n",
-       "                           equationNumbers: { autoNumber: \"AMS\", useLabelIds: true}\n",
-       "                           },\n",
-       "                tex2jax: {\n",
-       "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
-       "                    displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
-       "                },\n",
-       "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
-       "                \"HTML-CSS\": {\n",
-       "                    scale:95,\n",
-       "                        availableFonts: [],\n",
-       "                        preferredFont:null,\n",
-       "                        webFont: \"TeX\",\n",
-       "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
-       "                }\n",
-       "        });\n",
-       "</script>\n"
+       "        <style>\n",
+       "        .output_wrapper, .output {\n",
+       "            height:auto !important;\n",
+       "            max-height:100000px; \n",
+       "        }\n",
+       "        .output_scroll {\n",
+       "            box-shadow:none !important;\n",
+       "            webkit-box-shadow:none !important;\n",
+       "        }\n",
+       "        </style>\n",
+       "    "
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -286,10 +32,12 @@
    ],
    "source": [
     "#format the book\n",
-    "%matplotlib inline\n",
     "from __future__ import division, print_function\n",
-    "import sys;sys.path.insert(0,'..')\n",
-    "from  book_format import load_style;load_style('..')"
+    "%matplotlib inline\n",
+    "import sys\n",
+    "sys.path.insert(0, '..')\n",
+    "import book_format\n",
+    "book_format.set_style()"
    ]
   },
   {
@@ -428,9 +176,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.7.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }

Supporting_Notebooks/Interactions.ipynb

@@ -3,277 +3,23 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>\n",
-       "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
-       "@import url('http://fonts.googleapis.com/css?family=Lora');\n",
        "\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Open+Sans');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Karla');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Poppins');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Roboto');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Lato');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Domine');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Chivo');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Cardo');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Arvo');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Crimson+Text');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Ubuntu');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Fontin');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Raleway');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Merriweather');\n",
-       "\n",
-       "\n",
-       ".CodeMirror pre {\n",
-       "    font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
-       "}\n",
-       "    div.cell{\n",
-       "        width: 850px;\n",
-       "        margin-left: 0% !important;\n",
-       "        margin-right: auto;\n",
-       "    }\n",
-       "    div.text_cell_render{\n",
-       "        font-family: 'Lora';\n",
-       "        //font-family: 'Open Sans';\n",
-       "        //font-family: 'Karla',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Roboto',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Lato',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Domine',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Chivo',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Cardo',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Arvo',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Poppins',verdana,arial,sans-serif;   \n",
-       "        //font-family: 'Ubuntu',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Fontin',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Raleway',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Merriweather',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Crimson Text', verdana,arial,sans-serif;\n",
-       "        //font-family: verdana,arial,sans-serif;\n",
-       "        //font-family: arial,sans-serif;\n",
-       "        line-height: 125%;\n",
-       "        font-size: 130%;\n",
-       "        text-align: justify;\n",
-       "        text-justify:inter-word;\n",
-       "    }\n",
-       "    div.text_cell code {\n",
-       "        background: transparent;\n",
-       "        color: #000000;\n",
-       "        font-weight: 400;\n",
-       "        font-size: 12pt;\n",
-       "        //font-style: bold;\n",
-       "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
-       "   }\n",
-       "    h1 {\n",
-       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
-       "\t}\n",
-       "\t\n",
-       "    div.input_area {\n",
-       "        background: #F6F6F9;\n",
-       "        border: 1px solid #586e75;      \n",
-       "    }\n",
-       "\n",
-       "    .text_cell_render h1 {\n",
-       "        font-weight: 200;\n",
-       "        font-size: 30pt;\n",
-       "        line-height: 100%;\n",
-       "        color:#c76c0c;\n",
-       "        margin-bottom: 0.5em;\n",
-       "        margin-top: 1em;\n",
-       "        display: block;\n",
-       "        white-space: wrap;\n",
-       "        text-align: left;\n",
-       "    } \n",
-       "    h2 {\n",
-       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "    .text_cell_render h2 {\n",
-       "        font-weight: 200;\n",
-       "        font-size: 16pt;\n",
-       "        font-style: italic;\n",
-       "        line-height: 100%;\n",
-       "        color:#c76c0c;\n",
-       "        margin-bottom: 0.5em;\n",
-       "        margin-top: 1.5em;\n",
-       "        display: block;\n",
-       "        white-space: wrap;\n",
-       "        text-align: left;\n",
-       "    } \n",
-       "    h3 {\n",
-       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
-       "    }\n",
-       "    .text_cell_render h3 {\n",
-       "        font-weight: 200;\n",
-       "        font-size: 14pt;\n",
-       "        line-height: 100%;\n",
-       "        color:#d77c0c;\n",
-       "        margin-bottom: 0.5em;\n",
-       "        margin-top: 2em;\n",
-       "        display: block;\n",
-       "        white-space: wrap;\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "    h4 {\n",
-       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
-       "    }\n",
-       "    .text_cell_render h4 {\n",
-       "        font-weight: 100;\n",
-       "        font-size: 14pt;\n",
-       "        color:#d77c0c;\n",
-       "        margin-bottom: 0.5em;\n",
-       "        margin-top: 0.5em;\n",
-       "        display: block;\n",
-       "        white-space: nowrap;\n",
-       "    }\n",
-       "    h5 {\n",
-       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
-       "    }\n",
-       "\n",
-       "    .text_cell_render h5 {\n",
-       "        font-weight: 200;\n",
-       "        font-style: normal;\n",
-       "        color: #1d3b84;\n",
-       "        font-size: 16pt;\n",
-       "        margin-bottom: 0em;\n",
-       "        margin-top: 0.5em;\n",
-       "        display: block;\n",
-       "        white-space: nowrap;\n",
-       "    }\n",
-       "    div.output_subarea.output_text.output_pyout {\n",
-       "        overflow-x: auto;\n",
-       "        overflow-y: scroll;\n",
-       "        max-height: 50000px;\n",
-       "    }\n",
-       "    div.output_subarea.output_stream.output_stdout.output_text {\n",
-       "        overflow-x: auto;\n",
-       "        overflow-y: scroll;\n",
-       "        max-height: 50000px;\n",
-       "    }\n",
-       "    div.output_wrapper{\n",
-       "        margin-top:0.2em;\n",
-       "        margin-bottom:0.2em;\n",
-       "}\n",
-       "\n",
-       "    code{\n",
-       "        font-size: 6pt;\n",
-       "\n",
-       "    }\n",
-       "    .rendered_html code{\n",
-       "    background-color: transparent;\n",
-       "    }\n",
-       "    ul{\n",
-       "        margin: 2em;\n",
-       "    }\n",
-       "    ul li{\n",
-       "        padding-left: 0.5em; \n",
-       "        margin-bottom: 0.5em; \n",
-       "        margin-top: 0.5em; \n",
-       "    }\n",
-       "    ul li li{\n",
-       "        padding-left: 0.2em; \n",
-       "        margin-bottom: 0.2em; \n",
-       "        margin-top: 0.2em; \n",
-       "    }\n",
-       "    ol{\n",
-       "        margin: 2em;\n",
-       "    }\n",
-       "    ol li{\n",
-       "        padding-left: 0.5em; \n",
-       "        margin-bottom: 0.5em; \n",
-       "        margin-top: 0.5em; \n",
-       "    }\n",
-       "    ul li{\n",
-       "        padding-left: 0.5em; \n",
-       "        margin-bottom: 0.5em; \n",
-       "        margin-top: 0.2em; \n",
-       "    }\n",
-       "    a:link{\n",
-       "       font-weight: bold;\n",
-       "       color:#447adb;\n",
-       "    }\n",
-       "    a:visited{\n",
-       "       font-weight: bold;\n",
-       "       color: #1d3b84;\n",
-       "    }\n",
-       "    a:hover{\n",
-       "       font-weight: bold;\n",
-       "       color: #1d3b84;\n",
-       "    }\n",
-       "    a:focus{\n",
-       "       font-weight: bold;\n",
-       "       color:#447adb;\n",
-       "    }\n",
-       "    a:active{\n",
-       "       font-weight: bold;\n",
-       "       color:#447adb;\n",
-       "    }\n",
-       "    .rendered_html :link {\n",
-       "       text-decoration: underline; \n",
-       "    }\n",
-       "    .rendered_html :hover {\n",
-       "       text-decoration: none; \n",
-       "    }\n",
-       "    .rendered_html :visited {\n",
-       "      text-decoration: none;\n",
-       "    }\n",
-       "    .rendered_html :focus {\n",
-       "      text-decoration: none;\n",
-       "    }\n",
-       "    .rendered_html :active {\n",
-       "      text-decoration: none;\n",
-       "    }\n",
-       "    .warning{\n",
-       "        color: rgb( 240, 20, 20 )\n",
-       "    } \n",
-       "    hr {\n",
-       "      color: #f3f3f3;\n",
-       "      background-color: #f3f3f3;\n",
-       "      height: 1px;\n",
-       "    }\n",
-       "    blockquote{\n",
-       "      display:block;\n",
-       "      background: #fcfcfc;\n",
-       "      border-left: 5px solid #c76c0c;\n",
-       "      font-family: 'Open sans',verdana,arial,sans-serif;\n",
-       "      width:680px;\n",
-       "      padding: 10px 10px 10px 10px;\n",
-       "      text-align:justify;\n",
-       "      text-justify:inter-word;\n",
-       "      }\n",
-       "      blockquote p {\n",
-       "        margin-bottom: 0;\n",
-       "        line-height: 125%;\n",
-       "        font-size: 100%;\n",
-       "      }\n",
-       "</style>\n",
-       "<script>\n",
-       "    MathJax.Hub.Config({\n",
-       "                        TeX: {\n",
-       "                           extensions: [\"AMSmath.js\"],\n",
-       "                           equationNumbers: { autoNumber: \"AMS\", useLabelIds: true}\n",
-       "                           },\n",
-       "                tex2jax: {\n",
-       "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
-       "                    displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
-       "                },\n",
-       "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
-       "                \"HTML-CSS\": {\n",
-       "                    scale:95,\n",
-       "                        availableFonts: [],\n",
-       "                        preferredFont:null,\n",
-       "                        webFont: \"TeX\",\n",
-       "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
-       "                }\n",
-       "        });\n",
-       "</script>\n"
+       "        <style>\n",
+       "        .output_wrapper, .output {\n",
+       "            height:auto !important;\n",
+       "            max-height:100000px; \n",
+       "        }\n",
+       "        .output_scroll {\n",
+       "            box-shadow:none !important;\n",
+       "            webkit-box-shadow:none !important;\n",
+       "        }\n",
+       "        </style>\n",
+       "    "
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -286,10 +32,12 @@
    ],
    "source": [
     "#format the book\n",
-    "%matplotlib inline\n",
     "from __future__ import division, print_function\n",
-    "import sys;sys.path.insert(0,'..')\n",
-    "from  book_format import load_style;load_style('..')"
+    "%matplotlib inline\n",
+    "import sys\n",
+    "sys.path.insert(0, '..')\n",
+    "import book_format\n",
+    "book_format.set_style()"
    ]
   },
   {
@@ -347,15 +95,26 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Anaconda3\\lib\\site-packages\\IPython\\html.py:14: ShimWarning: The `IPython.html` package has been deprecated since IPython 4.0. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.\n",
+      "  \"`IPython.html.widgets` has moved to `ipywidgets`.\", ShimWarning)\n"
+     ]
+    },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAARUAAAEtCAYAAAAmx/QiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "609e5b01d9564d6b8c6e025cab5df9cb",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x74132e8>"
+       "interactive(children=(IntSlider(value=1, description='F00', max=2), FloatSlider(value=1.0, description='F01(dt…"
       ]
      },
      "metadata": {},
@@ -424,15 +183,17 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "94a56f8ac01144e0b32bbb39a353e40a",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8005f60>"
+       "interactive(children=(FloatSlider(value=5.0, description='var_x', max=20.0), FloatSlider(value=5.0, descriptio…"
       ]
      },
      "metadata": {},
@@ -489,15 +250,17 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "690c1af2d34845dd837f33de97469854",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x80526d8>"
+       "interactive(children=(FloatSlider(value=0.0, description='x', max=50.0, min=-50.0), FloatSlider(value=5.0, des…"
       ]
      },
      "metadata": {},
@@ -564,9 +327,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.7.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }

Supporting_Notebooks/Iterative-Least-Squares-for-Sensor-Fusion.ipynb

@@ -10,277 +10,23 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>\n",
-       "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
-       "@import url('http://fonts.googleapis.com/css?family=Lora');\n",
        "\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Open+Sans');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Karla');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Poppins');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Roboto');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Lato');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Domine');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Chivo');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Cardo');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Arvo');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Crimson+Text');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Ubuntu');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Fontin');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Raleway');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Merriweather');\n",
-       "\n",
-       "\n",
-       ".CodeMirror pre {\n",
-       "    font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
-       "}\n",
-       "    div.cell{\n",
-       "        width: 850px;\n",
-       "        margin-left: 0% !important;\n",
-       "        margin-right: auto;\n",
-       "    }\n",
-       "    div.text_cell_render{\n",
-       "        font-family: 'Lora';\n",
-       "        //font-family: 'Open Sans';\n",
-       "        //font-family: 'Karla',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Roboto',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Lato',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Domine',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Chivo',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Cardo',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Arvo',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Poppins',verdana,arial,sans-serif;   \n",
-       "        //font-family: 'Ubuntu',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Fontin',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Raleway',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Merriweather',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Crimson Text', verdana,arial,sans-serif;\n",
-       "        //font-family: verdana,arial,sans-serif;\n",
-       "        //font-family: arial,sans-serif;\n",
-       "        line-height: 125%;\n",
-       "        font-size: 130%;\n",
-       "        text-align: justify;\n",
-       "        text-justify:inter-word;\n",
-       "    }\n",
-       "    div.text_cell code {\n",
-       "        background: transparent;\n",
-       "        color: #000000;\n",
-       "        font-weight: 400;\n",
-       "        font-size: 12pt;\n",
-       "        //font-style: bold;\n",
-       "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
-       "   }\n",
-       "    h1 {\n",
-       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
-       "\t}\n",
-       "\t\n",
-       "    div.input_area {\n",
-       "        background: #F6F6F9;\n",
-       "        border: 1px solid #586e75;      \n",
-       "    }\n",
-       "\n",
-       "    .text_cell_render h1 {\n",
-       "        font-weight: 200;\n",
-       "        font-size: 30pt;\n",
-       "        line-height: 100%;\n",
-       "        color:#c76c0c;\n",
-       "        margin-bottom: 0.5em;\n",
-       "        margin-top: 1em;\n",
-       "        display: block;\n",
-       "        white-space: wrap;\n",
-       "        text-align: left;\n",
-       "    } \n",
-       "    h2 {\n",
-       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "    .text_cell_render h2 {\n",
-       "        font-weight: 200;\n",
-       "        font-size: 16pt;\n",
-       "        font-style: italic;\n",
-       "        line-height: 100%;\n",
-       "        color:#c76c0c;\n",
-       "        margin-bottom: 0.5em;\n",
-       "        margin-top: 1.5em;\n",
-       "        display: block;\n",
-       "        white-space: wrap;\n",
-       "        text-align: left;\n",
-       "    } \n",
-       "    h3 {\n",
-       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
-       "    }\n",
-       "    .text_cell_render h3 {\n",
-       "        font-weight: 200;\n",
-       "        font-size: 14pt;\n",
-       "        line-height: 100%;\n",
-       "        color:#d77c0c;\n",
-       "        margin-bottom: 0.5em;\n",
-       "        margin-top: 2em;\n",
-       "        display: block;\n",
-       "        white-space: wrap;\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "    h4 {\n",
-       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
-       "    }\n",
-       "    .text_cell_render h4 {\n",
-       "        font-weight: 100;\n",
-       "        font-size: 14pt;\n",
-       "        color:#d77c0c;\n",
-       "        margin-bottom: 0.5em;\n",
-       "        margin-top: 0.5em;\n",
-       "        display: block;\n",
-       "        white-space: nowrap;\n",
-       "    }\n",
-       "    h5 {\n",
-       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
-       "    }\n",
-       "\n",
-       "    .text_cell_render h5 {\n",
-       "        font-weight: 200;\n",
-       "        font-style: normal;\n",
-       "        color: #1d3b84;\n",
-       "        font-size: 16pt;\n",
-       "        margin-bottom: 0em;\n",
-       "        margin-top: 0.5em;\n",
-       "        display: block;\n",
-       "        white-space: nowrap;\n",
-       "    }\n",
-       "    div.output_subarea.output_text.output_pyout {\n",
-       "        overflow-x: auto;\n",
-       "        overflow-y: scroll;\n",
-       "        max-height: 50000px;\n",
-       "    }\n",
-       "    div.output_subarea.output_stream.output_stdout.output_text {\n",
-       "        overflow-x: auto;\n",
-       "        overflow-y: scroll;\n",
-       "        max-height: 50000px;\n",
-       "    }\n",
-       "    div.output_wrapper{\n",
-       "        margin-top:0.2em;\n",
-       "        margin-bottom:0.2em;\n",
-       "}\n",
-       "\n",
-       "    code{\n",
-       "        font-size: 6pt;\n",
-       "\n",
-       "    }\n",
-       "    .rendered_html code{\n",
-       "    background-color: transparent;\n",
-       "    }\n",
-       "    ul{\n",
-       "        margin: 2em;\n",
-       "    }\n",
-       "    ul li{\n",
-       "        padding-left: 0.5em; \n",
-       "        margin-bottom: 0.5em; \n",
-       "        margin-top: 0.5em; \n",
-       "    }\n",
-       "    ul li li{\n",
-       "        padding-left: 0.2em; \n",
-       "        margin-bottom: 0.2em; \n",
-       "        margin-top: 0.2em; \n",
-       "    }\n",
-       "    ol{\n",
-       "        margin: 2em;\n",
-       "    }\n",
-       "    ol li{\n",
-       "        padding-left: 0.5em; \n",
-       "        margin-bottom: 0.5em; \n",
-       "        margin-top: 0.5em; \n",
-       "    }\n",
-       "    ul li{\n",
-       "        padding-left: 0.5em; \n",
-       "        margin-bottom: 0.5em; \n",
-       "        margin-top: 0.2em; \n",
-       "    }\n",
-       "    a:link{\n",
-       "       font-weight: bold;\n",
-       "       color:#447adb;\n",
-       "    }\n",
-       "    a:visited{\n",
-       "       font-weight: bold;\n",
-       "       color: #1d3b84;\n",
-       "    }\n",
-       "    a:hover{\n",
-       "       font-weight: bold;\n",
-       "       color: #1d3b84;\n",
-       "    }\n",
-       "    a:focus{\n",
-       "       font-weight: bold;\n",
-       "       color:#447adb;\n",
-       "    }\n",
-       "    a:active{\n",
-       "       font-weight: bold;\n",
-       "       color:#447adb;\n",
-       "    }\n",
-       "    .rendered_html :link {\n",
-       "       text-decoration: underline; \n",
-       "    }\n",
-       "    .rendered_html :hover {\n",
-       "       text-decoration: none; \n",
-       "    }\n",
-       "    .rendered_html :visited {\n",
-       "      text-decoration: none;\n",
-       "    }\n",
-       "    .rendered_html :focus {\n",
-       "      text-decoration: none;\n",
-       "    }\n",
-       "    .rendered_html :active {\n",
-       "      text-decoration: none;\n",
-       "    }\n",
-       "    .warning{\n",
-       "        color: rgb( 240, 20, 20 )\n",
-       "    } \n",
-       "    hr {\n",
-       "      color: #f3f3f3;\n",
-       "      background-color: #f3f3f3;\n",
-       "      height: 1px;\n",
-       "    }\n",
-       "    blockquote{\n",
-       "      display:block;\n",
-       "      background: #fcfcfc;\n",
-       "      border-left: 5px solid #c76c0c;\n",
-       "      font-family: 'Open sans',verdana,arial,sans-serif;\n",
-       "      width:680px;\n",
-       "      padding: 10px 10px 10px 10px;\n",
-       "      text-align:justify;\n",
-       "      text-justify:inter-word;\n",
-       "      }\n",
-       "      blockquote p {\n",
-       "        margin-bottom: 0;\n",
-       "        line-height: 125%;\n",
-       "        font-size: 100%;\n",
-       "      }\n",
-       "</style>\n",
-       "<script>\n",
-       "    MathJax.Hub.Config({\n",
-       "                        TeX: {\n",
-       "                           extensions: [\"AMSmath.js\"],\n",
-       "                           equationNumbers: { autoNumber: \"AMS\", useLabelIds: true}\n",
-       "                           },\n",
-       "                tex2jax: {\n",
-       "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
-       "                    displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
-       "                },\n",
-       "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
-       "                \"HTML-CSS\": {\n",
-       "                    scale:95,\n",
-       "                        availableFonts: [],\n",
-       "                        preferredFont:null,\n",
-       "                        webFont: \"TeX\",\n",
-       "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
-       "                }\n",
-       "        });\n",
-       "</script>\n"
+       "        <style>\n",
+       "        .output_wrapper, .output {\n",
+       "            height:auto !important;\n",
+       "            max-height:100000px; \n",
+       "        }\n",
+       "        .output_scroll {\n",
+       "            box-shadow:none !important;\n",
+       "            webkit-box-shadow:none !important;\n",
+       "        }\n",
+       "        </style>\n",
+       "    "
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -293,10 +39,13 @@
    ],
    "source": [
     "#format the book\n",
-    "%matplotlib inline\n",
     "from __future__ import division, print_function\n",
-    "import sys;sys.path.insert(0,'..');sys.path.insert(0, '../code')\n",
-    "import book_format; book_format.load_style('..')"
+    "%matplotlib inline\n",
+    "import sys\n",
+    "sys.path.insert(0, '..');\n",
+    "sys.path.insert(0, '../kf_book')\n",
+    "import book_format\n",
+    "book_format.set_style()"
    ]
   },
   {
@@ -313,15 +62,13 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d00940>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -329,11 +76,8 @@
     }
    ],
    "source": [
-    "from book_format import set_figsize, figsize\n",
     "import ukf_internal\n",
-    "\n",
-    "with figsize(10, 6):\n",
-    "    ukf_internal.show_four_gps()"
+    "ukf_internal.show_four_gps()"
    ]
   },
   {
@@ -539,9 +283,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -549,25 +291,25 @@
      "text": [
       "iteration: 1\n",
       "innovation [-148.512    28.6789 -148.5361]\n",
-      "pos       [ 805.4175  205.2868]\n",
+      "pos       [805.4175 205.2868]\n",
       "\n",
       "\n",
       "iteration: 2\n",
       "innovation [-0.1177 -7.4049 -0.1599]\n",
-      "pos       [ 800.04    199.9746]\n",
+      "pos       [800.04   199.9746]\n",
       "\n",
       "\n",
       "iteration: 3\n",
       "innovation [-0.0463 -0.001  -0.0463]\n",
-      "pos       [ 800.  200.]\n",
+      "pos       [800. 200.]\n",
       "\n",
       "\n",
       "iteration: 4\n",
       "innovation [-0. -0. -0.]\n",
-      "pos       [ 800.  200.]\n",
+      "pos       [800. 200.]\n",
       "\n",
       "\n",
-      "Iterated solution:  [ 800.  200.]\n"
+      "Iterated solution:  [800. 200.]\n"
      ]
     }
    ],
@@ -673,7 +415,6 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -683,20 +424,20 @@
      "text": [
       "iteration: 1\n",
       "innovation [-0.0009 -1.3868 -0.0024]\n",
-      "pos       [ 800.0014  199.9991]\n",
+      "pos       [800.0014 199.9991]\n",
       "\n",
       "\n",
       "iteration: 2\n",
       "innovation [-0.0016 -0.     -0.0016]\n",
-      "pos       [ 800.  200.]\n",
+      "pos       [800. 200.]\n",
       "\n",
       "\n",
       "iteration: 3\n",
       "innovation [-0. -0. -0.]\n",
-      "pos       [ 800.  200.]\n",
+      "pos       [800. 200.]\n",
       "\n",
       "\n",
-      "Iterated solution:  [ 800.  200.]\n"
+      "Iterated solution:  [800. 200.]\n"
      ]
     }
    ],
@@ -717,40 +458,38 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "iteration: 1\n",
-      "innovation [ 129.8823  100.461   107.5398]\n",
-      "pos       [ 831.4474  186.1222]\n",
+      "innovation [129.8823 100.461  107.5398]\n",
+      "pos       [831.4474 186.1222]\n",
       "\n",
       "\n",
       "iteration: 2\n",
       "innovation [-31.6446  -7.4837 -30.7861]\n",
-      "pos       [ 800.3284  198.8076]\n",
+      "pos       [800.3284 198.8076]\n",
       "\n",
       "\n",
       "iteration: 3\n",
       "innovation [-0.6041 -0.3813  0.3569]\n",
-      "pos       [ 799.948   198.6026]\n",
+      "pos       [799.948  198.6026]\n",
       "\n",
       "\n",
       "iteration: 4\n",
       "innovation [-0.4803  0.0004  0.4802]\n",
-      "pos       [ 799.9476  198.6025]\n",
+      "pos       [799.9476 198.6025]\n",
       "\n",
       "\n",
       "iteration: 5\n",
       "innovation [-0.4802  0.0007  0.4803]\n",
-      "pos       [ 799.9476  198.6025]\n",
+      "pos       [799.9476 198.6025]\n",
       "\n",
       "\n",
-      "Iterated solution:  [ 799.948  198.602]\n"
+      "Iterated solution:  [799.948 198.602]\n"
      ]
     }
    ],
@@ -789,9 +528,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.7.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }

Supporting_Notebooks/Taylor-Series.ipynb

@@ -2,294 +2,42 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 3,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>\n",
-       "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
-       "@import url('http://fonts.googleapis.com/css?family=Lora');\n",
        "\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Open+Sans');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Karla');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Poppins');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Roboto');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Lato');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Domine');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Chivo');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Cardo');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Arvo');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Crimson+Text');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Ubuntu');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Fontin');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Raleway');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Merriweather');\n",
-       "\n",
-       "\n",
-       ".CodeMirror pre {\n",
-       "    font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
-       "}\n",
-       "    div.cell{\n",
-       "        width: 850px;\n",
-       "        margin-left: 0% !important;\n",
-       "        margin-right: auto;\n",
-       "    }\n",
-       "    div.text_cell_render{\n",
-       "        font-family: 'Lora';\n",
-       "        //font-family: 'Open Sans';\n",
-       "        //font-family: 'Karla',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Roboto',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Lato',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Domine',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Chivo',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Cardo',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Arvo',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Poppins',verdana,arial,sans-serif;   \n",
-       "        //font-family: 'Ubuntu',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Fontin',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Raleway',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Merriweather',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Crimson Text', verdana,arial,sans-serif;\n",
-       "        //font-family: verdana,arial,sans-serif;\n",
-       "        //font-family: arial,sans-serif;\n",
-       "        line-height: 125%;\n",
-       "        font-size: 130%;\n",
-       "        text-align: justify;\n",
-       "        text-justify:inter-word;\n",
-       "    }\n",
-       "    div.text_cell code {\n",
-       "        background: transparent;\n",
-       "        color: #000000;\n",
-       "        font-weight: 400;\n",
-       "        font-size: 12pt;\n",
-       "        //font-style: bold;\n",
-       "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
-       "   }\n",
-       "    h1 {\n",
-       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
-       "\t}\n",
-       "\t\n",
-       "    div.input_area {\n",
-       "        background: #F6F6F9;\n",
-       "        border: 1px solid #586e75;      \n",
-       "    }\n",
-       "\n",
-       "    .text_cell_render h1 {\n",
-       "        font-weight: 200;\n",
-       "        font-size: 30pt;\n",
-       "        line-height: 100%;\n",
-       "        color:#c76c0c;\n",
-       "        margin-bottom: 0.5em;\n",
-       "        margin-top: 1em;\n",
-       "        display: block;\n",
-       "        white-space: wrap;\n",
-       "        text-align: left;\n",
-       "    } \n",
-       "    h2 {\n",
-       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "    .text_cell_render h2 {\n",
-       "        font-weight: 200;\n",
-       "        font-size: 16pt;\n",
-       "        font-style: italic;\n",
-       "        line-height: 100%;\n",
-       "        color:#c76c0c;\n",
-       "        margin-bottom: 0.5em;\n",
-       "        margin-top: 1.5em;\n",
-       "        display: block;\n",
-       "        white-space: wrap;\n",
-       "        text-align: left;\n",
-       "    } \n",
-       "    h3 {\n",
-       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
-       "    }\n",
-       "    .text_cell_render h3 {\n",
-       "        font-weight: 200;\n",
-       "        font-size: 14pt;\n",
-       "        line-height: 100%;\n",
-       "        color:#d77c0c;\n",
-       "        margin-bottom: 0.5em;\n",
-       "        margin-top: 2em;\n",
-       "        display: block;\n",
-       "        white-space: wrap;\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "    h4 {\n",
-       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
-       "    }\n",
-       "    .text_cell_render h4 {\n",
-       "        font-weight: 100;\n",
-       "        font-size: 14pt;\n",
-       "        color:#d77c0c;\n",
-       "        margin-bottom: 0.5em;\n",
-       "        margin-top: 0.5em;\n",
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-       "    h5 {\n",
-       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
-       "    }\n",
-       "\n",
-       "    .text_cell_render h5 {\n",
-       "        font-weight: 200;\n",
-       "        font-style: normal;\n",
-       "        color: #1d3b84;\n",
-       "        font-size: 16pt;\n",
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-       "        margin-top: 0.5em;\n",
-       "        display: block;\n",
-       "        white-space: nowrap;\n",
-       "    }\n",
-       "    div.output_subarea.output_text.output_pyout {\n",
-       "        overflow-x: auto;\n",
-       "        overflow-y: scroll;\n",
-       "        max-height: 50000px;\n",
-       "    }\n",
-       "    div.output_subarea.output_stream.output_stdout.output_text {\n",
-       "        overflow-x: auto;\n",
-       "        overflow-y: scroll;\n",
-       "        max-height: 50000px;\n",
-       "    }\n",
-       "    div.output_wrapper{\n",
-       "        margin-top:0.2em;\n",
-       "        margin-bottom:0.2em;\n",
-       "}\n",
-       "\n",
-       "    code{\n",
-       "        font-size: 6pt;\n",
-       "\n",
-       "    }\n",
-       "    .rendered_html code{\n",
-       "    background-color: transparent;\n",
-       "    }\n",
-       "    ul{\n",
-       "        margin: 2em;\n",
-       "    }\n",
-       "    ul li{\n",
-       "        padding-left: 0.5em; \n",
-       "        margin-bottom: 0.5em; \n",
-       "        margin-top: 0.5em; \n",
-       "    }\n",
-       "    ul li li{\n",
-       "        padding-left: 0.2em; \n",
-       "        margin-bottom: 0.2em; \n",
-       "        margin-top: 0.2em; \n",
-       "    }\n",
-       "    ol{\n",
-       "        margin: 2em;\n",
-       "    }\n",
-       "    ol li{\n",
-       "        padding-left: 0.5em; \n",
-       "        margin-bottom: 0.5em; \n",
-       "        margin-top: 0.5em; \n",
-       "    }\n",
-       "    ul li{\n",
-       "        padding-left: 0.5em; \n",
-       "        margin-bottom: 0.5em; \n",
-       "        margin-top: 0.2em; \n",
-       "    }\n",
-       "    a:link{\n",
-       "       font-weight: bold;\n",
-       "       color:#447adb;\n",
-       "    }\n",
-       "    a:visited{\n",
-       "       font-weight: bold;\n",
-       "       color: #1d3b84;\n",
-       "    }\n",
-       "    a:hover{\n",
-       "       font-weight: bold;\n",
-       "       color: #1d3b84;\n",
-       "    }\n",
-       "    a:focus{\n",
-       "       font-weight: bold;\n",
-       "       color:#447adb;\n",
-       "    }\n",
-       "    a:active{\n",
-       "       font-weight: bold;\n",
-       "       color:#447adb;\n",
-       "    }\n",
-       "    .rendered_html :link {\n",
-       "       text-decoration: underline; \n",
-       "    }\n",
-       "    .rendered_html :hover {\n",
-       "       text-decoration: none; \n",
-       "    }\n",
-       "    .rendered_html :visited {\n",
-       "      text-decoration: none;\n",
-       "    }\n",
-       "    .rendered_html :focus {\n",
-       "      text-decoration: none;\n",
-       "    }\n",
-       "    .rendered_html :active {\n",
-       "      text-decoration: none;\n",
-       "    }\n",
-       "    .warning{\n",
-       "        color: rgb( 240, 20, 20 )\n",
-       "    } \n",
-       "    hr {\n",
-       "      color: #f3f3f3;\n",
-       "      background-color: #f3f3f3;\n",
-       "      height: 1px;\n",
-       "    }\n",
-       "    blockquote{\n",
-       "      display:block;\n",
-       "      background: #fcfcfc;\n",
-       "      border-left: 5px solid #c76c0c;\n",
-       "      font-family: 'Open sans',verdana,arial,sans-serif;\n",
-       "      width:680px;\n",
-       "      padding: 10px 10px 10px 10px;\n",
-       "      text-align:justify;\n",
-       "      text-justify:inter-word;\n",
-       "      }\n",
-       "      blockquote p {\n",
-       "        margin-bottom: 0;\n",
-       "        line-height: 125%;\n",
-       "        font-size: 100%;\n",
-       "      }\n",
-       "</style>\n",
-       "<script>\n",
-       "    MathJax.Hub.Config({\n",
-       "                        TeX: {\n",
-       "                           extensions: [\"AMSmath.js\"],\n",
-       "                           equationNumbers: { autoNumber: \"AMS\", useLabelIds: true}\n",
-       "                           },\n",
-       "                tex2jax: {\n",
-       "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
-       "                    displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
-       "                },\n",
-       "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
-       "                \"HTML-CSS\": {\n",
-       "                    scale:95,\n",
-       "                        availableFonts: [],\n",
-       "                        preferredFont:null,\n",
-       "                        webFont: \"TeX\",\n",
-       "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
-       "                }\n",
-       "        });\n",
-       "</script>\n"
+       "        <style>\n",
+       "        .output_wrapper, .output {\n",
+       "            height:auto !important;\n",
+       "            max-height:100000px; \n",
+       "        }\n",
+       "        .output_scroll {\n",
+       "            box-shadow:none !important;\n",
+       "            webkit-box-shadow:none !important;\n",
+       "        }\n",
+       "        </style>\n",
+       "    "
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "#format the book\n",
-    "%matplotlib inline\n",
     "from __future__ import division, print_function\n",
-    "import sys;sys.path.insert(0,'..')\n",
-    "from  book_format import load_style;load_style('..')"
+    "%matplotlib inline\n",
+    "import sys\n",
+    "sys.path.insert(0, '..')\n",
+    "import book_format\n",
+    "book_format.set_style()"
    ]
   },
   {
@@ -325,17 +73,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 4,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "estimate of sin(.3) is 0.30452025\n",
-      "exact value of sin(.3) is 0.295520206661\n"
+      "exact value of sin(.3) is 0.29552020666133955\n"
      ]
     }
    ],
@@ -374,9 +120,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.7.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }

animations/Gaussians_Animations.ipynb

@@ -8,237 +8,15 @@
    },
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<style>\n",
-       "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
-       "@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
-       "@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
-       "\n",
-       "    div.cell{\n",
-       "        width: 850px;\n",
-       "        margin-left: 0% !important;\n",
-       "        margin-right: auto;\n",
-       "    }\n",
-       "    div.text_cell code {\n",
-       "        background: transparent;\n",
-       "        color: #000000;\n",
-       "        font-weight: 600;\n",
-       "        font-size: 11pt;\n",
-       "        font-style: bold;\n",
-       "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
-       "   }\n",
-       "    h1 {\n",
-       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
-       "\t}\n",
-       "\t\n",
-       "    div.input_area {\n",
-       "        background: #F6F6F9;\n",
-       "        border: 1px solid #586e75;\n",
-       "    }\n",
-       "\n",
-       "    .text_cell_render h1 {\n",
-       "        font-weight: 200;\n",
-       "        font-size: 30pt;\n",
-       "        line-height: 100%;\n",
-       "        color:#c76c0c;\n",
-       "        margin-bottom: 0.5em;\n",
-       "        margin-top: 1em;\n",
-       "        display: block;\n",
-       "        white-space: wrap;\n",
-       "    } \n",
-       "    h2 {\n",
-       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
-       "    }\n",
-       "    .text_cell_render h2 {\n",
-       "        font-weight: 200;\n",
-       "        font-size: 16pt;\n",
-       "        font-style: italic;\n",
-       "        line-height: 100%;\n",
-       "        color:#c76c0c;\n",
-       "        margin-bottom: 0.5em;\n",
-       "        margin-top: 1.5em;\n",
-       "        display: inline;\n",
-       "        white-space: wrap;\n",
-       "    } \n",
-       "    h3 {\n",
-       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
-       "    }\n",
-       "    .text_cell_render h3 {\n",
-       "        font-weight: 200;\n",
-       "        font-size: 14pt;\n",
-       "        line-height: 100%;\n",
-       "        color:#d77c0c;\n",
-       "        margin-bottom: 0.5em;\n",
-       "        margin-top: 2em;\n",
-       "        display: block;\n",
-       "        white-space: nowrap;\n",
-       "    }\n",
-       "    h4 {\n",
-       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
-       "    }\n",
-       "    .text_cell_render h4 {\n",
-       "        font-weight: 100;\n",
-       "        font-size: 14pt;\n",
-       "        color:#d77c0c;\n",
-       "        margin-bottom: 0.5em;\n",
-       "        margin-top: 0.5em;\n",
-       "        display: block;\n",
-       "        white-space: nowrap;\n",
-       "    }\n",
-       "    h5 {\n",
-       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
-       "    }\n",
-       "    .text_cell_render h5 {\n",
-       "        font-weight: 200;\n",
-       "        font-style: normal;\n",
-       "        color: #1d3b84;\n",
-       "        font-size: 16pt;\n",
-       "        margin-bottom: 0em;\n",
-       "        margin-top: 0.5em;\n",
-       "        display: block;\n",
-       "        white-space: nowrap;\n",
-       "    }\n",
-       "    div.text_cell_render{\n",
-       "        font-family: 'Arimo',verdana,arial,sans-serif;\n",
-       "        line-height: 125%;\n",
-       "        font-size: 120%;\n",
-       "        width:740px;\n",
-       "        margin-left:auto;\n",
-       "        margin-right:auto;\n",
-       "        text-align:justify;\n",
-       "        text-justify:inter-word;\n",
-       "    }\n",
-       "    div.output_subarea.output_text.output_pyout {\n",
-       "        overflow-x: auto;\n",
-       "        overflow-y: scroll;\n",
-       "        max-height: 50000px;\n",
-       "    }\n",
-       "    div.output_subarea.output_stream.output_stdout.output_text {\n",
-       "        overflow-x: auto;\n",
-       "        overflow-y: scroll;\n",
-       "        max-height: 50000px;\n",
-       "    }\n",
-       "    div.output_wrapper{\n",
-       "        margin-top:0.2em;\n",
-       "        margin-bottom:0.2em;\n",
-       "}\n",
-       "\n",
-       "    code{\n",
-       "      font-size: 70%;\n",
-       "    }\n",
-       "    .rendered_html code{\n",
-       "    background-color: transparent;\n",
-       "    }\n",
-       "    ul{\n",
-       "        margin: 2em;\n",
-       "    }\n",
-       "    ul li{\n",
-       "        padding-left: 0.5em; \n",
-       "        margin-bottom: 0.5em; \n",
-       "        margin-top: 0.5em; \n",
-       "    }\n",
-       "    ul li li{\n",
-       "        padding-left: 0.2em; \n",
-       "        margin-bottom: 0.2em; \n",
-       "        margin-top: 0.2em; \n",
-       "    }\n",
-       "    ol{\n",
-       "        margin: 2em;\n",
-       "    }\n",
-       "    ol li{\n",
-       "        padding-left: 0.5em; \n",
-       "        margin-bottom: 0.5em; \n",
-       "        margin-top: 0.5em; \n",
-       "    }\n",
-       "    ul li{\n",
-       "        padding-left: 0.5em; \n",
-       "        margin-bottom: 0.5em; \n",
-       "        margin-top: 0.2em; \n",
-       "    }\n",
-       "    a:link{\n",
-       "       font-weight: bold;\n",
-       "       color:#447adb;\n",
-       "    }\n",
-       "    a:visited{\n",
-       "       font-weight: bold;\n",
-       "       color: #1d3b84;\n",
-       "    }\n",
-       "    a:hover{\n",
-       "       font-weight: bold;\n",
-       "       color: #1d3b84;\n",
-       "    }\n",
-       "    a:focus{\n",
-       "       font-weight: bold;\n",
-       "       color:#447adb;\n",
-       "    }\n",
-       "    a:active{\n",
-       "       font-weight: bold;\n",
-       "       color:#447adb;\n",
-       "    }\n",
-       "    .rendered_html :link {\n",
-       "       text-decoration: underline; \n",
-       "    }\n",
-       "    .rendered_html :hover {\n",
-       "       text-decoration: none; \n",
-       "    }\n",
-       "    .rendered_html :visited {\n",
-       "      text-decoration: none;\n",
-       "    }\n",
-       "    .rendered_html :focus {\n",
-       "      text-decoration: none;\n",
-       "    }\n",
-       "    .rendered_html :active {\n",
-       "      text-decoration: none;\n",
-       "    }\n",
-       "    .warning{\n",
-       "        color: rgb( 240, 20, 20 )\n",
-       "    } \n",
-       "    hr {\n",
-       "      color: #f3f3f3;\n",
-       "      background-color: #f3f3f3;\n",
-       "      height: 1px;\n",
-       "    }\n",
-       "    blockquote{\n",
-       "      display:block;\n",
-       "      background: #fcfcfc;\n",
-       "      border-left: 5px solid #c76c0c;\n",
-       "      font-family: 'Open sans',verdana,arial,sans-serif;\n",
-       "      width:680px;\n",
-       "      padding: 10px 10px 10px 10px;\n",
-       "      text-align:justify;\n",
-       "      text-justify:inter-word;\n",
-       "      }\n",
-       "      blockquote p {\n",
-       "        margin-bottom: 0;\n",
-       "        line-height: 125%;\n",
-       "        font-size: 100%;\n",
-       "      }\n",
-       "</style>\n",
-       "<script>\n",
-       "    MathJax.Hub.Config({\n",
-       "                        TeX: {\n",
-       "                           extensions: [\"AMSmath.js\"]\n",
-       "                           },\n",
-       "                tex2jax: {\n",
-       "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
-       "                    displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
-       "                },\n",
-       "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
-       "                \"HTML-CSS\": {\n",
-       "                    scale:85,\n",
-       "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
-       "                }\n",
-       "        });\n",
-       "</script>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "AttributeError",
+     "evalue": "module 'book_format' has no attribute 'load_style'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-1-088e253ce17b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;31m# consistant with that look and feel.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mbook_format\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mbook_format\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_style\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdirectory\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'..'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m: module 'book_format' has no attribute 'load_style'"
+     ]
     }
    ],
    "source": [
@@ -262,14 +40,15 @@
    },
    "outputs": [
     {
-     "data": {
-      "image/png": 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truncated
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x8474eb8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'gif_animate'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-2-5ca8afccfac5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mgif_animate\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0manimate\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mstats\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mgaussian\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpylab\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpylab\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'gif_animate'"
+     ]
     }
    ],
    "source": [
@@ -341,7 +120,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.3"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,

animations/Kalman_Filters_Animations.ipynb

@@ -1,270 +1,293 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:bacbdf37e5f8b4f5a0841d27be72d89c6cfab901e53ba2bc642b422618558c14"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from __future__ import division, print_function\n",
-      "%matplotlib inline\n",
-      "import sys\n",
-      "sys.path.insert(0,'..') # allow us to format the book\n",
-      "sys.path.insert(0,'../code') \n",
-      "\n",
-      "# use same formatting as rest of book so that the plots are\n",
-      "# consistant with that look and feel.\n",
-      "import book_format\n",
-      "book_format.load_style(directory='..')\n",
-      "\n",
-      "\n",
-      "\n",
-      "def update(mu1, var1, mu2, var2):\n",
-      "    mean = (var1*mu2 + var2*mu1) / (var1+var2)\n",
-      "    variance = 1 / (1/var1 + 1/var2)\n",
-      "    return (mean, variance)\n",
-      "\n",
-      "def predict(pos, variance, movement, movement_variance):\n",
-      "    return (pos + movement, variance + movement_variance)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This notebook creates the animations for the Kalman Filter chapter. It is not really intended to be a readable part of the book, but of course you are free to look at the source code, and even modify it. However, if you are interested in running your own animations, I'll point you to the examples subdirectory of the book, which contains a number of python scripts that you can run and modify from an IDE or the command line. This module saves the animations to GIF files, which is quite slow and not very interactive. "
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def plot_3d_covariance(ax, mean, cov):\n",
-      "    \"\"\" plots a 2x2 covariance matrix positioned at mean. mean will be plotted\n",
-      "    in x and y, and the probability in the z axis.\n",
-      "\n",
-      "    Parameters\n",
-      "    ----------\n",
-      "    mean :  2x1 tuple-like object\n",
-      "        mean for x and y coordinates. For example (2.3, 7.5)\n",
-      "\n",
-      "    cov : 2x2 nd.array\n",
-      "       the covariance matrix\n",
-      "\n",
-      "    \"\"\"\n",
-      "\n",
-      "    # compute width and height of covariance ellipse so we can choose\n",
-      "    # appropriate ranges for x and y\n",
-      "    o,w,h = stats.covariance_ellipse(cov,3)\n",
-      "    # rotate width and height to x,y axis\n",
-      "    wx = abs(w*np.cos(o) + h*np.sin(o))*1.2\n",
-      "    wy = abs(h*np.cos(o) - w*np.sin(o))*1.2\n",
-      "\n",
-      "\n",
-      "    # ensure axis are of the same size so everything is plotted with the same\n",
-      "    # scale\n",
-      "    if wx > wy:\n",
-      "        w = wx\n",
-      "    else:\n",
-      "        w = wy\n",
-      "\n",
-      "    minx = mean[0] - w\n",
-      "    maxx = mean[0] + w\n",
-      "    miny = mean[1] - w\n",
-      "    maxy = mean[1] + w\n",
-      "\n",
-      "    xs = np.arange(minx, maxx, (maxx-minx)/40.)\n",
-      "    ys = np.arange(miny, maxy, (maxy-miny)/40.)\n",
-      "    xv, yv = np.meshgrid (xs, ys)\n",
-      "\n",
-      "    zs = np.array([100.* stats.multivariate_gaussian(np.array([x,y]),mean,cov) \\\n",
-      "                   for x,y in zip(np.ravel(xv), np.ravel(yv))])\n",
-      "    zv = zs.reshape(xv.shape)\n",
-      "\n",
-      "    ax = plt.figure().add_subplot(111, projection='3d')\n",
-      "    ax.plot_surface(xv, yv, zv, rstride=1, cstride=1, cmap=cm.autumn)\n",
-      "\n",
-      "    ax.set_xlabel('X')\n",
-      "    ax.set_ylabel('Y')\n",
-      "\n",
-      "    ax.contour(xv, yv, zv, zdir='x', offset=minx-1, cmap=cm.autumn)\n",
-      "    ax.contour(xv, yv, zv, zdir='y', offset=maxy, cmap=cm.BuGn)\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from __future__ import print_function, division\n",
-      "import matplotlib.pyplot as plt\n",
-      "import numpy.random as random\n",
-      "import math\n",
-      "import stats\n",
-      "from gif_animate import animate\n",
-      "\n",
-      "\n",
-      "\n",
-      "sensor_error = 1.2**2\n",
-      "movement_error = .2\n",
-      "movement = 0\n",
-      "voltage = (25,20) #who knows what the first value is?\n",
-      "\n",
-      "volts = [14.44, 18.74, 17.21, 15.98, 16.76, 14.8, 16.01, 16.90, 15.41, 17.31,\n",
-      "        16.39, 17.96]\n",
-      "ps = [voltage[0]]\n",
-      "\n",
-      "\n",
-      "i = 0\n",
-      "pred = 0\n",
-      "def volt_animate(frame):\n",
-      "    global i, ps, voltage, Z, x, pred\n",
-      "    \n",
-      "    step = frame % 4\n",
-      "    plt.subplot(211)\n",
-      "    plt.ylim([14,26])\n",
-      "\n",
-      "    if step == 0:\n",
-      "        prev = voltage[0]\n",
-      "        voltage = predict(voltage[0], voltage[1], movement, movement_error)\n",
-      "        pred = voltage[0]\n",
-      "        plt.plot ([i, i+1], [prev, pred], c='g') \n",
-      "        \n",
-      "    elif step == 1:\n",
-      "        Z = volts[i]\n",
-      "        i += 1\n",
-      "        \n",
-      "        plt.scatter(i, Z, marker='+', s=64, color='r')\n",
-      "\n",
-      "    elif step == 2:\n",
-      "        plt.plot ([i,i], [pred, Z], c='r', alpha=0.3)\n",
-      "\n",
-      "    else:\n",
-      "        voltage = update(voltage[0], voltage[1], Z, sensor_error)\n",
-      "        ps.append(voltage[0])\n",
-      "        plt.plot(ps, c='b')\n",
-      "        \n",
-      "    plt.subplot(212)\n",
-      "    plt.cla()\n",
-      "    stats.plot_gaussian(voltage[0], voltage[1], xlim=[0,32])\n",
-      "    plt.ylim([0,1])\n",
-      "    plt.tight_layout()\n",
-      "        \n",
-      "N=12\n",
-      "animate('05_volt_animate.gif', volt_animate, N*3, 350)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x7f2d5f871b70>"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<img src='05_volt_animate.gif'>"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from __future__ import print_function, division\n",
-      "import matplotlib.pyplot as plt\n",
-      "import numpy.random as random\n",
-      "import math\n",
-      "import stats\n",
-      "from gif_animate import animate\n",
-      "\n",
-      "# assume dog is always moving 1m to the right\n",
-      "movement = 1\n",
-      "movement_error = .05\n",
-      "sensor_error = 4.5\n",
-      "pos = (0, 100)   # gaussian N(0,100)\n",
-      "\n",
-      "# this is the recorded output of a run of the dog sensor with an initial\n",
-      "# seed of 200\n",
-      "ZS = [-2.07, 6.05, 4.51, 3.47, 5.76, 5.93, 6.53, 9.01, 7.53, 11.68, \n",
-      "      11.15, 14.76, 13.45, 16.15, 19.05, 14.87, 20.90, 15.75, \n",
-      "      17.16, 20.50]\n",
-      "zs = []\n",
-      "ps = []\n",
-      "\n",
-      "N=20\n",
-      "\n",
-      "def dog_animate(frame):\n",
-      "    global pos, zs, ps,  N, ZS\n",
-      "    pos = predict(pos[0], pos[1], movement, movement_error)    \n",
-      "    Z = ZS[frame]\n",
-      "    zs.append(Z)\n",
-      "    \n",
-      "    pos = update(pos[0], pos[1], Z, sensor_error)\n",
-      "    ps.append(pos[0])\n",
-      "\n",
-      "\n",
-      "    plt.subplot(211)\n",
-      "    plt.plot(zs,c='r', linestyle='dashed')\n",
-      "\n",
-      "    plt.xlim([0,N*1.2])\n",
-      "    plt.ylim([0,N*1.2])\n",
-      "\n",
-      "    if len(ps) > 1:\n",
-      "        plt.plot(ps, c='#8EBA42')\n",
-      "        \n",
-      "    plt.subplot(212)\n",
-      "    plt.cla()\n",
-      "    stats.plot_gaussian(pos[0], pos[1], xlim=[0,N*1.2])\n",
-      "    plt.ylim(0, 1)\n",
-      "    plt.tight_layout()\n",
-      "\n",
-      "\n",
-      "animate('05_dog_track.gif', dog_animate, N, 200)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x7f2d585b7e10>"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<img src='05_dog_track.gif'>"
+     "ename": "AttributeError",
+     "evalue": "module 'book_format' has no attribute 'load_style'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-1-ec44a09e3350>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;31m# consistant with that look and feel.\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[1;32mimport\u001b[0m \u001b[0mbook_format\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mbook_format\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_style\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdirectory\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'..'\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     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mAttributeError\u001b[0m: module 'book_format' has no attribute 'load_style'"
      ]
     }
    ],
-   "metadata": {}
+   "source": [
+    "from __future__ import division, print_function\n",
+    "%matplotlib inline\n",
+    "import sys\n",
+    "sys.path.insert(0,'..') # allow us to format the book\n",
+    "sys.path.insert(0,'../code') \n",
+    "\n",
+    "# use same formatting as rest of book so that the plots are\n",
+    "# consistant with that look and feel.\n",
+    "import book_format\n",
+    "book_format.load_style(directory='..')\n",
+    "\n",
+    "\n",
+    "\n",
+    "def update(mu1, var1, mu2, var2):\n",
+    "    mean = (var1*mu2 + var2*mu1) / (var1+var2)\n",
+    "    variance = 1 / (1/var1 + 1/var2)\n",
+    "    return (mean, variance)\n",
+    "\n",
+    "def predict(pos, variance, movement, movement_variance):\n",
+    "    return (pos + movement, variance + movement_variance)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook creates the animations for the Kalman Filter chapter. It is not really intended to be a readable part of the book, but of course you are free to look at the source code, and even modify it. However, if you are interested in running your own animations, I'll point you to the examples subdirectory of the book, which contains a number of python scripts that you can run and modify from an IDE or the command line. This module saves the animations to GIF files, which is quite slow and not very interactive. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def plot_3d_covariance(ax, mean, cov):\n",
+    "    \"\"\" plots a 2x2 covariance matrix positioned at mean. mean will be plotted\n",
+    "    in x and y, and the probability in the z axis.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    mean :  2x1 tuple-like object\n",
+    "        mean for x and y coordinates. For example (2.3, 7.5)\n",
+    "\n",
+    "    cov : 2x2 nd.array\n",
+    "       the covariance matrix\n",
+    "\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # compute width and height of covariance ellipse so we can choose\n",
+    "    # appropriate ranges for x and y\n",
+    "    o,w,h = stats.covariance_ellipse(cov,3)\n",
+    "    # rotate width and height to x,y axis\n",
+    "    wx = abs(w*np.cos(o) + h*np.sin(o))*1.2\n",
+    "    wy = abs(h*np.cos(o) - w*np.sin(o))*1.2\n",
+    "\n",
+    "\n",
+    "    # ensure axis are of the same size so everything is plotted with the same\n",
+    "    # scale\n",
+    "    if wx > wy:\n",
+    "        w = wx\n",
+    "    else:\n",
+    "        w = wy\n",
+    "\n",
+    "    minx = mean[0] - w\n",
+    "    maxx = mean[0] + w\n",
+    "    miny = mean[1] - w\n",
+    "    maxy = mean[1] + w\n",
+    "\n",
+    "    xs = np.arange(minx, maxx, (maxx-minx)/40.)\n",
+    "    ys = np.arange(miny, maxy, (maxy-miny)/40.)\n",
+    "    xv, yv = np.meshgrid (xs, ys)\n",
+    "\n",
+    "    zs = np.array([100.* stats.multivariate_gaussian(np.array([x,y]),mean,cov) \\\n",
+    "                   for x,y in zip(np.ravel(xv), np.ravel(yv))])\n",
+    "    zv = zs.reshape(xv.shape)\n",
+    "\n",
+    "    ax = plt.figure().add_subplot(111, projection='3d')\n",
+    "    ax.plot_surface(xv, yv, zv, rstride=1, cstride=1, cmap=cm.autumn)\n",
+    "\n",
+    "    ax.set_xlabel('X')\n",
+    "    ax.set_ylabel('Y')\n",
+    "\n",
+    "    ax.contour(xv, yv, zv, zdir='x', offset=minx-1, cmap=cm.autumn)\n",
+    "    ax.contour(xv, yv, zv, zdir='y', offset=maxy, cmap=cm.BuGn)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'gif_animate'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-3-42bb287cbba9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmath\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mstats\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mgif_animate\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0manimate\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'gif_animate'"
+     ]
+    }
+   ],
+   "source": [
+    "from __future__ import print_function, division\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy.random as random\n",
+    "import math\n",
+    "import stats\n",
+    "from gif_animate import animate\n",
+    "\n",
+    "\n",
+    "\n",
+    "sensor_error = 1.2**2\n",
+    "movement_error = .2\n",
+    "movement = 0\n",
+    "voltage = (25,20) #who knows what the first value is?\n",
+    "\n",
+    "volts = [14.44, 18.74, 17.21, 15.98, 16.76, 14.8, 16.01, 16.90, 15.41, 17.31,\n",
+    "        16.39, 17.96]\n",
+    "ps = [voltage[0]]\n",
+    "\n",
+    "\n",
+    "i = 0\n",
+    "pred = 0\n",
+    "def volt_animate(frame):\n",
+    "    global i, ps, voltage, Z, x, pred\n",
+    "    \n",
+    "    step = frame % 4\n",
+    "    plt.subplot(211)\n",
+    "    plt.ylim([14,26])\n",
+    "\n",
+    "    if step == 0:\n",
+    "        prev = voltage[0]\n",
+    "        voltage = predict(voltage[0], voltage[1], movement, movement_error)\n",
+    "        pred = voltage[0]\n",
+    "        plt.plot ([i, i+1], [prev, pred], c='g') \n",
+    "        \n",
+    "    elif step == 1:\n",
+    "        Z = volts[i]\n",
+    "        i += 1\n",
+    "        \n",
+    "        plt.scatter(i, Z, marker='+', s=64, color='r')\n",
+    "\n",
+    "    elif step == 2:\n",
+    "        plt.plot ([i,i], [pred, Z], c='r', alpha=0.3)\n",
+    "\n",
+    "    else:\n",
+    "        voltage = update(voltage[0], voltage[1], Z, sensor_error)\n",
+    "        ps.append(voltage[0])\n",
+    "        plt.plot(ps, c='b')\n",
+    "        \n",
+    "    plt.subplot(212)\n",
+    "    plt.cla()\n",
+    "    stats.plot_gaussian(voltage[0], voltage[1], xlim=[0,32])\n",
+    "    plt.ylim([0,1])\n",
+    "    plt.tight_layout()\n",
+    "        \n",
+    "N=12\n",
+    "animate('05_volt_animate.gif', volt_animate, N*3, 350)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src='05_volt_animate.gif'>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'gif_animate'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-4-0138411cd6ba>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmath\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mstats\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mgif_animate\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0manimate\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;31m# assume dog is always moving 1m to the right\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'gif_animate'"
+     ]
+    }
+   ],
+   "source": [
+    "from __future__ import print_function, division\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy.random as random\n",
+    "import math\n",
+    "import stats\n",
+    "from gif_animate import animate\n",
+    "\n",
+    "# assume dog is always moving 1m to the right\n",
+    "movement = 1\n",
+    "movement_error = .05\n",
+    "sensor_error = 4.5\n",
+    "pos = (0, 100)   # gaussian N(0,100)\n",
+    "\n",
+    "# this is the recorded output of a run of the dog sensor with an initial\n",
+    "# seed of 200\n",
+    "ZS = [-2.07, 6.05, 4.51, 3.47, 5.76, 5.93, 6.53, 9.01, 7.53, 11.68, \n",
+    "      11.15, 14.76, 13.45, 16.15, 19.05, 14.87, 20.90, 15.75, \n",
+    "      17.16, 20.50]\n",
+    "zs = []\n",
+    "ps = []\n",
+    "\n",
+    "N=20\n",
+    "\n",
+    "def dog_animate(frame):\n",
+    "    global pos, zs, ps,  N, ZS\n",
+    "    pos = predict(pos[0], pos[1], movement, movement_error)    \n",
+    "    Z = ZS[frame]\n",
+    "    zs.append(Z)\n",
+    "    \n",
+    "    pos = update(pos[0], pos[1], Z, sensor_error)\n",
+    "    ps.append(pos[0])\n",
+    "\n",
+    "\n",
+    "    plt.subplot(211)\n",
+    "    plt.plot(zs,c='r', linestyle='dashed')\n",
+    "\n",
+    "    plt.xlim([0,N*1.2])\n",
+    "    plt.ylim([0,N*1.2])\n",
+    "\n",
+    "    if len(ps) > 1:\n",
+    "        plt.plot(ps, c='#8EBA42')\n",
+    "        \n",
+    "    plt.subplot(212)\n",
+    "    plt.cla()\n",
+    "    stats.plot_gaussian(pos[0], pos[1], xlim=[0,N*1.2])\n",
+    "    plt.ylim(0, 1)\n",
+    "    plt.tight_layout()\n",
+    "\n",
+    "\n",
+    "animate('05_dog_track.gif', dog_animate, N, 200)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src='05_dog_track.gif'>"
+   ]
   }
- ]
-}
+ ],
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

animations/discrete_bayes_animations.ipynb

@@ -15,276 +15,15 @@
    },
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<style>\n",
-       "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
-       "@import url('http://fonts.googleapis.com/css?family=Lora');\n",
-       "\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Open+Sans');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Karla');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Poppins');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Roboto');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Lato');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Domine');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Chivo');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Cardo');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Arvo');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Crimson+Text');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Ubuntu');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Fontin');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Raleway');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Merriweather');\n",
-       "\n",
-       "\n",
-       ".CodeMirror pre {\n",
-       "    font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
-       "}\n",
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-       "        margin-right: auto;\n",
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-       "        text-justify:inter-word;\n",
-       "    }\n",
-       "    div.text_cell code {\n",
-       "        background: transparent;\n",
-       "        color: #000000;\n",
-       "        font-weight: 400;\n",
-       "        font-size: 12pt;\n",
-       "        //font-style: bold;\n",
-       "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
-       "   }\n",
-       "    h1 {\n",
-       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
-       "\t}\n",
-       "\n",
-       "    div.input_area {\n",
-       "        background: #F6F6F9;\n",
-       "        border: 1px solid #586e75;\n",
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-       "\n",
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-       "        font-weight: 100;\n",
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-       "        color:#d77c0c;\n",
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-       "        display: block;\n",
-       "        white-space: nowrap;\n",
-       "    }\n",
-       "    h5 {\n",
-       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
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-       "\n",
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-       "        white-space: nowrap;\n",
-       "    }\n",
-       "    div.output_subarea.output_text.output_pyout {\n",
-       "        overflow-x: auto;\n",
-       "        overflow-y: scroll;\n",
-       "        max-height: 50000px;\n",
-       "    }\n",
-       "    div.output_subarea.output_stream.output_stdout.output_text {\n",
-       "        overflow-x: auto;\n",
-       "        overflow-y: scroll;\n",
-       "        max-height: 50000px;\n",
-       "    }\n",
-       "    div.output_wrapper{\n",
-       "        margin-top:0.2em;\n",
-       "        margin-bottom:0.2em;\n",
-       "}\n",
-       "\n",
-       "    code{\n",
-       "        font-size: 6pt;\n",
-       "\n",
-       "    }\n",
-       "    .rendered_html code{\n",
-       "    background-color: transparent;\n",
-       "    }\n",
-       "    ul{\n",
-       "        margin: 2em;\n",
-       "    }\n",
-       "    ul li{\n",
-       "        padding-left: 0.5em;\n",
-       "        margin-bottom: 0.5em;\n",
-       "        margin-top: 0.5em;\n",
-       "    }\n",
-       "    ul li li{\n",
-       "        padding-left: 0.2em;\n",
-       "        margin-bottom: 0.2em;\n",
-       "        margin-top: 0.2em;\n",
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-       "        padding-left: 0.5em;\n",
-       "        margin-bottom: 0.5em;\n",
-       "        margin-top: 0.5em;\n",
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-       "    ul li{\n",
-       "        padding-left: 0.5em;\n",
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-       "        margin-top: 0.2em;\n",
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-       "       color:#447adb;\n",
-       "    }\n",
-       "    a:visited{\n",
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-       "    blockquote{\n",
-       "      display:block;\n",
-       "      background: #fcfcfc;\n",
-       "      border-left: 5px solid #c76c0c;\n",
-       "      font-family: 'Open sans',verdana,arial,sans-serif;\n",
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-       "      text-align:justify;\n",
-       "      text-justify:inter-word;\n",
-       "      }\n",
-       "      blockquote p {\n",
-       "        margin-bottom: 0;\n",
-       "        line-height: 125%;\n",
-       "        font-size: 100%;\n",
-       "      }\n",
-       "</style>\n",
-       "<script>\n",
-       "    MathJax.Hub.Config({\n",
-       "                        TeX: {\n",
-       "                           extensions: [\"AMSmath.js\"],\n",
-       "                           equationNumbers: { autoNumber: \"AMS\", useLabelIds: true}\n",
-       "                           },\n",
-       "                tex2jax: {\n",
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-       "                    displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
-       "                },\n",
-       "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
-       "                \"HTML-CSS\": {\n",
-       "                    scale:95,\n",
-       "                        availableFonts: [],\n",
-       "                        preferredFont:null,\n",
-       "                        webFont: \"TeX\",\n",
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-       "</script>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "AttributeError",
+     "evalue": "module 'book_format' has no attribute 'load_style'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-1-730769ca9272>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'../code'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mbook_format\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mbook_format\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_style\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdirectory\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'..'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m: module 'book_format' has no attribute 'load_style'"
+     ]
     }
    ],
    "source": [
@@ -310,7 +49,19 @@
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'book_plots'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-2-1d7fb353c3e8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mbook_plots\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mbar_plot\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'matplotlib'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'inline'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'book_plots'"
+     ]
+    }
+   ],
    "source": [
     "from matplotlib import animation\n",
     "import matplotlib.pyplot as plt\n",
@@ -364,7 +115,7 @@
     {
      "data": {
       "text/plain": [
-       "'imagemagick'"
+       "'ffmpeg'"
       ]
      },
      "execution_count": 3,
@@ -381,30 +132,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
-     "data": {
-      "image/png": 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truncated
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x17c12128b38>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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truncated
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x17c10f16518>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'gif_animate'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-4-c8909b74d11e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mgif_animate\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0manimate\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mpos\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m1.0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mbar_animate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnframe\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      5\u001b[0m     \u001b[1;32mglobal\u001b[0m \u001b[0mpos\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'gif_animate'"
+     ]
     }
    ],
    "source": [
@@ -434,11 +176,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'animate' 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-5-0c3b6443d1db>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     19\u001b[0m \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m6.5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2.5\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---> 20\u001b[1;33m \u001b[0manimate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'02_simulate.gif'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbar_animate\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mframes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m40\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minterval\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m85\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[0m",
+      "\u001b[1;31mNameError\u001b[0m: name 'animate' is not defined"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 650x250 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "pos = np.array([.1]*10)\n",
     "hallway = np.array([1, 1, 0, 0, 0, 0, 0, 0, 1, 0])\n",
@@ -487,7 +250,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,

animations/multivariate_animations.ipynb

@@ -46,37 +46,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
-      "  if self._edgecolors == str('face'):\n"
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'gif_animate'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-3-c00fcbdb2b07>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;31m#import mkf_internal\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mgif_animate\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0manimate\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mellipse_animate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mframe\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;31mModuleNotFoundError\u001b[0m: No module named 'gif_animate'"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f9e208>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQcAAAEZCAYAAACad+BMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f9e320>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -105,21 +89,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "array([[ 0.0025,  0.005 ],\n",
-       "       [ 0.005 ,  0.01  ]])"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "NameError",
+     "evalue": "name 'f' 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-4-a2279ef61a95>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mQ\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m: name 'f' is not defined"
+     ]
     }
    ],
    "source": [
@@ -128,28 +112,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
-      "  if self._edgecolors == str('face'):\n"
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'gif_animate'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-5-518c7c0f45f6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mcm\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mmpl_toolkits\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmplot3d\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mAxes3D\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mgif_animate\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0manimate\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mrandom\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mDogSimulation\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mDogSimulation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'gif_animate'"
      ]
-    },
-    {
-     "data": {
-      "image/png": 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truncated
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x842bda0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -246,7 +223,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.0"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,

animations/particle_animate.ipynb

@@ -3,283 +3,8 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>\n",
-       "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
-       "@import url('http://fonts.googleapis.com/css?family=Lora');\n",
-       "\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Open+Sans');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Karla');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Poppins');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Roboto');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Lato');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Domine');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Chivo');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Cardo');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Arvo');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Crimson+Text');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Ubuntu');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Fontin');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Raleway');\n",
-       "//@import url('http://fonts.googleapis.com/css?family=Merriweather');\n",
-       "\n",
-       "\n",
-       ".CodeMirror pre {\n",
-       "    font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
-       "}\n",
-       "    div.cell{\n",
-       "        //width: 950px;\n",
-       "        margin-left: 0% !important;\n",
-       "        margin-right: auto;\n",
-       "    }\n",
-       "    div.text_cell_render{\n",
-       "        font-family: 'Lora';\n",
-       "        //font-family: 'Open Sans';\n",
-       "        //font-family: 'Karla',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Roboto',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Lato',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Domine',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Chivo',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Cardo',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Arvo',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Poppins',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Ubuntu',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Fontin',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Raleway',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Merriweather',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Crimson Text', verdana,arial,sans-serif;\n",
-       "        //font-family: verdana,arial,sans-serif;\n",
-       "        //font-family: arial,sans-serif;\n",
-       "        line-height: 125%;\n",
-       "        font-size: 130%;\n",
-       "        text-align: justify;\n",
-       "        text-justify:inter-word;\n",
-       "    }\n",
-       "    div.text_cell code {\n",
-       "        background: transparent;\n",
-       "        color: #000000;\n",
-       "        font-weight: 400;\n",
-       "        font-size: 12pt;\n",
-       "        //font-style: bold;\n",
-       "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
-       "   }\n",
-       "    h1 {\n",
-       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
-       "\t}\n",
-       "\n",
-       "    div.input_area {\n",
-       "        background: #F6F6F9;\n",
-       "        border: 1px solid #586e75;\n",
-       "    }\n",
-       "\n",
-       "    .text_cell_render h1 {\n",
-       "        font-weight: 200;\n",
-       "        font-size: 30pt;\n",
-       "        line-height: 100%;\n",
-       "        color:#c76c0c;\n",
-       "        margin-bottom: 0.5em;\n",
-       "        margin-top: 1em;\n",
-       "        display: block;\n",
-       "        white-space: wrap;\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "    h2 {\n",
-       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "    .text_cell_render h2 {\n",
-       "        font-weight: 200;\n",
-       "        font-size: 16pt;\n",
-       "        font-style: italic;\n",
-       "        line-height: 100%;\n",
-       "        color:#c76c0c;\n",
-       "        margin-bottom: 0.5em;\n",
-       "        margin-top: 1.5em;\n",
-       "        display: block;\n",
-       "        white-space: wrap;\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "    h3 {\n",
-       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
-       "    }\n",
-       "    .text_cell_render h3 {\n",
-       "        font-weight: 200;\n",
-       "        font-size: 14pt;\n",
-       "        line-height: 100%;\n",
-       "        color:#d77c0c;\n",
-       "        margin-bottom: 0.5em;\n",
-       "        margin-top: 2em;\n",
-       "        display: block;\n",
-       "        white-space: wrap;\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "    h4 {\n",
-       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
-       "    }\n",
-       "    .text_cell_render h4 {\n",
-       "        font-weight: 100;\n",
-       "        font-size: 14pt;\n",
-       "        color:#d77c0c;\n",
-       "        margin-bottom: 0.5em;\n",
-       "        margin-top: 0.5em;\n",
-       "        display: block;\n",
-       "        white-space: nowrap;\n",
-       "    }\n",
-       "    h5 {\n",
-       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
-       "    }\n",
-       "\n",
-       "    .text_cell_render h5 {\n",
-       "        font-weight: 200;\n",
-       "        font-style: normal;\n",
-       "        color: #1d3b84;\n",
-       "        font-size: 16pt;\n",
-       "        margin-bottom: 0em;\n",
-       "        margin-top: 0.5em;\n",
-       "        display: block;\n",
-       "        white-space: nowrap;\n",
-       "    }\n",
-       "    div.output_subarea.output_text.output_pyout {\n",
-       "        overflow-x: auto;\n",
-       "        overflow-y: scroll;\n",
-       "        max-height: 50000px;\n",
-       "    }\n",
-       "    div.output_subarea.output_stream.output_stdout.output_text {\n",
-       "        overflow-x: auto;\n",
-       "        overflow-y: scroll;\n",
-       "        max-height: 50000px;\n",
-       "    }\n",
-       "    div.output_wrapper{\n",
-       "        margin-top:0.2em;\n",
-       "        margin-bottom:0.2em;\n",
-       "}\n",
-       "\n",
-       "    code{\n",
-       "        font-size: 6pt;\n",
-       "\n",
-       "    }\n",
-       "    .rendered_html code{\n",
-       "    background-color: transparent;\n",
-       "    }\n",
-       "    ul{\n",
-       "        margin: 2em;\n",
-       "    }\n",
-       "    ul li{\n",
-       "        padding-left: 0.5em;\n",
-       "        margin-bottom: 0.5em;\n",
-       "        margin-top: 0.5em;\n",
-       "    }\n",
-       "    ul li li{\n",
-       "        padding-left: 0.2em;\n",
-       "        margin-bottom: 0.2em;\n",
-       "        margin-top: 0.2em;\n",
-       "    }\n",
-       "    ol{\n",
-       "        margin: 2em;\n",
-       "    }\n",
-       "    ol li{\n",
-       "        padding-left: 0.5em;\n",
-       "        margin-bottom: 0.5em;\n",
-       "        margin-top: 0.5em;\n",
-       "    }\n",
-       "    ul li{\n",
-       "        padding-left: 0.5em;\n",
-       "        margin-bottom: 0.5em;\n",
-       "        margin-top: 0.2em;\n",
-       "    }\n",
-       "    a:link{\n",
-       "       color:#447adb;\n",
-       "    }\n",
-       "    a:visited{\n",
-       "       color: #1d3b84;\n",
-       "    }\n",
-       "    a:hover{\n",
-       "       color: #1d3b84;\n",
-       "    }\n",
-       "    a:focus{\n",
-       "       color:#447adb;\n",
-       "    }\n",
-       "    a:active{\n",
-       "       font-weight: bold;\n",
-       "       color:#447adb;\n",
-       "    }\n",
-       "    .rendered_html :link {\n",
-       "       text-decoration: underline;\n",
-       "    }\n",
-       "    .rendered_html :hover {\n",
-       "       text-decoration: none;\n",
-       "    }\n",
-       "    .rendered_html :visited {\n",
-       "      text-decoration: none;\n",
-       "    }\n",
-       "    .rendered_html :focus {\n",
-       "      text-decoration: none;\n",
-       "    }\n",
-       "    .rendered_html :active {\n",
-       "      text-decoration: none;\n",
-       "    }\n",
-       "    .warning{\n",
-       "        color: rgb( 240, 20, 20 )\n",
-       "    }\n",
-       "    hr {\n",
-       "      color: #f3f3f3;\n",
-       "      background-color: #f3f3f3;\n",
-       "      height: 1px;\n",
-       "    }\n",
-       "    blockquote{\n",
-       "      display:block;\n",
-       "      background: #fcfcfc;\n",
-       "      border-left: 5px solid #c76c0c;\n",
-       "      font-family: 'Open sans',verdana,arial,sans-serif;\n",
-       "      width:680px;\n",
-       "      padding: 10px 10px 10px 10px;\n",
-       "      text-align:justify;\n",
-       "      text-justify:inter-word;\n",
-       "      }\n",
-       "      blockquote p {\n",
-       "        margin-bottom: 0;\n",
-       "        line-height: 125%;\n",
-       "        font-size: 100%;\n",
-       "      }\n",
-       "</style>\n",
-       "<script>\n",
-       "    MathJax.Hub.Config({\n",
-       "                        TeX: {\n",
-       "                           extensions: [\"AMSmath.js\"],\n",
-       "                           equationNumbers: { autoNumber: \"AMS\", useLabelIds: true}\n",
-       "                           },\n",
-       "                tex2jax: {\n",
-       "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
-       "                    displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
-       "                },\n",
-       "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
-       "                \"HTML-CSS\": {\n",
-       "                    scale:95,\n",
-       "                        availableFonts: [],\n",
-       "                        preferredFont:null,\n",
-       "                        webFont: \"TeX\",\n",
-       "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
-       "                }\n",
-       "        });\n",
-       "</script>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from __future__ import division, print_function\n",
     "%matplotlib inline\n",
@@ -289,15 +14,13 @@
     "# use same formattibng as rest of book so that the plots are\n",
     "# consistant with that look and feel.\n",
     "import book_format\n",
-    "book_format.load_style(directory='..')"
+    "#book_format.load_style(directory='..')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -379,17 +102,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x208af46e3c8>"
+       "<Figure size 400x400 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -466,9 +188,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.3"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }

environment.yml

@@ -1,6 +1,8 @@
 name: kf_bf
 dependencies:
+- pip
 - python
+- pip
 - ipython
 - jupyter
 - matplotlib

kf_book/gh_internal.py

@@ -172,26 +172,25 @@ def plot_estimate_chart_3():
         plt.ylim(157, 164.5)
 
 
-def plot_gh_results(weights, estimates, predictions, time_step=0):
-
+def plot_gh_results(weights, estimates, predictions, actual, time_step=0):
     n = len(weights)
     if time_step > 0:
         rng = range(1, n+1)
     else:
         rng = range(n, n+1)
     xs = range(n+1)
-    pred, = book_plots.plot_track(xs[1:], predictions, c='r', marker='v')
-    scale, = book_plots.plot_measurements(xs[1:], weights, color='k', lines=False)
-    est, = book_plots.plot_filter(xs, estimates, marker='o')
-
-    plt.legend([scale, est, pred], ['Measurement', 'Estimates', 'Predictions'], loc=4)
+    book_plots.plot_measurements(xs[1:], weights, color='k', lines=False)
+    book_plots.plot_filter(xs, estimates, marker='o', label='Estimates')
+    book_plots.plot_track(xs[1:], predictions, c='r', marker='v', label='Predictions')
+    plt.plot([xs[0], xs[-1]], actual, c='k', lw=1, label='Actual')
+    plt.legend(loc=4)
     book_plots.set_labels(x='day', y='weight (lbs)')
     plt.xlim([-1, n+1])
     plt.ylim([156.0, 173])
 
 
 def print_results(estimates, prediction, weight):
-    print('previous: {:.2f}, prediction: {:.2f} estimate {:.2f}'.format(
+    print('previous estimate: {:.2f}, prediction: {:.2f}, estimate {:.2f}'.format(
           estimates[-2], prediction, weight))
 
 

requirements.txt

@@ -1,2 +1,5 @@
 filterpy
-seaborn
+seaborn
+jupyter
+notebook
+sympy

table_of_contents.ipynb

@@ -10,106 +10,106 @@
     "\n",
     "## Table of Contents\n",
     "\n",
-    "[**Preface**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/00-Preface.ipynb)\n",
+    "[**Preface**](./00-Preface.ipynb)\n",
     " \n",
     "Motivation behind writing the book. How to download and read the book. Requirements for IPython Notebook and Python. github links.\n",
     "\n",
     "\n",
-    "[**Chapter 1: The g-h Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/01-g-h-filter.ipynb)\n",
+    "[**Chapter 1: The g-h Filter**](./01-g-h-filter.ipynb)\n",
     "\n",
     "Intuitive introduction to the g-h filter, also known as the $\\alpha$-$\\beta$ Filter, which is a family of filters that includes the Kalman filter. Once you understand this chapter you will understand the concepts behind the Kalman filter. \n",
     "\n",
     "\n",
-    "[**Chapter 2: The Discrete Bayes Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/02-Discrete-Bayes.ipynb)\n",
+    "[**Chapter 2: The Discrete Bayes Filter**](./02-Discrete-Bayes.ipynb)\n",
     "\n",
     "Introduces the discrete Bayes filter. From this you will learn the probabilistic (Bayesian) reasoning that underpins the Kalman filter in an easy to digest form.\n",
     "\n",
-    "[**Chapter 3: Probabilities, Gaussians, and Bayes' Theorem**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/03-Gaussians.ipynb)\n",
+    "[**Chapter 3: Probabilities, Gaussians, and Bayes' Theorem**](./03-Gaussians.ipynb)\n",
     "\n",
     "Introduces using Gaussians to represent beliefs in the Bayesian sense. Gaussians allow us to implement the algorithms used in the discrete Bayes filter to work in continuous domains.\n",
     "\n",
     "\n",
-    "[**Chapter 4: One Dimensional Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/04-One-Dimensional-Kalman-Filters.ipynb)\n",
+    "[**Chapter 4: One Dimensional Kalman Filters**](./04-One-Dimensional-Kalman-Filters.ipynb)\n",
     "\n",
     "Implements a Kalman filter by modifying the discrete Bayes filter to use Gaussians. This is a full featured Kalman filter, albeit only useful for 1D problems. \n",
     "\n",
     "\n",
-    "[**Chapter 5: Multivariate Gaussians**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/05-Multivariate-Gaussians.ipynb)\n",
+    "[**Chapter 5: Multivariate Gaussians**](./05-Multivariate-Gaussians.ipynb)\n",
     "\n",
     "Extends Gaussians to multiple dimensions, and demonstrates how 'triangulation' and hidden variables can vastly improve estimates.\n",
     "\n",
-    "[**Chapter 6: Multivariate Kalman Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/06-Multivariate-Kalman-Filters.ipynb)\n",
+    "[**Chapter 6: Multivariate Kalman Filter**](./06-Multivariate-Kalman-Filters.ipynb)\n",
     "\n",
     "We extend the Kalman filter developed in the univariate chapter to the full, generalized filter for linear problems. After reading this you will understand how a Kalman filter works and how to design and implement one for a (linear) problem of your choice.\n",
     "\n",
-    "[**Chapter 7: Kalman Filter Math**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/07-Kalman-Filter-Math.ipynb)\n",
+    "[**Chapter 7: Kalman Filter Math**](./07-Kalman-Filter-Math.ipynb)\n",
     "\n",
     "We gotten about as far as we can without forming a strong mathematical foundation. This chapter is optional, especially the first time, but if you intend to write robust, numerically stable filters, or to read the literature, you will need to know the material in this chapter. Some sections will be required to understand the later chapters on nonlinear filtering. \n",
     "\n",
     "\n",
-    "[**Chapter 8: Designing Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/08-Designing-Kalman-Filters.ipynb)\n",
+    "[**Chapter 8: Designing Kalman Filters**](./08-Designing-Kalman-Filters.ipynb)\n",
     "\n",
     "Building on material in Chapters 5 and 6, walks you through the design of several Kalman filters. Only by seeing several different examples can you really grasp all of the theory. Examples are chosen to be realistic, not 'toy' problems to give you a start towards implementing your own filters. Discusses, but does not solve issues like numerical stability.\n",
     "\n",
     "\n",
-    "[**Chapter 9: Nonlinear Filtering**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/09-Nonlinear-Filtering.ipynb)\n",
+    "[**Chapter 9: Nonlinear Filtering**](./09-Nonlinear-Filtering.ipynb)\n",
     "\n",
     "Kalman filters as covered only work for linear problems. Yet the world is nonlinear. Here I introduce the problems that nonlinear systems pose to the filter, and briefly discuss the various algorithms that we will be learning in subsequent chapters.\n",
     "\n",
     "\n",
-    "[**Chapter 10: Unscented Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/10-Unscented-Kalman-Filter.ipynb)\n",
+    "[**Chapter 10: Unscented Kalman Filters**](./10-Unscented-Kalman-Filter.ipynb)\n",
     "\n",
     "Unscented Kalman filters (UKF) are a recent development in Kalman filter theory. They allow you to filter nonlinear problems without requiring a closed form solution like the Extended Kalman filter requires.\n",
     "\n",
     "This topic is typically either not mentioned, or glossed over in existing texts, with Extended Kalman filters receiving the bulk of discussion. I put it first because the UKF is much simpler to understand, implement, and the filtering performance is usually as good as or better then the Extended Kalman filter. I always try to implement the UKF first for real world problems, and you should also.\n",
     "\n",
     "\n",
-    "[**Chapter 11: Extended Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/11-Extended-Kalman-Filters.ipynb)\n",
+    "[**Chapter 11: Extended Kalman Filters**](./11-Extended-Kalman-Filters.ipynb)\n",
     "\n",
     "Extended Kalman filters (EKF) are the most common approach to linearizing non-linear problems. A majority of real world Kalman filters are EKFs, so will need to understand this material to understand existing code, papers, talks, etc. \n",
     "\n",
     "\n",
-    "[**Chapter 12: Particle Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/12-Particle-Filters.ipynb)\n",
+    "[**Chapter 12: Particle Filters**](./12-Particle-Filters.ipynb)\n",
     "\n",
     "Particle filters uses Monte Carlo techniques to filter data. They easily handle highly nonlinear and non-Gaussian systems, as well as multimodal distributions (tracking multiple objects simultaneously) at the cost of high computational requirements.\n",
     "\n",
     "\n",
-    "[**Chapter 13: Smoothing**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/13-Smoothing.ipynb)\n",
+    "[**Chapter 13: Smoothing**](./13-Smoothing.ipynb)\n",
     "\n",
     "Kalman filters are recursive, and thus very suitable for real time filtering. However, they work extremely well for post-processing data. After all, Kalman filters are predictor-correctors, and it is easier to predict the past than the future! We discuss some common approaches.\n",
     "\n",
     "\n",
-    "[**Chapter 14: Adaptive Filtering**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/14-Adaptive-Filtering.ipynb)\n",
+    "[**Chapter 14: Adaptive Filtering**](./14-Adaptive-Filtering.ipynb)\n",
     "    \n",
     "Kalman filters assume a single process model, but manuevering targets typically need to be described by several different process models. Adaptive filtering uses several techniques to allow the Kalman filter to adapt to the changing behavior of the target.\n",
     "\n",
     "\n",
-    "[**Appendix A: Installation, Python, NumPy, and FilterPy**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Appendix-A-Installation.ipynb)\n",
+    "[**Appendix A: Installation, Python, NumPy, and FilterPy**](./Appendix-A-Installation.ipynb)\n",
     "\n",
     "Brief introduction of Python and how it is used in this book. Description of the companion\n",
     "library FilterPy. \n",
     "    \n",
     "\n",
-    "[**Appendix B: Symbols and Notations**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Appendix-B-Symbols-and-Notations.ipynb)\n",
+    "[**Appendix B: Symbols and Notations**](./Appendix-B-Symbols-and-Notations.ipynb)\n",
     "\n",
     "Most books opt to use different notations and variable names for identical concepts. This is a large barrier to understanding when you are starting out. I have collected the symbols and notations used in this book, and built tables showing what notation and names are used by the major books in the field.\n",
     "\n",
     "*Still just a collection of notes at this point.*\n",
     "\n",
     "\n",
-    "[**Appendix D: H-Infinity Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Appendix-D-HInfinity-Filters.ipynb)\n",
+    "[**Appendix D: H-Infinity Filters**](./Appendix-D-HInfinity-Filters.ipynb)\n",
     "    \n",
     "Describes the $H_\\infty$ filter. \n",
     "\n",
     "*I have code that implements the filter, but no supporting text yet.*\n",
     "\n",
     "\n",
-    "[**Appendix E: Ensemble Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Appendix-E-Ensemble-Kalman-Filters.ipynb)\n",
+    "[**Appendix E: Ensemble Kalman Filters**](./Appendix-E-Ensemble-Kalman-Filters.ipynb)\n",
     "\n",
     "Discusses the ensemble Kalman Filter, which uses a Monte Carlo approach to deal with very large Kalman filter states in nonlinear systems.\n",
     "\n",
     "\n",
-    "[**Appendix F: FilterPy Source Code**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Appendix-F-Filterpy-Code.ipynb)\n",
+    "[**Appendix F: FilterPy Source Code**](./Appendix-F-Filterpy-Code.ipynb)\n",
     "\n",
     "Listings of important classes from FilterPy that are used in this book.\n",
     "\n",
@@ -119,25 +119,25 @@
     "These notebooks are not a primary part of the book, but contain information that might be interested to a subest of readers.\n",
     "\n",
     "\n",
-    "[**Computing and plotting PDFs**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Supporting_Notebooks/Computing_and_plotting_PDFs.ipynb)\n",
+    "[**Computing and plotting PDFs**](./Supporting_Notebooks/Computing_and_plotting_PDFs.ipynb)\n",
     "\n",
     "Describes how I implemented the plotting of various pdfs in the book.\n",
     "\n",
     "\n",
-    "[**Interactions**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Supporting_Notebooks/Interactions.ipynb)\n",
+    "[**Interactions**](./Supporting_Notebooks/Interactions.ipynb)\n",
     "\n",
     "Interactive simulations of various algorithms. Use sliders to change the output in real time.\n",
     "\n",
-    "[**Converting the Multivariate Equations to the Univariate Case**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Supporting_Notebooks/Converting-Multivariate-Equations-to-Univariate.ipynb)\n",
+    "[**Converting the Multivariate Equations to the Univariate Case**](./Supporting_Notebooks/Converting-Multivariate-Equations-to-Univariate.ipynb)\n",
     "\n",
     "Demonstrates that the Multivariate equations are identical to the univariate Kalman filter equations by setting the dimension of all vectors and matrices to one.\n",
     "\n",
-    "[**Iterative Least Squares for Sensor Fusion**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Supporting_Notebooks/Iterative-Least-Squares-for-Sensor-Fusion.ipynb)\n",
+    "[**Iterative Least Squares for Sensor Fusion**](./Supporting_Notebooks/Iterative-Least-Squares-for-Sensor-Fusion.ipynb)\n",
     "\n",
     "Deep dive into using an iterative least squares technique to solve the nonlinear problem of finding position from multiple GPS pseudorange measurements.\n",
     "\n",
     "\n",
-    "[**Taylor Series**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Supporting_Notebooks/Taylor-Series.ipynb)\n",
+    "[**Taylor Series**](./Supporting_Notebooks/Taylor-Series.ipynb)\n",
     "\n",
     "A very brief introduction to Taylor series.\n",
     "\n",