diff --git a/Designing_Kalman_Filters.ipynb b/Designing_Kalman_Filters.ipynb index ed0dba8..8e17126 100644 --- a/Designing_Kalman_Filters.ipynb +++ b/Designing_Kalman_Filters.ipynb @@ -1,7 +1,7 @@ { "metadata": { "name": "", - "signature": "sha256:47e47ce6c78a985606fd325605b42a0e55e381111a625eb69820fb2771542ce2" + "signature": "sha256:6f0219267a66cc62e4a150a277649629939dd335bea9ca5857a6ff6f806acb53" }, "nbformat": 3, "nbformat_minor": 0, @@ -282,7 +282,7 @@ "source": [ "This first attempt at tracking a robot will closely resemble the 1-D dog tracking problem of previous chapters. This will allow us to 'get our feet wet' with Kalman filtering. So, instead of a sensor that outputs position in a hallway, we now have a sensor that supplies a noisy measurement of position in a 2-D space, such as an open field. That is, at each time $T$ it will provide an $(x,y)$ coordinate pair specifying the measurement of the sensor's position in the field.\n", "\n", - "Implemention of code to interact with real sensors is beyond the scope of this book, so as before we will program simple simuations in Python to represent the sensors. We will develop several of these sensors as we go, each with more complications, so as I program them I will just append a number to the function name. *pos_sensor1 ()* is the first sensor we write, and so on. \n", + "Implemention of code to interact with real sensors is beyond the scope of this book, so as before we will program simple simuations in Python to represent the sensors. We will develop several of these sensors as we go, each with more complications, so as I program them I will just append a number to the function name. $\\verb,pos_sensor1 (),$ is the first sensor we write, and so on. \n", "\n", "So let's start with a very simple sensor, one that travels in a straight line. It takes as input the last position, velocity, and how much noise we want, and returns the new position. " ] @@ -465,7 +465,7 @@ "\\end{aligned}\n", "$$\n", "\n", - "Perhaps the $\\frac{1}{0.3048}$ caught you off guard. At first I always found $\\small\\mathbf{H}$ a bit counter-intuitive to design because it takes you from the state variables to the measurements, but the Kalman filter is trying to take measurements and produce state variables to them. If you read the math chapter you will understand why $\\small\\mathbf{H}$ is designed to go in this direction. If not, well, you'll have to remember how this works and trust that it is correct.\n", + "Perhaps the $\\frac{1}{0.3048}$ caught you off guard. At first I always found $\\mathbf{H}$ a bit counter-intuitive to design because it takes you from the state variables to the measurements, but the Kalman filter is trying to take measurements and produce state variables to them. If you read the math chapter you will understand why $\\mathbf{H}$ is designed to go in this direction. If not, well, you'll have to remember how this works and trust that it is correct.\n", "\n", "So, here is the Python that implements this:" ] @@ -684,7 +684,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "I encourage you to play with this, setting $\\small\\mathbf{Q}$ and $\\small\\mathbf{R}$ to various values. However, we did a fair amount of that sort of thing in the last chapters, and we have a lot of material to cover, so I will move on to more complicated cases where we will also have a chance to experience changing these values.\n", + "I encourage you to play with this, setting $\\mathbf{Q}$ and $\\mathbf{R}$ to various values. However, we did a fair amount of that sort of thing in the last chapters, and we have a lot of material to cover, so I will move on to more complicated cases where we will also have a chance to experience changing these values.\n", "\n", "Now I will run the same Kalman filter with the same settings, but also plot the covariance ellipse for $x$ and $y$. First, the code without explanation, so we can see the output. I print the last covariance to see what it looks like. But before you scroll down to look at the results, what do you think it will look like? You have enough information to figure this out but this is still new to you, so don't be discouraged if you get it wrong." ] @@ -774,7 +774,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Did you correctly predict what the covariance matrix and plots would look like? Perhaps you were expecting a tilted ellipse, as in the last chapters. If so, recall that in those chapters we were not plotting $x$ against $y$, but $x$ against $\\dot{x}$. $x$ *is correlated* to $\\dot{x}$, but $x$ is not correlated or dependent on $y$. Therefore our ellipses are not tilted. Furthermore, the noise for both $x$ and $y$ are modelled to have the same value, 5, in $\\small\\mathbf{R}$. If we were to set R to, for example,\n", + "Did you correctly predict what the covariance matrix and plots would look like? Perhaps you were expecting a tilted ellipse, as in the last chapters. If so, recall that in those chapters we were not plotting $x$ against $y$, but $x$ against $\\dot{x}$. $x$ *is correlated* to $\\dot{x}$, but $x$ is not correlated or dependent on $y$. Therefore our ellipses are not tilted. Furthermore, the noise for both $x$ and $y$ are modelled to have the same value, 5, in $\\mathbf{R}$. If we were to set R to, for example,\n", "\n", "$$\\mathbf{R} = \\begin{bmatrix}10&0\\\\0&5\\end{bmatrix}$$\n", "\n", @@ -785,7 +785,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The final P tells us everything we need to know about the correlation beween the state variables. If we look at the diagonal alone we see the variance for each variable. In other words $P_{0,0}$ is the variance for x, $P_{1,1}$ is the variance for $\\dot{x}$, $P_{2,2}$ is the variance for y, and $P_{3,3}$ is the variance for $\\dot{y}$. We can extract the diagonal of a matrix using *numpy.diag()*." + "The final P tells us everything we need to know about the correlation beween the state variables. If we look at the diagonal alone we see the variance for each variable. In other words $\\mathbf{P}_{0,0}$ is the variance for x, $\\mathbf{P}_{1,1}$ is the variance for $\\dot{x}$, $\\mathbf{P}_{2,2}$ is the variance for y, and $\\mathbf{P}_{3,3}$ is the variance for $\\dot{y}$. We can extract the diagonal of a matrix using *numpy.diag()*." ] }, { @@ -848,9 +848,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The covariance contains the data for $x$ and $\\dot{x}$ in the upper left because of how it is organized. Recall that entries $\\small\\mathbf{P}_{i,j}$ and $\\small\\mathbf{P}_{j,i}$ contain $p\\sigma_1\\sigma_2$.\n", + "The covariance contains the data for $x$ and $\\dot{x}$ in the upper left because of how it is organized. Recall that entries $\\mathbf{P}_{i,j}$ and $\\mathbf{P}_{j,i}$ contain $p\\sigma_1\\sigma_2$.\n", "\n", - "Finally, let's look at the lower left side of $\\small\\mathbf{P}$, which is all 0s. Why 0s? Consider $\\small\\mathbf{P}_{3,0}$. That stores the term $p\\sigma_3\\sigma_0$, which is the covariance between $\\dot{y}$ and $x$. These are independent, so the term will be 0. The rest of the terms are for similarly independent variables." + "Finally, let's look at the lower left side of $\\mathbf{P}$, which is all 0s. Why 0s? Consider $\\mathbf{P}_{3,0}$. That stores the term $p\\sigma_3\\sigma_0$, which is the covariance between $\\dot{y}$ and $x$. These are independent, so the term will be 0. The rest of the terms are for similarly independent variables." ] }, { @@ -920,14 +920,19 @@ "The first step is to design our state variables. We will assume that the robot is travelling in a straight direction with constant velocity. This is unlikely to be true for a long period of time, but is acceptable for short periods of time. This does not differ from the previous problem - we will want to track the values for the robot's position and velocity. Hence,\n", "\n", "$$\\mathbf{x} = \n", - "\\begin{bmatrix}x\\\\v_x\\\\y\\\\v_y\\end{bmatrix}$$\n", - "\n", + "\\begin{bmatrix}x\\\\v_x\\\\y\\\\v_y\\end{bmatrix}$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "The next step is to design the state transistion function. This also will be the same as the previous problem, so without further ado,\n", "\n", "$$\n", "\\mathbf{x}' = \\begin{bmatrix}1& \\Delta t& 0& 0\\\\0& 1& 0& 0\\\\0& 0& 1& \\Delta t\\\\ 0& 0& 0& 1\\end{bmatrix}\\mathbf{x}$$\n", "\n", - "The next step is to design the control inputs. We have none, so we set ${\\small\\mathbf{B}}=0$.\n", + "The next step is to design the control inputs. We have none, so we set ${\\mathbf{B}}=0$.\n", "\n", "The next step is to design the measurement function $\\mathbf{z} = \\mathbf{Hx}$. We can model the measurement using the Pythagorean theorem.\n", "\n", @@ -945,7 +950,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Instead of computing $\\small\\mathbf{H}$ we will compute the partial derivative of $\\mathbf{H}$ with respect to the robot's position $\\small\\mathbf{x}$. You are probably familiar with the concept of partial derivative, but if not, it just means how $\\small \\mathbf{H}$ changes with respect to the robot's position.\n", + "Instead of computing $\\mathbf{H}$ we will compute the partial derivative of $\\mathbf{H}$ with respect to the robot's position $\\mathbf{x}$. You are probably familiar with the concept of partial derivative, but if not, it just means how $\\mathbf{H}$ changes with respect to the robot's position.\n", "\n", "$$\\frac{\\partial\\mathbf{h}}{\\partial\\mathbf{x}}=\n", "\\small\\begin{bmatrix}\n", @@ -974,12 +979,17 @@ "\\end{bmatrix}\n", "$$\n", "\n", - "However, this raises a huge problem. We are no longer computing $\\small\\mathbf{H}$, but $\\Delta\\small\\mathbf{H}$, the change of $\\small\\mathbf{H}$. If we passed this into our Kalman filter without altering the rest of the design the output would be nonsense. Recall, for example, that we multiply $\\small\\mathbf{Hx}$ to generate the measurements that would result from the given estimate of $\\small\\mathbf{x}$ But now that $\\small\\mathbf{H}$ is linearized around our position it contains the *change* in the measurement function. \n", + "However, this raises a huge problem. We are no longer computing $\\mathbf{H}$, but $\\Delta\\mathbf{H}$, the change of $\\mathbf{H}$. If we passed this into our Kalman filter without altering the rest of the design the output would be nonsense. Recall, for example, that we multiply $\\mathbf{Hx}$ to generate the measurements that would result from the given estimate of $\\mathbf{x}$ But now that $\\mathbf{H}$ is linearized around our position it contains the *change* in the measurement function. \n", "\n", - "We are forced, therefore, to use the *change* in $\\small\\mathbf{x}$ for our state variables. So we have to go back and redesign our state variables. \n", - "\n", - ">Please note this is a completely normal occurance in designing Kalman filters. The textbooks present examples like this as *fait accompli*, as if it is trivially obvious that the state variables needed to be velocities, not positions. Perhaps once you do enough of these problems it would be trivially obvious, but at that point why are you reading a textbook? I find myself reading through a presentation multiple times, trying to figure out why they made a choice, finally to realize that it is because of the consequences of something on the next page. My presentation is longer, but it reflects what actually happens when you design a filter. You make what seem reasonable design choices, and as you move forward you discover properties that require you to recast your earlier steps. As a result, I am going to somewhat abandon my **step 1**, **step 2**, etc., approach, since so many real problems are not quite that straightforward.\n", + "We are forced, therefore, to use the *change* in $\\mathbf{x}$ for our state variables. So we have to go back and redesign our state variables. \n", "\n", + ">Please note this is a completely normal occurance in designing Kalman filters. The textbooks present examples like this as *fait accompli*, as if it is trivially obvious that the state variables needed to be velocities, not positions. Perhaps once you do enough of these problems it would be trivially obvious, but at that point why are you reading a textbook? I find myself reading through a presentation multiple times, trying to figure out why they made a choice, finally to realize that it is because of the consequences of something on the next page. My presentation is longer, but it reflects what actually happens when you design a filter. You make what seem reasonable design choices, and as you move forward you discover properties that require you to recast your earlier steps. As a result, I am going to somewhat abandon my **step 1**, **step 2**, etc., approach, since so many real problems are not quite that straightforward." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "If our state variables contain the velocities of the robot and not the position then how do we track where the robot is? We can't. Kalman filters that are linearized in this fashion use what is called a *nominal trajectory* - i.e. you assume a position and track direction, and then apply the changes in velocity and acceleration to compute the changes in that trajectory. How could it be otherwise? Recall the graphic showing the intersection of the two range circles - there are two areas of intersection. Think of what this would look like if the two transmitters were very close to each other - the intersections would be two very long cresent shapes. This Kalman filter, as designed, has no way of knowing your true position from only distance measurements to the transmitters. Perhaps your mind is already leaping to ways of working around this problem. If so, stay engaged, as later sections and chapters will provide you with these techniques. Presenting the full solution all at once leads to more confusion than insight, in my opinion. \n", "\n", "So let's redesign our *state transition function*. We are assuming constant velocity and no acceleration, giving state equations of\n", @@ -994,7 +1004,7 @@ "$$\n", "\\mathbf{F} = \\begin{bmatrix}0 &1 & 0& 0\\\\0& 0& 0& 0\\\\0& 0& 0& 1\\\\ 0& 0& 0& 0\\end{bmatrix}$$\n", "\n", - "A final complication comes from the measurements that we pass in. $\\small\\mathbf{Hx}$ is now computing the *change* in the measurement from our nominal position, so the measurement that we pass in needs to be not the range to A and B, but the *change* in range from our measured range to our nomimal position. \n", + "A final complication comes from the measurements that we pass in. $\\mathbf{Hx}$ is now computing the *change* in the measurement from our nominal position, so the measurement that we pass in needs to be not the range to A and B, but the *change* in range from our measured range to our nomimal position. \n", "\n", "There is a lot here to take in, so let's work through the code bit by bit. First we will define a function to compute $\\frac{\\partial\\mathbf{h}}{\\partial\\mathbf{x}}$ for each time step." ] @@ -1059,7 +1069,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now let's write the code to compute $\\small\\frac{\\partial\\mathbf{h}}{\\partial\\mathbf{x}}$." + "Now let's write the code to compute $\\frac{\\partial\\mathbf{h}}{\\partial\\mathbf{x}}$." ] }, { diff --git a/Kalman_Filters.ipynb b/Kalman_Filters.ipynb index 60d0401..05af25f 100644 --- a/Kalman_Filters.ipynb +++ b/Kalman_Filters.ipynb @@ -1,7 +1,7 @@ { "metadata": { "name": "", - "signature": "sha256:3b30412bf2631dd597e1e619df3837d5c18dcb65903323a2b7bc4a1c899a076a" + "signature": "sha256:7bcd692a8ae1a36e1bb14d78bc58a3b8d1234f768fb0d0c6c00dee4155fa0676" }, "nbformat": 3, "nbformat_minor": 0, @@ -53,7 +53,7 @@ "\n", " .text_cell_render h1 {\n", " font-weight: 200;\n", - " font-size: 36pt;\n", + " font-size: 30pt;\n", " line-height: 100%;\n", " color:#c76c0c;\n", " margin-bottom: 0.5em;\n", @@ -70,8 +70,8 @@ " font-style: italic;\n", " line-height: 100%;\n", " color:#c76c0c;\n", - " margin-bottom: 1.5em;\n", - " margin-top: 0.5em;\n", + " margin-bottom: 0.5em;\n", + " margin-top: 1.5em;\n", " display: block;\n", " white-space: nowrap;\n", " } \n", @@ -242,13 +242,13 @@ ], "metadata": {}, "output_type": "pyout", - "prompt_number": 1, + "prompt_number": 2, "text": [ - "" + "" ] } ], - "prompt_number": 1 + "prompt_number": 2 }, { "cell_type": "heading", @@ -311,13 +311,13 @@ "language": "python", "metadata": {}, "outputs": [], - "prompt_number": 2 + "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The constructor *\\_\\_init()\\_\\_* initializes the DogSensor class with an initial position (x0), velocity (vel), and an noise scaling factor. The *sense()* function has the dog move by the set velocity and returns its new position, with noise added. If you look at the code for *sense()* you will see a call to *numpy.random.randn()*. This returns a number sampled from a normal distribution with a mean of 0.0. Let's look at some example output for that.\n", + "The constructor $\\verb,__init()__,$ initializes the DogSensor class with an initial position (x0), velocity (vel), and an noise scaling factor. The $\\verb,sense(),$ function has the dog move by the set velocity and returns its new position, with noise added. If you look at the code for $\\verb,sense(),$ you will see a call to $\\verb,numpy.random.randn(),$. This returns a number sampled from a normal distribution with a mean of 0.0. Let's look at some example output for that.\n", "\n" ] }, @@ -335,11 +335,11 @@ "output_type": "stream", "stream": "stdout", "text": [ - "-0.6891\t0.0651\t-1.3072\t-0.3641\t-0.9836\t-0.6302\t-0.1925\t-2.5600\t-0.2098\t0.4368\t0.1303\t-0.5265\t-1.1345\t-1.3365\t-0.1951\t0.6675\t2.3115\t-0.4293\t1.0739\t-2.2391\t" + "-0.8025\t0.4440\t1.6176\t-0.7325\t0.0304\t0.3592\t-0.1916\t1.3645\t0.2563\t-0.4732\t-0.3872\t0.2693\t0.4122\t0.1272\t0.7263\t-0.5945\t0.4633\t-1.5166\t0.5133\t1.1987\t" ] } ], - "prompt_number": 3 + "prompt_number": 4 }, { "cell_type": "markdown", @@ -347,7 +347,7 @@ "source": [ "You should see a sequence of numbers near 0, some negative and some positive. Most are probably between -1 and 1, but a few might lie somewhat outside that range. This is what we expect from a normal distribution - values are clustered around the mean, and there are fewer values the further you get from the mean.\n", "\n", - "Okay, so lets look at the output of the *DogSensor* class. We will start by setting the noise to 0 to check that the class does what we think it does" + "Okay, so lets look at the output of the $\\verb,DogSensor,$ class. We will start by setting the noise to 0 to check that the class does what we think it does" ] }, { @@ -381,11 +381,11 @@ "output_type": "display_data", "png": 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HI3cC9/vvv5++ffsCYDp7AksRA+q5FCPKCzGivCibLPHxBA3IfcjRlJICgMsF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\ne1jiB7Vq1aJRo0YA1K1bF6/Xy/fff698CAcPHiQrK4vGjRtjGAa7d+9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KaSxeHMCdd57LtlNDQnB268aURp9y/FAw1pCzvhqu+AmfJtrh4eEEBASQlJSE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\n8O677zJy5Eg+//xzAHJzc5WLeu7QoUPExMTwzDPPkJeXR2pqKgkJCcp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"text": [ - "" + "" ] } ], - "prompt_number": 5 + "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", - "> **Note**: numpy uses a random number generator to generate the normal distribution samples. The numbers I see as I write this are unlikely to be the ones that you see. If you run the cell above multiple times, you should get a slightly different result each time. I could use *numpy.random.seed(some_value)* to force the results to be the same each time. This would simplify my explanations in some cases, but would ruin the interactive nature of this chapter. To get a real feel for how normal distributions and Kalman filters work you will probably want to run cells several times, observing what changes, and what stays roughly the same.\n", + "> **Note**: numpy uses a random number generator to generate the normal distribution samples. The numbers I see as I write this are unlikely to be the ones that you see. If you run the cell above multiple times, you should get a slightly different result each time. I could use $\\verb,numpy.random.seed(some_value),$ to force the results to be the same each time. This would simplify my explanations in some cases, but would ruin the interactive nature of this chapter. To get a real feel for how normal distributions and Kalman filters work you will probably want to run cells several times, observing what changes, and what stays roughly the same.\n", "\n", "So the output of the sensor should be a wavering blue line drawn over a dotted red line. The dotted red line shows the actual position of the dog, and the blue line is the noise signal produced by the simulated RFID sensor. Please note that the red dotted line was manually plotted - we do not yet have a filter that recovers that information! \n", "\n", @@ -458,13 +458,13 @@ { "metadata": {}, "output_type": "display_data", - "png": 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B+hkzZJ+vTge8/XYQfvvbYDzxRA/WrtXKfqw5oxE4cYJKR9zBrwJtQAi2acAK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qxAm0Pf442hcvlt0D+7XXwvHKK+EoLr6E6GiPvx07b78dhpISFq+80oonnuiF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"text": [ - "" + "" ] } ], - "prompt_number": 6 + "prompt_number": 7 }, { "cell_type": "code", @@ -478,19 +478,19 @@ { "metadata": {}, "output_type": "display_data", - "png": 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i4jXcbnjn3u0sct9Kh6l9cbZvj8vgCtEivkCNtvgNzdaJEeVCjCgX3sm6dy/p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\nvy6/BQCo1xxWFwAAuHZCQkJqPs5dV1dX68SJE9q/f78effT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1LrZvzyIgAFJTLRw8aOXgwQAOHgxgbXIscXGFJCVlULmyz+3rIGWEdh0RERER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\nIRcwQy5ghlwg2NjRjiAOl0uxjz4qe+URzb5qq17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"text": [ - "" + "" ] } ], - "prompt_number": 7 + "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ - "You may not have a full understanding of the exact *meaning* of a noise value of 100.0, but as it turns out if you multiply *randn()* with a number $n$, the result is just a normal distribution with $\\sigma = \\sqrt{n}$. So the example with noise = 100 is using the normal distribution $\\mathcal{N}(0,100)$. Recall the notation for a normal distribution is $\\mathcal{N}(\\mu,\\sigma^2)$. If the square root is confusing, recall that normal distributions use $\\sigma^2$ for the variance, and $\\sigma$ is the standard deviation, which we do not use in this book. DogSensor.\\_\\_init__() takes the square root of the noise setting so that the *noise * randn()* call properly computes the normal distribution." + "You may not have a full understanding of the exact *meaning* of a noise value of 100.0, but as it turns out if you multiply $\\verb,randn(),$ with a number $n$, the result is just a normal distribution with $\\sigma = \\sqrt{n}$. So the example with noise = 100 is using the normal distribution $\\mathcal{N}(0,100)$. Recall the notation for a normal distribution is $\\mathcal{N}(\\mu,\\sigma^2)$. If the square root is confusing, recall that normal distributions use $\\sigma^2$ for the variance, and $\\sigma$ is the standard deviation, which we do not use in this book. DogSensor.$\\verb,__init__(),$ takes the square root of the noise setting so that the $\\verb,noise * randn(),$ call properly computes the normal distribution." ] }, { @@ -523,11 +523,11 @@ "output_type": "display_data", "png": 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kbceacENCGJtlD/jOJD32nmxBU7dFdilERMLYMKsM55PEMSsxvp5Th7kfW4814v7J+kH3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\nOp2289zpmTlz9kWh9OF0OOViz7T9fpIJ0/8cDr9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i2MR2df26kFQN4cpFb4hlEA2xyyqwT47lcGAkhW19cVKEO4jqdTFTFNEXDy/e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\nbPQhES6D9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NMPkIE+1BceEsgiTrSiMyMX9Nl6O4IPSguCCcxLAirDE1NYX7778fBw8exLlz5/D1r38d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\nIAiC2JK4H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K+TdEQCYf/ot+zF+s4fJPmBuMmspWNDd0CqjIfvDaF4v5SwsbZiKyWdXll9HZ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\n1QXUVnay691Sv0yaAfLiQvntDZPJjgnhG6+dYmupjDaLsSc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"text": [ - "" + "" ] } ], - "prompt_number": 10 + "prompt_number": 11 }, { "cell_type": "markdown", @@ -674,7 +674,7 @@ "\n", "\"Measure twice, cut once\" is a useful saying and practice due to this fact! The Gaussian is just a mathematical model of this physical fact, so we should expect the math to follow our physical process. \n", "\n", - "Now let's multiply two gaussians (or equivelently, two measurements) that are partially separated. In other words, their means will be different, but their variances will be the same. What do you think the result will be? Think about it, and then look at the graph." + "Now let's multiply two gaussians (or equivalently, two measurements) that are partially separated. In other words, their means will be different, but their variances will be the same. What do you think the result will be? Think about it, and then look at the graph." ] }, { @@ -706,11 +706,11 @@ "output_type": "display_data", "png": 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XYnbWnn1tJ/nbFmmYhRCilrh6PJHsrbtoOm2c6lJqlZCJo7hy6CSX9vymuhQh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0i7lTj2RaMuLNnzE3BwZLDsLnB5MJjSTAsadtzF9mMcZz0SOPPAIAePXVV6HT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\ncLUJCQY1YgP5P9iP17VcABAfpEaUzheHq2fmUcM8dzddug7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XNYIAnQDMASATht4+zAHg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\n", "text": [ - "" + "" ] } ], - "prompt_number": 12 + "prompt_number": 13 }, { "cell_type": "markdown", @@ -831,7 +831,7 @@ "language": "python", "metadata": {}, "outputs": [], - "prompt_number": 13 + "prompt_number": 14 }, { "cell_type": "markdown", @@ -850,15 +850,15 @@ "language": "python", "metadata": {}, "outputs": [], - "prompt_number": 14 + "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ - "That is less abstract, which perhaps helps with comprehension, but it is poor coding practice. We are writing a Kalman filter that works for any problem, not just tracking dogs in a hallway, so we don't use variable names with 'dog' in them. Still, the *sense_dog()* function should make what we are doing very clear. \n", + "That is less abstract, which perhaps helps with comprehension, but it is poor coding practice. We are writing a Kalman filter that works for any problem, not just tracking dogs in a hallway, so we don't use variable names with 'dog' in them. Still, the $\\verb,sense_dog(),$ function should make what we are doing very clear. \n", "\n", - "Let's look at an example. We will suppose that our current belief for the dog's position is $N(2,5)$. Don't worry about where that number came from. It may appear that we have a chicken and egg problem, in that how do we know the position before we sense it, but we will resolve that shortly. We will create a *DogSensor* object initialized to be at position 0.0, and with no velocity, and modest noise. This corresponds to the dog standing still at the far left side of the hallway. Note that we mistakenly believe the dog is at postion 2.0, not 0.0." + "Let's look at an example. We will suppose that our current belief for the dog's position is $N(2,5)$. Don't worry about where that number came from. It may appear that we have a chicken and egg problem, in that how do we know the position before we sense it, but we will resolve that shortly. We will create a $\\verb,DogSensor,$ object initialized to be at position 0.0, and with no velocity, and modest noise. This corresponds to the dog standing still at the far left side of the hallway. Note that we mistakenly believe the dog is at postion 2.0, not 0.0." ] }, { @@ -879,30 +879,30 @@ "output_type": "stream", "stream": "stdout", "text": [ - "time: 0 \tposition = 0.260 \tvariance = 2.500\n", - "time: 1 \tposition = 0.381 \tvariance = 1.667\n", - "time: 2 \tposition = 0.401 \tvariance = 1.250\n", - "time: 3 \tposition = 0.308 \tvariance = 1.000\n", - "time: 4 \tposition = 0.626 \tvariance = 0.833\n", - "time: 5 \tposition = 0.456 \tvariance = 0.714\n", - "time: 6 \tposition = 0.540 \tvariance = 0.625\n", - "time: 7 \tposition = 0.225 \tvariance = 0.556\n", - "time: 8 \tposition = 0.043 \tvariance = 0.500\n", - "time: 9 \tposition = -0.006 \tvariance = 0.455\n", - "time: 10 \tposition = -0.087 \tvariance = 0.417\n", - "time: 11 \tposition = 0.044 \tvariance = 0.385\n", - "time: 12 \tposition = 0.027 \tvariance = 0.357\n", - "time: 13 \tposition = 0.051 \tvariance = 0.333\n", - "time: 14 \tposition = 0.059 \tvariance = 0.312\n", - "time: 15 \tposition = 0.023 \tvariance = 0.294\n", - "time: 16 \tposition = 0.068 \tvariance = 0.278\n", - "time: 17 \tposition = 0.096 \tvariance = 0.263\n", - "time: 18 \tposition = 0.054 \tvariance = 0.250\n", - "time: 19 \tposition = 0.065 \tvariance = 0.238\n" + "time: 0 \tposition = 0.793 \tvariance = 2.500\n", + "time: 1 \tposition = 0.575 \tvariance = 1.667\n", + "time: 2 \tposition = 0.277 \tvariance = 1.250\n", + "time: 3 \tposition = 0.348 \tvariance = 1.000\n", + "time: 4 \tposition = 0.401 \tvariance = 0.833\n", + "time: 5 \tposition = 0.231 \tvariance = 0.714\n", + "time: 6 \tposition = 0.386 \tvariance = 0.625\n", + "time: 7 \tposition = 0.517 \tvariance = 0.556\n", + "time: 8 \tposition = 0.452 \tvariance = 0.500\n", + "time: 9 \tposition = 0.332 \tvariance = 0.455\n", + "time: 10 \tposition = 0.298 \tvariance = 0.417\n", + "time: 11 \tposition = 0.280 \tvariance = 0.385\n", + "time: 12 \tposition = 0.197 \tvariance = 0.357\n", + "time: 13 \tposition = 0.132 \tvariance = 0.333\n", + "time: 14 \tposition = 0.116 \tvariance = 0.312\n", + "time: 15 \tposition = 0.076 \tvariance = 0.294\n", + "time: 16 \tposition = 0.135 \tvariance = 0.278\n", + "time: 17 \tposition = 0.040 \tvariance = 0.263\n", + "time: 18 \tposition = 0.003 \tvariance = 0.250\n", + "time: 19 \tposition = 0.006 \tvariance = 0.238\n" ] } ], - "prompt_number": 15 + "prompt_number": 16 }, { "cell_type": "markdown", @@ -910,7 +910,7 @@ "source": [ "Because of the random numbers I do not know the exact values that you see, but the position should have converged very quickly to almost 0 despite the initial error of believing that the position was 2.0. Furthermore, the variance should have quickly converged from the intial value of 5.0 to 0.238.\n", "\n", - "By now the fact that we converged to a position of 0.0 should not be terribly suprising. All we are doing is computing $new\\_position = old\\_position * measurement$, and the measurement is a normal distribution around 0, so we should get very close to 0 after 20 iterations. But the truly amazing part of this code is how the variance became 0.238 despite every measurement having a variance of 5.0. \n", + "By now the fact that we converged to a position of 0.0 should not be terribly suprising. All we are doing is computing $\\verb,new_pos = old_pos * measurement,$ and the measurement is a normal distribution around 0, so we should get very close to 0 after 20 iterations. But the truly amazing part of this code is how the variance became 0.238 despite every measurement having a variance of 5.0. \n", "\n", "If we think about the physical interpretation of this is should be clear that this is what should happen. If you sent 20 people into the hall with a tape measure to physically measure the position of the dog you would be very confident in the result after 20 measurements - more confident than after 1 or 2 measurements. So it makes sense that as we make more measurements the variance gets smaller.\n", "\n", @@ -946,13 +946,13 @@ " \n", "In a nutshell, we shift the probability vector by the amount we believe the animal moved, and adjust the probability. How do we do that with gaussians?\n", "\n", - "It turns out that we just add gaussians. Think of the case without gaussians. I think my dog is at 7.3m, and he moves 2.6m to right, where is he now? Obviously, $7.3+2.6=9.9$. He is at 9.9m. Abstractly, the algorithm is $new\\_pos = old\\_pos + dist\\_moved$. It does not matter if we use floating point numbers or gaussians for these values, the algorithm must be the same. \n", + "It turns out that we just add gaussians. Think of the case without gaussians. I think my dog is at 7.3m, and he moves 2.6m to right, where is he now? Obviously, $7.3+2.6=9.9$. He is at 9.9m. Abstractly, the algorithm is $\\verb,new_pos = old_pos + dist_moved,$. It does not matter if we use floating point numbers or gaussians for these values, the algorithm must be the same. \n", "\n", "How is addition for gaussians performed? It turns out to be very simple:\n", "$$ N({\\mu}_1, {{\\sigma}_1}^2)+N({\\mu}_2, {{\\sigma}_2}^2) = N({\\mu}_1 + {\\mu}_2, {{\\sigma}_1}^2 + {{\\sigma}_2}^2)$$\n", "\n", "All we do is add the means and the variance separately! Does that make sense? Think of the physical representation of this abstract equation.\n", - "${\\mu}_1$ is the old position, and ${\\mu}_2$ is the distance moved. Surely it makes sense that our new position is ${\\mu}_1 + {\\mu}_2$. What about the variance? It is perhaps harder to form an intuition about this. However, recall that with the *update()* function for the histogram filter we always lost information - our confidence after the update was lower than our confidence before the update. Perhaps this makes sense - we don't really know where the dog is moving, so perhaps the confidence should get smaller (variance gets larger). I assure you that the equation for gaussian addition is correct, and derived by basic algebra. Therefore it is reasonable to expect that if we are using gaussians to model physical events, the results must correctly describe those events.\n", + "${\\mu}_1$ is the old position, and ${\\mu}_2$ is the distance moved. Surely it makes sense that our new position is ${\\mu}_1 + {\\mu}_2$. What about the variance? It is perhaps harder to form an intuition about this. However, recall that with the $\\verb,update(),$ function for the histogram filter we always lost information - our confidence after the update was lower than our confidence before the update. Perhaps this makes sense - we don't really know where the dog is moving, so perhaps the confidence should get smaller (variance gets larger). I assure you that the equation for gaussian addition is correct, and derived by basic algebra. Therefore it is reasonable to expect that if we are using gaussians to model physical events, the results must correctly describe those events.\n", "\n", "I recognize the amount of hand waving in that argument. Now is a good time to either work through the algebra to convince yourself of the mathematical correctness of the algorithm, or to work through some examples and see that it behaves reasonably. This book will do the latter.\n", "\n", @@ -969,13 +969,13 @@ "language": "python", "metadata": {}, "outputs": [], - "prompt_number": 16 + "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ - "What is left? Just calling these functions. The histogram did nothing more than loop over the *sense()* and *update()* functions, so let's do the same. " + "What is left? Just calling these functions. The histogram did nothing more than loop over the $\\verb,sense(),$ and $\\verb,update(),$ functions, so let's do the same. " ] }, { @@ -1019,47 +1019,47 @@ "stream": "stdout", "text": [ "UPDATE: 1.0000,\t502.0000\n", - "SENSE: -4.7569,\t9.8047\n", + "SENSE: -0.9237,\t9.8047\n", "\n", - "UPDATE: -3.7569,\t11.8047\n", - "SENSE: -0.2966,\t5.4138\n", + "UPDATE: 0.0763,\t11.8047\n", + "SENSE: 0.1727,\t5.4138\n", "\n", - "UPDATE: 0.7034,\t7.4138\n", - "SENSE: 3.9704,\t4.2574\n", + "UPDATE: 1.1727,\t7.4138\n", + "SENSE: 3.2003,\t4.2574\n", "\n", - "UPDATE: 4.9704,\t6.2574\n", - "SENSE: 5.5456,\t3.8490\n", + "UPDATE: 4.2003,\t6.2574\n", + "SENSE: 6.5697,\t3.8490\n", "\n", - "UPDATE: 6.5456,\t5.8490\n", - "SENSE: 6.3932,\t3.6904\n", + "UPDATE: 7.5697,\t5.8490\n", + "SENSE: 7.6101,\t3.6904\n", "\n", - "UPDATE: 7.3932,\t5.6904\n", - "SENSE: 6.2941,\t3.6267\n", + "UPDATE: 8.6101,\t5.6904\n", + "SENSE: 8.2152,\t3.6267\n", "\n", - "UPDATE: 7.2941,\t5.6267\n", - "SENSE: 7.2177,\t3.6007\n", + "UPDATE: 9.2152,\t5.6267\n", + "SENSE: 6.5868,\t3.6007\n", "\n", - "UPDATE: 8.2177,\t5.6007\n", - "SENSE: 7.4951,\t3.5900\n", + "UPDATE: 7.5868,\t5.6007\n", + "SENSE: 6.4438,\t3.5900\n", "\n", - "UPDATE: 8.4951,\t5.5900\n", - "SENSE: 7.8431,\t3.5856\n", + "UPDATE: 7.4438,\t5.5900\n", + "SENSE: 7.7971,\t3.5856\n", "\n", - "UPDATE: 8.8431,\t5.5856\n", - "SENSE: 8.2236,\t3.5838\n", + "UPDATE: 8.7971,\t5.5856\n", + "SENSE: 7.7203,\t3.5838\n", "\n" ] }, { "metadata": {}, "output_type": "display_data", - "png": 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Bo1s3eOrUgaduXchVqmhqhYNgxpleUoRl2jS4b7sNzoceUruU0OPxoFK7dnC1\naoX8UaO8Z9MSaYTL5V1GrGBcYc8eAxo2dKNDByfatXOhRQtXaYsXkJ+ZFi6EccsWGNLSIFss3neT\n2rSB48knoZnfPLTC6YR04QLkGjWK3WTYtg0RTz0Fd926hY2su25duBMS4G7ZUoViQ5PITC+bXio3\n3aFDiHzwQWRt3gy5hDNDyf+kCxdgmTYNpmXL4Hj8cXhuvx3uBg0095YmBT+PB/jlF33huML27UbE\nx7vRvr33aG7r1k7tnVfjcnnXw+3QQe1K1CHL0B06BMO2bTDs34+8f/4zpI88ShcvwrxwYeFRW93R\no94TBNu0Qc4XXxR/QEEbFcKZaQFPZAsQAT3bJMuwjhoF28sv+73hDeic/EyOjkb+22/D1r8/MqZM\nQd2DByFdvlxi06vfsweW6dPhqVEDcvXq8FSvDrlGDbjr1YPnjjtUqF4dbrf3NdWhA19TN3Oj7z1Z\nBg4f1hWOK6SmGhAVJaN9exd69XLgww/zULWqf1dYKA8pIwPh/foBFgtykpNR3ku0BeTPKUmCp0ED\nOBo0gKOUu+gOHULYpElwXjm3wNOgQbmaPFVystu9Kx8cPQr9sWOQLl2C7eWXi99PkiBlZcGVlATP\nI4/AU7cuPLVqlX4hIT82uwH5etIwNr1ULqaVKyFdvAj73/6mdikEQK5dG4eeeAI1bvBD0lO7Nhw9\ne0J39iykM2dg2L0b0pkz8DRsiPw33yx2f/3evTCtWuVtjq9pkj2xsQF1Rnh2NvD99wZs327Ajh0G\n7N1rQH5+d+h03msDmEwyzObS/zabZZhMKHbbzR5b0u3X7stsLnpbICzMceqUVDiusGWLER6Pdxmx\nzp2dePvtPNSurd0m91qGrVsR3r8/7H36wDZyZLkb3mAmV6sG5733wpCWBsuMGZDy8+Fq3RqORx+F\ns0cPtcvzKu1yt3l5iGrZEtLZs/DExl4dQSjl4gty5col/iykwMfxBvKZdOkSKrVujZylS+Fu1kzt\ncshPdH/+CePXX0N35gykM2egO3MGusxMONu3R/6kScXuX3DWeGFzXL06PDVqAJGRFfr237lzErZv\nv9rk/v67Ho0bu9C6tQutWnlnSSMjvTOndjvgcEiw2wG7XSqyfe3fNtu12977Fvxd8NjrP+5wADZb\nyR8v6XEGQ/GG2dsgF//7Rs14SQ11WZtxk8nbhF+4ICE11TuusHWrEefOSUhOdl0ZWXDi9tsrbhkx\nRXg8sHxVePifAAAgAElEQVTwAcxz5yJ31iy4OnVSu6KAI504AWNaGmSrVZWm17BhA/TXnDimO3oU\n+vR0XPr99xKvcKc7dsx7tJazykGL4w3kX/n5yH/lFTa8Qc5z222wDxki/gC7HbqjR2HYubNIk2x/\n+ukSm2TdwYPQ//HH1Sa5WrUyXwBAloHjx3WFTe727QZkZOjQooW3yZ04MR9JSSWfMGU0FrxrWfD7\nv3pHKWUZcDpRSpMtCTXnBR/PzQUuXND53Hw7HN59Go3ehrhVKxfatXPi2Wdz0aiResuIKUG6eBH6\nPXuQtXEj5Nq11S4nIMm1a8PRq1ept1umTIF+797CpRbdiYliDacse39uXBlBcHTvXuLKNObly+Gp\nXBmeOnXgatXKe+S2Tp1SL+nsqVNH+HOj4MUjvRrAmR0xzEmMZnNyu0t8+9jw7bcwL1oEXWZmYZMs\nWyzeSy8PHVrs/rrjxyGfv4hfsuOw/bdq2L7LjO3bDXC5vI1Z69bePwkJ7pu+W63ZrDRClr1HtHfs\nSEOHDjdfI5r4miognTkDQ1oaDNu2wZiaCt2pU3C1bIn8N9+EOyGhWE7W4cNh2LkTuvR0yFZr4QhC\n3sSJkKtXV/EzURdfT+L8eqR3586d+Nvf/gaXy4XExEQsX77c110RUSgopQN1dekCV5cuVz8gy5Au\nX756RjS8jdf+/Xps327Ars8jsePXeNyCi2jn+QIPhO/B2zV+R9yIh+B86sli+5cuXAAkCXLlyjy7\nuowkyTvqoNcHxowuaYdcvTqcjzwC5yOPIB+AdO4cDNu2wVOlSon3t/fqBXu/fnDHx3tHoYj8wKcj\nvR6PBw0bNsQnn3yCNm3a4Pz586hy3QuZR3qJyFc5OcAPPxQ96axuXXfhPG6rVi7ExMiA2w3p/Hno\nzpyBp3LlEt+qtrz/PswzZ0LKy4NcrRo8V+aM7X37wnXffSp8dlShcnO9b3nzJDWioOa3I727d+9G\ntWrV0KZNGwAo1vASEZXF+fMSduy42uQePKhHo0ZutG7txEsv2dCihRtRUSX8fq7XQ65eHe4bvP1p\nGznSe2a+zeZdsSIz09sk163rv0+INEH322+IeO455I8bB2f37mqXQ0Qq8+lUhPT0dERFReHBBx9E\n06ZNMXv2bKXrCimpqalqlyBGlmGZOhXSuXOqPH3A5KSyQMjp+HEdVqwwYfhwK1q1qoSmTaPwySdm\n3HKLjAkT8vH775ewbl023njDhs6dXSU3vGVlscATFwf3PffA2bUrPLffHhBZaUEg5mT8/HNE9ugB\n25AhFdrwBmJWamBOYpiTsnw60muz2ZCWloaffvoJUVFRuOeee/DAAw+gXr16StdHGmL8+muYVqyA\n7aWX1C6FAogsAwcP6rBjhwHbtnmv0GW3Xz3prE8fOxo1cmtiJSH9gQOwjhqF/FGjvMtYcQY48Njt\nCHvtNRg3b0bO6tVwN2qkdkVEpBE+/TdTs2ZN3HXXXah9ZX6uWbNm+O2334o1vYMGDUJ8fDwAICoq\nComJiYVnIRb89sLtZCQnJ2uqnpK2t3/7LToOH468BQsAs1m1egqonYeWt9V+PTmdwJIlP+Hnn6OR\nmVkfO3YYYDTm4667LuDhh6MxapQNGRlbIEnayKvIduvWsP3tb8CIEUBYGAzvvAPXvfciNS1NG/Wp\ntF3wMa3Uc6PtsDffxIXffsP+d95BqysNbyh9/wXSdgGt1KPFbb6ebvz6SU1NRXp6OgCgX79+uBmf\nTmS7fPkyEhIScODAAYSHh6NZs2ZYtWoVGjRoUHgfnsgWXMLGjYN04QLyZs1SuxTSmLy8oied7d5t\nQHz81ZPOWrd2ITY2wM7+d7thXLMGYe+9Bzk8HLmzZnkvu0ral5MDhIfzKD1RiBE5kc2nmd6oqChM\nnz4dnTp1QtOmTfH0008XaXipbK7/rVdr9AcOwLRyJfInTFC1Dq3npBX+zuniRQnr1hnxxhth6Nw5\nEg0aVMbEiWHIz5cwYIAdP/54Gamp2XjvvXw8+qhT0w1vqVnp9XD27ImstDTYXnoJcrVqFVuYxgTU\n915EhKoNb0BlpSLmJIY5Kcvg6wMfe+wxPPbYY0rWQhql/+kn5L/1FuSqVdUuhVRw4kTBygpGbN9u\nwIkTOtxzj/cI7ptv5qNpU1dJF0wKDjodnH/9q9pVEBGRAnhFNiIqJMvA77/rCkcVtm83IC9PKjKq\nkJiojZPO1KbfsQO6y5fh7NKFb6WrwJCWBtPixcibM4f5E5F/r8hGRIHP5QIOHPBe6ayg0Q0Plwsv\n5TtihA3163vYU5RAcjpheecdWCZPhm3UKDgfeIDNV0XweGCeMQOWuXORO2sWMyciYT7N9JKyOLMj\nhjmJuVFO+flAaqoB771nQc+eEbj11sp46aVwHD6sw1//6sD//peFffuyMHt2Hvr0caBBg+BueMvz\nmnK1a4fszZthGzECln/8A5EdO8L49ddFLp8cLLTyvSddvIjwZ56Bad06ZG3cCFfHjmqXVIxWstI6\n5iSGOSmLR3qJgtilSxJ27jQUHsn9+Wc97rrLu7LCiy/aMX9+Lm65JfiatAqj08HZvTucXbvCuG4d\nzAsXwtmpk/eyt6Qo6cQJRHbvDme3bshdvBgwGtUuiYgCDGd6qTibDYatW+Hq3FntSqiM8vKAb74x\nFja56el6NGt2dR63WTMXwsPVrpLIB243DNu2wdWundqVEJEGcaaXfGKZNg36X39l0xtATp+WMH++\nGYsXm9G4sRsdOjjx5JN5uPtuNw+IaYB08iTkmBhAx4kyn+n1bHiJqFz4E1gDtDSzozt0COb585H3\nj3+oXUoxWspJK378UY+BA61o27YSsrMlrFuXjaFDv8FLL9nRrBkb3pupqNeU9fXXEdmhA4xffgl4\nPBXynEri9544ZiWGOYlhTspi00tXyTKso0bB9vLLkGvVUrsaKoXHA6xbZ8RDD0Xg6acj0LChG3v2\nZGHy5HzcdlvgNVShIHf+fOS//josH3yASu3awfif/wRk81tRjKtXQ8rIULsMIgoynOmlQqYVK2Ce\nNQvZGzeCC7FqT24u8O9/mzF3rhmVKskYNMiGhx5y8mhuIJFlGDZuRNjkyfDUqoXcRYvUrkhb7HaE\nvfYajJs3I2fpUnjuuEPtiogoQHCml8TJMkwLFyJv6lQ2vBpz8qSEefMsWLrUhNatXfjXv3LRsqU7\nqJcSC1qSBFfnzsi+7z5IZ8+qXY2m6NLTEf788/DUro2sTZuASpXULomIggzHGzRAEzM7koSctWvh\nbtZM7UpKpYmcKtDevXq8+KIV7dpVgs0GbNiQjcWLc9Gq1Y0b3lDLqTxUy0qSIFevrs5z+8DfORnX\nr0dk585wPPYYchcuDOiGl99/YpiTGOakLB7So6v0erUrCHlut3ded/ZsM44f1+HFF+14//28QO4B\nqCzy8xHxxBOwP/ccnH/9a8h8T+qOHkXO4sVwt2ypdilEFMQ400ukAdnZwLJl3nndqlW987rduzs5\naRJqZBmG775D2OTJkC5dQv6oUXA+8kjINL9ERL4SmenleAORik6ckPDGG2Fo0iQKO3YYMGdOLr79\nNht//Ssb3pAkSXB16oTsb75B3qRJsMybh0pt2sDw3XdqV0ZEFPDY9GqAWjM70tmz3kt4BYhgmm36\n4Qc9XnghHB06VILHA3z3XTY++SQXLVq4y73vYMrJ3zSblSTB1bEjstetQ97kyZBVnm9RLCePB7pj\nx5TZl0Zp9jWlMcxJDHNSFo8lhSpZhvXvf4erXTvYBw1Su5qQ4HIBKSlGzJ5twZkzEvr3t2P69FzO\n61LpJAmuv/xF7SoUIV28COugQYBej9ylS9Uuh4hCEGd6Q5QxJQVhb7+NrC1bALNZ7XKCWlYWsGSJ\nGR99ZEZsrHdet2tXJ8c0qVykc+dg3LgRjsce0/wyg/q9exH+/PNwdu2K/LfeAkwmtUsioiDDmV4q\nWXY2rGPGeNfkZcPrN8eO6fDqq2FISorCvn0GfPJJLtaty0aPHmx4qfykS5dg+vRTVGrVCqZ//9v7\nVoLWyDJMCxYg4oknkD9+PPLffZcNLxGphk2vBlT0zE7YpElwdugAV3JyhT5veQXCbJMsAzt26NGn\nTzjuvTcSJhOweXMWPv44F02bln9eV0Qg5KQVgZyV5/bbkbN2LfKmT4fp3/9GpZYtYVq2zC/Nr685\n6X79FebFi5G9bh2cDz+scFXaFMivqYrEnMQwJ2Vp+z0xUpzu6FGYPv8cWWlpapcSVJxO4MsvvfO6\nly5553VnzsxFRITalVGwcyUnIyc5GYa0NFimTIGrSRN47rpL7bIAAJ677kL2f/8L6Hh8hYjUx5ne\nECSdPg05JkbtMoLC5csSFi0y4eOPLahb142BA+24/36OLxAREVUkkZleHukNQWx4y+/wYR3mzjVj\n5UoTunRxYunSHDRuXDHjC0RlJV26BDk8HDAa/fckHg+P6BKRpvEnlAZwZkeM2jnJMrBtmwG9e4fj\n/vsjEREhIzU1C3Pm5Gmq4VU7p0ASKlmZlixBpebNYVq0CHA4yvz4m+WkS09H5P33Q79/v68lBo1Q\neU2VF3MSw5yUxaaX6CYcDmDlShM6dYrEsGFWdOrkxL59l/H66zbExvplOohIUfYhQ5A7Zw5Ma9Z4\nm9+FC31qfktiXL8ekZ07w/HII3Dffbci+yQi8gfO9IYCp9O/b2sGqYsXr87r1q/vndft3NnJd3Ap\noOl37kTYlCnQpad7T2j1dQkxlwuWf/wD5uXLkTNvHtytWilbKBFRGXCmlwCbDZU6dULO0qXw3Hqr\n2tUEhD/+0GHOHDO++MKEBx90YvnyHDRqpJ3xBaLycLdsiZxVq6BLTy/Xmrnh/ftDungRWf/7H+Sq\nVRWskIjIP3jMSgP8ObNjmTYN7vr1g6Lh9WdOsgxs3WrAU0+Fo2vXSERHy9i+PQszZ+YFXMPLGTBx\noZyVJz5e+L4l5ZQ/bhxyVq5kw3udUH5NlQVzEsOclMUjvUFMd+gQzAsWIGvzZrVL0Sy7HfjiCxNm\nzzbD4ZAwcKANCxbkIixM7cqI1GEdPhzuRo1g7937hlds9NSrV4FVERGVH2d6g5UsI+Lhh+Hs2hX2\nAQPUrkZzzp+X8MknZixYYEbDhm4MHGhDp04uzutSyNPv2QPLlCkw/PQTbMOGeZtfi0XtsoiIbkhk\nppf/xQcp04oVkLKyYO/XT+1SNOXgQR2GD7finnsqIT1dh88/z8aqVTm47z42vEQA4G7aFLmffYac\nJUtg2LQJUU2bwjx9utplERGVG/+b1wB/zOy42rRB7uzZgCF4Jlh8zUmWge++M+CJJyLw8MORqFnT\ng127sjBjRh7uusujcJXq4wyYOGZVOndSEnL//W/kLFuG/Vz9RRhfU2KYkxjmpKzg6YioCE9cnNol\nqM5mAz7/3ITZsy2QZWDQIBsWL3bwnVqiMnA3aYJzOTlql0FEVG6c6aWgc/ashAULzPjkEzPuvts7\nr/uXv7ggSWpXRkRERP7g95ne7OxsxMbGYurUqeXZDZEifvlFhyFDrGjRohIyMnRYsyYbK1bkoGNH\nNrxEREShrlxN78SJE3HPPfdAYkdRLpzZEVNSTh4PsGGDAT17RuCxxyJRp44HP/yQhWnT8nDHHcE3\nryuCrydxzEoMcxLHrMQwJzHMSVk+z/QePHgQZ8+eRbNmzeCnCQkqA93x4wh76y3kzpuHUDismZ8P\nLF9uwpw5FphMMgYOtKNnT8eNlhUlIiKiEObzTG/Pnj3xwQcfYMGCBYiIiMDLL79c5HbO9FYgWUb4\n00/D3bw5bCNGqF2NX2VmSpg3z4xFi8xo1syFQYPsSE7m+AIREVEoE5np9elI79q1a9GgQQPExcXx\nKK8GGL/6CvojR5C7aJHapfjNkSM6vP++BV9/bcSjjzrw1VfZqF8/NMcXiIiIqOx8anp37dqFVatW\nYc2aNTh37hx0Oh1iY2Px1FNPFbnfoEGDEH/l+u5RUVFITExEcnIygKtzKtxOLjKzU+bHN24M69ix\n2DF4MM7v2qWJz0fp7cxMCQ88YERi4i7s3p2I6GgZqampyMzURn1a2y7X6ynEtq/PTO16tLo9e/Zs\n/vwW3Ob3n9j2gQMHMHDgQM3Uo9Vtvp5u/PM7NTUV6enpAIB+AhfjKveSZePHj0dkZCRGXPe2Oscb\nxKWmphZ+Mcsq7LXXIF26hLyZMxWuShtsNqBHj0h07uxEmzYbfc4plJTn9RRqmJUY5iSOWYlhTmKY\nkzi/jTeQssrzgvbUqgXH8OEKVqMdsgz8/e9WxMd7MGqUDZLEb3wR/AEpjlmJYU7imJUY5iSGOSmr\n3E3vm2++qUQd5CP7oEFql+A306ZZ8OefeqSkZPNENSIiIiqXcq3TS8q4dj6FvNauNWLBAjOWLs1B\nWJj3Y8xJDHMSx6zEMCdxzEoMcxLDnJTF8QbSnB9/1GPECCtWrsxBTAxXByEiIqLyK/eJbKXhiWzk\ni8xMCZ07R2LChHz89a9OtcshIiKiACByIhvHGwKMYetWGDZsULsMv7DZgN69I9C7t4MNLxERESmK\nTa8GCM/s5OfDOmyYf4tRyfUrNZSEs01imJM4ZiWGOYljVmKYkxjmpCzO9AYQy7RpcCcmwtW5s9ql\nKI4rNRAREZE/caY3QOh+/x2R3boha/NmyLGxapejqLVrjRg71ooNG7J44hoRERGVGS9OESxkGdaR\nI2EbOTLoGl6u1EBEREQVgTO9GnCzmR3pzBnI1avDLnBd6UCSmSmhd+9wvPdeHpo0cd/0/pxtEsOc\nxDErMcxJHLMSw5zEMCdl8UhvAJBr1EDuvHlql6GogpUannmGKzUQERGR/3GmlyqcLAMDBljhckmY\nNy+XJ64RERFRuXCmlzRp2jQL/viDKzUQERFRxeFMrwaE0sxOSooRCxaYsXRpDsLCyvbYUMqpPJiT\nOGYlhjmJY1ZimJMY5qQsNr0aZdiyBcjJUbsMRf34ox7Dh1uxZAlXaiAiIqKKxZleDdKlpyOyUydk\nb9wIT926apejiMxMCZ07R2LChHyeuEZERESKEpnp5ZFerZFlhI0eDfugQUHT8HKlBiIiIlIbm14N\nuHZmx5iSAv3Ro7C99JKKFSlHloGhQ62Ij/dg9GhbufbF2SYxzEkcsxLDnMQxKzHMSQxzUhZXb9CS\n7GxYx45F7kcfASaT2tUogis1EBERkRZwpldDTMuXw7B1K/I+/FDtUhSRkmLEmDFWbNiQxRPXiIiI\nyG+4Tm+AcfTqBcejj6pdhiIKVmpYsYIrNRAREZH6ONOrAUVmdgyB/3tIZqaE3r3D8d57eUhKciu2\nX842iWFO4piVGOYkjlmJYU5imJOy2PSSorhSAxEREWkRZ3pJMbIMDBhghdMpYf78XJ64RkRERBWC\nM70BQDpxAnLt2mqXoYiClRrWruVKDURERKQtHG9Qkf7AAUR264bUzZvVLqXcUlKMWLDAjKVLc2C1\n+uc5ONskhjmJY1ZimJM4ZiWGOYlhTsrikV4VWaZPh71fP0CvV7uUcuFKDURERKR1nOlVie7PPxH5\nwAO4vGcPEBmpdjk+y8yU0LlzJMaPz8cjj/DENSIiIqp4IjO9HG9QiWXGDNhfeCGgG95rV2pgw0tE\nRERaxqZXBdLJkzCuXQt7//4AAnNmR5aBoUOtiIvzYPRoW4U8ZyDmpAbmJI5ZiWFO4piVGOYkhjkp\nizO9ajCZkDdjBuToaLUr8RlXaiAiIqJAwpleKrOUFCPGjLFiw4YsnrhGREREquM6vaQ4rtRARERE\ngYgzvRoQKDM7mZkSevcOx5QpeUhKclf48wdKTmpjTuKYlRjmJI5ZiWFOYpiTsnxuek+ePInk5GQ0\natQIzZo1w8aNG5WsizSGKzUQERFRIPN5pvfMmTPIzMxEYmIi0tPT0aZNG5w4caLwds70Xsduh/7H\nH+Fu3lztSspMloEBA6xwOiXMn5/LE9eIiIhIU/w601u9enVUr14dABAfHw+HwwGn0wmj0ejrLoOa\n6bPPYEpJQc7KlWqXUmbTp3OlBiIiIgpsisz0rl+/Hs2aNWPDWxqXC5YZM2AbPrzEm7U8s5OSYsT8\n+WYsWZIDq1XdWrSck5YwJ3HMSgxzEsesxDAnMcxJWeVevSEjIwMjR47El19+Wey2QYMGIT4+HgAQ\nFRWFxMREJCcnA7j6hQyFbeOaNbhssSDN7UbylWy0VF9p24cPV8Lbb7fDihU5OHx4Kw4fVreeAwcO\naCofbgf+dgGt1KPV7QMHDmiqHm4H/jZ/nnNbiZ/fqampSE9PBwD069cPN1OudXptNhs6d+6M119/\nHV26dClyG2d6r5BlRHbogPxx4+C6LiMty8yU0LlzJMaPz+eJa0RERKRpIjO9Po83yLKM559/Hk8/\n/XSxhpeuMmzcCMgyXJ07q12KMK7UQERERMHG56Y3LS0Nq1atwkcffYSkpCQkJSUhIyNDydqCgrtF\nC+R+9BFudAbY9W+1qkmWgaFDrYiL82D0aJva5RShpZy0jDmJY1ZimJM4ZiWGOYlhTsoy+PrA5ORk\nOBwOJWsJSnJUFOSoKLXLEMaVGoiIiCgYlWum90Y40xt4UlKMGDPGim+/zUJsLC8xTERERIHBr+v0\nUnA5cECP4cOtWLEihw0vERERBR1F1uml8lF7ZiczU8Izz4RjypQ8JCW5Va3lRtTOKVAwJ3HMSgxz\nEsesxDAnMcxJWWx6/UB39CgMGzaoXYYQrtRAREREoYAzvX5gHToUnpo1YRs7Vu1SbkiWgQEDrHA6\nJcyfn8sT14iIiCggcaZXBdKpUzCuXYusH35Qu5Sb4koNREREFCo43qAwy6xZcDz5JOToaOHHqDGz\nk5JixLx5ZixZkgOrtcKf3iecbRLDnMQxKzHMSRyzEsOcxDAnZfFIr4KkCxdgWrYMWVu3ql3KDXGl\nBiIiIgo1nOlVkPmDD6D/80/kzZihdimlysyU0LlzJMaPz+eJa0RERBQUONNbwewDB0LKzVW7jFIV\nrNTw9NNcqYGIiIhCC2d6lWQyQb7lljI/rCJmdmQZGDrUirg4D0aPtvn9+fyBs01imJM4ZiWGOYlj\nVmKYkxjmpCwe6Q0R167UoOOvOkRERBRiONMbAlJSjHjlFSs2bMjiiWtEREQUdDjTS1ypgYiIiAic\n6S03w4YNMH7zTbn24a+ZncxMCc88E44pU/KQlOT2y3NUJM42iWFO4piVGOYkjlmJYU5imJOy2PSW\nhywj7O23IWtwSJYrNRARERFdxZnecjBs2ICwCROQvWULtHQdX1kGBgywwumUMG9eLk9cIyIioqDG\nmV4/s0ybBtuwYZpqeAGu1EBERER0PbZEPjJs3w5dRgacDz9c7n0pObOTkmLEvHlmLFmSA6tVsd1q\nAmebxDAnccxKDHMSx6zEMCcxzElZPNLrI+Pq1bD9/e+AQTsRFqzUsHw5V2ogIiIiuhZnen0ly4Db\nrZmmNzNTQufOkRg/Pp8nrhEREVFIEZnp5XiDryRJMw2vzQb83/9xpQYiIiKi0rDp1YDyzOzIMjB0\nqBW1ankwerRNwaq0h7NNYpiTOGYlhjmJY1ZimJMY5qQsbRyqJJ9Nn27BoUN6pKRwpQYiIiKi0nCm\nN4ClpBjxyitWbNiQxRPXiIiIKGRxpldhlvfeg+G//1W7DABXV2pYsoQrNRARERHdDJteQdKFCzDP\nng33HXcovu+yzuxkZkp45plwTJmSh6ZN3YrXo1WcbRLDnMQxKzHMSRyzEsOcxDAnZbHpFWT+6CM4\nu3eHXKuWqnVwpQYiIiKisuNMr4jsbEQ1bYrsdevguf121cqQZWDAACscDgnz5+fyxDUiIiIiiM30\ncvUGAeZFi+BKTla14QW4UgMRERGRr9g6CdD/8gtsw4b5bf8iMzspKUbMm2fG0qU5sFr9VoqmcbZJ\nDHMSx6zEMCdxzEoMcxLDnJTFI70C8mbNUvX5C1ZqWL6cKzUQERER+cLnmd4VK1Zg3LhxkCQJU6dO\nRffu3YvcHlQzvSrKzJTQuXMk3norHz178sQ1IiIiouv5babX4XBgzJgx2LlzJ2w2Gzp27Fis6aXy\nu3alBja8RERERL7zaaZ3586dSEhIQLVq1RAXF4e4uDjs379f6dpCRkkzO7IMDB1qRa1aHowebVOh\nKu3hbJMY5iSOWYlhTuKYlRjmJIY5KcunI72ZmZmIiYnB3LlzER0djZo1a+L06dNo3Lix0vWpR5YB\nSVLt6blSAxEREZFyytVO9e/fH48//jgAQFKxQfSHiEcfhX7v3gp5ruTk5CLbXKmhZNfnRCVjTuKY\nlRjmJI5ZiWFOYpiTsnw60hsTE4PTp08XbmdkZCAmJqbY/QYNGoT4+HgAQFRUFBITEwu/gAWH7LW4\nrd+xA47ffsOWrCy0vfK5VNTzR0V1wPDhVrz6aioOH76M2Fj18+A2t7nNbW5zm9vc1tJ2wb/T09MB\nAP369cPN+LR6g8PhwJ133ll4IlunTp1w6NChIvcJ5NUbInr1guPBB+F47rkKeb7U1FQkJydzpYab\nKMiJbow5iWNWYpiTOGYlhjmJYU7i/LZ6g8lkwqRJk9C2rfc46PTp033ZjSbpDxyA/qef4Fi8uEKf\nlys1EBEREfmPz+v03kygHukN79sXriZNYB8ypMKeU5aBAQOscDgkzJ+fyxPXiIiIiMrAb0d6g5bH\nA7lyZdgraKyhAFdqICIiIvIvtljX0umQN3UqEBlZYU+ZkmLErFngSg0Crh1ep9IxJ3HMSgxzEses\nxDAnMcxJWWx6VfTLL7orKzV8j9hYv0yZEBERERE406uaS5ck3HtvJEaPtqFXL4fa5RAREREFLJGZ\nXh7pVYHbDbz4Yji6dHGy4SUiIiKqAGx6ASArq0KfbtIkC/LzgQkT8gFwZkcUcxLDnMQxKzHMSRyz\nEsOcxDAnZbHpzclBVIsWkDIzK+Tp1q41YvlyExYsyIXRWCFPSURERBTyQn6m1zxzJgy7dyN3wQK/\nPw9YdEkAABcBSURBVNdvv+nQo0ckli/PQdOmbr8/HxEREVEo4Dq9N2O3wzJrFnL+/W+/P1VWlveK\na+PH57PhJSIiIqpgIT3eYPrsM7jvugvuu+/26/N4PED//uHo2NGJp58ufuIaZ3bEMCcxzEkcsxLD\nnMQxKzHMSQxzUlboHul1u2H517+QN2OG359qyhQLLl+WsGhRvt+fi4iIiIiKC92Z3txcmJcsgb1/\nf0CS/PY069YZMWqUFZs2ZaFGDV6AgoiIiEhpnOm9kfBw2AcM8OtT/P67Dn//uxXLluWw4SUiIiJS\nUUjP9PpTwYlrr7+ej+bNb3ziGmd2xDAnMcxJHLMSw5zEMSsxzEkMc1IWm14/8HiAQYPC0batC88+\nyyuuEREREaktdGd6/ei99yzYtMmIL7/MhsmkdjVEREREwU1kpjfkjvTqfvnFr/tfv96IhQvNWLgw\nhw0vERERkUaEVNOrP3AAkY8/Djj8M3Lwxx86DBlixYIFOahZU/wAOmd2xDAnMcxJHLMSw5zEMSsx\nzEkMc1JWSDW9lunTYRswAP44BJud7T1x7dVX89GyJa+4RkRERKQlITPTq/vzT0Q+8AAu79kDREYq\num9ZBvr0Ccctt8iYPj3Pn8v+EhEREdF1uE7vNSwzZsD+wguKN7wAMH26BadP6/Dxx9lseImIiIg0\nKCTGG6RTp2Bcu9Z79TWFbdxowMcfm7FoUQ7MZt/2wZkdMcxJDHMSx6zEMCdxzEoMcxLDnJQVEkd6\n5UqVkLtwIeToaEX3e+SIDoMHh2PhwlzExvKKa0RERERaFTIzvUrLyQHuv78Snn/ejn797GqXQ0RE\nRBSyuE6vn8gy8Pe/h6NJExf69mXDS0RERKR1bHp98K9/mXHsmA5TpyqzUgNndsQwJzHMSRyzEsOc\nxDErMcxJDHNSVkjM9Crpu+8MmD3bgg0bsmCxqF0NEREREYkI3pleux3GjRvh7NZNsV0eO6ZDly6R\nmD8/F8nJLsX2S0RERES+C+mZXtNnn8G8cKFi+8vLA/7v/8IxfLiNDS8RERFRgAnOptflgmXGDNiG\nD1dkd7IMDB0ajoQEN/r3V/7ENc7siGFOYpiTOGYlhjmJY1ZimJMY5qSsoJzpNa5ZA7laNbhat1Zk\nf7NmmXHokA7r1vGKa0RERESBKPhmemUZkR06IH/cOLi6dCn37rZsMeDFF8OxYUM24uI8ChRIRERE\nREoKyZlew3//C8gyXJ07l3tfx4/r8OKL4fjoo1w2vEREREQBrMxN78mTJ5GcnIxGjRqhWbNm2Lhx\noz/q8pmrfXvkfvopyjuHkJ/vPXFtyBAb2rf374lrnNkRw5zEMCdxzEoMcxLHrMQwJzHMSVllnuk1\nGo2YPXs2EhMTkZ6ejjZt2uDEiRP+qM03RiM88fHl2oUsA8OHW1G/vgeDBvGKa0RERESBrtwzvdWr\nV8fJkydhNBqLfFz1dXrLYe5cMz791IRvvsmG1ap2NURERER0IyIzveVavWH9+vVo1qxZsYY3kKWl\nGfDPf1rw7bdseImIiIiCxQ1neqdPn47ExMQif9544w0AQEZGBkaOHIlZs2ZVSKEV4cQJCf36hWPO\nnFzUqVNxJ65xZkcMcxLDnMQxKzHMSRyzEsOcxDAnZd3wSO+wYcMwbNiwYh+32Wx4/PHHMXXqVNSr\nV6/Uxw8aNAjxV+Zro6KikJiYiOTkZABXv5BKbOsOH8bhJUtw4t57fd7ff/+7DWPGtMXAgTZ07OhS\ntD5uK7N94MABTdXD7cDfLqCVerS6feDAAU3Vw+3A3+bPc24r8fM7NTUV6enpAIB+/frhZso80yvL\nMp5++mm0b98eAwcOLPV+FTnTax06FJ6aNWEbO9anx8sy8NJLVuTlSViwIJcXoCAiIiIKIH6Z6U1L\nS8OqVavw22+/4aOPPgIArFu3DjVr1vStynKSTp2Cce1aZP3wg8/7WLDAjH37DFi/PosNLxEREVEQ\nKvM6vcnJyXA4HNi7d2/hH7UaXgCwzJoFx5NPQo6O9unxO3boMWWKBUuW5CAiQuHiBF3/ViuVjDmJ\nYU7imJUY5iSOWYlhTmKYk7LKfKRXS6QLF2BatgxZW7f69PhTpyS88EIEPvwwF7feyiuuEREREQWr\ncq/TW5qKmOk1z5sH/Y8/Im/GjDI/1m4HunePxIMPOjFihM0P1RERERFRRfD7Or1qs/ft671ecBnJ\nMjBqlBWxsR4MH86Gl4iIiCjYlXmmV1MkCb5cQWLRIhO+/96ADz/UxkoNnNkRw5zEMCdxzEoMcxLH\nrMQwJzHMSVkBfaTXFzt36vHuu2FYty4bkZFqV0NEREREFSGgZ3rL6vRpCffdVwnTpuWiSxeX2uUQ\nERERkQJEZnoDe7yhDBwO4LnnIvDcc3Y2vEREREQ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OD6fbvBkxb7+N/W++iaf79n3g8+5cIiztJg83b6pRrpw3wxXcUC0RJiepzymP\nB1i+XI9Jk0yoXNmLIUMcKFUq/Id++XdPPGZ125EjGsyZY8CXX+rQrJkHb7zhROnSgb8PSs/J50tr\nxO9svjM25Q9r0O/fxN+/4b6z6b/7OTNm7OSVXiKiu3lefhnW/PlhPX0aQOCt97//VuPw4cAV3N+W\nHceh6wlwC3o8E/cHKuY8izZ5LmLM1P9D4f8UhPquj/9qjhwBPB4IZnPgP4sFiI0FNBoZvrvwoNMB\nXbq40aaNG59+akDz5hbUqePBf//rRJEi4d/8Ej2Kzwd8+aUOc+YYcOaMBj16OHHggAM5coRoxt9m\nC/ycyiaNJvBf4J2ttNpD/zmFgwfFPY9Xeokoqvj9wP79GmzbpsehQ4EruGlLhJUr50PFgldQMf8l\nFIq5DpXNClVqKlRWKzz160PIk+ee/ZkGDoT24EGorNb0/2CzIXXrVviqVr3n+cb33oP68uXbTfK/\njbKncWMIuXLdW7DLFXj/TgmDc0GSkgJ88okR8+YZ0Ly5GwMGOJEvX2R+wI+ijNOJO2edbt1SYelS\nPebPNyBPHgGJiU40aeIJ7bJ+Ph/iatWCs2dPuDt1CuGBg0fsTC+bXiKKeH4/8PPPGmzYoMeGDYHl\nwV5+2Y0qVQKjCnnySPxjMG19rrsvCwPQfv011BcupDfTaf85BwyA/9+b+dzJUq8eNL/+CiE2Frij\nUbbNng1/sWL3PF/3xRdQORz3NNX+p56C0geNr19XYdo0I5Yv16NjRzf69XOG7soXkcTUJ07A3KED\nUnbvxl//s2DuXAPWrNGjbl0PEhNdqFxZvnl29V9/wdyuHTwNG8IxalTYvzPFpjdMKH1eR0lCkpXL\nhZi334bjvfcgxMcH91hBwnMqwO8HfvlFg/Xr9di4UY+4OAHNm7vRrJkbTz/tD6+cvF6obDbgjkbZ\nV6rUfd+eNE6eDPWpUxkaalVqKqyLF8NfvPg9z4/t1Om+V56db7+dfmU71FldvKjCRx+ZsGGDDj16\nuNCrlzMUK81lW1idUzKL+KxSU2F5sS6+rDcJH//ZCL/+qkHnzi68/roL+fOLb7uCmZPq5k3EduoE\nIT4etjlzJBl3kAtXbyDKAtP48VBZrRDC4V9YuocgZGx0zeZAo7tmTSpKlgzjWVGtNvBLWHz8I6fl\nnP37Z2rXjmHDoLp5854rz5n6SLTE8ucX8NFHdvTpo8bEiUZUrRqPvn2d6NbNBZNJtrKIRLHbBKxv\n9iU+vvrHqmlWAAAgAElEQVQd1DvzIDHRhUWL3Io7d4XHH4d17VrE9O8PS7NmSN26FZH+qVxe6SX6\nl3b3bsT26oWU3bsh5MwpdzkkkiAABw4EGt0NG/SIjb19RTcSVgQg4PhxNd57z4SDBwO3Nu7Qgbc2\nJuU5f16F+fONWPGpH9U1+9D90/KoWUet/HF8QYDmyBH4KlSQu5Is45VeokxQ/fMPYpOSYJs+nQ1v\nGEhrdAMzujqYTEDz5m58/nkqSpXyK/8fmTCkunEDwuOPy/KButKl/Vi61IYDBzQYN86EGTOMGDzY\niRYt3OE+ikhhThCAffs0mDPHiF27tGjXPAU/mhsj99aZ8Be+d6ZfkVSqsG54MyNM/kQiV9TefzwL\ngpaVICBmwAC4GzeGV8RvikoXqedUWqM7cqQJFSvGISkpFiaTgJUrrfjppxQMGRJY21JsTxapOQXD\nnj17YG7fHrqvvpK1jsqVfVi3zoopU+yYN8+A2rXj8OWXOgTn/crM4zklXrhn5XYDq1bpUbeuBUlJ\nsXjuOS8OHbqF8ZMF5Prlc/gLF5bkOOGek9LwSi8RAE/9+nA3aSJ3GXQXQQB+/TVtdEEHgwFo1syN\nzz6z8opuiDkGDkTM0KHwvPQS5J4tqF3bi23bUrFtmw7jxhkxZYoRI0Y4ULs2b21MwXX16u27pj39\ntA/vvOPESy95Mr7jEMYfCLuT5tgx+EqUkHW+X2qc6SUiRREE4NCh242uThcYXWje3IPSpX1sdGVk\nbtkSngYN4OrRQ+5S0vn9wLp1Orz3ngmFCvkxbJgDVarw1sYkrWPHNJg924AtW3Ro2tSDxMTbd02L\nVDE9e0L9v//BtmhRYLRJwTjTS0RhQxCAw4dvN7pabaDRXbbMhjJl2OgqhWPsWJhbtICrTRsoZQ0x\ntRpo2dKDpk09WLFCjy5dzKhY0YuhQx0R35RQcPl8wNatOsyebcDp0xp06+bCL7+kIGdOhczTBJl9\n5kyYRo2CpX59WD/7DP6iReUuKds40yszzuuIx6zECZec0q7ojhljQuXKcejePRZarYAlS2zYvz8F\nw4Y5UbZs8BrecMlJCdKy8pUpA0+9ejBNnixzRffS6YDOnd345ZdbqFHDi1desSAxMQanT4funzme\nU+IpOauUFGDmTAOqVInD1KlGdOniwqFDt9C/v/Oehld99ix0GzYErRZZc9Jo4Bg3Ds7evWFp3Bja\nH36QrxaJ8EovRSdBiOjbuiqVIABHjmiwYYMO69froVIFruguWmRDuXK8ohsOHMOGQX39utxlPJDR\nCPTu7cJrr7kwe7YR9epZ0LSpBwMHOjJ1UwCKPqdOqTF3rgGrV+tRp44Xc+faULXqQ0ZlHA7Edu4M\nd4cOoStSBu4uXeAvUgSxiYlI2bUrrFc44kwvRR+7HZamTWFbuBD+QoXkribiCQJw9OjtRlcQ8O86\nuh6UL89Gl4Lrxg0Vpk83YulSPdq3d+Ott+69WkfRSxCAnTu1mDPHgIMHtejUyYWuXcXdNS2mb1+o\nHA7Y5s2LjosodjsQEyN3FffFmV6iBzCNHg1f0aJseINIEAIf/Fi/XocNG/Tw+YDmzT1YsMDGRpdC\nKkcOAaNHO5CY6MTkyUZUqxaH7t1dSEoKj1sbU3DY7YElx+bMMUKtBhITnVi40Cb6rmn6pUuh/fln\npHzzTXQ0vIBiG97M4EyvzJQ816Q0UmSl/fpr6LZtg+ODDySoSJnkOqfSGt1x4wKNRadOsfD5VJg/\n34aDB1MwapQDFSoop+Hl3z3xIiGrfPkETJrkwI4dqfj7bzWqVInH9OkG2O3SHSMScgoVubI6f16F\nMWNMqFAhHtu36/D++3bs2ZOCTp3E3yZYc+QITGPHwrpkCWA2B7VenlPS4pVeihqqq1cR268fbPPm\nQYiPl7uciCAIwG+/3R5d8HiAZs08mDvXhooVldPgEqUpUsSPWbPs+P33wK2N58wxYsAABzp2dEfS\ncqR0B0EAfv5Zg9mzjdi5U4vWrd3Yti0VTz2V9dU97DNmwF+ihIRVhifdtm3wFygAX9mycpciCmd6\nKWrEdukC/5NPwjFqlNylhDVBAI4fvz264HIFGt3mzd145hk2ulHF6YRp/Hg4Ro6U/YYVWXXwoAbj\nx5tw+rQagwc70aoVb20cKdxuYMMGPebMMeDGDRV69HChQwcXx1okpNuwATHvvAP79OnwNGggWx2c\n6SW6i+O//42IdQblIAjA77+r/11HVw+HIzCj+8knNlSqxEY3ahkM0Bw7BsOSJXB16yZ3NVlSqZIP\na9dasWePFuPGmTB1qhHDhjnQuLGH53WYunbt9l3TihXzYcAAJ+rV8/CXmSDwNGsGa4ECMHfuDOfp\n03D16qXoGWfO9MqM8zriZTcrf6lSEXU7xQeR6pwKXNFVY8IEI557Lg5t2ljgcKgwc6YNhw+nYOxY\nBypXDt+Gl3/3xHtgVioVHGPHwjhpUmBx0zBWs6YXX32VijFj7PjgAyNeesmC777TIjPvhfKcEi8Y\nWf32mwZ9+8agatU4JCersWqVFRs2WNGwYfg2vOFwTvmqVEHq1q3Qr1iBmP79AY9H7pIeiFd6iSiD\nO6/o2mwqNGvmxscf21ClSvg2uBQ8vnLl4HnxRRinTYNzxAi5y8kWlQqoV8+LunVTsX69DoMGxSBf\nPj+GD3egWjXe2liJfD5g27bAXdNOndKga1cXfv45BblySTi56XQGFoCmB/IXKoTUr75CbN++UP/1\nV+AikwJxppeIcOLE7UY3NTXQ6DZv7kblyj6o+X4QPYLqwgXE1a4dWLi+YEG5y5GM1wusXKnHxIkm\nlC3rxfDhTpQpw+ZXCVJSgGXLDJg/34AcOQT07OlE06Ye6d/Mc7thadIEjkGD4BUxM0ry4EwvRT3V\nP/9AeOwxuctQrBMn1NiwQY/16wONbtOmbkybFriiy0aXMkMoUACunj2h/ekneFq1krscyWi1QMeO\nbrRq5cbixQa0bGlGrVpeDB7sQNGiWf/kP2XdqVNqzJtnwKpVerzwghdz5jzirmnZZBo5Ev6cOeF9\n4YWgHYNCh/+0ySwc5nWUIlNZpabCUqcONIcOBa8ghXpYTidPqvHBB0bUqBGHli0t+OcfFaZOteHI\nkVuYMCHwFm60NLz8uyeemKyc77wTUQ3vnYxGIDHRhV9+uYWSJX2oX9+Ct96KwfnzGed9eE6Jl5ms\n0u6a1q5dLBo0sCAmRsDu3Sn49NPgNry6tWuh274d9lmzINcPRp5T0uKVXopIMUOGwFurFnwVK8pd\niuz++OP2Fd1//lGhSRM3Jk+2RVWDSyQFsxkYMMCJrl1dmDHDgP/8Jw5t27rx9ttOaWdICQDgcNy+\naxoQuGvap5/aQnJjMPWJE4gZPBjWL77gO4bZpFu3Dprjx+EcMkS2Xx7ScKaXIo5u40aYxo5Fys6d\nQb9bjlL9+Wdao6vDjRtqNG0amNFlo0skncuXVZg82Yg1a/To2tWFnj1dyJFD4Ac+s+nCBRUWLDBg\nyRIDqlTxIjHRhf/8xxvSXGPbtYOncWO4O3YM3UEjlOrqVZg7doS/QAHYZs6E6FvfZYLYmV42vRRR\nVP/7H+L+7/9gXbYMvqpV5S4n5L7+WouxY024fj3Q6DZr5sGzz3rZ6BIFUXKyGhMnGrF+vR5OJxAb\nC8TGCun/xcTc3jabBcTECBmeExMTePzex25v6/WKXv5UEml3Tfvuu8Bd03r0cMk3O223IySXlKOF\n0xlY2eHMGViXL4eQJ4+ku+cH2cLEnj17ULNmTbnLCAtisjK9/z5cXbtGZcO7Z48WSUmxSEr6GX36\nlAjbdSlDhX/3xMtKVqqrVyE88USQKlKWhAQ/Zs60o1277ahevSZsNsBmU6X/Z7erYLUiw3bac27c\nUP+7ffuxwPMzPub3pzXAuKNxFu5psO98LO05aQ317dcEGuqYGEG2G+mlnVNuN7Bxow6zZxtx/Xrg\nrmlTptjkv2uaQhreiPk5ZTTCNncujB98AEu9erB+9hn8pUuHvAw2vRRR7OPHR+V6iocOadC1ayw+\n/dQGleoKNBreE57koz57FpYGDXBr/37I372ElkYT+Jbj4gQA0r2R6nYjQ/N8u1EOPHb39pUr6jsa\nZxXs9oyNuNUaeEyjwT3Ncuaa54xXstOubD/ql+6UFD0++siIBQsMKFrUh/79nahfP3xvIkEiqFRw\n/ve/8BUrJl8JHG8gCm9//qlG06YWTJpkx8svK/dOOBRdYpKS4M+XD87hw+UuhR5AEACX685m+O4r\n1Lircb79nEDTfO8V6rTXGQz3H/EwmwMtx549Wrz8sgeJiS6ULcu1jyl7ON5AFAXOn1ehVSszhg1z\nsOElRXEMHYq42rXh6tIlom5YEUlUqsAbY0ajgJw5pbv+5fcHVl64++pzWmPscgFTptiVs+KFzwfD\n3LlwdesWFbeqj2b8eIvMuAafeMwqo+vXVWjZ0oLu3V3o2NGd/jhzEoc5iZeVrIQCBeDq2hWmCROC\nUJEy8ZwKUKsDH+Z74gkBRYr4UaaMD88+60OdOl68/LIHLVt6cOLE93KXmc743nvQbd/+6JkMGUTV\nOeV0Bv0QbHoprKlPnAh8yjbKpKYCbdqY0aiRB337uuQuh+i+nG++Cd1330Fz+LDcpRDdl27rVhg+\n/xy2efMU2fRGC+0PPyCuTh2ok5ODehzO9FLYUv3zD+Jq1YJt5kx4a9eWu5yQcbmAtm3NSEjwY+pU\ne8QvY0ThTXPwIHzFiwMWi9ylEGWgPnsWlvr1o3aJS0URBBjmzoVx2jRYFy2Cr1q1TL1c7Ewvr/RS\neBIExAwYAHfjxlHV8Pp8wBtvxMJiETB5MhteUj5fpUpseEl5HA7Edu4M54ABbHiVQKWCKzERtqlT\nYe7YEbq1a4NyGDa9MouqeZ1sujMr/Zo10Bw/DseoUTJWFFqCAPTvH4OUFBXmzbM98J04nlPiMCfx\nmJU4zEk82bPyeuFu3x6uHj3kreMRZM8pxLz16sG6bh1MY8ZAv2CB5PvPctO7b98+lC9fHqVLl0ab\nNm2krInoodTJyTANGwbb3LlBuZ2hUr37rhG//abBkiVWGAxyV0NEFMYsFrgSEyP/NndhyFemDFK/\n/hreOnUk33eWZnr9fj9KlSqFhQsXokaNGrh+/Tpy5syZ4Tmc6aVgMU6eDEGrhevNN+UuJWRmzDBg\n2TIDvvwyVdKlhYiIiMJdUNfpPXDgAJ544gnUqFEDAO5peImCydm/f+C9/iixfLke8+cbsGULG14K\nb5pDh6A5eBDurl3lLoWIolCWxhuSk5MRHx+Phg0bolKlSvjkk0+kritqRNu8TnZkyCpK3pLaskWH\nceNMWLPGioIFxTW8PKfEYU7iSZWVP1cumMaPh+rCBUn2pzQ8p8QLeVZ+f0jWgZUaz6k7CALUx49n\naxdZutLrdDrxww8/4NixY4iPj0eVKlXQoEEDPPnkkxme17t3byQkJAAA4uPjUa5cOdSsWRPA7T/I\naN9Oo5R6lLx99OhRRdUT7O0jR3Ji6tTnsGqVFZcv78bly8qqL9y3o+18ys720aNHJduf6/XXkdqv\nHw699ZZivj/+PI/8v3/FV63Ck2o17FOnKuL753bmt2sVKQJLixY41qIFNhcqhFu3bgEIXIjt3r07\nxMjSTO+OHTswYsQI/PjjjwCA9u3b47XXXkPDhg0zPIczvURZc+iQBq1bm7FggQ01a3rlLodIOikp\niK9WDdZVq+ArX17uaigKaHfuRGyvXkjZsQNC/vxyl0PZoD59GuZ27eCpUweOcePSbygS1HV6q1Sp\nguTkZNy8eRNutxtHjx5F0aJFs7IrokdS//lntt/SCCd//qlGu3ZmTJliZ8NLkScuDo5Bg2AaOTKq\nZvNJHqoLFxDbqxdsc+aw4Y0A/qeeQuq2bdCcOIHYDh0CtyfNhCw1vfHx8Zg6dSrq1KmDSpUqoX37\n9ihRokRWdhX17n5bjO4VM2QIdD/8EBVZnT+vQsuWZgwf7kDjxp4s7SMacpICcxJP6qzcr70GaLVQ\nXb0q6X7lxnNKvJBk5XbD/PrrcCYmhu1NjHhO3Ut47DFYV62CkC8fYgYMyNRrtVk9aKtWrdCqVaus\nvpxIFO1330F99ixcnTsD+/fLXU5QXb+uQsuWFrzxhgsdOrjlLocoeHQ6WNeskbsKinD6NWvgf+KJ\nqFreMmrodLBPnpzpK71ZmukVgzO9lG1+PywvvABn//7wNGsmdzVBlZoKvPKKBbVrezByZPh9wpiI\nSHEEAXC5AKNR7kooyII600sUCvrVqwGDAZ6mTeUuJahcLqBTJzPKlvVhxAg2vEREklCp2PBSBmx6\nZcZ5nQcQBBinTYN97Nj0NXkjMSufD+jRIxZxcQI++sguyfLDkZhTMDAn8ZiVOMxJPGYlDnOSVpZn\neomCSqVCytatQFyc3JUEjSAA/fvHIDVVhZUrrWkrrxBFH6cTKqcTwmOPyV0JEUUwzvQSyWTMGBO+\n/16LdetSYbHIXQ2RfIyTJ0N9+jTsH38sdykUxnRr18JXqRL8d90oiyIfZ3qJFGz6dAO++kqHzz+3\nsuGlqOfs3h26b76B5tgxuUuhMKXZtw8xQ4cCarY19GA8O2TGeR3xIiWrZcv0+PRTA9auTUXOnNK/\n0RIpOQUbcxIv6FnFxcE5cCBMI0aE9Q0reE6JJ2VWqqtXYe7WDbYZM+AvXFiy/SoBzylpseklCqHN\nm3UYP96EtWutKFAgfP9xJ5Kaq3NnqC9ehHbHDrlLoXDi8yG2Rw+42rWDt149uashheNMLymG5tdf\nYZw2DbZFi+QuJSi+/16Lbt1isWqVFRUr+uQuh0hxdF99BdO77yLl++/BT3aSGMZx46A9cCBwsxOe\nM1FL7EwvV28gZRAEmEaNgrtFC7krCYpff9WgW7dYLFhgY8NL9ACeBg0CKzhwLpNE8hcpAlvPnmx4\nSRT+ZJEZ53UCtN98A/WVK3B37PjA54RrVn/8oUb79mZMnWpHzZreoB8vXHMKNeYkXsiyUqngrV4d\nkixYLQOeU+JJlZW7Y0cIuXJJsi8l4jklLTa9JD+fDzGjRsExejSgjaw3H86fV6FVKzOGD3egUSOP\n3OUQERFFLc70kuz0y5ZBv3IlrJs2he0Vnvu5fl2FRo0seO01F/r0ccldDhERUUTiOr0UPjQaOO64\n3XAkSE0FWrc24+WX3Wx4iYikYrfLXQGFMTa9MuO8DuBu1w4+Ee8KhEtWTifw2mtmlC/vw/DhzpAf\nP1xykhtzEk+urAwzZ4bVDSt4TomXlaw0hw8jrmbNwA/ZKMFzSlpseokk5PUCb7wRi8ceE/Dhh/ZI\nunhNFHp6fdjfsIKkobp5E7FdusAxYgRgNMpdDoUpzvQSSUQQgH79YvD332qsXGmFwSB3RURhzuNB\nXI0asL/3Hrx168pdDcnF70ds+/bwP/UUHBMmyF0NKRBneolCbMwYE44f12DpUja8RJLQ6eAYPRox\nI0cG3kahqGScMgXqf/6BY8wYuUuhMMemV2bROq+j3bUL8GXuJg1Kzmr6dAO2btVh1SorzGZ5a1Fy\nTkrCnMSTMytPo0bwP/449CtWyFaDWDynxBOblerKFeiXLoV1wQJApwtyVcrDc0pabHop5DT79yM2\nKQlwRcaqBkuX6vHppwasXZuKHDk4e0gkKZUKjnffhfbgQbkrIRkIuXMj5aefIOTPL3cpFAE400uh\nJQiwNGoE12uvwd2+vdzVZNumTTr8978x2LgxFcWK+eUuh4iIKOqInemNrNtfkeLptmwBUlPhbtNG\n7lKybfduLQYMiMHq1VY2vERERArH8QaZRdW8jscD09ixgdsNazSZfrmSsjp4UIPu3WOxcKENFSpk\nbjY52JSUk5IxJ/GYlTjMSTxmJQ5zkhabXgoZ/bp18BcoAK+ItyCU7I8/1Gjf3oxp0+x4/nl+opyI\nSCrqEyegW7dO7jIoQnGml0LH54Pqn38g5MwpdyVZdv68Co0aWTBkiBPt2rnlLocoKqkuXuQHmyJR\nairi6taF88034e7QQe5qKIxwnV5SHo0mrBvea9dUaNnSgsREFxteIrkIAiytWkG7Y4fclZCUBAGx\n/frB+9xzbHgpaNj0yozzOuLJmVVqKtC6tRlNmriRlKTspdZ4TonDnMRTVFYqFRxDh8I0alSm1/oO\nNkXlpHB3Z2WYMwfqM2dgnzhRpoqUieeUtNj0Ej2C0wm89poZFSr4MGyYU+5yiKKep3FjCHFx0H/2\nmdylkAQ0P/0E45QpsC1aBBiNcpdDEYwzvUQP4fUCXbvGQqMB5s+3ZWXRCSIKAs0vv8DcuTNu7dsH\n2W+DSNmiOXoUqmvX4H3hBblLoTDFmV5ShJikJGgOH5a7jCwRBODtt2NgtaowezYbXiIl8VWpAm/1\n6jDOnCl3KZRNvnLl2PBSSLDplVkkz+tof/gB2h9+gK9kSUn2F+qsRo824fffNViyxAqDIaSHzpZI\nPqekxJzEU2pW9rFjFXVnR6XmpETMShzmJC3ekY2Cw++HaeRIOEaMQFh1jP+aPt2A7dt12LIlle+c\nEimUkD8/gjKfR0QRiTO9FBS6L76A8eOPkfrNN4A6vN5QWLJEj8mTjfjyy1Tkz89/UomIJGWzAbGx\ncldBEYQzvSQflwumcePgGDMm7BreTZt0eP99E9assbLhJSKSmDo5GXF160LLt+1JBuHVkUSgSJzX\nUV27Bvcrr8Bbq5ak+w12Vrt2aTFgQAxWrrSiWDF/UI8VTJF4TgUDcxKPWYnDnB5Ou2sXLPXqwfXa\na9gZnDeZIw7PKWmx6SXJCQUKwDlihNxlZMrBgxp07x6LhQttKF9eWQveE5EIKSkwDRqkuBtWEABB\ngGHmTMQmJsI2bx5cvXsDKpXcVVEU4kwvRb2TJ9Vo1syCKVPsaNjQI3c5RJQVggBLo0ZwdezI29gq\njPH996HbuhW2pUvhL1RI7nIoAnGml0iE8+dVaNXKgtGjHWx4icKZSgX7u+/CNGFC4INSpBiuzp2R\n+tVXbHhJdmx6ZcZ5HfGkzuraNRVatrSgVy8n2rZ1S7pvOfGcEoc5iRcuWfmqVIH3uedgnDVLluOH\nS06hJuTLB5hMGR5jVuIwJ2llq+lNTU1F/vz58dFHH0lVD4Up9blzUF29KncZoqWkAK1bm9GkiRu9\ne7vkLoeIJOIYORKG2bOhunxZ7lKISGGy1fSOHz8eVapUgYoD6VlWs2ZNuUuQRMyAAdCvXx/UY0iV\nldMJvPaaGRUr+jBsmFOSfSpJpJxTwcacxAunrPyFC8PVowc0R46E/NjhlFNQ2O0wzJ0buIf7I0R9\nViIxJ2ll+Y5sJ0+exNWrV1G5cmUE6bNwFCa0330H9dmzcHXuLHcpj+T1Aj16xCJnTgGTJtn5AWKi\nCOQcPFjuEqKO+tw5xL72GnylSwMeD6DXy10S0T2yfKV3yJAhGD16tISlRKewn9fx+2EaPTpwu+Eg\n/5DLblaCALz1VgzsdhVmz7ZBo5GoMIUJ+3MqRJiTeMxKnGjNSbtzJyz168Pdvj3sn3wi6t+CaM0q\ns5iTtLJ0pXfTpk0oUaIEChUq9NCrvL1790ZCQgIAID4+HuXKlUu/VJ/2Bxnt22mUUk9mt+tcuAAY\nDPguRw5gz56gHu/o0aPZev3ChaWQnGzBunWp2L9fGflxW77t7J5P0bR99OhRRdWj1O00Sqkn6NvP\nPw/DzJlQT5mCnwYMQKmePUW/nn//uJ3dn9+3bt0CACQnJ6N79+4QI0vr9I4YMQIrV66EVqvFtWvX\noFarMXXqVLRr1y79OVynNwr4fIirUgW2Tz6B77nn5K7moaZNM2DlSgO2bElFjhwcxyEiyja3GzHv\nvAPnwIFcjoxkJXad3mzfnGLMmDGwWCzo379/hsfZ9EYHdXIy/P9ezVeqxYv1mDLFiC+/TEX+/Gx4\niaKKIEB14QKEggXlroSIgoQ3pwgTd78tFm5C2fBmJauNG3WYONGEtWutUdPwhvs5FSrMSbxwzkrz\n22+Ia9AAsNuDfqxwzinUmJU4zEla2uzuYNSoUVLUQSS5nTu1GDgwBmvWWFG0qF/ucohIBr6yZeGt\nVg3GTz6Bc8AAucsJX4IAuFyA0Sh3JURZlu3xhgfheAPJ6cABDdq2NWPxYhtq1PDKXQ4RyUh99iws\ndesi5ccfIeTOLXc54cduR2y/fvDnywfH2LFyV0N0D443UNQ6eVKNDh3MmDHDzoaXiOAvUgTutm1h\nmjhR7lLCjvrcOVgaNICg1cIxZIjc5RBlC5temYXbvI7m119hGjZMlmOLyervv9Vo1cqC0aMdaNDA\nE4KqlCfczim5MCfxIiEr54AB0G3aBPXJk0E7RiTkdKf09Xc7dIB91izAZJJs35GWVbAwJ2mx6SXx\nBAGmUaPgK15c7kru69o1FVq2NKN3byfatnXLXQ4RKYjw+OOwrlkD/5NPyl1KWNDu2oXYXr1gmz8f\nrsRE8PaVFAk400uiab/+GjEjRiBlzx5Am+3PQEoqJQVo1syCunU9GDbMKXc5REThze2G6upVCAUK\nyF0J0SNxppek5fUiZtQoOEaPVlzD63QCHTuaUamSD0OHsuElIso2vZ4NL0UcNr0yC5d5Hf1nn8Gf\nIwc89evLVsP9svJ6ge7dY5Erl4APPrDzHTiEzzklN+YkHrMShzmJx6zEYU7SYtNLoqivXoVjzBhF\nzXUJAtCvXwwcDhVmz7ZBo5G7IiKiMCMI0C9YANWNG3JXQhR0nOmlsCQIwMiRJuzbp8W6damIjZW7\nIiIKJ9o9e6D+4w+4u3aVuxT52GyI7dcP6tOnYV22DEL+/HJXRJQlnOmliDZtmgE7dujw+edWNrxE\nlGn+AgVgmjABqitX5C5FFunr7+r1SN2yhQ0vRQU2vTLjvI54aVktXqzHokUGrFmTiscfD8obFWGN\n55Q4zEm8SMzK/+STcLdpA+MHH0i2z3DJSfvdd7DUqwd3x46wz5wp6fq7YoVLVnJjTtJi00thZcMG\nHYvNYdgAAB2ASURBVCZONGHtWivy52fDS0RZ5xw4EPqNG4N6wwol0h48CNuCBVx/l6IOZ3rpgdS/\n/w5/qVJyl5Huu++0SEyMxZo1VpQv75O7HCKKAIaZM6H94QfYVqyQuxQiyiLO9FK2aPbvh6V1a8Ct\njDubHTigwRtvxGLRIhsbXiKSjKt798DVzpQUuUshoiBj0yszRc7rCELgRhRDhwJ6vdzV4NdfNWjf\n3oxevX5BjRpeuctRPEWeUwrEnMSL6KwMBtiWLwfi4rK9K0XmZLfLXcF9KTIrBWJO0mLTS/fQbdkC\nWK1wt24tdyn4+WcN2rQxY8oUO6pVuyx3OURE4UEQYJg+HZamTQNrPBIRZ3rpLh4P4p5/Hvb33oNX\nxHxMMO3dq0WnTrGYNcuGl17iFV4iIlFsNsS++SbUZ8/CungxhIIF5a6IKKg400tZol++HP4CBeCt\nU0fWOnbvDjS8c+ey4SUiEkt99iws9etDMBoD6++y4SVKx6ZXZkqb13G3bAnbjBmyLmOzY4cW3brF\nYuFCG1544XbDq7SslIo5icOcxIumrFS3bkH1zz9Zeq3cOamuX4elQQO4O3eG/eOPAaNR1noeRu6s\nwgVzkpZW7gJIYSwWCBaLbIfftk2Hvn1jsHSpFc89x1UaiCi0jFOmADYbHJMmyV1Kpgk5cyL166/h\nL1RI7lKIFIkzvaQYmzfrMGBADJYvt6JKFTa8RBR6qhs3EPfss0jdsgX+EiXkLoeIROBML4WVL77Q\nYeDAGKxaxYaXiOQj5MgB55tvwjRmjNylEJHE2PTKjPM6wOef6zF8eAzWrrWiQoUHN7zMShzmJA5z\nEi/asnL16AHNb79Bm8nvO5Q5ab/9Fpp9+0J2PKlF2zmVVcxJWmx6CaahQ6E+fVqWYy9bpsfYsSZ8\n8UUqypThFV4iUgCjEY6RI2EaOVJ5a9wKAgzTpiG2Tx+o/H65qyEKK5zpjXLaH35ATFISUvbtAwyG\nkB57wQI9pkwxYd26VBQrxh/eRKQgggDN0aPwlS8vdyW3Wa2I7dsX6r//hnXRIi5HRvQvzvTSo/n9\nMI0cCcfw4SFveGfPNmD6dCM2bWLDS0QKpFIpquFVnzkDS4MGEGJikLp5Mxteoixg0yszOed1dOvX\nA4IAT4sWIT3u9OkGzJtnwKZNVhQpIr7h5WyTOMxJHOYkHrMSJ5g5qf/6C+4uXRS//q5YPKfEYU7S\n4jq90crlgmncONinTQPUofvdZ9IkI1av1mPTplTkz6+wWTkiIoXyvvQSeG9KouzhTG+UUv/xB4yz\nZ8M+eXJIjicIwIQJRmzerMf69anIk4cNLxEREWUfZ3rpofwlSoS04R01yoStW3XYtIkNLxGFH+O7\n70L955+hOZjNFprjEEUZNr0yi/R5HUEAhgwx4fvvtdiwwYpcubLe8EZ6VlJhTuIwJ/GYFSDExz/y\nhhVS5KTdsQPx1apBdflytvelZDynxGFO0mLTS0Hj9wMDBsTgwAEt1q+3IkcOXuElovDkeuMNaI4e\nhfaHH4JzgLT1d/v2hW3+fAh58gTnOERRjDO9FBQ+H9CvXwxOn1Zj5Uor4uLkroiIKHt0a9fCOGsW\nUr/+WtoPAN+5/u7ixRAKFJBu30RRgDO9dA/VpUuA0xn043i9QO/eMfj7bzVWrWLDS0SRwdOiBaBS\nQffFF9LtVBBgbt8eQmxsYP1dNrxEQcOmV2ahnNeJ7dMH+s8/D+oxPB6gR49YXL2qxmefWWE2S7dv\nzjaJw5zEYU7iMat/qVRwvPsuNCdP3vfLWcpJpYJt1izYZ8yIiPV3xeI5JQ5zkhbX6Y0S2u++g/rc\nObjbtw/aMVwuoHv3WHg8wIoV1mj6+U1EUcJbvTq81atLuk/eXY0oNDjTGw38flheeAHOAQPgado0\nKIdwOoHOnc0wGATMn2+DXh+UwxARERFlwJleSqdfvRowGuFp0iQo+7fbgfbtzTCbBXz6KRteIqL7\nUZ8+Df3y5XKXQRS1stT0XrhwATVr1kTZsmVRuXJlfPPNN1LXFTWCPq/jcsE4fjzsY8YAKpXku7da\ngbZtzciTx485c2zQ6SQ/RDrONonDnMRhTuIxK3EelpP2m29gadgwMAdGPKdEYk7SytJMr06nwyef\nfIJy5cohOTkZNWrUwPnz56WujaSg18O2aBF8QRg1SUkB2rSxoFgxH6ZOtUOjkfwQRESKpj59Gv6n\nnnrwEwQBxqlTYZg3D9bFi+F77rnQFUdEGUgy05s7d25cuHABujsu83GmN7LduqVCq1ZmVKjgxQcf\nOCRdspKIKCx4vYirUgX2WbPgrVHj3q9brYhNSoL6wgWuv0sURCGb6d22bRsqV66coeGlyHbjhgrN\nm5tRpYoXkyax4SWiKKXVwjF8OEwjRwZuQXkX1a1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"text": [ - "" + "" ] } ], - "prompt_number": 17 + "prompt_number": 18 }, { "cell_type": "markdown", @@ -1070,7 +1070,7 @@ " movement = 1 \n", " movement_error = 2\n", " \n", - "For the moment we are assuming that we have some other sensor that detects how the dog is moving. For example, there could be an inertial sensor clipped onto the dog's collar, and it reports how far the dog moved each time it is triggered. The details don't matter. The upshot is that we have a sensor, it has noise, and so we represent it with a Gaussian. Later we will learn what to do if we do not have a sensor for the *update()* step.\n", + "For the moment we are assuming that we have some other sensor that detects how the dog is moving. For example, there could be an inertial sensor clipped onto the dog's collar, and it reports how far the dog moved each time it is triggered. The details don't matter. The upshot is that we have a sensor, it has noise, and so we represent it with a Gaussian. Later we will learn what to do if we do not have a sensor for the $\\verb,update(),$ step.\n", "\n", "For now let's walk through the code and output bit by bit.\n", "\n", @@ -1080,9 +1080,15 @@ " pos = (0, 500) # gaussian N(0,500)\n", " \n", " \n", - "The first lines just set up the initial conditions for our filter. We are assuming that the dog moves steadily to the right 1m at a time. We have a relatively low error of 2 for the movement sensor, and a higher error of 10 for the RFID position sensor. Finally, we set our belief of the dog's initial position as $N(0,500)$. Why those numbers. Well, 0 is as good as any number if we don't know where the dog is. But we set the variance to 500 to denote that we have no confidence in this value at all. 100m is almost as likely as 0 with this value for the variance. \n", - "\n", + "The first lines just set up the initial conditions for our filter. We are assuming that the dog moves steadily to the right 1m at a time. We have a relatively low error of 2 for the movement sensor, and a higher error of 10 for the RFID position sensor. Finally, we set our belief of the dog's initial position as $N(0,500)$. Why those numbers. Well, 0 is as good as any number if we don't know where the dog is. But we set the variance to 500 to denote that we have no confidence in this value at all. 100m is almost as likely as 0 with this value for the variance. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "Next we initialize the RFID simulator with\n", + "\n", " dog = DogSensor(pos[0], velocity=movement, noise=sensor_error)\n", "\n", "It may seem very 'convienent' to set the simulator to the same position as our guess, and it is. Do not fret. In the next example we will see the effect of a wildly inaccurate guess for the dog's initial position.\n", @@ -1092,16 +1098,20 @@ " zs = []\n", " ps = []\n", " \n", - "This is the first time that I am introducing standard nomenclature used by the Kalman filtering literature. It is traditional to call our measurement $Z$, and so I follow that convention here. As an aside, I find the nomenclature used by the literature very obscure. However, if you wish to read the literature you will have to become used to it, so I will not use a much more readable variable name such as $m$ or $measure$.\n", - " \n", - " \n", - "Now we just enter our *sense()->update()* loop.\n", + "This is the first time that I am introducing standard nomenclature used by the Kalman filtering literature. It is traditional to call our measurement $Z$, and so I follow that convention here. As an aside, I find the nomenclature used by the literature very obscure. However, if you wish to read the literature you will have to become used to it, so I will not use a much more readable variable name such as $m$ or $measure$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we just enter our $\\verb,sense() - >update(),$ loop.\n", "\n", " for i in range(10):\n", " pos = update(pos[0], pos[1], movement, sensor_error)\n", " print 'UPDATE:', \"%.4f\" %pos[0], \", %.4f\" %pos[1]\n", "\n", - "Wait, why *update()* before sense? It turns out the order does not matter once, but the first call to DogSensor.sense() assumes that the dog has already moved, so we start with the update step. In practice you will order these calls based on the details of your sensor, and you will very typically do the *sense()* first.\n", + "Wait, why $\\verb,update(),$ before sense? It turns out the order does not matter once, but the first call to $\\verb,DogSensor.sense(),$ assumes that the dog has already moved, so we start with the update step. In practice you will order these calls based on the details of your sensor, and you will very typically do the $\\verb,sense(),$ first.\n", "\n", "So we call the update function with the gaussian representing our current belief about our position, the another gaussian representing our belief as to where the dog is moving, and then print the output. Your output will differ, but when writing this I get this as output:\n", "\n", @@ -1126,7 +1136,7 @@ " \n", "as the result. What is happening? Well, at this point the dog is really at 1.0, however the predicted position is 1.6279. What is happening is the RFID sensor has a fair amount of noise, and so we compute the position as 1.6279. That is pretty far off from 1, but this is just are first time through the loop. Intuition tells us that the results will get better as we make more measurements, so let's hope that this is true for our filter as well. Now look at the variance: 9.8047. It has dropped tremendously from 502.0. Why? Well, the RFID has a reasonably small variance of 2.0, so we trust it far more than our previous belief. At this point there is no way to know for sure that the RFID is outputting reliable data, so the variance is not 2.0, but is has gotten much better.\n", "\n", - "Now the software just loops, calling *update()* and *sense()* in turn. Because of the random sampling I do not know exactly what numbers you are seeing, but the final position is probably between 9 and 11, and the final variance is probably around 3.5. After several runs I did see the final position nearer 7, which would have been the result of several measurements with relatively large errors.\n", + "Now the software just loops, calling $\\verb,update(),$ and $\\verb,sense(),$ in turn. Because of the random sampling I do not know exactly what numbers you are seeing, but the final position is probably between 9 and 11, and the final variance is probably around 3.5. After several runs I did see the final position nearer 7, which would have been the result of several measurements with relatively large errors.\n", "\n", "Now look at the plot. The noisy measurements are plotted in with a dotted red line, and the filter results are in the solid blue line. Both are quite noisy, but notice how much noisier the measurements (red line) are. This is your first Kalman filter shown to work!" ] @@ -1181,9 +1191,9 @@ { "metadata": {}, "output_type": "display_data", - "png": 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8YqWiKlYBnJmZSa1atZg6dSoACxcuJDExkaSkJFasWFGqAYqIiEjZdvCghRdf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\nM/6V3rif+E+83bsH6ksQERERkSogMzOTXr16lfr1lbYT3Ni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upO+Iv7VF8YMMC8N1xx040tPJ+8uSriWtMueF+I96jEVERMqBoPnzsS9fjmP5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\nCuNa4ti+XQ369VPEJ59Uv7Zpk0O9e8fpppv8WrW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"text": [ - "" + "" ] }, { @@ -1202,7 +1212,7 @@ ] } ], - "prompt_number": 18 + "prompt_number": 19 }, { "cell_type": "markdown", @@ -1299,7 +1309,7 @@ "metadata": {}, "source": [ "#####Excercise:\n", - "Modify the values of *movement_error* and *sensor_error* and note the effect on the filter and on the variance. Which has a larger effect on the value that variance converges to. For example, which results in a smaller variance:\n", + "Modify the values of mov$\\verb,ement_error,$ and $\\verb,sensor_error,$ and note the effect on the filter and on the variance. Which has a larger effect on the value that variance converges to. For example, which results in a smaller variance:\n", "\n", " movement_error = 40\n", " sensor_error = 2\n", @@ -1338,20 +1348,25 @@ "language": "python", "metadata": {}, "outputs": [], - "prompt_number": 19 + "prompt_number": 20 }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We generate white noise with a given variance using the equation *random.randn() * variance*. The specification gives us the standard deviation of the noise, not the variance, but recall that variance is just the square of the standard deviation. Hence we raise 2.13 to the second power.\n", - "> **Sidebar**: spec sheets are just what they sound like - specificiations. Any individual sensor will exhibit different performance based on normal manufacturing variations. Numbers given are often maximums - the spec is a guarantee that the performace will be at least that good. So, our sensor might have standard deviation of 1.8. If you buy an expensive piece of equipment it often comes with a sheet of paper displaying the test results of your specific item; this is usually very trustworthy. On the other hand, if this is a cheap sensor it is likely it received little to no testing prior to being sold. Manufacturers typically test a small subset of their output to verify that everything falls within the desired performance range. If you have a critical application you will need to read the specification sheet carefully to figure out exactly what they mean by their ranges. Do they guarantee their number is a maximum, or is it, say, the $3\\sigma$ error rate? Is every item tested? Is the variance normal, or some other distribution. Finally, manufacturing is not perfect. Your part might be defective and not match the performance on the sheet.\n", + "We generate white noise with a given variance using the equation $\\verb,random.randn() * variance,$. The specification gives us the standard deviation of the noise, not the variance, but recall that variance is just the square of the standard deviation. Hence we raise 2.13 to the second power.\n", + "> **Sidebar**: spec sheets are just what they sound like - specifications. Any individual sensor will exhibit different performance based on normal manufacturing variations. Numbers given are often maximums - the spec is a guarantee that the performace will be at least that good. So, our sensor might have standard deviation of 1.8. If you buy an expensive piece of equipment it often comes with a sheet of paper displaying the test results of your specific item; this is usually very trustworthy. On the other hand, if this is a cheap sensor it is likely it received little to no testing prior to being sold. Manufacturers typically test a small subset of their output to verify that everything falls within the desired performance range. If you have a critical application you will need to read the specification sheet carefully to figure out exactly what they mean by their ranges. Do they guarantee their number is a maximum, or is it, say, the $3\\sigma$ error rate? Is every item tested? Is the variance normal, or some other distribution. Finally, manufacturing is not perfect. Your part might be defective and not match the performance on the sheet.\n", "\n", "> For example, I just randomly looked up a data sheet for an airflow sensor. There is a field \"Repeatability\", with the value \"$\\pm0.50\\%$ Reading\". Is this a Gaussian? Is there a bias? For example, perhaps the repeatibility is nearly $0.0\\%$ at low temperatures, and always nearly $+0.50$ at high temperatures. Data sheets for electrical components often contain a section of \"Typical Performance Characteristics\". These are used to capture information that cannot be easily conveyed in a table. For example, I am looking at a chart showing output voltage vs current for a LM555 timer. There are three curves showing the performance at different temperatures. The response is ideally linear, but all three lines are curved. This clarifies that errors in voltage outputs are probably not Gaussian - in this chip's case higher temperatures leads to lower voltage output, and the voltage output is quite nonlinear if the input current is very high. \n", "\n", - "> As you might guess, modeling the performance of your sensors is one of the harder parts of creating good Kalman filter. \n", - "\n", - "Now we need to write the Kalman filter processing loop. As with our previous problem, we need to perform a cycle of sensing and updating. The sensing step probably seems clear - call *volt()* to get the measurement, pass the result into *sense()* function, but what about the update step? We do not have a sensor to detect 'movement' in the voltage, and for any small duration we expect the voltage to remain constant. How shall we handle this?\n", + "> As you might guess, modeling the performance of your sensors is one of the harder parts of creating good Kalman filter. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we need to write the Kalman filter processing loop. As with our previous problem, we need to perform a cycle of sensing and updating. The sensing step probably seems clear - call $\\verb,volt(),$ to get the measurement, pass the result into $\\verb,sense(),$ function, but what about the update step? We do not have a sensor to detect 'movement' in the voltage, and for any small duration we expect the voltage to remain constant. How shall we handle this?\n", "\n", "As always, we will trust in the math. We have no movement, and no error associated with them, so we will just set both to zero. Let's see what happens. " ] @@ -1397,9 +1412,9 @@ { "metadata": {}, "output_type": "display_data", - "png": 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Ev4eAAMe7r4c4nA6tfm+1/vf8/62sEVl93i8sLEy/vO6XWvTaIv37F/+utZev9eEoYQUc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\n7XlJK6auUER4xJAHCwAAAJg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FWpyzWNuu2qYFWQuUHJvs9d9hsOY9EDhHIHzERMcMFj8peR4/VjjmItif8zab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\nZEiS8vLytGfPHhUWFvpskAg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"text": [ - "" + "" ] }, { @@ -1407,7 +1422,7 @@ "output_type": "display_data", "png": 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LLldeKGXMVsGEXMCEXMCEXMAJWR1d4Fq6AAAAcEsWiy5ndEsZs1UwIRcwIRcwIRdwQnZn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\nUfq5uH34+8RsDTp+LGPtMVu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"text": [ - "" + "" ] }, { @@ -1415,17 +1430,17 @@ "stream": "stdout", "text": [ "Variance converges to 0.0907297673624\n", - "Last voltage is 16.2989678826\n" + "Last voltage is 15.6491261618\n" ] } ], - "prompt_number": 20 + "prompt_number": 21 }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The first plot shows the individual sensor measurements marked with '+'s vs the filter output. Despite a lot of noise in the sensor we quickly discover the approximate voltage of the sensor. In the run I just completed at the time of authorship, the last voltage output from the filter is $16.213$, which is quite close to the $16.4$ used by the *volt()* function. On other runs I have gotten up to around $16.9$ as an output and also as low as 15.5 or so.\n", + "The first plot shows the individual sensor measurements marked with '+'s vs the filter output. Despite a lot of noise in the sensor we quickly discover the approximate voltage of the sensor. In the run I just completed at the time of authorship, the last voltage output from the filter is $16.213$, which is quite close to the $16.4$ used by the $\\verb,volt(),$ function. On other runs I have gotten up to around $16.9$ as an output and also as low as 15.5 or so.\n", "\n", "The second plot shows how the variance converges over time. Compare this plot to the variance plot for the dog sensor. While this does converge to a very small value, it is much slower than the dog problem. The next section **Explaining the Results - Multi-Sensor Fusion** explains why this happens.\n", "\n", @@ -1444,7 +1459,7 @@ "language": "python", "metadata": {}, "outputs": [], - "prompt_number": 21 + "prompt_number": 22 }, { "cell_type": "markdown", @@ -1478,13 +1493,13 @@ { "metadata": {}, "output_type": "display_data", - "png": 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I318ojAEAAABRGDuHXic7ZG/L1R7jZMHxb4fsbZG/v1AYAwAAAKIwdg69TnbI3hY9xrY4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\nBoxnmbqm5t/d3R1raWmJjY6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dF3zPnmh86qljd2RlaSKlJO2/kLUxFCBDvNzC9+4tJu0T2RlWroQlDVdFU83w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L48R/PFz6A7deRXQgmgQ9V/sW1WFirMKBdwDkQQJiEA+EBW4N7Eli0LtmxZ6eMhd5UrTOVcco9t\nWrDApUEuBJeY6GH1TJ08Cc306TAvXizbHKKBQp9BVn/3nTgnHrsd+hEjJGeV2AoVJGW8AN4+kwlH\nEBAknNFYsA1CvtDpXBnkYGvHuIQEsOXLyzmrQg9dpw7Y0qUlNeqFDY3GM+hSKnmXKpE81sfma8RD\n/VuaVq0SlBt75hkn7twhcPgwBduHH4KLj/d4n+OAr7/W4O2m+8EV8a4dr1+fwaZNOZg/X42zZynM\nnm0Oqr+4bl0Gy5aZMHKkHtu388mFtWtVaNqUFqxjLujzon7pGzjxw5nAG1qt0VVuF0l0OrAlSgAO\nB2a8fAXPds1B2bLCNfhc6dLgjEZeNYqAKzgGAOWmTR41v51KHMHWHcKfaf7zgrh3jzfLkkg4rKYJ\nmy1spWx+A2Sz2UsZJhBs1ap+z1suMdFDto7IyCiQGEP78cdhfQAt9AGyZto0UQLcwbro2V9+GY7n\nnw9malEDFxMT8QyyGOgGDWBavdr/RlE476gnJgbm+fOjrsQgFIgbNwq9Nniw6AcPhnLDhrCMTVHA\nkCF2zJ0rfDM8cECBrCwCXYof8gqe8yhblsWOHdn49deckJrK69dnsHixCUOH6rF7t0KwOS9aSK1h\nwZ/pwta+7hA2W1SV20UUgkD20aO49rcdP+0rh9Gj/a8s0I0bQ3HwoOcQt26B/PdfD4Oalo1M+PN2\nOVHeRmRaGthgAmS9XnZZRvqpp2AKoh5aDP7iG+Xevbxus4zkL7GIpEmIO4rt20FduRK28Qt9gOy+\njOMPLxmoRwjb0KGwucvtRAsKhesp1VdNobFdOyg3bozkrB4TReSdF+olS6BeskTWsYn0dCh//VXW\nMeWGvHQJ1NmzcLZrB/LcOUnqJGLp29eO3bsVgmYNM2dqMHKkDYrMdHB+1Ed0OnmUFhs1YrBokRkv\nv6zHrVsk2rQRXh0syBpkAGjRzI4D2TVw4kSA2v1oa9gOAxcvknjpJT1mzVLj8GHKywhm8vt2DK+4\nCUWT/DfX2V96CUytWh6vKTdvhrNtWw+JR11yAhobTmPXLm/Zx/znBZGWFlwG2WCQfQVOuXp1SMYW\n6jlzQPko02KTknzaPIcjG85UrgxH796unyNpM+0OW6FCSBbegSj8AXJsrOgAGVFuMx02DIaw6wWG\nBbsd5OXL0MyaVdAzeUwBE9AoJAjIf/6B5uuvZR1TbtQLFsDety+g0UC9ciVUy5fLfoyYGKBvXwe+\n+86z/vjcORKnT1Po08cBrkgRMBI794OlSRMaS5aY8fnnFkRRK4cHiRWM+Eo/DkOH6v32cEs1TCqM\nTJmihVbL4cYNEu+8o0OFCnHo3NmA//1PgyVLVNh5oghGvBK4SZxJTQWTmurxGt20KWxvvunxGlus\nGDopt7iaQ/3h7NYNTNWq0n4hAEzDhjCtXfvgBRmCZd24cSDu3Qt6f+rkSVA+TKBMq1eDK1lS8D1O\nq5U9QOaSkuB48UXXz0ROToFkkJlKlUBdvhy28Qt/gCwyg4ycHFm7PB/jH+qPPyS5BgnWFCqVyD5w\nAMTdu/x4j5Ed4tYtyfVpkcR1Xmg0wrqkuajnzfO0SmaYgNeFiOuVOxx+fwcvLBaofvrJ5RzmbNoU\niv37wzK1116zYflylUdF06xZGrz2mo3/6N9/H3S7dmE5thCNG9Po2tV3b0lB1yBzCQl4wTIfVavS\n+Owz3yUU9gEDYHv//QjOLLJcvEhi924FJk+2YMoUK3bvzsHZs5l4+20bKApYvUqJSeQ4aLq0DGp8\nNiWFr4d1gytWDM/YVmPbNqWXrHj+88Lxwgtgk5N9ju90iqviUy1bBu2HH4qetxBCutCS9g/Sbjq/\n1GY4KLAMcsWKIC9dCtv4hT5AZuPiQIoIkLnYWL86gnKi/v573if8ESama9fQfdJJEmxKCuzDhwv7\nwjsciGnVKjqa0Qop6kWLoA5j5zF58SJiWrZ0/RzTvLlH97NYOI3Gr5oMee2aR80gef26x3EFifDy\nt/azz/imYpGo1qwBXb++6wZPN2wIxfHj0u1VWRbkuXN+N0lK4tC6yg0sm8lLW924QWDLFiUGD47O\nGuACR6WC89ne+GJiJtatU2HvXh+pboNBUlNqYWPGDA1efdXuUV5jNAKtW9MYO9aGDe9txoDKB31m\nN4OBi49H6Y/6Ii6OC1zi4gebDejTx4DBgwMnzpSbNoGpWTPoYwG5dtGhBsjBuOn5UOTwhXLjRmjE\nqiDlYh82jFcsijBMhQqPM8j+cHbqBMaPHEkebJUqsAVRqE5kZQW8ueRHtXz5I6ER6BOG4YNjlXhB\nd381hfa+fWGZPt3rdeLuXZB37z5UzWhyoti2DdTZs363CaeLHgBAowHptqxIXrsmKWBwnRdumtlC\nEGazxwqRGHlGwmKJ6PI3ZzBI6vTmYmJ4O9U8jEYwlSpBceyYtANbLDB26BBws7eIGZi3KA40zWvW\n9u3rQGxsdD58FnQNMgBYZs9GQkkVZswwY+RI3SPXT3z1Konff1diyBDfD2xMxYqwTJki74FJEvaX\nX0b79k6vMgux54XDAQwerIfBwOH4cQq3b/u5h1gsUO7dC2eIKyic0RhSXMAmJoIMJoNsMICTYKZF\nXr0quUGRi4312cQbTpgqVWAbNSps4xf+ALl7d97oIExQJ0/y+owSeBTqzvxitfIBjYjANS45ObBM\nn1Yr2AFE3r792EXPD4pDh6Bcv97vNkR2dlgDZE6vf5C9cDj4oDSI47ElSrhstAWxWDxE+DmdLnCA\nbLNFtMSCMxgk3Xic3bp5dO8DfE2mQmJ5AeFweJmECFG/+FWUjjdh6VIVVqxQYehQeWu+H1bataPR\npg2NDz6IYLlOFPD11xoMHmz3+xDFlSgRtvtzhw5ObNkS+LzOD00DQ4boQRDA//2fGR07OrFmje9k\njnLXLtC1a4ccAKo2bw5ptY5LSAgqg8wVKYKczZtFbx/IZjqqMBjg7NIlbMMX+gA53ARV4J4XIEYJ\nREYGjDLqMit//91vCQlht4MTMBzwidUaVE0heffu4wDZD2Ic9cKdQXbvoCYyMvgLrwSh3Lzzgm7R\nArbx431u56UDqtXyQbOf8humbFnQLVqInkuocDExIUtHObp2BVOhgrSd7HZBA5D8cGo1Xm95DO+9\np0OnTk6UKhWd2WOg4GuQ8/PppxYcOqR44Er4kHPzJoF165QYOlRiuY8I9AMHiirDatCAxtWrJP77\n70EiJtB5wbLA66/rkJVFYP58M1Qq4LnnHFi1SiBAzskBOM5LhzlYMs+dg2n+/KD3p+vXh33IEO83\nzGZZlRzI9HSwfhRr8lD9+CNIH02DDwuPA+RAuJlZiCXatC85nU62mmjt2LHQ9+0L5bp1vjeSII7P\naTRBqxMQd+5IWjp61KBTU0EdPQqvThY3wl5ioVbzKRunE6REm2kpEBaLZzZYoeD/+anXZRo1guOF\nF8IyHyGkZpCFYOrVg7NnT0n7EE4nODHlTmo1nkn5G82b03jjDbfvpNnsU17qMTwGAzB7thnvvKND\nWtrDX/L1zTcavPiiA0WKhOEhiiAQ07Ur1PPm+d1MqQRataKxdavwQwl15gxUq1a5fuY4YPRoHa5f\nJ7F0qcl1i2rWjMbduyQuXPAMh2Jr1QJx/z7IK1fg7NQptN8JfDYdITSycSVLgm7a1Ot16vRp6F9/\nPZSpeUBkZIjKliu3bQN1RoRZTiHmcYAcgEDNQUIQFktUBciuK4EMMlmOvn1h/egj/3qsJAm6YUNR\n4+UFyF61Y9nZMNau7TcD+LjEwj9csWLgYmNB+mliYJOTwRYrFr5JEASQ20VNpKdLtn0VW1NomTED\ndL6lXLZUKdml4UKBi40tmIZSu11UPwCnUkHhtOHnn01ISXnwUEVduCC70UCoREMNcn4aNWLwwgt2\nvPWWzvVn1o0cCcW2bQU7MZm5e5fATz+pMHJkeL5bdNOmIG/eBN2kScBtO3RwegTI7ucFdewYFHv2\nAOC/dmPHanHmDIXly03u1VigKKBHD4Essk4HwmyGaePGoO2lI4HcNtNERoZfzfM82MTEkJoOCwOP\nTIBM/fGH30DBF2KaffJjHTPGox4yGuBiYmSxgmSqVQNbuTIoPxlprlQp3slNDHlL4fmgrlzhZWPy\n1TFThw5B+dtvAAD7kCGwv/qq+Mk/gjCpqVAcOeLzfcucOWBTUsI6h8xz58AZjaCbNYNpxYqwHIMt\nW9ZL5zz72LGoUhCgW7eG2V2KTgiWDVyTLxWSBFO5csDN6KZNwVSp4vU6EcbMf2GGPHcOVL6GyTFj\nbLh2jcT772tx6RIJ8vbth05lZ84cDXr3dghagMuBs0ULMCkpYKpX97mN4uBBqJYuRZs2TuzZoxTM\n/ZD37rlc9CZO1ODAAQVWrTIJGto8+6wDq1erPP5UnF4PmM2h/jphR+4A2fLNN6Dz6VELwcXHu5oO\nY1q0CEnjOVop9AEykZkJ1YIFAbdTL1oERRBaupzBAKZaNUn72IcPR7Qp3HNGo2x202yZMrI5euUZ\nQOSvHSMvXwZb0dvKlWBZ3n+dZcElJICTmJF81LD37w+6Tp2CnYRezz/okKTkB8doqzUNN4rt22Ho\n10/WMdkKFWBeujTgds4uXUALSOORGRmiahIjSTScF8p9+6DK98CnUgHLl/PL9126xKDF4a+wcE/l\nh0bhIiODwJIlKs8SHJlhU1KQfeiQ3yZv4u5dKLdsQWIihypVGOzfz99v3c8LIi0NXJEimD5dg40b\nVVizxoS4OOGgvmZNBkolcPjwA9m4SOgHywFhMnmZdOQ/38hr10SvILPJyaKMxdxl56iLFyOrKe8G\ncfcudMOGhWXsQh8gw26HVoSMjD+vcr8YjTC51TEVVqQGyOS5c9B8+aXge0ylSshZuVKWeeX8/ruX\nvSgAUJcuCTYj0Y0bg4uNhfL332U5/sMO3bIl2CefLOhphI7T6ZWtexhRz58PRxi7soPhcQZZGDYh\nQVB2q3RpDp9+asWpU1l4v9h8bD1ZAjVrxmLIEB1271YU6oTyt9+q0bmzE0lJBftLsMWL8xKf8C6z\nyINMS8PXJ9vgp59UWLcuB4mJvudMEHwW2b3MIhwWzVLhOOD2bcLvOZOXQWZZYMcOBfr106N8+Tgc\nOvQg2NcPGgTqr79knZtLds7h4PtMCqislIuNhWrdOvlX3vAQBMhcXBzvThPgqkM84k56pnXrJAmd\nK86cAeVL/1mlAudPcksKGg1AEF41hb4yyCAI2EaMgPqbb+Q5/mOimrzzgjCZYOjVS9axFbt3h9WF\nSSrktWtQHD4Mh4gmPPWMGaBOnozArNzUR6KIaKhB5uLj/cpuKZVAZ9UWLP38Io4ezUbdugzeeUeH\n8eO1hTJIzs4G5s9XY9Sogq/r54oVA+EKkB3YvFkJjvM8L+adaobv9tTAmjU5KF488Afeu7cDv/yi\ncsVZXEICH/wVIF9/rUbt2rGoUSMWI0fq8Gv/jcg44KlYcY8ogq9u9kGDBkZ88okWbds6MXiwHXv2\nPHhoCEewz9StC0ePHrxUaExMwfkRqNVgS5UC+e+/sg9d6ANkqNX8lSjAH58wmWSt0ylscLGxfDeC\nSIjMTLAFeFOkrlzxKWfl7NIF5K1boA4fjvCsHlNQhKJ24gv14sURCzLFoFqxAo5evUSVoZB370K5\nfXsEZsUHI8zDsAohM1xCQmDrYJsNnEaDxEQOQ4bYsXVrDvbvV+Djj6MvSM7JAUaM0KFbNwPeeEOH\nL7/UYPVqJY4epZCeTuD77zVo29aJJ57wrYoTKdhixfgMMsehShUWNE14qFAsXarC1IxXsW7Jf6Kz\n3eXKsShfnsXOnXy5hnnxYtCtW4dl/mLYt0+Bb7/V4Mj/t3fncU7U5x/APzO5k72XXXaRZblERUE5\nVI5VqUhBERSB1qMiKBa592e5enmgKLVFsVWstdhWLYgXoIAoVZFTETlbuZb7hgX2yOae+f7+yO66\nxySZJJPMJHner1dfNdfMV/wyefKd5/s8WyuxdGk1unYV8N6WDug+8jr065eOp2O2n74AACAASURB\nVJ+2YOJEK7rOGY2dGTdhwYIarF1bjdGjPRgwwItNmxqkeQbY6xMNsX17eAcP9i8+qtBmutFYOnSA\nLgaLHYkfIMMf/IW6UEnl6ZDAuIqKiMt/cWfPgt+/P6zPNM0prF61CsJ110m/Wa+He/x4mP7974jG\nR2pVVYXdJTIqEUQE9fPCbPav5kiVrLPbkX7bbc2fr672lxwMRIV65VxFRcCye8alS+EZNkzWcXwl\nJdBv3Kjk0AJyP/oovEOHxuVccmkhB1lO44bq1asbVUDIymL46CM7vv5aj9mztRMk793L47bbMmA0\nAqWlLnTv7kN1NYdPPjFi2jQrevTIwIsvmjWxegzAvyGX4wC7HRz34yryhg0b8OGHBjz/vAUfrRbQ\npmd4ufP+NIswavjHyJkzHH75SxsWLKhB69YMl18u4pe/dOPD2/6Co8+9gblzHTAaGa68UsDWrVV4\n7TUHbrhBqF/E7dXLh23b9PVVLlltRY5Y4KqqVA+QPcOHQ4xBJz9t7SSLkJidDb6yEkKQ2/7eAQPi\nsqGLP34chpUrVelLriSuogJiq1YRfdawZg30mzfD8eqrkQ8gRFkq9+jRYTWcIAB34gQ4UfRvwgCg\n37ED5nnzYF++PKbntU6dCl+fPjC98gpq/vY3iBKVEkLiOH+Q7HI1W2HlamrAnzjR7CO2//s/eAYN\ngnfECOlDqtDxMqNHD1Rt2dK8jFJ1NYTOnSHIbOjj690btsce8+fdheiSx1VUAHZ7yLQovqwM/NGj\n8PXvL2sMqU5s0SJkAwlWW0WhoexshqVL7bj77jTwvBm/+51LtbvTALB0qQEzZljx1FNOPPBA4JQC\nj0dWtcC4qXnllfq7ogMHevHyy2a4XAX4xz+s+PDDanTsGP5K9913e/DMM2bY7c2K4sSNzweMHWvD\nQw+5ceutvkavsZwcGCvL0auXgF69hIDHyMgAOnQQsH27Dr16CbI3HOo3boTx3Xfh+MtfZI9X6NxZ\n9X1anp//PCbHTYoIw/3IIxBDrHY6Z88Gi7Deq27nTkBmgX/++PGQ7X0TAVdZGXGJrEgapYSdU1iX\nWkNkMy5d6q8AUivmTULq6HTg7Hbw58+H3a614bwIlGYRqMxRqC6YnMsV9xXkgM1C0tNRs3Ch7B99\nLDsbQtu20O3YEfK9hs8+g+WZZ0K+T7d7N0wyql1ogRZykGGzwSljg7iUnBx/kPzZZwY895xZlZVk\nr9dfG3j2bAs++MAeNDgGtBUcA/5W7HU/lktKfNi1S4+FC3tgyRI7OneOLA0kN5ehVy8fVq1S7192\nzhwLTCZg2rTm1zoxNxe8zHbTffr4sHGj/ztSbNNG1mIAd/p02GVtYTBEHFtpXVIEyJ4xY5TbNCbB\nOnUqdHJrKGuszXSk3GPGBG3Dq//6a9jGjJF+MYxOeubZs2nDXZy4H34Y+s2bwdfuZuYqK+Nya6yu\nnmi0m718vXpJpidwNTWSJYZCrpo4nfFv6KNAN706vj595KVZeDzyfkyaTKpvSkolubkMy5bZsWqV\nEXPnxvdOxpkzHO66Kw1lZTp8+WU1rr028GpkIrBYgGnTnFi0yI6uXaP7dxk50oP33lMnQP70UwM+\n+MCI11+vkdwyxHJy6msPh9K3r6++/J1r1ixZHTj5ixdV3XukNYoGyDqdDt26dUO3bt1QWlqq5KHV\nZbEEz2VsQGttpusYFy+GZeZM2e8XevaEWFQU8HXWogV0Afqwc263/FvXej04h0MTOYVJz2aDa8IE\nWP70JwDR5ZmHg1mt4E+d8gexYS5DNZwXNf/+t3SaVE2NdN3OEE1+vIMHx7aLoASWlubPjVaAa/Jk\neB54IOT7OI/HH/yGwIxGcEFac2tJslwvWrRgWLasGh9/bMSf/hTbILm6Gli/Xo+XXjKjf/8M3HKL\nD+++a0d2tkYSoaM0ZYobTufXUR9n0CAvtm7V4dy5+Oa9HDnCY+pUKxYutAds4e299Va4H3mk0XP8\nnj2SDU169/Zh61Z9WNXPuAsXwrrLZ3rlFfBHj8o/QQh2OzBsWBouXtRGu3ZFA2Sr1Yrt27dj+/bt\nmD9/vpKHVlVY7aY1uoLMjEbwCna6EYqK/HmfUvcGXS7ZK8iwWJoXMK+uTrruU1rhfvhh6NevB793\nb9xSLJjNBv7YMVntSyMRcAU5xM5t1/TpYBHm2UcqYIpFJMe67DLJHNdm3G4wOT9MTCagaYDsdkO/\nfn1kAySy5OUxLF9ejVdeMSkaGJSV8XjnHSNKS60oKUnHVVdl4dlnLSgv5/DGGzWYOdOVtNs4dN99\nB+OSJRF91mYDbr/di6VL47eK7HIBY8bY8PjjLtxwQ+AVcNa6dbPN67Zx4yTvcGdnMxQXC9ixI4zq\nVZcuhXWdNnzxBfhDh2S/P5Q//9mMr782SNa1VkNSbNKLNWa1yg6Q1dj4IwdLT1eskx4AICMDTKfz\n/4VqcktGbNVKduDFzGbwZ882yinMGDAA9jffhNi5s3LjJX5paXCPHw/ziy9CuP76+HRIs9nAnzgR\nUYAsJ9fUd+ONcEi0y2Y5OZprfyq2bAkuBgXtg4piBZk/fRrWyZNRJSPXOZ40kYMcAnf2LNJGjEC1\njB8Y+fkMt97qw8cfGzB6dHRpLl4v8MwzFixZYkS/fl706CFg1Cg3rrlG0FwesdLq5oV++3Z/jfMI\nN2+NGOHB889bMG6cMndU3G7g5ZfN+OEHHSwWBosFMJsZrFYGsxnYuVOH4mIxovMFazXdt68Pmzbp\ncf318tJO+IsX4QsjxUJOJRe5Tp7ksHChCTNmOLF6tQE//7n66V6KBsgulws9evSAxWLB888/j5tu\nuknJw0eMu3AB+i1bQu44Dshslp1iIXTvHjQ1QS0sIwOcQrd264ht2oA/dgxCk79Qnocflj+u2lbT\n9QQB/NGjENu1U2qYpAnX2LHgy8vj9mfsHjUK7oceit0J0tIgSmw5d48dG7tzRsixYEGjx7odO2D4\n/HO4ZsyI2TlZVhZEGbWVWatW8Nx5Z6PnuAsXYrbynwx0334L2GwQrrmm2WucwxHWNfeeezx44w1T\nVAHyiRMcxo5NQ2Ymw+bNVcjJSd47cbr//Q+GFSvgkkgdrGszHalbbvFh4kQe8+aZIYpAdTUHu52D\n3e7/Z8aAhx7yYNAgb8gKJN9/r8OkSTZ07Cjgnns8cLk4uFyAw8HB5eLgdAJXXCFgypTIqpkEC5D7\n9PHhnXdMmDpVXuDteP75sBb4xNxc2MaPh0MU4Rk5UvbnpMyZY8GYMW6MGePGjTdmaKJqSkQB8vz5\n87Fw4cJGz9199904efIk8vPzsXXrVgwbNgxlZWUwyVi5iBZ/4AB0u3bBG6DTFn/gAMzz50ccIAtX\nXy17M5Nw9dURnSPmlF5BRoMAOVC9Yjlq87s3bNiAkpIS8MePQ8zL02SaStJIT4cYz5rgUVQbqZsX\nycr4/vsx7/DpGT1a1vvEoiK4p0xp9JwWu+gB2pkXhs8+A9LSJAPkcDYrA8Btt3kxZYoVZ85wKCgI\nP7Bds0aPyZNtGD/ehcmT3UmbPlHP44Hh008bBcj13yPl5fCF0Tm2Kb0e+OMfHdi4UY/0dIbcXBHF\nxUB6OkNaGkNNDYc5c8x48UUzfvtbJ265xdcsuHU6gblz/Sv5c+Y4cM89oYPpSHB2e8BrSJ8+Pkye\nbIPvUjWM9oqQi3eBU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hjVfALp2FFEQYGIuXMt+PJLZdPjkoWYnw/+3DkIrVsD8KcJCe3a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\nfEoIKHeMSKN5QaTQvCBSaF4QJVGATAghhBBCSAMcY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5U5x9koih4wURU8AB8uTJk1GzZk20bNnS/dzatWvRtGlTNGvWDN99950oAySEEEJCwTJ9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\ngTkJlKFYegeZKC7dnwX5aZsoJ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lYblulk9DDL1dMCEXMCEXMCEXcFJalk9jRhgAAABuw8NySCt6u2BCLmBCLmBCLuAk1hEG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\nlOux5PNa8sVf90Rfj2/7PNG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tWxtqGdeKae1aMJWV8RbDUJiiIlhee01bY5UPx0TzfRXy8uBSUnDF5YLFwGw2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\nj+gbb3jVD06F4/dnEtkDGZddRo2V4JD9oCiQ0mnqeDp6tPjbEAtYjfPO8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U2b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\ndi6/EyTPYa5B8aty882mWQfPdc4ym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LMx8WgqZFH8pYLo2laYULt2RZzLHgo0+yZu347Q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\nCqAMtj17wBw/nvTnwSFD4B8/HtyXX2p+RlIsDkY8f/gDgoMHW2pDO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xcGvf02EJoQW/37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Bsqm/0JB0WzaKIhkLUwDD2zgSJVoXDN8uCNUk0YmbtAtCokh9LUtHxAgfyUjH1V53/Phx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ho4VqxovhkxQhCkuGyNlcDarVtjnu0UCgpgU2ceCcwgC506NU9uGBzYps+fD+/11ys5zWXh\n9nkpMcNhjmX73/+kyYIEbDx1P/CAciz3PffAq1VKXC9Va0g/Fvr1C5pcUmeyUVKltvZwDjgcSUmr\nA0h3epo71EPy0PpLShJz0YkwiLZ/8QUyAn/ETKB8rrKcE+1JTt4drqb699i2bEHWddeB27hR8+N8\n9+7wqqtMyh0sEb+DMJkbuLIyabCuQ8zIaM6K4vUmvpxwhJ+v0LUrPFGEKbTg84X92fH9+sH7058C\nbjdsX3wBoUcP8EYufgb4S0p0k9e3YLeD8fmQ9uqrUv7TUKEnH3lDTaIuxoHqd5oyMhJe8CFWgkap\n92QSnc6wG9T01Ozdq+xN8I0fD7+qWEq8HEuXgq2sBLdvH+zr15uT4s5uh2fatJQfVslWEWe/z54w\nAdlhCiBF3Z7cXJw5fRre6dMhnH9+9B+M0H724EFkxZICLgppL79s6kw48+OPyLn44tj7bZjrmtCz\nZ+Q0uHpfO3iwkhVM+jJBWalUBoMGr8tcWVlzeEa0WBb+4cMh6twAAkDmzJmGU4DqSktrvr7n5Gj3\nY53wEzE3t8W+A6a6Wklx7HrqKSn6IJB9ynD4ZASWHER77rkHLnXWilQILeYQTW7daAQ6PK8x++2b\nMAFNjz3WfByeB+z25oIXamFS/olOZ8vZDNW/R2RZcAcPIkOnOp/31luD88DKu7hjSAvXQpjMDWlL\nlijpBrXw552HjYGNZozX2zyrnyoxroiInTopIQ9ahPPOg2/SJDhfegnZU6c2F19JAt9Pfwrm+HGw\nhw9HfK/ocAA+H+z/+592CqnQk0+i4389nqhnzRMZ42hU00svJbSyYESZmXA98ojhj6nPCf7LLktO\nKjrVpraUczjguf12OAJV17iyMmyRV9MCbF9+iXQD4RTRUGKt4+z37OnT4KKo2JcwoihVcoQU1hhp\nttTx4YdJC3lzvvxy9Nmf6urinjlscb4QRWkzaKyD6EQUfNH62u7dm+tTALB98w1s27bBN3GiUggu\nXFpYTRrXMba8HPZwGdA4TgpR0vk3socPS7mrU1mIR6fgluhwSLnqVdKWLFGKgsmr+3yvXtIMd7zF\n5EJYchCdMg0Nzcm6Q39BMZbt1MIXF6NRK84tJ0dKs6Pe7W+3t9ws5vMhr2tXpdJiKKG4GA0hOWZ9\nV12FRrkTMYw0CNf597gfeABC377SZqEjR6Tk8Q5HQv794cJSvBMnwnPHHWE+rNpN3KEDmqKIuzXW\nuPAXwaZnn5VmjA3iL7gAnvvui/zGwKwrt3MnsuWsAlHK/slPIqaokqUtWxa+alqA6+GHleVgViuN\nUujJx+Ag2rZmDdgwG0mZhgZw334b1Xe1Jf5hw8AbmWFMBbkfyBdZk0IDuCNHlHSSac8/j4KQSmdM\nXV1chSq0KLNzJhWYkTkXLIj6HAAAaGhAu6IicFu3BocH6En0HoQY5Y4eDUadxSoR5AmmWAfCgSwc\nts8/Nz7La4DnzjtRv3IlPLNmQTj/fAg5OWHTwmrSGDSyhw4hTac8PAD4f/ITuMLtKZBTzSV6dTgM\nobAQnttua/G8f+TI4JV0oMUkqG/CBPBDhsB7550QunWLrciOjlY7iGaOH4+YkD3r1luRLsdDac1E\nJ+gkWbdhA6BRqls+rrwcXv/pp1Lwf0iMEbdvHxiPp7kMeCieB/v990FPsQcPNg+E9Mp+h7CtWYP0\nRx9NbMyQKOrOIAvduoXPk8owGCzHwWZnGz95RGpapB3fGRlhiyXETd48G8MNG1tZKWU2iWYDrqqP\nhSP07auUq9bKKy06nfAG0okBgP+ii6TNnlG2Pe3dd2HbsUP3df/Ikfp9PISpMdEp5ps0Cf4ELPsn\nkpibCyEnJ+Yl5oRRXSwZvx/nybGQLhdQVyedNxOcDs1/8cXS8rHqbypr8mSwoVkCkilQgMdQFcrA\n32nakiUQzjlHM5tIkGSnwIvyxos9flw3a0+0WpwvwmXYiUJNRQXAMEh/6qmIaTLjkp6u7MsS7faY\niiWJLBuc8g2IWDxMzM+HEC7Vonoza4Lk9u0rFQbTO+S332r2d6Fv35b7UELGb65nn4XYoQMyp00D\n3G5wCcws1GoH0Zn33QfH0qUtnnc+9ZRysmMrK+EMzNaKnTrBc/fdzW/UWTpIOI3jCO3bB8+6yrPl\nOgH8TEMDskNSAjr+9S845KXNKCtV2TZsaB7QJegE6h89Gg3//a/2ixFik1yPPNJcvSnB6t97D77x\n45Py3VGTT2KxhA6lpSFnwoTo0tdF+7sUBGROn44z1dXwaW3syMmBa8GC5q9t3x6uJ56IOkaYPXhQ\nWv0J106TZ/fixW3ZgpzRo8Ft3252U5LKP2oUXI8+KsVODh2qXco8FVR9htuyRcls4Hj/fSl7iNud\nlGIhviuvhBiYGLG//z643btbDlQiiGcvgePtt8HW1Bj7e1FtFvNffDE8WmGDanFmyEqkFmnM4iVv\nYnvggdi/4vvvYdu1KyXnrJyBAwG7PfLmbC0cB9s33wSnpBSE+IovyWEciZyJ1sm1LmOqqzWL1oVi\n9+yRUgxq/F5sW7eCqa+HY/nyuJoadLyEfZPF2L/8EtzevS2edz7/PJx//av0QPVDTvvb34IGJGKH\nDuELQySImJ3dcsNURoYSuwagOZm7Hq1BmPpO0+EIn/83wPnqq1JZTY5Dw7/+Fe0/IXYRBtH+ceOw\nLkm5aP2XXabk99TDVFSEjxuLV+Dfbtu+HbatW419NHDyiioXbrQ3hPKAxECcm3faNN3CLKFsO3bA\nprO5FYChmwkzY6LDsW3aBO777+OeObM635gxUsGGTp2kGy6zbn5UfYYrL8fpVauk5wMTAYzHk5Sy\n1a4nn1TO22lvvAH2zBnDN8JCUVHsOWtDwmi4LVuUjVS6jO5hSGI4h9C+vfaNepKEni9EhoGYlRXT\nzK5MGYSmoO+z1dUx/z5cDz8MprIS9jVrmp+Mc5JMmYFOUEy0bcMG6aYwXN8MqViohysvlxIWaP1e\n5Gqfrb1iYcJodBTG65V2kwPBP8iQAR23e3fYpYVE8V11VYtNlELHjkGdM2LJaq0ZPFVIBj9gAOpW\nrYJfZ2OR89lnYVdXQWIY+FMwS8sXFyubJbQwJ06gwMQZPe7wYaQFNi0lg1wxUzP+OOKHA301wkwA\nU1kpFVWI5sKZqM204Q4RLhVeqlZ/4sQePQouUP2qhSQUW0FdHRxh4hfN4PvpT+EfNQpC9+5wR1kZ\nNRnsH30klScG4Jo7F4fkQZG8mpakmegggZAvozPL9Z9/jvrVq2M7Zkge35yJE2GXbyD0RLkiKePP\nPz9psfiemTPhnj07Kd8dlaws1GpUwDVCTs+akkqgcYRYCv36SaGLBtuZM2KEfiVPm02Kq0/QIDq0\nhLrme9LSNMM5mJMnkTFjRvP7An8b8uSj889/hvNPf5JelDNnJfB31roH0TqUzh9mEA2OS/pylu3r\nr5GhcQHyzJ4Nj5ETjNcrzYSohfzRCeefD9dTT2l+3PHOO8pyWSpLA3unT28u06uB27MHw7/8MmXt\naSGBcfFaPLNn40xVVUxxZbpFSUJw+/YBQMucz1qSPIg9c/hw+MITmZlRZ2AxMyY68667kHPlldov\nJmEQzf74I5x/+UvCvq81se3YoRSRcD/6KC6QQ/IC5z/v7bfD/f/+X5IbEdtAQuzYMar86prkrCjq\n81OkQZacdYllkX3ZZbob1WW+yZNRJ084JZh73jxj6fjijL3ViomOVA4+Ivn3noJrJuPxgK2uBvvd\nd7FtZAyZaIsmBI89ckT33ybm56MhxqqMmt8XTcYbp7M5tFWFcbthV98QcRx8F18Mr5z+UhQBnw9M\nRQXYM2ek6y3NREdJb8lC7vzqctGhccCJqlgYTmNjQjYlaOVqdHzwAdKjzGbBlZcrg2gjJWqTLtkb\nWyJIf+IJ2NetS94BOA6w28GPGAHvxImGPtrw+utKdctwxPR0+IcNk4r5RJD21lvNZYWTIScn7ICj\n6bnn4FNXmrKqcOeFJAyiuR07wEWRorAt8g8eDP+oUS1fkHPrtmtnrJhJNAQhaCN3NLNoiabceKtv\nOiOdL2021C9bBn7QICmProVinsPxDxgQfgO6WeTJuBirExrFlpcj44knYNu82fiHQyZIhKKisGFO\n3K5dUgpAvZsXhwN8IquyBtoSbhKPqamBQ2O1hduyJXg1N3QClOPgWLFCmVBiq6piW/3V0aoH0bqd\nO9AxGl96Ce5Zs6TnQmZuxRTMROsu0fB8c8GVKAjFxaj76qvgrzC6IY9hIOTmxla9KVlEETWB0rqJ\n5liyJGIMoaGd73EQWdbwDZvYqVN0Mfvp6dKSdhSiSYOnxm3a1JzdJsVMjYkON1hJwiA64jK9CZjq\n6rDpClNFzM0Nij+X+4XodAIJrNAYxOdDjjoPvN2OxoULNSvhJg3HSZlyVIPoaM4h/vHj4b3tNnBH\nj5416ST5AQPiLnyWjPOFaLPBe/31UpraFLF//nnw6kO0QkM+I23ilvdkparst9MJIT9fP4sZAO91\n17UY5wAAd/BgyBMhYzeWlTKceb0xFa2KpNUOor2TJ0tlOUOfv+IK+APlL4WePeH6wx8ASEsXae+8\n0/zGVAyidbJgMFVVUjWlaDGMsqQp811/PTzqykcRiCwrpRizUHaE9GeeQWYC7xjVMufOhV3jD9IU\nMcS7CT17oiGKHcZiejoYvbi20PdmZsJlIL6VqauD88UXwVRXR/2ZViHcZtjBg+F65BH4BwxIyfHM\nYl+9WgkxSX/oIaQtXmxKO8TsbDAaN9q+SZPQ9NxzyTmowyHN0gUG776rrwZfUpLSnLm+yZPR+Prr\nwU8avF451Nc7C2t64QXNlJvxYvfsQVos2S4ChF694Asp8pEsQocOSjaYWG7Q+ZIS8MXFzU84HHA9\n9pj+B6Io+51QGRnSKmi4f1t6eotxjha+Z0947rpLeSyv2og2m7Qqm5sL/+DBcTdZZp0RU4I1vvGG\nZh5M98MPB6eyC/DMnCndCQVE2vSWECwLpqam5YxoIjZ4qZdv/P7IM9sMg8Z//jPmcqXJYNuyBelJ\nquQHSAnnw+F79DA+ox8Dvn//pMVtik5n9INoh8NYOIecnzcFG3BDmZonOsyglh8xAu5f/cpSf0fJ\nYCstlXKt1tXBsWyZadk5xJycoJnolPSLQL+XV268N90E/oILjH/NsWMJLSKivn5ZHfftt7AlqRqi\nFq1+wR47Bnsce274oUPhnT49nmZFLTTZgBFpf/0rhHbt4L/oouYnHQ7NcZAiGRukwxBzclATUusi\naiFtFLt0gU89garKCc4kOIUv0IoH0Xr4Cy4AP2xYyxdCfqie++6DP0zp5oRgGNh27ED6008HP+12\ngzl5Mr7vVg2i2cOHkVNSopu7lu/TJ/n/1hgIubnwmThY4ouL4b311qQfRywshN/IyoOR787JgS8k\nh7gum01z44Yu+eRlodWLVBC6dlU2J6dCtAVoUsnx4Yew7doF9swZaVOzSX3AX1ICf0mJKceOd7Nb\nzuWXI2fcuIQ05czp0/AZWHkEEHGAxG3Zgox7742jVfrSXngBzpdeSsp3R8XvR/bUqeYVCTKIUU+s\nGRzYcvv3G6/aabNBiFSMLJEYRrcORiRCp05hS6F7Zs6UYq1ttuZxUQL3u7Wtq184JqTX8g8bBtfc\nuS2Oy33/ffwbvNT/HpYFW1vbYrAu89x6a0pyYhvFDxiALT/5SfIOEOn3zXGWKX0bs5wc8OeeG92M\nl8OhlCKPisGUWYlkZkx045IlqElhCItQWNi8d8MqApMOylKpSYMRfuRI+K67Tnkc2i8y5s2D7X//\nS/hxa8rK4o6BZqqqpPy/qdTYCIgi3DNnQujRI+xbHe+/jzSNgmWJkLZ0KbiQEu3J1OJ8ocpUcjbw\nXnedMqgVw8QNa9KoTshUV8Pxj3/of4bjkr8SnyBiRgb8I0fqv0FOeVdUBO/kyRHrUxh1dvSgZHG5\nmvMgmpGjNitLSj0Wcly+f/+4A+C9N96IptCiMjonDM//+3/Sju0ff4yY9iilGAZmZg12z5sH95w5\nJrZAX/r8+bCvWBHVex3//KdSyS0c79VXwzNzZvSNaKMz0anG9+8P/9ChZjcjmHwRMvFGKhL20CFp\nU2YSMs5Ek+0m2dIffdTwTX67rl3Bbd0q5dCNlE7SgrH4CSP31zhv/uwrV0o3Jknm/s1vIHbpAjEz\n0/iEl8Ygmq2qCruPge/dG01mrhQYwA8eDM/tt4d9j+/aayH06AHPvfdC6NixOf1dArTaqx9TUaG5\n4UQt45FHkCUv15tV6EGr7HfPnqiJVH0qAvbYManMMhD1Xbf9ww+RvnBhXMdNKIbBBf37J+3rhUi7\nqtPTpdyUFsQeOQL2hx+ie3OUfVts315JAxQNPrBB14xBtKkx0SnmHzcuOMbPAoSOHSF07tw8CLHI\nsri6X9hXrZLilpNQsTBU9oQJYMvLDX2mrrQUtbGuqIginC+8ENNHHe+/D6FXr8iFVJK9CpfC622L\n80WCJgAyHnxQM8VssnivvdZ4m7VWVDUG1kEyM8En8dqbSEKvXvBHCItqfPNNMKdPI/NnP4PYuXPQ\nxsN4tdpBdOYvfgH7Bx+0eD7tb38DF8izyB48qGRo4Pv0gffGG1PZRAAAI4pJWVKyf/wx0pYsAaAa\nPEc4adm++SYpszaxcv/qV0mrmNXwxhuWjAOPlmPlyugLcEQ5iGYaG5FhIGxAzM1F09NPS9WwCAAp\nv2ruBReADeRdb61811wD9733Ssu+7dqlZO+AUXJF0GgL+MSKPXQItm3bDM/cCsXFEGIMo7PJRVBi\nHIj6Jk2Cd+rUsO9Jep0EC/DceWfMn2Wqq6WbtBRMImRfdBGY6mo0vfgiIBcmiZLIsrCvWwf2wIHm\nJxsbYfvuuwS30uJcLth2707417baQbR940bNpOQZjz8Oh5waTNX5na+9lvr4NEgn+oQXAwCCd6CG\nlIjVk/bee2BCKx+ayP+Tn2BdknLR+iZPjljFjz1wILgcutUEyg1HwkS7yhLDaozn3ntNGUSbmic6\nDG7bNrDHj4OR893l1LkAAB6VSURBVKy2Uv6RI8EPHAgxPR3em26yTDiHul/Ig+ikrybJIYGp/BmE\nTIxw33wT/2b0UEkcRPPdusGfwtUkrfOFyHHBGSsMYo8cCfxP8odR7MmTMafc9fziF2DOnAkaD6Wq\nBoKVMD4f5Yk2TGdmQEltpur8YoKDzaPlvfHG8KWQY6VarhELC1H38cdR5Vi0EqaiAu2TcOcYLe67\n7+B47z3Tjh9O3bp1qP/kk6jey+3ZE5QGTJdZIU1nGfaHH8Bt3ar9YhJSQzEVFbD/5z8J+75E8F19\ntbSSk5kJl86GZbMpM9FJHkQLcv7dVFYADOlfOVdcgbTQvNF6orzO+UePhl8jTWwiuO+/H55f/CIp\n3x2tum3bYk4bByClZb/jScsmnHeeFHplpJ11dchJUQ7seLFHjyJj7lzd1x3vvYeMOXOkVXYaRCeG\nmJcn/Y/6DtKEQTRXVobMBMbmBAmJeeJHjoT7N7+J/DkLDaJs27Zh+IYN5jVAECy7aY7v3z/qAgSN\nL7wAfzQ3UJGqWFmImTHRWT/7GXIuu0z7xWSU/T50CGl//3vCvq81U/cLMScHQm4uhG7dkntQjkPt\nli0Rs10kkmZ5ZAMzx1k//SnYCMv53htvRP2aNUabFhXv9Onwjx6dlO/WonW+0NrUb4g8IEvFTHRl\nJZi6OunmPZaNjCHndjFCTnFGEBKawzyp6uth27Qp/Ht8PrCVleDUIS0JcnZcMWOlMSiu3bwZTX/6\nk/SyesNJghNwR8XjAXv4cFK+2r5iBZxvvGH4c4IFdp0rTN4dnvbKK3B8+KGpbUgE7y23RFUCmdu+\nHawJhVPOOuEGK8kYRO/dG/kiQVoQOnaUNmRGGfYU17F69UrtBITWDGoUGygb3noL/pEjwZw6FX8a\n1TZOSe+Y5Jh7GVNfj8xZs8AePWr8syEF3IT8/LDXeqa6GmyExAxWwX33Hbi9e/XfIAhwrFgBsaAA\ngjyBmkCtehCtlU9R6N1bqSbmfvBBuB54QHohwQm4o5LEYza+8QZqt20z9Bm+a1d4klQ5LyaiiFOn\nT5t2eMZI4ZFWgPH7Db2f/e47ZNx/f5JaE56pMdEpHkTb1q+33ICHqagwnI0iFYJiort0QZOVsg0l\nUlpaUOni2q+/jiqXuO/aa+GbMgXc3r3BG81auaScL2w28L17A0bzNseg7n//Az98OLj9+2O7LoUm\nMIgw7rDa+SYcLtJ5yO0G43aD798ftdFmtDKg1Q6iPXfcAd/VV4d9D9+/P9wPPwxA2lUfbd7dhEni\n7LeYn294eZEfPjwpgfexcv71r8hNQqePmoVCW1JBKCgwlI+YaWxE2ttvA/X1SWyVBYUr+33BBXDN\nmyddXFuxtPfegyOQ/SfzjjukfLkkZfgBA1CvKlkt9O0bOe+zCuP1topVtnjYvv4ajn/+M+bPi3l5\nUsq5FOAHDFCuR5FS92rxjR0bNB4Qc3PhCrMXi+/ZE56bbjJ8nLao1Q6imxYtgt9AtTvvtGkQioqS\n2CINLAv2xx8Tv6s6lCCAiSLvdOPrr1uiiIDMtnMnMkwcoPF9+yZl+ceyHA7AyGx0YGbDjEwUpuaJ\nDjeIHjwY7t/9DsjKSmGDUs+2aRNsO3YAkNItWuWGsy3lD4+bib8z29dfg0thiJJWv2D37pXSusZI\n7NAB7kcfjadZsR1XzjoTJcfbbwPp6eCHDWt+Mjsb3jvu0P9QRsZZU2zF7HNPqx1EG2ZGZgKGAXvs\nGJyLFiX3OI2NyDv/fHDffpvc4ySY0L49/CNGmHZ8fvBg+KZMMe34qSba7caWCttoxUKhZ08Iubmp\nO14KN6xFy/7FF7AFcuwTYpTz2WeRFsOenUTK/NWvUlJtMJFq9u0DbzBjCltebsnQq0The/SAGGbS\nwjdxIoSCgqQdv21d/cIxYRMbf+658EyfnvzBeyBmy6z41Vj5S0qwfeRI044valV6as3sdmPFdkws\n+WxmTHTD8uWoldNkpgDfty88JhSCCsffv3/wxdwiJaKtmj/cMpqaAEGA54Yb4JcrjprA/tVXSqGz\nVNDrF4xF+m20xFgGg5GqE57lxOxs+EtKwrxBTEpqO1nbHkR7PEBDg/T/ZsxEO53gu3dPyXFFjoNt\n+/aw72GqqsBUVia9LVFjGJi5UOP92c/QZNEcuMkgFBWh8eWXo/9AG52JTjW+V6+UpgOLRv3nn6Pe\nyoWIWrumJjifesrwx9oVFcG2dq2U4ixFWSWsTDNVoAH2f/87tfnBY8Gy1m9jHPi+fcPnHHc64bvi\niqQdv01f/dJee605obiZhSZScNzGV1+F66GHwr4n7Z13kPbaa0lvS9QYBuf37Wve8dPSokob1WqE\nxs1FoGyeM2EQ3ZZiX/mSEninTTO7GcGczuBKgBaZ0Wsr/YJxu+FcvDimz9o/+gh8v34QevZMcKui\nV79sGRrefjtlx9PtF3FeezNnzza2j8QMrXxFVSwqgu/yy/VfLyhIapaeOMr1nP243bvBBXIu+i+8\nELwZA7YUFbjwTZ6MSAv13Natloq/9MyeDT5CaW5ioqwsND36aMrypJ4N2D17kHP55ahbtw5C9+5m\nNyclxKwsQ5u4SfzYY8fAyKuoMQi7qSwF/OPHm3p8me+662L/MM9Le0gsvhInOhxwfPCBlDyBrqcJ\nZ+3ffrKpOn/aP/4B9siRlDdBzM2F0L59yo+rxfHpp2ArKsxuhsI/ahTWJakYDUkMz9y5pszWWzX2\nlSsrkwY3brfZTUkZz223WSY1plX7RcJZfOBmNVr9QujY0VBKzxbk1ReL/y68118PxusFF6FCJYmN\ntX/7Sea94QZ4J02SHjCMKZsMvHfeaa0CJxbCHjqEdvSHTyyG3bcP3K5d2i8modiK1bmefjqqipgk\ncfju3eGbMCG2D1sk9MZsdatXQ+zQIfYvCFQstPrfulhUBKF797jjv62K3bsX6WFCVW0bNiBLHucl\n4/hJ++azgH/cODS++SaAwAYDE04umT/7mbUqR1noD822YQNKArloCVEzM/Y1+9prkXPJJdovtsFB\ntJW0lZhoZGWhYenS2D7LMMj8+c/BbdyY2DZZmFa/EM85p3kgHAuGwZljxyw/Ew3A3D1fScbU1sIW\npjqzyDBJrT58Fvz2UySJ1QPDHvbMGTCnTqX8uHqEjh3NbkKzVvyHT85iKS77TUiiNL70EvwTJoA5\nfRpMGwo5SpqzZeO5IJwdg/0YcAcPwrZli+7rTH19XEV1Ion5p1pfX48uXbpgoWrX49KlS9GnTx8U\nFxdjpaoMrN7zlsIwpuxgFfLywNbUpPy4Wvz9+1srC4Aooqq62uxWEB1MRQUyf/5zU45tZuyrWFCg\nHwNMg2hTtZmY6Bh5b7oJvssvB7dzJ9jApvq2oM33C0Foteck9sSJ8K8fO5bc48f6waeeegrDhw9X\nHnu9Xjz00ENYv349vvjiC8ydOzfs81ZjX7cOtnXrUn5cMS8PjEUG0fzgwRAtdGedtngx2u3da3Yz\niA7G7Ybjv/+1foqnBKtfuRK1O3dqvsb36QP3zJkQunRJcavMkz1uHDgKuzqrsLW1sLXxgaV95UrY\nV60yuxkp4ZswodWek3wXXQT/wIG6r/svvBD8eecl7fgxpbjbu3cvTp48iWGqnLKbNm1C//790SEQ\nqN+1a1fs3LkTdXV1ms8PMli6Mtm8N94IIYk/aF2CAPaHH1J/XA1Nzz9vdhOC2MrKkJWZCWvcYpAW\n5OVBE8KgzIx9FcNk0xH69YMrhiIYZzPbjh1xpVtLpDYTE00M0eoX3LZtQFZW2BzDrQG7fz+4Awfg\nmT3b7KYkBT9sGOrDVL8U+vVD3aZNSTt+TDPRv/3tb/H4448DAJjAEsGJEydQWFiIxYsXY9myZejc\nuTMqKytRVVWl+bzliKIpu1cZrxcspXHTJHTuDL+B4h8kxahiIQmw0r4OEqVWurwfrfRFi5K+1G8F\njNeb1Jjgti7s1W/RokUYMGBA0H/Dhw9Hnz590LVrV4iiCDEwCyUPpmfMmIEbb7yxxXepn2es+Mdr\n0ia2xjffRNMrr6T8uGcD39ix2E2DaOuSB88m/N20+RhHixEzMsxuAgDqFxG5XIAgwHfxxfBfdJHZ\nrUkZ3X7Risthy0Sns03lrU+1sOEcc+fObRHDPH/+fPzrX//CBx98gFOnToFlWXTp0gXdunULmmE+\nceIEunTpgvr6+hbPFxYWah5v1qxZ6NatGwAgNzcXAwYMUJZh5D+CZD0+UVmJ2sxMyPV8kn08Kz52\n/vgjSkaPhtihgyXaM+TkSTCBCktWaA89Dn6cXlWFCQDAMCk//u7du03/99Nj6XHtpk1Ye+IEUFpq\nentkVvr5WOnxtZMmoWHJEpz0+3G8vBy928jPS+t8cS2gTASY3b5kPhadTnhra1Fqgb9Psx/L/19e\nXg4AuPvuuxEvRhRjD2h84oknkJ2djXnz5sHr9aJv377YtGkT3G43xo0bh/379+s+H2r16tUYGk/1\noDhl3H8//EOGwHvnnaa1wWzpv/sdhE6dLFP8JePee+G/5BJ4b77Z7KYQLS4X2p1zDs6cPm12Swgh\nUWiXnw/P9Ongu3eHf8QI8CNGmN0k08g/iyZVhrHWiDl9GjnDh6PWInuvrGTbtm0YH2cJeluC2gKH\nw4EFCxZgzJgxAKRQkHDPW41v4kTwPXua3QxTcTt3Qhw92uxmKDx33QXRSnmrSbC0NLgefNDsVlgK\ne/AgcktKUPP999R3iTWJIjz33Wd2KyzB95OfmN2EpBOdTrA1NWCqq+mclARx7Qh67LHHMG/ePOXx\n1KlTsW/fPuzbtw9XX311xOetxLF8Obg9e8xuhqns69eD27fP7GYo+JISrG1DuUzPOiwLt0mD6NDl\ne6vg5FW2JFbIIvqs2i+IubT6BV9cDL5PHxNak2KBtLWUwCA5aFu9zKSy30Qfu28f8iw0qCckIiq2\nQshZoX75cgjnnmt2M5KPYeAfOpTOSUmSsHCOs53IsmBoEG0p9jVrUPLDD3CZ3RBiOfKGEasRaRBt\nKqv2CysR22BKSq1+IeokOGi12uDvPRVoEC0zqey31QhhCkmknElpBwmJGQ2iiYU1LlzYNmZfSTBB\noEF0ktBPVUbhHPCNGgXflClmN6OZKOK4FQvzEAAAU1ODrKlTTTm25WNfaRBtCsv3C5N5p0+Hvw1s\npgvV5vuFINA5KUloEC2z2SA6HGa3wlT8gAEQs7PNboYi7a23ULBrl9nNIHqammD/4guzW2EpQs+e\n8EyfDjE/3+ymEELCcLz1Frg2UsnPd9VVEK20ytyKxJUnOpHMzhNNrKddfj7EjAzUVFSY3RSigT1y\nBLlDhlCeaELIWSfznnvgmzgRXo0Ky6RtSESeaJqJJpbFFxXBP2iQ2c0gOkSOM7sJhBASE8e//w2u\nrMzsZpCzHA2iiWX5J0zAHhpEW5ZYVIS61atNOXabj3EkmqhfROB2AzxvditSTrdfUD53EicaRBMF\ne/QoGCstzdNGCMvjhwwxuwmEkCjldesGx9KlZjfDOihjBYkT9SCicC5YAPsnn5jdDIXIMDi3Vy+z\nm0EsiPIBEy3UL8Jj/H7YNm0yuxkpp9svaKKGxIkG0UTBffstmNpas5uh8N56K3yXXmp2MwiJGnv0\nKNrl5wP19WY3hRASAX/BBWY3gZzlaBBNFLbdu2Hbvt3sZij4IUOwlvJEEw1WjX1lDx8GADBUuMkU\nVu0XxFxa/cI3ejSEc84xoTWkNaFBNLEs9rvvkHvwoNnNICR6geVhkZaJiVVZI6ut6Rpffx3+4cPN\nbgY5y1HZb2JZjk8+QUlTE9xmN4RYjmVjX2mjkqks2y+spA2mptTqF2KnTia0hLQ2NIgmQYS8PLOb\n0EwUaeMHObvI/ZX6LbGgpiefhH/kSLObQUirQdMmROG9/HL4x40zuxnNRBFHqVoh0WDV2FeRBtGm\nsmq/sArP7Nnghw0zuxkpR/2CJAvNRBMF368fhHbtzG6GwvHee+gEgLZokbOFeM458Nx8M5CebnZT\nCCGEJBkjitbYZbB69WoMHTrU7GYQC2mXnw8xMxM1R4+a3RRCCCGEtCLbtm3D+PHj4/oOmokmlsX3\n6AGxQwezm0EIIYQQ0gLFRBPL8l1zDfb162d2M4gFUYwj0UL9gmihfkGShQbRxLpocxYhhBBCLIoG\n0cS6GAY9unc3uxXEgigfMNFC/YJooX5BkoVioolleW+4AaKNuig5ezAnTiCvXz+cqa4GqO8SQkir\nRjPRxLL4Cy7A2lOnzG4GsSCrxjiylZXS/1Aokims2i+IuahfkGShQTQhhCQKFVshhJA2gwbRxNIo\nlo1osWy/oEG0qSzbL4ipqF+QZKFBNCGEJAoNogkhpM2gQTSxNIplI1qoXxAt1C+IFuoXJFloEE0I\nIQkidOwI73XXmd0MQgghKcCIoiia3QgAWL16NYYOHWp2MwghhBBCSCu3bds2jB8/Pq7voJloQggh\nhBBCDKJBNLE0imUjWqhfEC3UL4gW6hckWWgQTQghhBBCiEEUE00IIYQQQtoUiokmhBALYSor0S4/\n3+xmEEIISQEaRBNLo1g2osWq/YI9edLsJrRpVu0XxFzUL0iy0CCaEEIIIYQQg2gQTSxt7NixZjeB\nWBD1C6KF+gXRQv2CJAsNogkhJFEYxuwWEEIISZGYBtGbNm3CwIED0a9fP9x0003K80uXLkWfPn1Q\nXFyMlStXRnyekEgolo1ooX5BtFC/IFqoX5BksRn9gCAIuOOOO/D3v/8do0ePxqlTpwAAXq8XDz30\nEDZt2gS3241LL70U11xzje7zhBDS2giFhfDS+Y0QQtoEw4PorVu3okOHDhg9ejQAoKCgAIA0O92/\nf3906NABANC1a1fs3LkTdXV1ms8PGjQoUf8G0opRLBvRYtV+IRYUoHHJErOb0WZZtV8Qc1G/IMli\neBBdXl6O3NxcXHnllaiqqsI999yDmTNn4sSJEygsLMTixYuRn5+Pzp07o7KyEg0NDZrP0yCaEEII\nIYScrcIOohctWoTXX3896LnGxkacPn0a3377LXJzczF8+HBcccUVYAIbambMmAEAWL58edDn1M8z\ntPmGRKm0tJRmEUgL1C+IFuoXRAv1C5IsYQfRc+fOxdy5c4OeW716NebPn4+ioiIAwLBhw/D999+j\nsLAQlZWVyvtOnDiBLl26oL6+vsXzhYWFmsebNWsWunXrBgDIzc3FgAEDlI4vbwygx23rscwq7aHH\n1ni8e/duS7WHHlvjscwq7aHH1nhM5wt6LCstLUV5eTkA4O6770a8GFEURSMfqK2tRf/+/bF7925k\nZmZi2LBh+Pe//40ePXqgb9++ygbCcePGYf/+/fB6vZrPh1q9ejWGDh0a9z+IEEIIIYSQcLZt24bx\n48fH9R02ox/Izc3FokWLMG7cOPh8Ptx6663o06cPAGDBggUYM2YMACkUBAAcDofm84QQ0towFRXI\nGzgQZ06fNrsphBBCkszwTHSy0Ew00VJaSrFspCWr9gtu1y7kXHIJDaJNYtV+QcxF/YJoScRMNFUs\nJIQQQgghxCAaRBNLo9kDosWq/ULo0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FKUnzBXL+DDIRERERUUWoFAUy10Ku+tg7RiLMBYkwFyTCXJCSNF95RkY6cPSo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\nGFlpzgAAAABJRU5ErkJggg==\n", 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I84JEmBckwrwgEW9bLPTevNnff/+Nxx57DIGBgdDpdPjiiy8QHh6OWbNmoUePHgCA5OTk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//op2MQgh5UhQQfT06dPRoUMHMAwDs9mMSZMmYdeuXdi6dSvGjRsHAD6Xh4PI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\nbtsWgdmzoVx2GSkjZ8Mb7xWMZuNE6L8Nvvii2ShkN693hM2uyTJprnn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jR7KjMbRlaIxz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KJAl6rVpwz54N7wsvEMHv558z5i2hL76A2r07PFOmwDd2LMSLF6F26UKbKEVB9PnnodmUf/i1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lpaWCy++9916X/W3duhUdO3b0OM6FCxdQv379wP46EhXoOyaEEEJIVdq/f7/LUMKBCKgl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\nk6bIlmXmvsKb1G6scakUtGbNbE35yuWXwzDXXy4SAW9CS7hkMid23vB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Be1XC5d4Cd9+N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437q14xyaTYXNrFxZNI7QAbT5hVkTY64N3pNuvESay1UkZeIlN9Ka4PhC4Vm2zFrEDAOxv/6V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\nR2jGDKPZlt+2LT0udk3CIxhkwe/YAWnZMmjZ2RC//x65NupHxZt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Pr7CLUYrm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ZM/6Dt56NYwRtJLHhQGjfHi4VccTpP/6Q/hBF1L/9NlxXX62xotin/aYaGmBe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Z29YfuEE5296Sa2uPKiBGGHRxMJPxm6tcC4xAo2b0bFoEGgLVuCtmkDs1UraMcd599FhplAN2Wn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\nyMLxdVN4zjkUxGzf7q6JnIdwch36dJoyF08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kfMrKEKikUI2VlpKutVA1UeSjwBjJdS1f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KDSayPDoXrOHEKBKyy03U905Eir9oOmQdyyBZExY8h7btgQ4Q8+gDJ3LuTvvw/W5xZF/yJf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pu56ifsEwqJkxA+wFFyRfIBOhS0vB7N6d8LhAz57i/zFGDwC2n36CfcWK2BON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\nC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TfZmGb+/ndo559vv9eaBrNePRTdcQfZxHXdlpH0gd65M7IBhW/6SSdBs96znFQxvwwP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YMQtjxnhZ/NZloh4fwLnMGBYWLyT58GYqnPEzcryfSZO8RNy6h7epqAgVz59PtWeeoeb27cix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"text": [ - "" + "" ] } ], - "prompt_number": 27 + "prompt_number": 28 }, { "cell_type": "markdown", @@ -1843,7 +1858,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Implement the Kalman filter using IPython Notebook's animation features to allow you to modify the various constants in real time using sliders. Refer to the section **Interactive Gaussians** in the Gaussian chapter to see how to do this. You will use the *interact()* function to call a calculation and plotting function. Each parameter passed into *interact()* automatically gets a slider created for it. I have built the boilerplate for this; just fill in the required code." + "Implement the Kalman filter using IPython Notebook's animation features to allow you to modify the various constants in real time using sliders. Refer to the section **Interactive Gaussians** in the Gaussian chapter to see how to do this. You will use the $\\verb,interact(),$ function to call a calculation and plotting function. Each parameter passed into $\\verb,interact(),$ automatically gets a slider created for it. I have built the boilerplate for this; just fill in the required code." ] }, { @@ -1869,13 +1884,13 @@ { "metadata": {}, "output_type": "pyout", - "prompt_number": 28, + "prompt_number": 29, "text": [ "" ] } ], - "prompt_number": 28 + "prompt_number": 29 }, { "cell_type": "markdown", @@ -1926,19 +1941,19 @@ "output_type": "display_data", "png": 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27drhdrvZuXMnFoslr5g6ceJE3nHaLp/bhmFQvXp1oqOj837Cb9euHYC2tX3W7ZP7QiUe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\n2rf3afRok5v8Cgr0hxU2AAAATFWpdZD9fmncOLc2bIjUO+9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"text": [ - "" + "" ] }, { "metadata": {}, "output_type": "pyout", - "prompt_number": 29, + "prompt_number": 30, "text": [ "" ] } ], - "prompt_number": 29 + "prompt_number": 30 }, { "cell_type": "heading", @@ -1958,7 +1973,7 @@ "\n", "Do you suppose that this filter works well or poorly with nonlinear systems?\n", "\n", - "Implement a Kalman filter that uses the following *sin()* to generate the measurement value for i in range(100):\n", + "Implement a Kalman filter that uses the following equation to generate the measurement value for i in range(100):\n", "\n", " Z = math.sin(i/3.) * 2\n", " \n", @@ -1974,7 +1989,7 @@ "language": "python", "metadata": {}, "outputs": [], - "prompt_number": 30 + "prompt_number": 31 }, { "cell_type": "markdown", @@ -2007,7 +2022,7 @@ "\n", "p1, = plt.plot(zs,c='r', linestyle='dashed')\n", "p2, = plt.plot(ps, c='b')\n", - "plt.legend([p1,p2], ['measurement', 'filter'], 2)\n", + "plt.legend([p1,p2], ['input', 'filter'], 2)\n", "plt.show()" ], "language": "python", @@ -2016,13 +2031,13 @@ { "metadata": {}, "output_type": "display_data", - "png": 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xQVIYFySFcUFyCngizFkjiIiIiEiLAj5rBOcRpnuxtoukMC5ICuOCpDAuSE4BTYRDQgCX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\nvaccRs25wgOeCOv++guC1ZqnVwv5TeiYMTCtXu3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A9C+/QHfvntvj0tMp7NpFo1s3wSr4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l+L//+z/ExcVh1KhRRX6elpaG+vXrF36uVi0BP/2UhZtuCuh0RERERERF7NmzB61bt/Z5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"text": [ - "" + "" ] } ], - "prompt_number": 31 + "prompt_number": 32 }, { "cell_type": "markdown", @@ -2030,10 +2045,11 @@ "source": [ "######Discussion\n", "\n", - "Here we set a bad initial guess of 100. We can see that the filter never 'acquires' the signal. Note now the peak of the filter output always lags the peak of the signal by a small amount. More clearely we can see the large gap in height between the measurement and filter. \n", - "**REWriTE - not seeing heigh gap now**\n", + "Here we set a bad initial guess of 100. We can see that the filter never 'acquires' the signal. Note now the peak of the filter output always lags the peak of the signal by a small amount, and how the filtered signal does not come very close to capturing the high and low peaks of the input signal.\n", "\n", - "Maybe we just didn't adjust things 'quite right'. After all, the output looks like a sin wave, it is just offset in $x$ and $y$. Let's test this assumption." + "If we recall the g-h filter chapter we can understand what is happening here. The structure of the g-h filter requires that the filter output chooses a value part way between the predition and measurement. A varying signal like this one is always accelerating, whereas our process model assumes constant velocity, so the filter is mathematically guaranteed to always lag the input signal. \n", + "\n", + "Maybe we just didn't adjust things 'quite right'. After all, the output looks like a sin wave, it is just offset some. Let's test this assumption." ] }, { @@ -2060,7 +2076,7 @@ "language": "python", "metadata": {}, "outputs": [], - "prompt_number": 32 + "prompt_number": 33 }, { "cell_type": "markdown", @@ -2104,11 +2120,11 @@ "output_type": "display_data", "png": 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qBfMTT3js/e/fZ7BpkwYvvWTA779n4eRJHi+8EAhThQhaGS7FuIsXZdMkiG9o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\nWiqGRURERMRr/Q+qK/+8gQ67GwAAAABJRU5ErkJggg==\n", "text": [ - "" + "" ] } ], - "prompt_number": 33 + "prompt_number": 34 }, { "cell_type": "markdown", @@ -2117,7 +2133,7 @@ "###### Discussion\n", "This is terrible! The output is not at all like a sin wave, except in the grossest way. With linear systems we could add extreme amounts of noise to our signal and still extract a very accurate result, but here even modest noise creates a very bad result.\n", "\n", - "Very shortly after practioners began implementing Kalman filters they realized the poor performance of them for nonlinear systems and began devising ways of dealing with it. Much of this book is devoted to this problem and its various solutions." + "Very shortly after practioners began implementing Kalman filters they recognized the poor performance of them for nonlinear systems and began devising ways of dealing with it. Much of this book is devoted to this problem and its various solutions." ] }, { @@ -2144,68 +2160,6 @@ "language": "python", "metadata": {}, "outputs": [], - "prompt_number": 33 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def volt():\n", - " return random.randn()*4. + 14.4\n", - "\n", - "sensor_error = 30\n", - "movement_error = 2\n", - "pos = (12,500)\n", - "\n", - "zs = []\n", - "ps = []\n", - "vs = []\n", - "\n", - "\n", - "for i in range(100):\n", - " Z = volt()\n", - " zs.append(Z)\n", - " \n", - " pos = sense(pos[0], pos[1], Z, sensor_error)\n", - " ps.append(pos[0])\n", - " vs.append(pos[1])\n", - " #pos = update(pos[0], pos[1], 0, movement_error)\n", - "\n", - "\n", - "p1, = plt.plot(zs,c='r', linestyle='dashed')\n", - "p2, = plt.plot(ps, c='b')\n", - "plt.legend([p1,p2], ['measurement', 'filter'], 3)\n", - "plt.show()\n", - "plt.plot(vs)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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wLheYzSb7mJiXF/T/YEc9MUeYO3bseMURxmB9/nmknHUW+J9/1ndiHQijRsEQ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\nx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RGgEAAABPynprxAgzwp5GbxdMyAVMyAVMyAWcxIwwAAAAPCn7hTAzwp5GbxdMyAVMyAVM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\n5mnnzp2SLl1rTjkftsPGxsb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- "text": [ - "" - ] - } - ], "prompt_number": 34 }, { diff --git a/Multidimensional_Kalman_Filters.ipynb b/Multidimensional_Kalman_Filters.ipynb index 9affed2..bdcb75f 100644 --- a/Multidimensional_Kalman_Filters.ipynb +++ b/Multidimensional_Kalman_Filters.ipynb @@ -1,7 +1,7 @@ { "metadata": { "name": "", - "signature": "sha256:00dc5e666bed6e6a433a1c086c374cef621d05ba381473abe5a36db5a5cd4ef7" + "signature": "sha256:df1d770cec87d6a479373130e0f54d7645b1762707c5d4ae14ccfc6562a26688" }, "nbformat": 3, "nbformat_minor": 0, @@ -328,16 +328,16 @@ "\n", "If you are reasonably well-versed in linear algebra this equation should look quite managable; if not, don't worry! If you want to learn the math we will cover it in detail in the next optional chapter. If you choose to skip that chapter the rest of this book should still be managable for you\n", "\n", - "I have programmed it and saved it in the file *stats.py* with the function name *multivariate_gaussian*. I am not showing the code here because I have taken advantage of the linear algebra solving apparatus of numpy to efficiently compute a solution - the code does not correspond to the equation in a one to one manner. If you wish to view the code, I urge you to either load it in an editor, or load it into this worksheet by putting *%load -s multivariate_gaussian stats.py* in the next cell and executing it with ctrl-enter. " + "I have programmed it and saved it in the file *stats.py* with the function name *multivariate_gaussian*. I am not showing the code here because I have taken advantage of the linear algebra solving apparatus of numpy to efficiently compute a solution - the code does not correspond to the equation in a one to one manner. If you wish to view the code, I urge you to either load it in an editor, or load it into this worksheet by putting $\\verb,%load -s multivariate_gaussian stats.py,$ in the next cell and executing it with ctrl-enter. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - ">As of version 0.14 scipy.stats has implemented the multivariate normal equation with the function **multivariate_normal()**. It is superior to my function in several ways. First, it is implemented in Fortran, and is therefore faster than mine. Second, it implements a 'frozen' form where you set the mean and covariance once, and then calculate the probability for any number of values for x over any arbitrary number of calls. This is much more efficient then recomputing everything in each call. So, if you have version 0.14 or later you may want to substitute my function for the built in version. Use **scipy.version.version** to get the version number. I deliberately named my function **multivariate_gaussian()** to ensure it is never confused with the built in version.\n", + ">As of version 0.14 scipy.stats has implemented the multivariate normal equation with the function $\\verb,multivariate_normal(),$. It is superior to my function in several ways. First, it is implemented in Fortran, and is therefore faster than mine. Second, it implements a 'frozen' form where you set the mean and covariance once, and then calculate the probability for any number of values for x over any arbitrary number of calls. This is much more efficient then recomputing everything in each call. So, if you have version 0.14 or later you may want to substitute my function for the built in version. Use $\\verb,scipy.version.version,$ to get the version number. I deliberately named my function $\\verb,multivariate_gaussian(),$ to ensure it is never confused with the built in version.\n", "\n", - "> If you intend to use Python for Kalman filters, you will want to read the tutorial for the scipy.stats module, which explains 'freezing' distributions and other very useful features. As of this date, it includes an example of using the multivariate_normal function, which does work a bit differently from my function." + "> If you intend to use Python for Kalman filters, you will want to read the tutorial for the $\\verb,scipy.stats,$ module, which explains 'freezing' distributions and other very useful features. As of this date, it includes an example of using the multivariate_normal function, which does work a bit differently from my function." ] }, { @@ -396,7 +396,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Finally, we have to define our covariance matrix. In the problem statement we did not mention any correlation between $x$ and $y$, and we will assume there is none. This makes sense; a dog can choose to independently wander in either the $x$ direction or $y$ direction without affecting the other. If there is no correlation between the values you just fill in the diagonal of the covariance matrix with the variances. I will use the seemingly arbitrary name $P$ for the covariance matrix. The Kalman filters use the name $P$ for this matrix, so I will introduce the terminology now to avoid explaining why I change the name later. " + "Finally, we have to define our covariance matrix. In the problem statement we did not mention any correlation between $x$ and $y$, and we will assume there is none. This makes sense; a dog can choose to independently wander in either the $x$ direction or $y$ direction without affecting the other. If there is no correlation between the values you just fill in the diagonal of the covariance matrix with the variances. I will use the seemingly arbitrary name $\\textbf{P}$ for the covariance matrix. The Kalman filters use the name $\\textbf{P}$ for this matrix, so I will introduce the terminology now to avoid explaining why I change the name later. " ] }, { @@ -534,7 +534,7 @@ "source": [ "The result is clearly a 3D bell shaped curve. We can see that the gaussian is centered around (2,7), and that the probability quickly drops away in all directions. On the sides of the plot I have drawn the Gaussians for $x$ in greens and for $y$ in orange.\n", "\n", - "As beautiful as this is, it is perhaps a bit hard to get useful information. For example, it is not easy to tell if $x$ and $y$ both have the same variance or not. So for most of the rest of this book we will display multidimensional Gaussian using contour plots. I will use some helper functions in *gaussian.py* to plot them. If you are interested in linear algebra go ahead and look at the code used to produce these contours, otherwise feel free to ignore it." + "As beautiful as this is, it is perhaps a bit hard to get useful information. For example, it is not easy to tell if $x$ and $y$ both have the same variance or not. So for most of the rest of this book we will display multidimensional Gaussian using contour plots. I will use some helper functions in $\\verb,gaussian.py,$ to plot them. If you are interested in linear algebra go ahead and look at the code used to produce these contours, otherwise feel free to ignore it." ] }, { @@ -581,10 +581,10 @@ "The first plot uses the mean and covariance matrices of\n", "$$\n", "\\begin{aligned}\n", - "\\mu &= \\begin{bmatrix}2\\\\7\\end{bmatrix} \\\\\n", - "\\sigma^2 &= \\begin{bmatrix}2&0\\\\0&2\\end{bmatrix}\n", + "\\mathbf{\\mu} &= \\begin{bmatrix}2\\\\7\\end{bmatrix} \\\\\n", + "\\mathbf{\\sigma}^2 &= \\begin{bmatrix}2&0\\\\0&2\\end{bmatrix}\n", "\\end{aligned}\n", - "$$\n", + "$$ \n", "\n", "Let this be our current belief about the position of our dog in a field. In other words, we believe that he is positioned at (2,7) with a variance of $\\sigma^2=2$ for both x and y. The contour plot shows where we believe the dog is located with the '+' in the center of the ellipse. The ellipse shows the boundary for the $1\\sigma^2$ probability - points where the dog is quite likely to be based on our current knowledge. Of course, the dog might be very far from this point, as Gaussians allow the mean to be any value. For example, the dog could be at (3234.76,189989.62), but that has vanishing low probability of being true. Generally speaking displaying the $1\\sigma^2$ to $2\\sigma^2$ contour captures the most likely values for the distribution. An equivelent way of thinking about this is the circle/ellipse shows us the amount of error in our belief. A tiny circle would indicate that we have a very small error, and a very large circle indicates a lot of error in our belief. We will use this throughout the rest of the book to display and evaluate the accuracy of our filters at any point in time. " ] @@ -768,11 +768,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "So in general terms we can show how a multidimensional Kalman filter works. In the example above, we compute velocity from the previous position measurements using something called the **measurement function**. Then we predict the next position by using the current estimate and something called the **state transition function**. In our example above,\n", + "So in general terms we can show how a multidimensional Kalman filter works. In the example above, we compute velocity from the previous position measurements using something called the *measurement function*. Then we predict the next position by using the current estimate and something called the *state transition function*. In our example above,\n", "\n", "$$new\\_position = old\\_position + velocity*time$$ \n", "\n", - "Next, we take the measurement from the sensor, and compare it to the prediction we just made. In a world with perfect sensors and perfect airplanes the prediction will always match the measured value. In the real world they will always be at least slightly different. We call the difference between the two the **residual**. Finally, we use something called the **Kalman gain** to update our estimate to be somewhere between the measured position and the predicted position. I will not describe how the gain is set, but suppose we had perfect confidence in our measurement - no error is possible. Then, clearly, we would set the gain so that 100% of the position came from the measurement, and 0% from the prediction. At the other extreme, if he have no confidence at all in the sensor (maybe it reported a hardware fault), we would set the gain so that 100% of the position came from the prediction, and 0% from the measurement. In normal cases, we will take a ratio of the two: maybe 53% of the measurement, and 47% of the prediction. The gain is updated on every cycle based on the variance of the variables (in a way yet to be explained). It should be clear that if the variance of the measurement is low, and the variance of the prediction is high we will favor the measurement, and vice versa. \n", + "Next, we take the measurement from the sensor, and compare it to the prediction we just made. In a world with perfect sensors and perfect airplanes the prediction will always match the measured value. In the real world they will always be at least slightly different. We call the difference between the two the *residual*. Finally, we use something called the *Kalman gain* to update our estimate to be somewhere between the measured position and the predicted position. I will not describe how the gain is set, but suppose we had perfect confidence in our measurement - no error is possible. Then, clearly, we would set the gain so that 100% of the position came from the measurement, and 0% from the prediction. At the other extreme, if he have no confidence at all in the sensor (maybe it reported a hardware fault), we would set the gain so that 100% of the position came from the prediction, and 0% from the measurement. In normal cases, we will take a ratio of the two: maybe 53% of the measurement, and 47% of the prediction. The gain is updated on every cycle based on the variance of the variables (in a way yet to be explained). It should be clear that if the variance of the measurement is low, and the variance of the prediction is high we will favor the measurement, and vice versa. \n", "\n", "The chart shows a prior estimate of $x=1$ and $\\dot{x}=1$ ($\\dot{x}$ is the shorthand for the derivative of x, which is velocity). Therefore we predict $\\hat{x}=2$. However, the new measurement $x^{'}=1.3$, giving a residual $r=0.7$. Finally, Kalman filter gain $k$ gives us a new estimate of $\\hat{x^{'}}=1.8$.\n", "\n", @@ -1144,30 +1144,30 @@ "\n", "**1**: We just assign the initial value for our state. Here we just initialize both the position and velocity to zero. \n", "\n", - "**2**: We set $\\small\\mathbf{F}=(\\begin{smallmatrix}1&1\\\\0&1\\end{smallmatrix})$, as in design step 2 above. \n", + "**2**: We set $\\textbf{F}=(\\begin{smallmatrix}1&1\\\\0&1\\end{smallmatrix})$, as in design step 2 above. \n", "\n", - "**3**: We set $H=(\\begin{smallmatrix}1&0\\end{smallmatrix})$, as in design step 3 above.\n", + "**3**: We set $\\textbf{H}=(\\begin{smallmatrix}1&0\\end{smallmatrix})$, as in design step 3 above.\n", "\n", - "**4**: We set $\\small\\mathbf{R} = 5$ and $\\small\\mathbf{Q}=0$ as in steps 5 and 6.\n", + "**4**: We set $\\textbf{R} = 5$ and $\\mathbf{Q}=0$ as in steps 5 and 6.\n", "\n", "**5**: Recall in the last chapter we set our initial belief to $\\mathcal{N}(\\mu,\\sigma^2)=\\mathcal{N}(0,500)$ to signify our lack of knowledge about the initial conditions. We implemented this in Python with a list that contained both $\\mu$ and $\\sigma^2$ in the variable $pos$:\n", "\n", " pos = (0,500)\n", " \n", - "Multidimensional Kalman filters stores the state variables in $\\mathbf{x}$ and their *covariance* in $\\small\\mathbf{P}$. These are $f.x$ and $f.P$ in the code above. Notionally, this is similar as the one dimension case, but instead of having a mean and variance we have a mean and covariance. For the multidimensional case, we have\n", + "Multidimensional Kalman filters stores the state variables in $\\mathbf{x}$ and their *covariance* in $\\mathbf{P}$. These are $\\verb,f.x,$ and $\\verb,f.P,$ in the code above. Notionally, this is similar as the one dimension case, but instead of having a mean and variance we have a mean and covariance. For the multidimensional case, we have\n", "\n", "$$\\mathcal{N}(\\mu,\\sigma^2)=\\mathcal{N}(\\mathbf{x},\\mathbf{P})$$\n", "\n", - "$\\small\\mathbf{P}$ is initialized to the identity matrix of size $n{\\times}n$, so multiplying by 500 assigns a variance of 500 to $x$ and $\\dot{x}$. So $f.P$ contains\n", + "$\\mathbf{P}$ is initialized to the identity matrix of size $n{\\times}n$, so multiplying by 500 assigns a variance of 500 to $x$ and $\\dot{x}$. So $\\verb,f.P,$ contains\n", "\n", "$$\\begin{bmatrix} 500&0\\\\0&500\\end{bmatrix}$$\n", "\n", "This will become much clearer once we look at the covariance matrix in detail in later sessions. For now recognize that each diagonal element $e_{ii}$ is the variance for the $ith$ state variable. \n", "\n", - "> Summary: For our dog tracking problem, in the 1-D case $\\mu$ was the position, and $\\sigma^2$ was the variance. In the 2-D case $\\small\\mathbf{x}$ is our position and velocity, and $\\small\\mathbf{P}$ is the *covariance* of the position and velocity. It is the same thing, just in higher dimensions!\n", + "> Summary: For our dog tracking problem, in the 1-D case $\\mu$ was the position, and $\\sigma^2$ was the variance. In the 2-D case $\\mathbf{x}$ is our position and velocity, and $\\mathbf{P}$ is the *covariance* of the position and velocity. It is the same thing, just in higher dimensions!\n", "\n", "\n", - "All that is left is to run the code! The *DogSensor* class from the previous chapter has been placed in *DogSensor.py*." + "All that is left is to run the code! The $\\tt DogSensor$ class from the previous chapter has been placed in $\\verb,DogSensor.py,$." ] }, { @@ -1217,9 +1217,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This is the complete code for the filter, and most of it is just boilerplate. The first function *dog_tracking_filter()* is a helper function that creates a *KalmamFilter* object with specified $\\small\\mathbf{R}$, $\\small\\mathbf{Q}$ and $\\small\\mathbf{P}$ matrices. We've shown this code already, so I will not discuss it more here. \n", + "This is the complete code for the filter, and most of it is just boilerplate. The first function $\\verb,dog_tracking_filter(),$ is a helper function that creates a $\\verb,KalmanFilter,$ object with specified $\\mathbf{R}$, $\\mathbf{Q}$ and $\\mathbf{P}$ matrices. We've shown this code already, so I will not discuss it more here. \n", "\n", - "The function *filter_dog()* implements the filter itself. Lets work through it line by line. The first line creates the simulation of the DogSensor, as we have seen in the previous chapter.\n", + "The function $\\verb,filter_dog(),$ implements the filter itself. Lets work through it line by line. The first line creates the simulation of the DogSensor, as we have seen in the previous chapter.\n", "\n", " dog = DogSensor(velocity=1, noise=noise)\n", "\n", @@ -1233,7 +1233,7 @@ " zs = [None] * count\n", " cov = [None] * count\n", " \n", - "Finally we get to the filter. All we need to do is perform the update and predict steps of the Kalman filter for each measurement. The *KalmanFilter* class provides the two functions *update()* and *predict()* for this purpose. *update()* performs the measurement update step of the Kalman filter, and so it takes a variable containing the sensor measurement. \n", + "Finally we get to the filter. All we need to do is perform the update and predict steps of the Kalman filter for each measurement. The $\\verb,KalmanFilter,$ class provides the two functions $\\verb,update(),$ and $\\verb,predict(),$ for this purpose. $\\verb,update(),$ performs the measurement update step of the Kalman filter, and so it takes a variable containing the sensor measurement. \n", "\n", "Absent the bookkeeping work of storing the filter's data, the for loop reads:\n", "\n", @@ -1242,9 +1242,9 @@ " dog_filter.update (z)\n", " dog_filter.predict()\n", " \n", - "It really cannot get much simpler than that. As we tackle more complicated problems this code will remain largely the same; all of the work goes into setting up the $KalmanFilter$ variables; executing the filter is trivial.\n", + "It really cannot get much simpler than that. As we tackle more complicated problems this code will remain largely the same; all of the work goes into setting up the $\\verb,KalmanFilter,$ variables; executing the filter is trivial.\n", "\n", - "Now let's look at the result. Here is some code that calls *filter_track()* and then plots the result. It is fairly uninteresting code, so I will not walk through it." + "Now let's look at the result. Here is some code that calls $\\verb,filter_track(),$ and then plots the result. It is fairly uninteresting code, so I will not walk through it." ] }, { @@ -1285,7 +1285,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Finally, call it. We will start by filtering 100 measurements with a noise factor of 30, $\\small\\mathbf{R}=5$ and $\\small\\mathbf{Q}=0$." + "Finally, call it. We will start by filtering 100 measurements with a noise factor of 30, $\\mathbf{R}=5$ and $\\mathbf{Q}=0$." ] }, { @@ -1324,7 +1324,7 @@ "\n", "The first plot plots the output of the Kalman filter against the measurements and the actual position of our dog (drawn in green). After the initial settling in period the filter should track the dog's position very closely.\n", "\n", - "The next two plots show the variance of $x$ and of $\\dot{x}$. If you look at the code, you will see that I have plotted the diagonals of $\\small\\mathbf{P}$ over time. Recall that the diagonal of a covariance matrix contains the variance of each state variable. So $\\small\\mathbf{P}[0,0]$ is the variance of $x$, and $\\small\\mathbf{P}[1,1]$ is the variance of $\\dot{x}$. You can see that despite initializing $\\small\\mathbf{P}=(\\begin{smallmatrix}500&0\\\\0&500\\end{smallmatrix})$ we quickly converge to small variances for both the position and velocity. We will spend a lot of time on the covariance matrix later, so for now I will leave it at that.\n", + "The next two plots show the variance of $x$ and of $\\dot{x}$. If you look at the code, you will see that I have plotted the diagonals of $\\mathbf{P}$ over time. Recall that the diagonal of a covariance matrix contains the variance of each state variable. So $\\mathbf{P}[0,0]$ is the variance of $x$, and $\\mathbf{P}[1,1]$ is the variance of $\\dot{x}$. You can see that despite initializing $\\mathbf{P}=(\\begin{smallmatrix}500&0\\\\0&500\\end{smallmatrix})$ we quickly converge to small variances for both the position and velocity. We will spend a lot of time on the covariance matrix later, so for now I will leave it at that.\n", "\n", "In the previous chapter we filtered very noisy signals with much simpler code than the code above. However, realize that right now we are working with a very simple example - an object moving through 1-D space and one sensor. That is about the limit of what we can compute with the code in the last chapter. In contrast, we can implement very complicated, multidimensional filter with this code merely by altering are assignments to the filter's variables. Perhaps we want to track 100 dimensions in financial models. Or we have an aircraft with a GPS, INS, TACAN, radar altimeter, baro altimeter, and airspeed indicator, and we want to integrate all those sensors into a model that predicts position, velocity, and accelerations in 3D (which requires 9 state variables). We can do that with the code in this chapter." ] @@ -1341,9 +1341,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The code in the $KalmanFilter$ is a nearly verbatim transcription of the linear algebra equations. I take advantage of numpy matrices to implement the linear algebra. It is worth looking at this code if for no other reason than to realize how easy it is to implement linear algebra with Python and numpy. For most of this book you will only really need to know how to call this class, not how to implement it from scratch.\n", + "The code in the $\\verb,KalmanFilter,$ is a nearly verbatim transcription of the linear algebra equations. I take advantage of numpy matrices to implement the linear algebra. It is worth looking at this code if for no other reason than to realize how easy it is to implement linear algebra with Python and numpy. For most of this book you will only really need to know how to call this class, not how to implement it from scratch.\n", "\n", - "> **sidebar**: numpy provides two data structures which can be used to perform linear algebra: *numpy.array* and *numpy.matrix*. The usual advice is to use *numpy.array*, not *numpy.matrix*. Ever the contrarian, I have chosen to use *numpy.matrix*, but for what I think are good pedalogical reasons. *numpy.array* is usually recommended because it can be sized to any arbitrary number of dimensions, and *numpy.matrix* is constrained to two dimensions. However, for Kalman filters we only need 2 dimensions. More importantly, *numpy.matrix* allows you to use very natural sytax. Multipying a by b is written *a*b* if using *numpy.matrix*, but *a.dot(b)* if they are *numpy.array*. It is also more natural to mix scalars and matrices using *numpy.matrix*. Finally, the resulting code is extremely close to the equivelent Matlab code; if you are more familiar with Matlab than Python this code should feel very familiar to you.\n", + "> **sidebar**: numpy provides two data structures which can be used to perform linear algebra: $\\verb,numpy.array,$ and $\\verb,numpy.matrix,$. The usual advice is to use $\\verb,numpy.array,$, not $\\verb,numpy.matrix,$. Ever the contrarian, I have chosen to use $\\verb,numpy.matrix,$, but for what I think are good pedalogical reasons. $\\verb,numpy.array,$ is usually recommended because it can be sized to any arbitrary number of dimensions, and $\\verb,numpy.matrix,$ is constrained to two dimensions. However, for Kalman filters we only need 2 dimensions. More importantly, $\\verb,numpy.matrix,$ allows you to use very natural sytax. Multipying a by b is written $\\verb,a*b,$ if using $\\verb,numpy.matrix,$, but $\\verb,a.dot(b),$ if they are $\\verb,numpy.array,$. It is also more natural to mix scalars and matrices using $\\verb,numpy.matrix,$. Finally, the resulting code is extremely close to the equivalent Matlab code; if you are more familiar with Matlab than Python this code should feel very familiar to you.\n", "\n", "\n", "The constructor of the class creates variables for each of the Kalman filter variables, and assigns them a reasonable default value. This is the code in its entirety:\n", @@ -1361,7 +1361,7 @@ " self.R = np.matrix(np.eye(1))\n", " self.I = np.matrix(np.eye(dim))\n", "\n", - "The function *predict()* implements the Kalman filter prediction equations.\n", + "The function $\\verb,predict(),$ implements the Kalman filter prediction equations.\n", "\n", " def predict(self):\n", " self.x = (self.F*self.x) + (self.B * self.u)\n", @@ -1376,7 +1376,7 @@ "\\end{aligned}\n", "$$\n", "\n", - "Finally, the *update()* function implements the Kalman filter update equations in an equally straightforward way:\n", + "Finally, the $\\verb,update(),$ function implements the Kalman filter update equations in an equally straightforward way:\n", "\n", " def update(self, Z):\n", " \"\"\"\n", @@ -1390,7 +1390,7 @@ " self.x = self.x + (K*y)\n", " self.P = (self.I - (K*self.H))*self.P\n", " \n", - "Finally, for those reading this online or in a printed form, here is the code in KalmanFilter.py absent the unit testing code that is included in that file." + "Finally, for those reading this online or in a printed form, here is the code in $\\verb,KalmanFilter.py,$ absent the unit testing code that is included in that file." ] }, { @@ -1458,9 +1458,9 @@ "source": [ "Your results will vary slightly depending on what numbers your random generator creates for the noise componenet of the noise, but the filter in the last section should track the actual position quite well. Typically as the filter starts up the first several predictions are quite bad, and varies a lot. But as the filter builds its state the estimates become much better. \n", "\n", - "Let's start varying our parameters to see the effect of various changes. This is a *very normal* thing to be doing with Kalman filters. It is difficult, and often impossible to exactly model our sensors. An imperfect model means imperfect output from our filter. Engineers spend a lot of time tuning Kalman filters so that they perform well with real world sensors. We will spend time now to learn the effect of these changes. As you learn the effect of each change you will develop an intuition for how to design a Kalman filter. As I wrote earlier, designing a Kalman filter is as much art as science. The science is, roughly, designing the $\\small{\\mathbf{H}}$ and $\\small{\\mathbf{F}}$ matrices - they develop in an obvious manner based on the physics of the system we are modelling. The art comes in modelling the sensors and selecting appropriate values for the rest of our variables.\n", + "Let's start varying our parameters to see the effect of various changes. This is a *very normal* thing to be doing with Kalman filters. It is difficult, and often impossible to exactly model our sensors. An imperfect model means imperfect output from our filter. Engineers spend a lot of time tuning Kalman filters so that they perform well with real world sensors. We will spend time now to learn the effect of these changes. As you learn the effect of each change you will develop an intuition for how to design a Kalman filter. As I wrote earlier, designing a Kalman filter is as much art as science. The science is, roughly, designing the ${\\mathbf{H}}$ and ${\\mathbf{F}}$ matrices - they develop in an obvious manner based on the physics of the system we are modelling. The art comes in modelling the sensors and selecting appropriate values for the rest of our variables.\n", "\n", - "Let's look at the effects of the noise parameters $\\small{\\mathbf{R}}$ and $\\small{\\mathbf{Q}}$. I will only run the filter for twenty steps to ensure we can see see the difference between the measurements and filter output. I will start by holding $\\small{\\mathbf{R}}$ to 5 and vary $\\small{\\mathbf{Q}}$. " + "Let's look at the effects of the noise parameters ${\\mathbf{R}}$ and ${\\mathbf{Q}}$. I will only run the filter for twenty steps to ensure we can see see the difference between the measurements and filter output. I will start by holding ${\\mathbf{R}}$ to 5 and vary ${\\mathbf{Q}}$. " ] }, { @@ -1498,15 +1498,15 @@ "source": [ "The filter in the first plot should follow the noisy measurement almost exactly. In the second plot the filter should vary from the measurement quite a bit, and be much closer to a straight line than in the first graph. \n", "\n", - "In the Kalman filter $\\small{\\mathbf{R}}$ is the *measurement noise* and $\\small{\\mathbf{Q}}$ is the *process uncertainty*. $\\small{\\mathbf{R}}$ is the same in both plots, so ignore it for the moment. Why does $\\small{\\mathbf{Q}}$ affect the plots this way?\n", + "In the Kalman filter ${\\mathbf{R}}$ is the *measurement noise* and ${\\mathbf{Q}}$ is the *process uncertainty*. ${\\mathbf{R}}$ is the same in both plots, so ignore it for the moment. Why does ${\\mathbf{Q}}$ affect the plots this way?\n", "\n", "Let's remind ourselves of what the term *process uncertainty* means. Consider the problem of tracking a ball. We can accurately model its behavior in statid air with math, but if there is any wind our model will diverge from reality. \n", "\n", - "In the first case we set $\\small{\\mathbf{Q}}=100$, which is quite large. In physical terms this is telling the filter \"I don't trust my motion prediction step\". Strictly speaking, we are telling the filter there is a lot of external noise that we are not modeling with $\\small{\\mathbf{F}}$, but the upshot of that is to not trust the motion prediction step. So the filter will be computing velocity ($\\dot{x}$), but then mostly ignoring it because we are telling the filter that the computation is extremely suspect. Therefore the filter has nothing to use but the measurements, and thus it follows the measurements closely. \n", + "In the first case we set ${\\mathbf{Q}}=100$, which is quite large. In physical terms this is telling the filter \"I don't trust my motion prediction step\". Strictly speaking, we are telling the filter there is a lot of external noise that we are not modeling with $\\small{\\mathbf{F}}$, but the upshot of that is to not trust the motion prediction step. So the filter will be computing velocity ($\\dot{x}$), but then mostly ignoring it because we are telling the filter that the computation is extremely suspect. Therefore the filter has nothing to use but the measurements, and thus it follows the measurements closely. \n", "\n", - "In the second case we set $\\small{\\mathbf{Q}}=0.1$, which is quite small. In physical terms we are telling the filter \"trust the motion computation, it is really good!\". Again, more strictly this actually says there is very small amounts of process noise, so the motion computation will be accurate. So the filter ends up ignoring some of the measurement as it jumps up and down, because the variation in the measurement does not match our trustworthy velocity prediction. \n", + "In the second case we set ${\\mathbf{Q}}=0.1$, which is quite small. In physical terms we are telling the filter \"trust the motion computation, it is really good!\". Again, more strictly this actually says there is very small amounts of process noise, so the motion computation will be accurate. So the filter ends up ignoring some of the measurement as it jumps up and down, because the variation in the measurement does not match our trustworthy velocity prediction. \n", "\n", - "Now let's leave $\\small{\\mathbf{Q}}=0.1$, but bump $\\small{\\mathbf{R}}$ up to $1000$. This is telling the filter that the measurement noise is very large. " + "Now let's leave ${\\mathbf{Q}}=0.1$, but bump ${\\mathbf{R}}$ up to $1000$. This is telling the filter that the measurement noise is very large. " ] }, { @@ -1537,7 +1537,7 @@ "\n", "The filter is strongly preferring the motion update to the measurement, so if the prediction is off it takes a lot of measurements to correct it. It will eventually correct because the velocity is a hidden variable - it is computed from the measurements, but it will take awhile.\n", "\n", - "To some extent you can get similar looking output by varying either $\\small{\\mathbf{R}}$ or $\\small{\\mathbf{Q}}$, but I urge you to not 'magically' alter these until you get output that you like. Always think about the physical implications of these assignments, and vary $\\small{\\mathbf{R}}$ and/or $\\small{\\mathbf{Q}}$ based on your knowledge of the system you are filtering." + "To some extent you can get similar looking output by varying either ${\\mathbf{R}}$ or ${\\mathbf{Q}}$, but I urge you to not 'magically' alter these until you get output that you like. Always think about the physical implications of these assignments, and vary ${\\mathbf{R}}$ and/or ${\\mathbf{Q}}$ based on your knowledge of the system you are filtering." ] }, { @@ -1552,7 +1552,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "So far I have not given a lot of coverage of the covariance matrix. $\\small\\mathbf{P}$, the covariance matrix is nothing more than the variance of our state - such as the position of our dog. It has many elements in it, but don't be daunted; we will learn how to interpret a very large $9{\\times}9$ covariance matrix, or even larger.\n", + "So far I have not given a lot of coverage of the covariance matrix. $\\mathbf{P}$, the covariance matrix is nothing more than the variance of our state - such as the position of our dog. It has many elements in it, but don't be daunted; we will learn how to interpret a very large $9{\\times}9$ covariance matrix, or even larger.\n", "\n", "Recall the beginning of the chapter, where we provided the equation for the covariance matrix. It read:\n", "\n", @@ -1565,7 +1565,7 @@ " \\end{pmatrix}\n", "$$\n", "\n", - "(I have subtituted $\\small\\mathbf{P}$ for $\\Sigma$ because of the nomenclature used by the Kalman filter literature).\n", + "(I have subtituted $\\mathbf{P}$ for $\\Sigma$ because of the nomenclature used by the Kalman filter literature).\n", "\n", "The diagonal contains the variance of each of our state variables. So, if our state variables are\n", "\n", @@ -1731,13 +1731,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The output on these is a bit messy, but you should be able to see what is happening. In both plots we are drawing the covariance matrix for each point. We start with the covariance $\\small\\mathbf{P}=(\\begin{smallmatrix}50&0\\\\0&50\\end{smallmatrix})$, which signifies a lot of uncertainty about our initial belief. After we receive the first measurement the Kalman filter updates this belief, and so the variance is no longer as large. In the top plot the first ellipse (the one on the far left) should be a slighly squashed ellipse. As the filter continues processing the measurements the covariance ellipse quickly shifts shape until it settles down to being a long, narrow ellipse tilted in the direction of movement.\n", + "The output on these is a bit messy, but you should be able to see what is happening. In both plots we are drawing the covariance matrix for each point. We start with the covariance $\\mathbf{P}=(\\begin{smallmatrix}50&0\\\\0&50\\end{smallmatrix})$, which signifies a lot of uncertainty about our initial belief. After we receive the first measurement the Kalman filter updates this belief, and so the variance is no longer as large. In the top plot the first ellipse (the one on the far left) should be a slighly squashed ellipse. As the filter continues processing the measurements the covariance ellipse quickly shifts shape until it settles down to being a long, narrow ellipse tilted in the direction of movement.\n", "\n", "Think about what this means physically. The x-axis of the ellipse denotes our uncertainty in position, and the y-axis our uncertainty in velocity. So, an ellipse that is taller than it is wide signifies that we are more uncertain about the velocity than the position. Conversely, a wide, narrow ellipse shows high uncertainty in position and low uncertainty in velocity. Finally, the amount of tilt shows the amount of correlation between the two variables. \n", "\n", - "The first plot, with $\\small\\mathbf{R}=5$, finishes up with an ellipse that is wider than it is tall. If that is not clear I have printed out the variances for the last ellipse in the lower right hand corner. The variance for position is 3.85, and the variance for velocity is 3.0. \n", + "The first plot, with $\\mathbf{R}=5$, finishes up with an ellipse that is wider than it is tall. If that is not clear I have printed out the variances for the last ellipse in the lower right hand corner. The variance for position is 3.85, and the variance for velocity is 3.0. \n", "\n", - "In contrast, the second plot, with $\\small\\mathbf{R}=0.5$, has a final ellipse that is taller than wide. The ellipses in the second plot are all much smaller than the ellipses in the first plot. This stands to reason because a small $\\small\\mathbf{R}$ implies a small amount of noise in our measurements. Small noise means accurate predictions, and thus a strong belief in our position. " + "In contrast, the second plot, with $\\mathbf{R}=0.5$, has a final ellipse that is taller than wide. The ellipses in the second plot are all much smaller than the ellipses in the first plot. This stands to reason because a small $\\small\\mathbf{R}$ implies a small amount of noise in our measurements. Small noise means accurate predictions, and thus a strong belief in our position. " ] }, { @@ -1752,7 +1752,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Why are the ellipses for $R=5$ shorter, and more tilted than the ellipses for $\\small\\mathbf{R}=0.5$. Hint: think about this in the context of what these ellipses mean physically, not in terms of the math. If you aren't sure about the answer,change $\\small\\mathbf{R}$ to truly large and small numbers such as 100 and 0.1, observe the changes, and think about what this means. " + "Why are the ellipses for $\\mathbf{R}=5$ shorter, and more tilted than the ellipses for $\\mathbf{R}=0.5$. Hint: think about this in the context of what these ellipses mean physically, not in terms of the math. If you aren't sure about the answer,change $\\mathbf{R}$ to truly large and small numbers such as 100 and 0.1, observe the changes, and think about what this means. " ] }, { @@ -1767,11 +1767,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The $x$ axis is for position, and $y$ is velocity. An ellipse that is vertical, or nearly so, says there is no correlation between position and velocity, and an ellipse that is diagnal says that there is a lot of correlation. Phrased that way, it sounds unlikely - either they are correlated or not. But this is a measure of the *output of the filter*, not a description of the actual, physical world. When $\\small\\mathbf{R}$ is very large we are telling the filter that there is a lot of noise in the measurements. In that case the Kalman gain $\\small\\mathbf{K}$ is set to favor the prediction over the measurement, and the prediction comes from the velocity state variable. So, there is a large correlation between $x$ and $\\dot{x}$. Conversely, if $\\small\\mathbf{R}$ is small, we are telling the filter that the measurement is very trustworthy, and $\\small\\mathbf{K}$ is set to favor the measurement over the prediction. Why would the filter want to use the prediction if the measurement is nearly perfect? If the filter is not using much from the prediction there will be very little correlation reported. \n", + "The $x$ axis is for position, and $y$ is velocity. An ellipse that is vertical, or nearly so, says there is no correlation between position and velocity, and an ellipse that is diagnal says that there is a lot of correlation. Phrased that way, it sounds unlikely - either they are correlated or not. But this is a measure of the *output of the filter*, not a description of the actual, physical world. When $\\mathbf{R}$ is very large we are telling the filter that there is a lot of noise in the measurements. In that case the Kalman gain $\\mathbf{K}$ is set to favor the prediction over the measurement, and the prediction comes from the velocity state variable. So, there is a large correlation between $x$ and $\\dot{x}$. Conversely, if $\\mathbf{R}$ is small, we are telling the filter that the measurement is very trustworthy, and $\\mathbf{K}$ is set to favor the measurement over the prediction. Why would the filter want to use the prediction if the measurement is nearly perfect? If the filter is not using much from the prediction there will be very little correlation reported. \n", "\n", "**This is a critical point to understand!**. The Kalman filter is just a mathematical model for a real world system. A report of little correlation *does not mean* there is no correlation in the physical system, just that there was no correlation in the mathematical model. It's just a report of how much measurement vs prediction was incorporated into the model. \n", "\n", - "Let's bring that point home with a truly large measurement error. We will set $\\small\\mathbf{R}=500$. Think about what the plot will look like before scrolling down. To emphasize the issue, I will set the amount of noise injected into the measurements to 0, so the measurement will exactly equal the actual position. " + "Let's bring that point home with a truly large measurement error. We will set $\\mathbf{R}=500$. Think about what the plot will look like before scrolling down. To emphasize the issue, I will set the amount of noise injected into the measurements to 0, so the measurement will exactly equal the actual position. " ] }, { @@ -1805,7 +1805,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Keep looking at these plots until you grasp how to interpret the covariance matrix $\\small\\mathbf{P}$. When you start dealing with a, say, $9{\\times}9$ matrix it may seem overwhelming - there are 81 numbers to interpret. Just break it down - the diagonal contains the variance for each state variable, and all off diagonal elements are the product of two variances and a scaling factor $p$. You will not be able to plot a $9{\\times}9$ matrix on the screen because it would require living in 10-D space, so you have to develop your intution and understanding in this simple, 2-D case. \n", + "Keep looking at these plots until you grasp how to interpret the covariance matrix $\\mathbf{P}$. When you start dealing with a, say, $9{\\times}9$ matrix it may seem overwhelming - there are 81 numbers to interpret. Just break it down - the diagonal contains the variance for each state variable, and all off diagonal elements are the product of two variances and a scaling factor $p$. You will not be able to plot a $9{\\times}9$ matrix on the screen because it would require living in 10-D space, so you have to develop your intution and understanding in this simple, 2-D case. \n", "\n", "> **sidebar**: when plotting covariance ellipses, make sure to always use *plt.axis('equal')* in your code. If the axis use different scales the ellipses will be drawn distorted. For example, the ellipse may be drawn as being taller than it is wide, but it may actually be wider than tall." ] diff --git a/Unscented_Kalman_Filter.ipynb b/Unscented_Kalman_Filter.ipynb index cdec725..32745e1 100644 --- a/Unscented_Kalman_Filter.ipynb +++ b/Unscented_Kalman_Filter.ipynb @@ -1,7 +1,7 @@ { "metadata": { "name": "", - "signature": "sha256:388572360fc3834b2b47f1ac29bc861fe9c4c097c10c029badd3c3aab4674e6c" + "signature": "sha256:1512602ec041fd03daea2ab69da5d9ef557e2b0b23bc7eeabfed009bbf2d9fd0" }, "nbformat": 3, "nbformat_minor": 0, @@ -242,13 +242,13 @@ ], "metadata": {}, "output_type": "pyout", - "prompt_number": 2, + "prompt_number": 1, "text": [ - "" + "" ] } ], - "prompt_number": 2 + "prompt_number": 1 }, { "cell_type": "markdown", @@ -281,13 +281,13 @@ { "metadata": {}, "output_type": "display_data", - "png": 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QE5stMmUKatzi0NCqw+GcGjS0/jZksLxRgxUTwgV9tkMTcMTD3I4buH1y8cmF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\n6rcfLhljUDZo8E6aCqcL6yCVADdrWiC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oQCQUu2t633nnHeTl5aG+vh7nzp1Tfed55VtpIlNTVkBdedWUFRAnb3ZpLVbu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\noAq1DWaWN7gY1vQSERH1IGW1DagwNSodQ3iZJbU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"text": [ - "" + "" ] } ], - "prompt_number": 3 + "prompt_number": 2 }, { "cell_type": "markdown", @@ -297,7 +297,7 @@ "\n", "It has perhaps occured to you that this sampling process constitutes a solution to our problem. This is called a 'monte carlo' approach, and it used by some Kalman filter designs, such as the *Ensemble filter*. Sampling requires no specialized knowledge programming, and does not require a closed form solution. No matter how nonlinear or poorly behaved the tranfer function is, as long as we sample with enough points we will build an accurate output distribution.\n", "\n", - "\"Enought points\" is the rub. The graph above was created with 500,000 points, and the output is still not smooth. You wouldn't need to use that many points to get a reasonable estimate of the mean and variance, but it will require many points. What's worse, this is only for 1 dimension. In general, the number of points required increases by the power of the number of dimensions. If you need 50 points for 1 dimension, you need 50^2 for two dimensions, 50^3 for three dimensions, and so on. So while this approach does work, it is very computationally expensive. The Unscented Kalman filter, the topic of this chapter, uses a somewhat similar technique but reduces the amount of computation needed by a drastic amount. \n", + "\"Enought points\" is the rub. The graph above was created with 500,000 points, and the output is still not smooth. You wouldn't need to use that many points to get a reasonable estimate of the mean and variance, but it will require many points. What's worse, this is only for 1 dimension. In general, the number of points required increases by the power of the number of dimensions. If you need $50$ points for 1 dimension, you need $50^2$ for two dimensions, $50^3$ for three dimensions, and so on. So while this approach does work, it is very computationally expensive. The Unscented Kalman filter, the topic of this chapter, uses a somewhat similar technique but reduces the amount of computation needed by a drastic amount. \n", "\n", "It is somewhat hard to understand some aspects of this problem by looking at the histogram, so consider this alternative representation, this time for 2 variables/dimensions." ] @@ -315,13 +315,13 @@ { "metadata": {}, "output_type": "display_data", - "png": 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iIXR3d6O6oR0J47yNFUsqfZnFMgCp2AGFk14YkfBEDofDMdIXd+3ahblznf82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\n17RGRam0co9qG45o975GJSanLPgcWHq9XR3auT5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"text": [ - "" + "" ] } ], - "prompt_number": 4 + "prompt_number": 3 }, { "cell_type": "markdown", @@ -348,7 +348,7 @@ "\n", "The fewest number of points that we can use is one per dimension. This is the number that the linear Kalman filter uses. The input to a Kalman filter for the distribution $\\mathcal{N}(\\mu,\\sigma^2)$ is just $\\mu$ itself. So while this works for the linear case, it is not a good answer for the nonlinear case.\n", "\n", - "If we were to pass some value $\\mu+\\Delta$ instead, the *identity* system would not converge, so this is not a possible algorithm. Since we cannot set our one point sample to $\\mu$, or any value that is not $\\mu$, we must conclude that a one point sample will not work.\n", + "If we were to pass some value $\\mu+\\Delta$ instead, the identity system would not converge, so this is not a possible algorithm. Since we cannot set our one point sample to $\\mu$, or any value that is not $\\mu$, we must conclude that a one point sample will not work.\n", "\n", "So, what is the next lowest number we can choose? Consider the fact that Gaussians are symmetric, and that we probably want to always have one of our sample points be the mean of the input. Two points would require us to select the mean, and then one other point. That one other point would introduce an asymmetry in our input that we probably don't want. I recognize that this is rather vague, but I don't want to spend a lot of time on a scheme that doesn't work. \n", "\n", @@ -367,19 +367,19 @@ { "metadata": {}, "output_type": "display_data", - "png": 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XccaIpKg9Fz09QHPzF6t34VPPAQLQcjS6/2MtLWxWhkvtuRhP3d2Ay/V5BkWh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\nFigkr+bOHvz4v+UI99fh58smyfqmFua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"text": [ - "" + "" ] } ], - "prompt_number": 5 + "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ - "For this to work for all *identity* cases we will want the sums of the weights to equal one. We can always come up with counterexamples, but in general if the sum is greater or less than one the sampling will not yield the correct output. Given that, we then have to select *sigma points* $\\mathcal{X}$ and their corresponding weights so that they compute to the mean and variance of the input Gaussian. So we can write\n", + "For this to work for identity we will want the sums of the weights to equal one. We can always come up with counterexamples, but in general if the sum is greater or less than one the sampling will not yield the correct output. Given that, we then have to select *sigma points* $\\mathcal{X}$ and their corresponding weights so that they compute to the mean and variance of the input Gaussian. So we can write\n", "\n", "$$\\begin{aligned}\n", "1 &= \\sum_i{w_i}\\;\\;\\;&(1) \\\\\n", @@ -407,13 +407,13 @@ { "metadata": {}, "output_type": "display_data", - "png": 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Dfv3bDxCLxUTHoTtYWGUgk8ngw4+OIKR3cZ/VJWBzFcHevAP/8u8fIBKJiI5DSyCZTOI3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\ngf3P8oo8/GSZTEafnejRRyfPqbZ1mwKtG4v+6sq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3Hx1tYsKENmUaKPqcJH0rqL1G50i2xuLHPzYwcmR7ktHedIbKnVGYWi13+YMG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\n7TW6nnxaWMPJ6mYW9MJdlLP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6Yd6wYQNiYmIQGxuLbdu2XXa/4uJiJCUlYfDgwUhMTMT27ds7fQwiIgBoNYtI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\nTnUOa0FygjatyNU7T1coPyPF6qkBABDXFttT9Xf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"text": [ - "" + "" ] } ], - "prompt_number": 7 + "prompt_number": 6 }, { "cell_type": "markdown", @@ -511,7 +511,7 @@ "source": [ "Here I have plotted two different choices for lambda - 05 and 1.4 - to show how lambd affects the distribution of the points. \n", "\n", - "Perhaps the reason for calling these *sigma points* is clear. While we are not selecting exactly $1\\sigma$ for the position of our points, we are scaling by some factor of $\\sigma$.\n", + "Perhaps the reason for calling these sigma points is clear. While we are not selecting exactly $1\\sigma$ for the position of our points, we are scaling by some factor of $\\sigma$.\n", "\n", "\n", "On to larger dimensions ($n>1$). The term $(\\sqrt{(n+\\lambda)\\Sigma})_i$ has to be a matrix because $\\Sigma$ is a matrix. The subscript $i$ is choosing the column vector of the matrix. And thus the square root is not the square root of a scalar, but of a matrix.\n", @@ -533,7 +533,7 @@ "\n", "If this makes you uncomfortable, I advise not worrying about it. We are just defining an algorithm for choosing our sigma points; if the 'square' root in method of computation ($SS$) is slightly different than the other case ($SS^T), it shouldn't really matter. Plus, this still works for the one dimensional case, as the transpose of a scalar is just the scalar. \n", "\n", - "I will derive the math for computing this in a later section in this chapter. For now I will say that this is a well worn area of linear algebra, and performing this 'square root' uses something called the Cholesky decomposition. This is a library procedure that is available in any linear algebra library. For example, numpy provides it in $\\verb ,numpy.linalg.cholesky(),$, and we will be using that function in our code. If your language of choice is Fortran, C, C++, or the like the standard libraries like LAPACK also provide this routine. And, of course, matlab provides $\\tt chol()$, which does the same thing." + "I will derive the math for computing this in a later section in this chapter. For now I will say that this is a well worn area of linear algebra, and performing this 'square root' uses something called the Cholesky decomposition. This is a library procedure that is available in any linear algebra library. For example, numpy provides it in $\\verb,numpy.linalg.cholesky(),$, and we will be using that function in our code. If your language of choice is Fortran, C, C++, or the like the standard libraries like LAPACK also provide this routine. And, of course, matlab provides $\\verb,chol(),$, which does the same thing." ] }, { @@ -601,7 +601,7 @@ "language": "python", "metadata": {}, "outputs": [], - "prompt_number": 8 + "prompt_number": 7 }, { "cell_type": "markdown", @@ -708,7 +708,7 @@ "language": "python", "metadata": {}, "outputs": [], - "prompt_number": 10 + "prompt_number": 8 } ], "metadata": {} diff --git a/book.ipynb b/book.ipynb deleted file mode 100644 index 6f60833..0000000 --- a/book.ipynb +++ /dev/null @@ -1,6615 +0,0 @@ -{ - "metadata": { - "name": "_merged", - "signature": "sha256:0b133277ac85f975c916bc21148ad9bfba4634dc26c82d0842b53aee56e01439" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Kalman and Bayesian Filters in Python

\n", - "
Table of Contents
\n", - "\n", - "### Version 0.0\n", - "\n", - "Not ready for public consumption. In development." - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Motivation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is a book for programmers that have a need or interest in Kalman filtering. The motivation for this book came out of my desire for a gentle introduction to Kalman filtering. I'm a software engineer that spent almost two decades in the avionics field, and so I have always been 'bumping elbows' with the Kalman filter, but never had the need to implement one myself. As I moved into solving tracking problems with computer vision I needed to start implementing them. There are classic textbooks in the field, such as Grewal and Andrew's excellent *Kalman Filtering*. But sitting down and trying to read these books is a dismal and trying experience if you do not have the background. Typcially the first few chapters fly through several years of undergraduate math, blithely referring you to textbooks on, for example, It\u014d calculus, and presenting an entire semester's worth of statistics in a few brief paragraphs. These books are good textbooks for an upper undergraduate course, and an invaluable reference to researchers and professionals, but the going is truly difficult for the more casual reader. Symbology is introduced without explanation, different texts use different words and variables names for the same concept, and the books are almost devoid of examples or worked problems. I often found myself able to parse the words and comprehend the mathematics of a defition, but had no idea as to what real world phenomena these words and math were attempting to describe. \"But what does that *mean?*\" was my repeated thought.\n", - "\n", - "However, as I began to finally understand the Kalman filter I realized the underlying concepts are quite straightforward. A few simple probability rules, some intuition about how we integrate disparate knowledge to explain events in our everyday life and the core concepts of the Kalman filter are accessible. Kalman filters have a reputation for difficulty, but shorn of much of the formal terminology the beauty of the subject and of their math became clear to me, and I fell in love with the topic. \n", - "\n", - "As I began to understand the math and theory more difficulties itself. A book or paper's author makes some statement of fact and presents a graph as proof. Unfortunately, why the statement is true is not clear to me, nor is the method by which you might make that plot obvious. Or maybe I wonder \"is this true if R=0?\" Or the author provide pseudocode - at such a high level that the implementation is not obvious. Some books offer Matlab code, but I do not have a license to that expensive package. Finally, many books end each chapter with many useful exercises. Exercises which you need to understand, but excercises with no answers. If you are using the book in a classroom, perhaps this is okay, but it is terrible for the independent reader. I loathe that an author witholds information from me, presumably to avoid 'cheating'. \n", - "None of this necessary, from my point of view. Certainly if you are designing a Kalman filter for a aircraft or missile you must be a thorough master of all of the mathematics and topics in a typical Kalman filter textbook. I just want to track an image on a screen, or write some code for my Arduino project. I want to know how the plots in the book are made, and chose different parameters than the author chose. I want to run simulations. I want to inject more noise in the signal and see how a filter performs. \n", - "\n", - "I wrote this book to address all of those needs. This is not the book for you if you program avionics for Boeing or design radars for Ratheon. Go get a degree at Georgia Tech, UW, or the like, because you'll need it for that kind of work. This book is for the hobbiest, the curious, and the working engineer that needs to filter or smooth data. \n", - "\n", - "This book is interactive. While you can read it online as static content, I urge you to use it as intended. It is written using IPython Notebook, which allows you to combine text, python, and python output in one place. Every plot, every piece of data in this book is generated from Python that is available to you right inside the notebook. Want to double the value of a parameter. Click on the Python cell, change the parameter's value, and click 'Run'. A new plot or printed output will appear in the book. \n", - "\n", - "This book has exercises, but it also has the answers. I trust you. If you just need an answer, go ahead and read the answer. If you want to internalize this knowledge, try to implement the exercise before you read the answer. \n", - "\n", - "This book has supporting libraries for computing statistics, plotting various things related to filters, and for the various filters that we cover. This does require a strong caveat; most code is written for didactic purposes. It is rare that I chose the most efficient solution (which often obscures the intent of the code), and I mostly did not concern myself with numerical stability. This is important to understand - Kalman filters in aircraft are carefully designed and implemented to be numerically stable; the naive implemention is not stable in many cases. If you are serious about Kalman filters this book will not be the last book you need. My intention is to introduce you to the concepts and mathematics, and to get you to the point where the textbooks are approachable.\n", - "\n", - "Finally, this book is free. The cost for the books required to learn Kalman filtering is somewhat prohibitive even for a Silicon Valley engineer like myself; I cannot believe the are within the reach of someone in a depressed economy, or a financially struggling student. I have gained so much from free software like Python, and free books like those from Allen B. Downey [here](http://www.greenteapress.com/). It's time to repay that. So, the book is free, it is hosted on free servers, and it uses free software for all of the code. " - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Reading the book" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are multiple ways to read this book. However, it is intended to be interactive and I recommend using it in that form. If you install IPython on your computer and then clone this book you will be able to run all of the code in the book yourself. You can perform experiments, see how filters react to different data, see how different filters react to the same data, and so on. I find this sort of immediate feedback both vital and invigorating. You do not have to wonder \"what happens if\". Try it and see!\n", - "\n", - "If you do not want to do that you can read this book online. the website http://nbviewer.org provides an IPython Notebook server that renders a notebook stored at github (or elsewhere). The rendering is done in real time when you load the book. If you read my book today, and then I make a change tomorrow, when you go back tomorrow you will see that change. \n", - "\n", - "You may access this book via nbviewer at any by using this address:\n", - "http://nbviewer.ipython.org/github/rlabbe/Kalman-Filters-and-Random-Signals-in-Python/blob/master/Introduction.ipynb\n", - "\n", - "Finally, you may generate output in a variety of formats. I will not cover how to do that, other than to point you to [IPython nbconvert](http://ipython.org/ipython-doc/rel-1.0.0/interactive/nbconvert.html). You can convert this book into static HTML pages, latex, or PDF. While I don't recommend it particularly, it is useful for those that don't want to program and/or are working offline." - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Installation and Software Requirements" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you want to run the notebook on your computer, which is what I recommend, then you will have to have IPython installed. I do not cover how to do that in this book; requirements change based on what other python installations you may have, whether you use a third party package like Anaconda Python, what operating system you are using, and so on. \n", - "\n", - "To use all features you will have to have Ipython 2.0 installed, which is released and stable as of April 2014. Most of the book does not require that, but I do make use of the interactive plotting widgets introduced in this release. A few cells will not run if you have an older version installed.\n", - "\n", - "You will need Python 2.7 or later installed. Almost all of my work is done in Python 2.7, but I periodically test on 3.3. I do not promise any specific check in will work in 3.X, however. I do use Python's \"from __future__ import ...\" statement to help with compatibility. For example, all prints need to use parenthesis. If you try to add, say, \"print 3.14\" into the book your script will fail; you must write \"print (3.4)\" as in Python 3.X.\n", - "\n", - "You will need a recent version of NumPy, SciPy, and Matplotlib installed. I don't really know what the minimal might be. \n", - "I have numpy 1.71, SciPy 0.13.0, and Matplotlib 1.3.1 installed on my machines.\n", - "\n", - "Personally, I use the Anaconda Python distribution in all of my work, [available here](https://store.continuum.io/cshop/anaconda/). I am not selecting them out of favoritism, I am merely documenting my environment. Should you have trouble running any of the code, perhaps knowing this will help you." - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Provided Libraries" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "I've not structured anything nicely yet. For now just look for any .py files in the base directory. As I pull everything together I will turn this into a python library, and probably create a separate git project just for the python code.\n", - "\n", - "There are python files with a name like *xxx*_internal.py. I use these to store functions that are useful for the book, but not of general interest. Often the Python is the point and focus of what I am talking about, but sometimes I just want to display a chart. IPython Notebook does not allow you to collapse the python code, and so it sometimes gets in the way. Some IPython books just incorporate .png files for the image, but I want to ensure that everything is open - if you want to look at the code you can. \n", - "\n", - "Some chapters introduce functions that are useful for the rest of the book. Those functions are initially defined within the Notebook itself, but the code is also stored in a Python file that is imported if needed in later chapters. I do document when I do this where the function is first defined. But this is still a work in progress." - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "License" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\"Creative
Kalman Filters and Random Signals in Python by Roger Labbe is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
\n", - "\n", - "Based on a work at https://github.com/rlabbe/Kalman-Filters-and-Random-Signals-in-Python." - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Contact" - ] - }, - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "Signals and Noise" - ] - }, - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "The g-h Filter" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Building Intuition via Thought Experiments" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Imagine that we live in a world without scales - the devices you stand on to weigh yourself. One day at work a coworker comes running up to you and announces her invention to you. After she explains, you eagerly stand on it and announce the results: \"172 lbs\". You are estatic - for the first time in your life you know what you weigh. More importantly, dollar signs dance in your eyes as you imagine selling this device to weight loss clinics across the world! This is fantastic!\n", - "\n", - "Another coworker hears the commotion and comes over to find out what has you so excited. You explain the invention and once again step onto the scale, and proudly proclaim the result: \"161 lbs.\" And then you hesitate, confused.\n", - "\n", - "\"What? It read 172 lbs just a few seconds ago\" you complain to your coworker. \n", - "\n", - "\"I never said it was accurate,\" she replies.\n", - "\n", - "Sensors are inaccurate. This is the motivation behind a huge body of work in filtering, and solving this problem is the topic of this book. I could just provide the solutions that have been developed over the last half century, but these solutions developed by asking very basic, fundamental questions into the nature of what we know and how we know it. Before we attempt the math, let's follow that journey of discovery, and see if it does not inform our intuition about filtering. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Try Another Scale\n", - "\n", - "Is there any way we can improve upon this result? The obvious, first thing to try is get a better sensor. Unfortunately, your co-worker informs you that she has built 10 scales, and they all operate with about the same accuracy. You have her bring out another scale, and you weigh yourself on one, and then on the other. The first scale (A) reads \"160lb\", and the second (B) reads \"170lbs\". What can we conclude about your weight?\n", - "\n", - "Well, what are our choices?\n", - "\n", - "* We could choose to only believe A, and assign 160lbs to our weight estimate. \n", - "* we could choose to only believe B, and assign 170lbs to our weight.\n", - "* We could choose a number less than either A or B\n", - "* We could choose a number greater than either A or B\n", - "* We could choose a number between A and B\n", - "\n", - "The first two choices are plausible, but we have no reason to favor one scale over the other. Why would we choose to believe A more than B? We have no reason for such a belief. The third and fourth choices are irrational. The scales are admittedly not very accurate, but there is no reason at all to choose a number outside of the range of what they measure. The final choice is the only reasonable one. If both scales are inaccurate, and as likely to give a result above my actual weight as below it, more often than not probably the answer is somewhere between A and B. \n", - "\n", - "In mathematics this concept is formalized as *expected value*, and we will cover it in depth later. For now ask youself what would be the 'usual' thing to happen if we made one million separate readings. Some of the times both scales will read too low, sometimes that will both read too high, and the rest of the time they will straddle the actual weight. If they straddle the actual weight then certainly we should choose a number between A and B. If they don't straddle then we don't know if they are both too high or low, but by choosing a number between A and B we at least mitigate the effect of the worst measurement. For example, suppose our actual weight is 180lbs. 160lbs is a big error. But if we choose a weight between 160lbs and 170lbs our estimate will be better than 160lbs. The same argument holds if both scales returned a value greater than the actual weight.\n", - "\n", - "We will deal with this more formally later, but for now I hope it is clear that our best estimate is just the average of A and B. $\\frac{160+170}{2} = 165$.\n", - "\n", - "Let's play 'what if' some more. What if we are now told that A is three times more accurate than B? Consider the 5 options we listed above. It still makes no sense to choose a number outside the range of A and B, so we will not consider those. It perhaps seems more compelling to choose A as our estimate - after all, we know it is more accurate, why not just use it instead of B? Can B possibly improve our knowledge over A alone?\n", - "\n", - "The answer, perhaps counterintuitively, is yes, it can. Consider this case. We know scale A is accurate to 1 lb. In other words, if we weight 170 lbs, it could report 169,170, or 171 lbs. We know that scale B is accurate to 9 lbs. We do a reading, and A=160, and B=170. What should we estimate our weight to be?\n", - "\n", - "Well, if we say 160 lbs we would be wrong, because B can only be 9 pounds off, and 170 lbs - 160 lbs is 10 lbs. 160 is not a possible measurement for B. In fact, the only number that satifies all of the constraints is 161 lbs. That is 1 lb within the reading of A, and 9 lbs within the reading of B. \n", - "\n", - "This is an important result. With two relatively inaccurate sensors we were able to deduce an extremely accurate result. Now sure, that was a specially constructed case, but it generalizes. What if A is accurate to 3 lbs, B is accurate to 11 lbs, and we get the measurements of A=160 lbs and B=170 lbs? The result can only be from 159 lbs to 163 lbs, which is better than the range of 157 lbs to 163 lbs that is the range of values that A alone allows.\n", - "\n", - "> So two sensors, even if one is less accurate than the other, is better than one.\n", - "\n", - "However, we have strayed from our problem. No customer is going to want to buy multiple scales, and besides, we initially started with an assumption that all scales were equally (in)accurate. \n", - "\n", - "So, what if I have one scale, but I weigh myself many times? We concluded that if we had two scales of equal accuracy we should average the results of their measurements. What if I weigh myself 1,000,000 times with one scale? We have already stated that the scale is equally likely to return a number too large as it is to return one that is too small. I will not prove it, but it can be proved that the average of a large number of weighings will be extremely close to my actual weight. Consider a simple case - the scale is accurate to within 1 lb. If I weigh 170, it will return one of 169, 170, and 171. The average of a bunch of 170 is 170, so we can exclude those. What is left is measurements of 169 and 171. But we know there will be as many 169s as there are 171s. The average of those will also be 170, and so the average of all must be 170, my true weight. It's not that hard to extend this to any arbitrary accuracy.\n", - "\n", - "Okay, great! We have an answer, and phone the UB Thin chain to tell them the good news. As you might expect, their CEO does not share your enthusiam. First, no one has the patience to weigh themselves a million, or even a hundred times. Second, people are far less interested in what they weigh than whether they are actually losing weight or not. \n", - "\n", - "So, let's play 'what if' again. What if you measured your weight once a day, and got the readings 170, 161, and then 169. Did you gain weight, lose weight, or is this all just noisy measurements? \n", - "\n", - "We really can't say. The first measurement was 170, and the last was 169, implying a 1 lb loss. But if the scale is only accurate to 10 lbs, that is explainable by noise - bad measurements. I could have actually gained weight; maybe my weight on day one was 165 lbs, and on day three it was 172. It is possible to get those weight readings with that weight gain. My scale tells me I am losing weight, and I am actually gaining weight! The CEO of UB Thin will not be interested in buying this.\n", - "\n", - "Shall we give up? No, let's play 'what if'. Suppose I take a different scale, and I get the following measurements: 169, 170, 169, 171, 170, 171, 169, 170, 169, 170. What does your intuition tell you? It is possible, for example, that you gained 1 lb each day, and the noisy measurements just happens to look like you stayed the same weight. Equally, you could have lost 1 lb a day and gotten the same readings. But is that likely? How likely is it to flip a coin and get 10 heads in a row? Not very likely. We can't prove it, but it seems pretty likely that my weight held steady.\n", - "\n", - "Another what if: what if the readings were 158.0, 164.2, 160.3, 159.9, 162.1, 164.6, 169.6, 167.4, 166.4, 171.0? Let's look at a chart of that and then answer some questions." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%matplotlib inline\n", - "from __future__ import division, print_function\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.pylab as pylab\n", - "import numpy as np\n", - "\n", - "# make all plots a reasonable size\n", - "pylab.rcParams['figure.figsize'] = 12,6\n", - "\n", - "weights = [158.0, 164.2, 160.3, 159.9, 162.1, 164.6, 169.6, 167.4, 166.4, 171.0]\n", - "plt.plot(weights)\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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eDle1yV0SORGDyYyXd5bgronhimmCRbERViilzv8oDXMSx6zEMCcxzpZTZ48Ja/aU4r7M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\n52zwYGw5WoeNv9Ri1Zx4eKtd5S6HHJz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- "text": [ - "" - ] - } - ], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Does it 'seem' likely that I lost weight and this is just really noisy data? Not really. Does it seem likely that I held the same weight? Again, no. This data trends upwards over time; not evenly, but definitely upwards. Lets look at that in a chart. We can't be sure, but that surely looks like a weight gain, and a signifant weight gain at that. Let's test this assumption with some more plots. It is easier to 'eyeball' data sometimes.\n", - "\n", - "So let's look at two hypothesis. First, let's assume we held the same weight. To get that number, we agreed that we should just average all the measurements. Let's look at that." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "ave = np.sum(weights) / len(weights)\n", - "plt.plot([0,9], [ave,ave], c='r')\n", - "plt.plot(weights, c='b')\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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RUu3/kRrmJI5ZiWFOYkItJ5MJGDNGi7ffNiMsrGpf66us9Hpg1iwzpk61IDk5HP/8Zxjs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\nr4kxG3wZ8z9myMkcWZkhJzPkZI6szJCTObKKHcc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- "text": [ - "" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That doesn't look very convincing. \n", - "\n", - "Now, let's assume we we gained weight. How much? I don't know, but numpy does! We just want to draw a line through the measurements that looks 'about' right. numpy has functions that will do this according to a rule called \"least squared fit\". Let's not worry about the details of that computation, and just plot the results." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "xs = range(len(weights))\n", - "line = np.poly1d(np.polyfit(xs, weights, 1))\n", - "plt.plot (xs, line(xs), c='r')\n", - "\n", - "plt.plot(weights, c='b')\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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SBKSk6PHCCw4WwQGgUgEvvuhEixYePPusAdu2afDGGyVoT/H5oN69GzqTCeqd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\nExEREZFUrtoIT58+HVFRUUhISKj6sYCAAHTo0AEdOnTA1KlTq3785Zd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- "text": [ - "" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This looks much better, at least to my eyes. It seems far more likely to be true that I gained weight than I didn't gain any weight. Did I actually gain 13 lbs? Who can say? That seems impossible to answer.\n", - "\n", - "\"But is it impossible?\" pipes up a coworker.\n", - "\n", - "Let's try something crazy. Let's assume that somehow I know I am gaining about one lb a day. It doesn't matter how I know that, right now, just assume I know it somehow. Maybe I am eating a 6000 calorie a day diet, which would result in such a weight gain. Maybe there is another way. Let's just see if we can make use of such information if it was available.\n", - "\n", - "The first measurement was 158. We have no way of knowing any different, so let's just accept that as our estimate. If our weight today is 158, what will it be tomorrow? Well, we think we are gaining weight at 1 lb/day, so our prediction is 159, like so:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import gh_internal\n", - "gh_internal.plot_estimate_chart_1()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Okay, but what good is this? Sure, we could just assume the 1 lb/day is accurate, and just predict our weight for 10 days, but then why use a scale at all if we don't incorporate its readings? So let's look at the next measurement." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "gh_internal.plot_estimate_chart_2()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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WAACgDUpPd2rVqpOKiam7SB4woEKLFhUpKYmbhQSDBgvkCRMm6J133vFsu1wuTZs2TU8/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\n+ZtF/uaQffCiQG4ih8Ohxx9/vMkFcmp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- "text": [ - "" - ] - } - ], - "prompt_number": 6 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here the measurement is in blue, the previous estimate (output of the filter) is black, and the estimate is red. So we have a problem. Our prediction doesn't match our measurement. But, that is what we expected, right?. If the prediction was always exactly the same as the measurement, it would not be capable of adding any information to the filter. \n", - "\n", - "> The key insight to this entire book follows. Read it carefully!\n", - "\n", - "So what do we do? If we only take data from the measurement than the prediction will not affect the result. If we only take data from the prediction then the measurement will be ignored. If this is to work we need to take some kind of blend of the prediction and measurement. \n", - "\n", - "Blending two values - this sounds a lot like the two scale problem earlier. Using the same reasoning as before we can see that the only thing that makes sense is to choose a number between the prediction and the measurement. For example, an estimate of 165 makes no sense, nor does 157. Our estimates should like between 159 and 164.2. \n", - "\n", - "Should it be half way? Maybe, but in general it seems like we might know that our prediction is very accurate or very inaccurate. Probably the accuracy of our prediction differs from the accuracy of the scale. Recall what we did when A was much more accurate than B - we scaled the answer to be closer to A than B. Let's look at that in a chart.\n", - "**ESTIMATE AND PREDICTION SHOULD NOT USE THE SAME SYMBOLOGY HAT**" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "gh_internal.plot_estimate_chart_3()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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lZXjwwQexbt26OgtkUTf92LlzJ6KiorB3717rGNLT0x3e0a0xY3Ulr8bq2LEj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\ncalUivHjx+PAgQMsjomIiIjqwAL5LtatWzesWrU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- "text": [ - "" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now tet's try a randomly chosen number: $\\frac{6}{10}$. Our estimate will be six tenth the measurement and the rest will be from the prediction. Let's just code that up and see the result. We have to take into account one other factor. Weight gain has units of lbs/time, so to be general we will need to add a time step $t$, which we will set to 1 (day)." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "w = 160\n", - "gain_rate = 1\n", - "time_step = 1\n", - "scale_factor = 6/10\n", - "\n", - "# store the filtered results\n", - "estimates = []\n", - "\n", - "# most filter literature uses 'z' for measurements\n", - "for z in weights: \n", - " # predict new position\n", - " w = w + gain_rate * time_step\n", - " \n", - " # update filter \n", - " w = w * (1-scale_factor) + z * scale_factor\n", - " estimates.append(w)\n", - "\n", - " \n", - "plt.xlim([0,10])\n", - "\n", - "p1, = plt.plot (estimates, 'b--')\n", - "p2, = plt.plot(weights, c='r')\n", - "p3, = plt.plot([0,9],[160,170],c='g')\n", - "plt.legend([p1,p2,p3], ['filter', 'measurements', 'actual'], 2)\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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D94ceIv377zE3bKh2OOVScXuK7zpSHBQURPXq1S2+Fh0dTWhoKAD79u2jefPm3HfffXh5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\nl99N8/II6NqVwmnTsERFld99SujCBQ2bNnmyZ4+OvXt1/PSTB6GhVp5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- "text": [ - "" - ] - } - ], - "prompt_number": 8 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That is pretty good! The blue dots, showing our estimates, are not a straight line, but they are straighter than the measurements and somewhat close to the trend line we created. Also, it seems to get better over time. This may strike you as silly; of course the data will look good if we assume the conclusion, that our weight gain is around 1 lb.\n", - "\n", - "But, what if? What if instead of just leaving the weight gain at 1 lb, we compute it from our estimate. In other words, our first estimate is 162.28. We started at 158; this implies a weight gain not of 1, but 4.28 lbs. Can we use this information somehow? It kind of seems plausible. After all, the weight measurement itself is based on a real world measurement of our weight, so there is useful information. Our estimate of our weight gain may not be perfect, but it is surely better than just guessing our gain is 1 lb. Data is better than a guess, even if it is noisy.\n", - "\n", - "So, should we just set the new gain/day to 4.28 lbs? Hmm, sounds like our same problem again. We have two numbers (the gain/day for yesterday and for today), and want to combine them somehow to get tomorrow's gain. Let's use our same tool, and the only tool we have so far - take a combination of the two. This time I will use another arbitrarily chosen number, $\\frac{2}{3}$." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy.random as random\n", - "w = 160\n", - "gain_rate = 1\n", - "time_step = 1\n", - "weight_scale = 6/10\n", - "gain_scale = 2/3\n", - "\n", - "estimates = []\n", - "\n", - "for z in weights:\n", - " # prediction step\n", - " w_prediction = w + gain_rate*time_step\n", - " gain_rate = gain_rate\n", - " \n", - " # update step\n", - " gain_rate = gain_rate * 1-gain_scale + ((z - w) * gain_scale/time_step)\n", - " w = w_prediction * (1-weight_scale) + (z * weight_scale)\n", - " \n", - " estimates.append(w)\n", - "\n", - "p1, = plt.plot (estimates, 'b--')\n", - "p2, = plt.plot(weights, c='r')\n", - "p3, = plt.plot([0,9],[160,170],c='g')\n", - "plt.legend([p1,p2,p3], ['filter', 'measurements', 'actual'], 2)\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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aNTMgIkJbqrH19i678Rw7VoNx40zXeD6uRQsDWrSwngX+RTu9bbm4Bbsu76rS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\nsRs26PDoo84S7ezqaae3Nd3XyLrJhVK4/vlP2MeMQejAgcj94guUx2b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- "text": [ - "" - ] - } - ], - "prompt_number": 9 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "I think this is starting to look really good. We used no methodology for choosing our scaling factors of $\\frac{6}{10}$ and $\\frac{2}{3}$ (actually, they are not particularly good choices for this problem), and we 'luckily' choose 1 lb/day as our initial guess for the weight gain, but otherwise all of the reasoning followed from very reasonable assumptions.\n", - "\n", - "One final point before we go on. In the prediction step I wrote the line\n", - "\n", - " gain_rate = gain_rate\n", - " \n", - "This obviously has no effect, and can be removed. I wrote this to emphasize that in the prediction step you need to predict next value for **all** state variables, both *weight* and *gain_rate*. In this case we are assuming that the the gain does not vary, but when we generalize this algorithm we will remove that assumption. " - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "The g-h Filter" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This algorithm is known as the g-h filter. *g* and *h* refer to the two scaling factors that we used in our example. *g* is the scaling we used for the measurement (weight in our example), and *h* is the scaling for the change in measurement over time (lbs/day in our example)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This filter is the basis for a huge number of filters, including the Kalman filter. In other words, the Kalman filter is a form of the g-h filter. So is the Least Squares filter, which you may have heard of, and so is the Benedict-Bordner filter, which you probably have not. Each filter has a different way of assigning values to *g* and *h*, but otherwise the algorithms are identical. For example, the $\\alpha$-$\\beta$ filter just assigns a constant to *g* and *h*, constained to a certain range of values. Other filters will vary *g* and *h* dynamically, and filters like the Kalman filter will vary them based on the number of dimensions in the problem.\n", - "\n", - "\n", - "**Let me repeat the key insights as they are so important**. If you do not understand these you will not understand the rest of the book. If you do understand them, then the rest of the book will unfold naturally for you as mathematically elaborations to various 'what if' questions we will ask about *g* and *h*.\n", - "\n", - "* Multiple measurements are more accurate than one measurement\n", - "* Always choose a number part way between two measurements to create a more accurate estimate\n", - "* Predict the next measurement based on the current estimate and how much we think it will change\n", - "* The new estimate is then chosen as part way between the prediction and next measurement\n", - "\n", - "Let's look at a visual depiction of the algorithm." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "gh_internal.create_predict_update_chart()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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Ro0c3+JjVpk2bhi1btuDo0aNmpa8KCgowb948AMDHH3+MoKAgrFixAqWlpfD3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\nuXOxYMECi1gmT56M33//HU8++STKy8sxa9Ysea7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Y+RcIUQ6HQ3l5eT2ub2xs1LFjxxQf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\n2t38ut1JSEhQQUGBCgoKOFcViHCEVgARKyoqSiNGjNCIESPU1tamQ4c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- "text": [ - "" - ] - } - ], - "prompt_number": 10 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "I'll begin to introduce the nomenclature and variable names used in the literature. Measurement is typically denoted $z$, and that is what we will use in this book (some literature uses $y$). Subscript $k$ indicates the time step, so $\\mathbf{z_k}$ is the data for this time step. A bold font denotes a vector. So far we have only consided having one sensor, and hence one sensor measurement, but in general we may have *n* sensors and *n* measurements. $\\mathbf{x}$ denotes our data, and is bold to denote that it is a vector. For example, for our scale example, it represents both the initial weight and inital weight gain rate, like so:\n", - "$$\\mathbf{x} = \\begin{bmatrix}x \\\\ \\dot{x}\\end{bmatrix}$$\n", - "\n", - "Finally, a hat '$\\hat{}$' indicates an *estimate*. So the output of the predict step time $k$ at is the *estimate* of our state $\\mathbf{\\hat{x}_k}$, and again this is a vector of all the state variables. So for our scale example it containes the estimate of both the weight and the gain rate.\n", - "\n", - "So, the algorithm is simple. The state is initialized with $\\mathbf{x_0}$. We then enter a loop, predicting the state for time $k$ from the values from time $k-1$. We then get the measurement $z_k$ and choose some intermediate point between the measurements and prediction, creating the estimate $\\mathbf{\\hat{x}_k}$." - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise: Write Generic Algorithm" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the example above, I explicitly coded this to solve the weighing problem that we've been discussing throughout the chapter. For example, the variables are named \"weight_scale\", \"gain\", and so on. I did this to make the algorithm easy to follow - you can easily see that we correctly implemented each step. But, this is specialized code. Rewrite it to work with any data. Use this function signature:\n", - "\n", - " def g_h_filter (data, x0, dx, g, h)\n", - " \"\"\"\n", - " Performs g-h filter on 1 state variable with a fixed g and h.\n", - "\n", - " 'data' contains the data to be filtered.\n", - " 'x0' is the initial value for our state variable\n", - " 'dx' is the initial change rate for our state variable\n", - " 'g' is the g-h's g scale factor\n", - " 'h' is the g-h's h scale factor\n", - " 'dt' is the length of the time step \n", - " \"\"\"\n", - "\n", - "Test it by passing in the same weight data as before, plot the results, and visually determine that it works." - ] - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Solution and Discussion" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def g_h_filter (data, x0, dx, g, h, dt=1., pred=None):\n", - " \"\"\"\n", - " Performs g-h filter on 1 state variable with a fixed g and h.\n", - " \n", - " 'data' contains the data to be filtered.\n", - " 'x0' is the initial value for our state variable\n", - " 'dx' is the initial change rate for our state variable\n", - " 'g' is the g-h's g scale factor\n", - " 'h' is the g-h's h scale factor\n", - " 'dt' is the length of the time step \n", - " 'pred' is an optional list. If provided, each prediction will\n", - " be stored in it\n", - " \"\"\"\n", - " \n", - " x = x0\n", - " results = []\n", - " for z in data:\n", - " #prediction step\n", - " x_est = x + (dx*dt)\n", - " dx = dx\n", - " \n", - " if pred is not None:\n", - " pred.append(x_est)\n", - " \n", - " # update step\n", - " residual = z - x_est\n", - " dx = dx + h * (residual) / dt\n", - " x = x_est + g * residual\n", - " \n", - " results.append(x)\n", - " \n", - " return results\n", - "\n", - "def plot_g_h_results (measurements, filtered_data, title=''): \n", - " p1, = plt.plot (filtered_data, 'b--')\n", - " p2, = plt.plot(measurements,c='r')\n", - " plt.legend((p1,p2), ('filter', 'measurement'), 4)\n", - " plt.title(title)\n", - " plt.show()\n", - "\n", - "\n", - "plt.xlim([0,10])\n", - "plt.plot([0,9],[160,170],c='g')\n", - "data = g_h_filter (data=weights, x0=160, dx=1, g=6./10, h = 2./3, dt=1.)\n", - "plot_g_h_results (weights, data)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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FO3Ze3ImErQl4JvkZ5P6ei6XdlmLvkL1IbJnosQWxMz8v/ObNg61VKx7rXQWU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- "text": [ - "" - ] - } - ], - "prompt_number": 11 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that I rewrote the equations somewhat:\n", - "\n", - "$$\\begin{aligned}\n", - "\\hat{x}_t &= gz + \\hat{x}_{t-1} (1-g) \\\\\n", - "&= gz + \\hat{x}_{t-1} - g\\hat{x}_{t-1} \\\\\n", - "&= \\hat{x}_{t-1} + g*(z-\\hat{x}_{t-1}) \\\\\n", - "&= \\hat{x}_{t-1} + g*residual\n", - "\\end{aligned}\n", - "$$\n", - "\n", - "I'll let you decide which form of the equation is more expressive. One form explicitly uses $g$ and $(1-g)$ to compute the point between the two values, the other finds the difference between the points, and adds a fraction of that to the first value. Both are computing the same thing. You'll see both forms in the literature, so I have used both to expose you to them. " - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Choice of g and h" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The g-h filter is not one filter - it is a classification for a family of filters. Eli Brookner in *Tracking and Kalman Filtering Made Easy* lists 11, and I am sure there are more. Not only that, but each type of filter has numerous subtypes. Each filter is differentiated by how g and h are chosen. So there is no 'one fits all' advice that I can give here. Some filters set g and h as constants, others vary them dynamically. The Kalman filter varies them dynamically at each step $k$ and bases it on the number of state variables there are in $\\mathbf{x}$. Some filters allow g and h to take any value within a range, others constrain one to be dependent on the other by some function $f(\\dot{}), \\mbox{where }g = f(h)$.\n", - "\n", - "The topic of this book is not the entire family of g-h filters; more importantly, we are interested in the *Bayesian* aspect of these filters, which I have not addressed yet. Therefore I will not cover selection of g and h in depth. Eli Brookner's book mentioned in the previous paragraph is an excellent resource for that topic, if it interests you. If this strikes you as an odd position for me to take, recognize that the typical formulation of the Kalman filter does not use g and h at all; the Kalman filter is a g-h filter because it mathematically reduces to this algorithm. When we design the Kalman filter we will be making a number of carefully considered choices to optimize it's performance, and those choices indirectly affect g and h. Don't worry if this is not too clear right now, it will be much clearer later after we develop the Kalman filter theory.\n", - "\n", - "However, it is worth seeing how varying g and h affects the results, so we will work through some examples. This will give us strong insight into the fundamental strengths and limitations of this type of filter, and help us understand the behavior of the rather more sophisticated Kalman filter." - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise: create measurement function" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's write a function that generates noisy data for us. Recall from chapter 0 that we model a noisy signal as the signal plus white noise generated by *numpy.random.randn()*. We want a function that we call with the starting value, the amount of change per step, the number of steps, and the amount of noise we want to add. It should return a list of the data. Test it by creating 30 points, filtering it with *g_h_filter()*, and plot the results with *plot_g_h_results()*" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# your code here" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 12 - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Solution" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def gen_data (x0, dx, count, noise_factor):\n", - " return [x0 + dx*i + random.randn()*noise_factor for i in range (count)]\n", - "\n", - "measurements = gen_data (0, 1, 30, 1)\n", - "data = g_h_filter (data=measurements, x0=0, dx=1, dt=1, g=.2, h=0.02)\n", - "plot_g_h_results (measurements, data)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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45pkkQqHY1KbszE35JRY1wCIiUvLl5pLUrx8pDz1E7iuv4B06FFJTC3RodjY8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\nGTMG58+fb3RMZmYmhgwZ4uypHbPZoDhwAKqdO6E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- "text": [ - "" - ] - } - ], - "prompt_number": 13 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise: Bad Initial Conditions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now write code that uses *gen_data* and *g_h_filter* to filter 100 data points that starts at 5, has a derivative of 2, a noise scaling factor of 10, and uses g=0.2 and h=0.02. Set you initial guess for x to be 100." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# your code here" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 14 - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Solution and Discussion" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "zs = gen_data (x0=5, dx=2, count=100, noise_factor=10)\n", - "data = g_h_filter (data=zs, x0=100., dx=2., dt=1.,g=0.2, h=0.01)\n", - "plot_g_h_results (measurements=zs, filtered_data=data)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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zz0I7382dW+TWzp7hw0m69FIcixfjz5esxLz5ZmhL6HIkx4Dl7ndl7qtOHfwd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\n5dIIefddbF9/TfasWXlPjp9+GntyMpmffopRp85lXVtEREREqr6UlBT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- "text": [ - "" - ] - } - ], - "prompt_number": 15 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The filter starts out with estimates that are far from the measured data due to the bad initial guess of 100. You can see that it 'rings' before settling in on the measured data. 'Ringing' means that the signal overshoots and undershoots the data in a sinusodial type pattern. This is a very common phenomena in filters, and a lot of work in filter design is devoted to minimizing ringing. That is a topic that we are not yet prepared to address, but I wanted to show you the phenomenon." - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise: Extreme Noise" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Rerun the same test, but this time use a noise factor of 100. Remove the initial condition ringing by changing the initial condition from 100 down to 5." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# your code here" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 16 - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Solution and Discussion" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "zs = gen_data (x0=5, dx=2, count=100, noise_factor=100)\n", - "data = g_h_filter (data=zs, x0=5., dx=2., g=0.2, h=0.02)\n", - "plot_g_h_results (measurements=zs, filtered_data=data)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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PwY9/bIPBoPA1GqLAmehauteyEnnpvi6YzQZRZgAKoN9bvfGg1bpgZrNUHpCd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\nIk9MFc5cY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- "text": [ - "" - ] - } - ], - "prompt_number": 17 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This doesn't look so wonderful to me. We can see that perhaps the filtered signal varies less than the noisey signal, but it is far from the straight line. If we were to plot just the filtered result no one would guess that the signal with no noise starts at 5 and increments by 2 at each time step. And while in locations the filter does seem to reduce the noise, in other places it seems to overshoot and undershoot.\n", - "\n", - "At this point we don't know enough to really judge this. We added **a lot** of noise; maybe this is as good as filtering can get. However, the existance of the multitude of chapters beyond this one should suggest that we can do much better than this." - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Excercise: The Effect of Acceleration" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Write a new data generation function that adds in a constant acceleration factor to each data point. In other words, increment dx as you compute each data point so that the velocity (dx) is ever increasing. Set the noise to 0, $g=0.2$ and $h=0.02$ and plot the results. Explain what you see." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# your code here" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 18 - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Solution and Discussion" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def gen_data (x0, dx, count, noise_factor, accel=0):\n", - " zs = []\n", - " for i in range (count):\n", - " zs.append (x0 + dx*i + random.randn()*noise_factor)\n", - " dx += accel\n", - " return zs\n", - " \n", - "predictions = []\n", - "zs = gen_data (x0=10, dx=0, count=20, noise_factor=0, accel = 2)\n", - "data = g_h_filter (data=zs, x0=10, dx=0, g=0.2, h=0.02, pred=predictions)\n", - "plt.xlim([0,20])\n", - "plot_g_h_results (measurements=zs, filtered_data=data)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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kHJWUEPPwwzgXLqTwlVfwdulidkSWs3u3jeHD4znlFINVq1zUq2eYHZJY3HFf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\nWpKTk6+6TPHx8R2eO3PmDLt37/b3EIOvt7ttOEV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- "text": [ - "" - ] - } - ], - "prompt_number": 19 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Each prediction lags behind the signal. If you think about what is happening this makes sense. Our model assumes that velocity is constant. The g-h filter computes the first derivative of $x$ (we use $\\dot{x}$ to denote the derivative) but not the second derivative $\\ddot{x}$. So we are assuming that $\\ddot{x}=0$. At each prediction step we predict the new value of x as $x + \\dot{x}*t$. But because of the acceleration the prediction must necessarily fall behind the actual value. We then try to compute a new value for $\\dot{x}$, but because of the $h$ factor we only partially adjust $\\dot{x}$ to the new velocity. On the next iteration we will again fall short.\n", - "\n", - "Note that there is no adjustment to $g$ or $h$ that we can make to correct this problem. This is called the *lag error* or *systemic error* of the system. It is a fundamental property of g-h filters. Perhaps your mind is already suggesting solutions or workarounds to this problem. As you might expect, a lot of research has been devoted to this problem, and we will be presenting various solutions to this problem in this book.\n", - "> The 'take home' point is that the filter is only as good as the mathematical model used to express the system. " - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise: Varying g" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's look at the effect of varying g. Before you perform this exercise, recall that g is the scale factor for choosing between the measurement and prediction. What do you think of a large value of g will be? A small value? \n", - "\n", - "Now, let the $noise\\_factor=50$ and $dx=5$. Plot the results of $g = 0.1\\mbox{, } 0.5,\\mbox{ and } 0.9$." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# your code here" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 20 - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Solution and Discussion" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "zs = gen_data (x0=5, dx=5, count=100, noise_factor=50)\n", - "\n", - "data = g_h_filter (data=zs, x0=0., dx=5., dt=1.,g=0.1, h=0.01)\n", - "plot_g_h_results (zs, data, 'g = 0.1')\n", - "\n", - "data = g_h_filter (data=zs, x0=0., dx=5., dt=1.,g=0.5, h=0.01)\n", - "plot_g_h_results (zs, data, 'g = 0.5')\n", - "\n", - "data = g_h_filter (data=zs, x0=0., dx=5., dt=1.,g=0.9, h=0.01)\n", - "plot_g_h_results (zs, data, 'g = 0.9')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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wahUQGwsYDLBdeqlfu86mf/8bqtmMpmXL2iTNLS3A6tVGLF0aaX99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E1cUU+8Jx277GxJmIiIj8mv34ZLGwUN/15eVQBg8GAOzbF9vqtMC2DAOjkBhS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WJSoKxvhkVP1dPVFX01rYiDMdzpW4fFUxcSYiIiKvJp47yEIsL3eqf0B7PQIz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\n", - "text": [ - "" - ] - } - ], - "prompt_number": 21 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It is clear that as $g$ is larger we more closely follow the measurement instead of the prediction. When $g=0.9$ we follow the signal almost exactly, and reject almost none of the noise. One might naively conclude that $g$ should always be very small to maximize noise rejection. However, that means that we are mostly ignoring the measurements in favor of our prediction. What happens when the signal changes not due to noise, but an actual state change? Let's look. I will create data that has $\\dot{x}=1$ for 9 steps before changing to $\\dot{x}=0$. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "zs = [5,6,7,8,9,10,11,12,13,14]\n", - "\n", - "for i in range(100):\n", - " zs.append(14)\n", - "\n", - "data = g_h_filter (data=zs, x0=4., dx=1., dt=1.,g=0.1, h=0.01)\n", - "plot_g_h_results (zs, data, 'g = 0.1')\n", - "\n", - "data = g_h_filter (data=zs, x0=4., dx=1., dt=1.,g=0.5, h=0.01)\n", - "plot_g_h_results (zs, data, 'g = 0.5')\n", - "\n", - "data = g_h_filter (data=zs, x0=4., dx=1., dt=1.,g=0.9, h=0.01)\n", - "plot_g_h_results (zs, data, 'g = 0.9')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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Pf6h8hhdAZAvpBTWcKjonR77Bg+0uwxZcb91sdud30UVeLV9epF/9qkx33FFf\np52WoH/8I0bemp9gcyS787NTUZFLS5ZE6fXXY/TII3G64456Ve6XkGBpxIgyTZ16QIsWFWvjxj3K\nzd1rexPs5OwiAfk5U41fLOcErs2b5dq1S+VHXU0PQPXUqydddZVXY8d6lZPj0T//GaOLLqITRtUO\nHJB69UpSSYlLHTqUq0MHv9q3L1efPlUv0ZeYKD6fAAQFoxFViJk5U9GffKL9L7xgdykAYKTiYmnt\n2qif39wV///002LVq+JE79atLiUnW4zRAKixOl1H2Ak8ubkqc+hYBFCXZs6M0aJFHl14oVf9+/vk\n4TuSUY63tu4ZZySqfn1LnTqVq1Mnvy64wKtOnfxVXuJXklq0iOx16QGEJ37sHMXyW9r7zmc6MOFu\nJdhdjE1YQsZsJuU3dGiZduxw6d5762nrVrfOPrtM553n1YABzm2KwzW/H35wa+XK/53hXbMmSmvW\nRGnOnL1KT6+8+sJXXxU77uxuuGaH6iE/Z+LFckfZOGe19quBErpzOTkg1Fq0sDRx4kHl5OzVBx/s\nVdu25XrwwXr6+muHdsE2273bpX37qr7v8cfj9NprMdqzx6WsLJ+mTDmgFSuKqmyCparPEgNAuGFG\n+CiLL39WnvXr1POrx+wuBcBRvvsuSl26lDv2bHEwff11lBYs8GjduiitW+fWunVRKi116fnn92nE\niKpfpAYA4YgZ4SBK/DpXBy66zO4yABylpET6v/9roB9/dCsz06e+fX3q18+n3r19Sky0u7rwUloq\n5ee7lZ8fpTZtynXKKZUvbrJyZZS2bXOrVy+fxozxq1OncjVvzovVADgLoxG/4N3n1SmF89X2miy7\nS7EVaymaLVLzq1dP+uKLYi1bVqTrrz+osjJp2rQ4ZWdH1jR/oPm98060zjknXt26Jaldu4a68sp4\nvfBCrDZurPq6wFdd5dWUKSW65hqvTjvNx4oNQRCpX3tOQX7OxBnhX9j6zlLF1O+kFh0a2V0KgGNo\n3NjSmWeW6cwzyyT9b+WCoy1dGqWHHqqntm39ateuXO3bH1qbtk0bvxo0qMOCA/Tjj24tWhSl7dvd\n2rrVrU2b3Nq82a1f/apMkyaVVto/La1ct91WqjZt/GrVys/4CABUAzPCvxA3ZYosr08H77/P7lIA\n1FJRkUuLF0cpPz9KP/zg/vlyvFFKT/fp2WcPVNp//Xq35s/3KCnJUlKSpcTEQ/82amSpSZPjf5u0\nLMnvl8oTaL6PAAAgAElEQVTKDv1bv37lfTZudGvBAo+Ki10qLnZpzx6Xdu1yKSOjXOPHH6y0/8cf\nezRzZqyaN/erRQu/Wrc+1OC2b+9Xs2YsNQYAhzEjHCTROTkquftuu8sAEARJSZaGD/dJqt4Lvw5d\n3tejoqJDjerhf08/vUxTp1a+dO/rr8fohhuOPLUcG2vp4ou9mj69cqO9ebNbubkeJSQcarKbN/cr\nLc3SKadUverCGWf4dMYZvGgNAEKJM8KHFRerYVqa9qxdK8XF2V2NrVhL0WzkVzcs69CbyxXcpcLI\nz1xkZzbyMxdnhIMgev58+Xr3dnwTDKB6gt0AAwDqHqtG/MyTm6uyoUPtLiMs8Bux2cjPbORnLrIz\nG/k5E43wz3z/ydHBgYPtLgMAAAB1hEZY0vYlW3Rwy0/yd0+3u5SwwFqKZiM/s5GfucjObOTnTDTC\nkja/PF+rWwyR28OHAwAAwCno/CTFfDZPJQOH2F1G2GBOymzkZzbyMxfZmY38nMnxjbDlt3Tyj/PU\ncuxpdpcCAACAOuT4RnjjnNU6EBWvFlmpdpcSNpiTMhv5mY38zEV2ZiM/Z3J8I9xwcY6K+w6xuwwA\nAADUMcc3wq1XzVO78YxF/BJzUmYjP7ORn7nIzmzk50zOboS9Xnm++kq+02iEAQAAnMbRjbBn8WKV\nd+okq3Fju0sJK8xJmY38zEZ+5iI7s5GfMzm7Ec7JUdlgriYHAADgRI5uhKNzcuSjEa6EOSmzkZ/Z\nyM9cZGc28nMmxzbCezcVq2zZapX17Wd3KQAAALCBYxvhDS8u0LcJp8pVL87uUsIOc1JmIz+zkZ+5\nyM5s5OdMjm2Eyz/M1Z5MxiIAAACcymVZlhWKA8+dO1cZGRmhOHRQ7G7eXz/97Vl1GJNudykAAAAI\n0NKlSzV8+PCAHusJci1G2LFks5r6CpU0Ks3uUgAAAGATR45GbHr5C61qOVRujyPf/RNiTsps5Gc2\n8jMX2ZmN/JzphJ3g5MmTlZycrPT0QyMEmzdv1sCBA9WtWzdlZmbqk08+CXmRwXbq/rlqP55lUgAA\nAJzshDPCCxYsUExMjK6++mrl5eVpx44d2r59u9LT01VQUKCsrCxt2rSp0uPCdkbYspR0yina+9FH\n8qem2l0NAAAAaiGkM8L9+/dXfn5+xXazZs3UrFkzSVJqaqq8Xq/KysoUHR0dUAF1zb1qlawGDWiC\nAQAAHK5WQ7IffvihMjMzjWmCJa4mVx3MSZmN/MxGfuYiO7ORnzMFvGrEtm3bNHnyZL3//vvH3OfG\nG29U6s9nXpOSkpSenl5xCcPDn3B1vf2r3FwdvOwy256fbbbZZpvtyNw+LFzqYZv8InU7Ly9PRUVF\nkqSCggKNGzdOgarWOsL5+fnKzs5WXl6eJKm0tFRnnHGG7r33Xo0YMaLKx4TjjLDvgFdNTu6o4uXL\nZTVqZHc5AAAAqKXazAjXeDTCsixdc801uuyyy47ZBIerdf/4Rqv8XWiCAQAAcOJGeMKECcrKytKa\nNWuUkpKihx9+WLNmzdKzzz6rXr16qVevXtq2bVtd1Fpr+97N1dauQ+0uI+wd/WcimIX8zEZ+5iI7\ns5GfM3lOtMOTTz6pJ5988ojb7r333pAVFErJefNUdNvddpcBAACAMFCtGeFAhNuM8N5NxWrUvZv2\n/bBGcQ3j7C4HAAAAQVCnM8Km2vDiAq1udCpNMAAAACQ5qBFuvDRHpQNYP7g6mJMyG/mZjfzMRXZm\nIz9nOuGMcKTouuVT7X/2WZXbXQgAAADCgiNmhF2bNilxyBAVrVkjuR1zEhwAACDiMSN8AtGffSbf\noEE0wQAAAKjgiM7Qk5OjssHMB1cXc1JmIz+zkZ+5yM5s5OdMkd8IW5aic3PlGzLE7koAAAAQRiL+\nxXLbPlkttxLkb9PG7lKMMXDgQLtLQC2Qn9nIz1xkZzbyc6aIPyO86aXPtbTRMLvLAAAAQJiJ+EY4\naVGOdDrzwTXBnJTZyM9s5GcusjMb+TlTRDfC3n1enbzrC7W9JsvuUgAAABBmInpG+IeZ3yixXme1\n7NDI7lKMwpyU2cjPbORnLrIzG/k5U0SfEd73bo62pg21uwwAAACEoYhuhPsWfaqUa06zuwzjMCdl\nNvIzG/mZi+zMRn7OFLmjEcXFSvxxlfyj+thdCQAAAMJQxJ4Rjp4/X77MTCkuzu5SjMOclNnIz2zk\nZy6yMxv5OVPENsKenByVDWU+GAAAAFWL2EY4OjdXvsGsHxwI5qTMRn5mIz9zkZ3ZyM+ZIrIRdm3a\nJNeuXSpPT7e7FAAAAISpiGyEl/7xSy1rPFRyR+S7F3LMSZmN/MxGfuYiO7ORnzNFZKcYnTtPP2UM\nsbsMAAAAhLGIa4Qtv6WTf5ynVlexfnCgmJMyG/mZjfzMRXZmIz9nirhGeOOc1doflaDk/ql2lwIA\nAIAwFnGNcOG/PteG9iybVhvMSZmN/MxGfuYiO7ORnzNFXCN80rJ50uksmwYAAIDji6xG2OtVj71f\nqMfE/nZXYjTmpMxGfmYjP3ORndnIz5kiqhH2LF6s8k6d5Gra2O5SAAAAEOYiqxHOyVEZV5OrNeak\nzEZ+ZiM/c5Gd2cjPmSKqEY7OyeGyygAAAKiWyGmEi4sVtXq1fP362V2J8ZiTMhv5mY38zEV2ZiM/\nZ/LYXUCw7HrrC7nT+khxcXaXAgAAAAOc8Izw5MmTlZycrPT09Irb3njjDXXu3FldunTRv//975AW\nWF0FL3yuRQnD7C4jIjAnZTbyMxv5mYvszEZ+znTCRvjCCy/UnDlzKra9Xq/uuOMOffHFF/rkk080\nceLEkBZYXW3XzlOjMYPsLgMAAACGOGEj3L9/fzVp0qRie+HChUpLS9NJJ52klJQUpaSkaPny5SEt\n8kR2LNmsJF+h2o1Ks7WOSMGclNnIz2zkZy6yMxv5OVONZ4S3bdumFi1aaMaMGWrcuLGSk5O1detW\n9ejRIxT1Vcuml7/QzpZDleaJnNf+AQAAILQCfrHcddddJ0l6++235XK5glZQIGI+y1HpAJZNCxbm\npMxGfmYjP3ORndnIz5lq3Ai3bNlSW7durdg+fIa4KjfeeKNSU1MlSUlJSUpPT6/4RDv8J4habw8Y\noF4/far/ZIzU/Pnzg398ttlmm2222WabbbbDZjsvL09FRUWSpIKCAo0bN06BclmWZZ1op/z8fGVn\nZysvL09er1cnn3yyFi5cqNLSUg0bNkxr166t9Ji5c+cqIyMj4MKqy71ypeKvvFLFS5aE/LmcYv78\n//1CAfOQn9nIz1xkZzbyM9fSpUs1fPjwgB7rOdEOEyZM0DvvvKPCwkKlpKToqaee0tSpUzVgwABJ\n0vTp0wN64mDhanIAAAAIRLXOCAeirs4Ix198sQ5edpnKzjsv5M8FAACA8FKbM8JmL7Pg9cqzYIF8\ng1g/GAAAADVjdCPsWbxY5Z06yWrUyO5SIsrhwXSYifzMRn7mIjuzkZ8zGd0Ifzv9c61JGWp3GQAA\nADCQ0Y1w/IIc/ZQ5xO4yIg6vmjUb+ZmN/MxFdmYjP2c64aoR4WrvpmK13f+d9l3Z2+5SAAAAYCBj\nzwhveHGBVjc6VXEN4+wuJeI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yxNEjDAAA0BcVFxersLAwqGv77Ixw5cqt2hE/niIYAAAAHeqzhTA7ygWPXimz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\nsHtzibx5eb39sgAAAMAxer0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H7RGWpC1btmjGjBkqLy/Xu+++q4ULF2rZsmWSpLPOOksPPvigxo5t3ZbQmT3C\n8UuWKHbDBh184IFOeT8AAABEj5PpEfZ2ZPDEiRO1bt06VVVVqa6uTtu2bQsogjtbbFmZGk8/PaJz\nAAAAgD3H7RGeO3euCgoKtGHDBmVmZur111/XwoUL9fWvf12FhYVatGhRZ82zXd6SEvnzeHRaKB37\nT0WwhfzsIjvbyM8usnOv414RXrx4sRYvXhzw+mWXXRa2CXXEwapaJW72yT9iRKSnAgAAAGNMryzn\nW7pen3QbJcWxolwoNTelwybys4vsbCM/u8jOvUwXwqwoBwAAgGCZLoTj15bKnzsu0tPocuiVso38\n7CI728jPLrJzL9OFcHpliXp/k0IYAAAAHfeVzxEORmc8R/hgVa16D8/W574KxSXSIwwAAOBGJ/Mc\nYbNXhD1la1WdMZIiGAAAAEExWwinbCpV72mRXcyjq6JXyjbys4vsbCM/u8jOvcwWwrFlZWocR38w\nAAAAgmO2EPaWlrKiXJjwPEXbyM8usrON/OwiO/eyWQjX1ipm61ZWlAMAAEDQTBbCseXlR4pgVpQL\nC3qlbCM/u8jONvKzi+zcy2Qh/NHTa/VpL9oiAAAAEDyThfDh98q0PY1COFzolbKN/OwiO9vIzy6y\ncy+ThfDASh6dBgAAgJNjrhA+WFWrQfWblXHO8EhPpcuiV8o28rOL7GwjP7vIzr3MFcK+petV0WMU\nK8oBAADgpJgrhGuWr9GuTPqDw4leKdvIzy6ys4387CI79/JGegIdNTHmQ1VfOjnS0wAAAIBx5q4I\n9/qkVCmF3CgXTvRK2UZ+dpGdbeRnF9m5l61CuHlFuZEjIz0TAAAAGGeqEGZFuc5Br5Rt5GcX2dlG\nfnaRnXuZKoS9ZWXyjxsX6WkAAACgCzBVCMeWlamRQjjs6JWyjfzsIjvbyM8usnMvU4XwtlfWaGtf\nHp0GAACAk+dxHMcJ9UGLioqUn58f0mMerKpV7+HZ+txXwWIaAAAAkCQVFxersLAwqH3NXBFmRTkA\nAACEkplCuGb5Gu3KoC2iM9ArZRv52UV2tpGfXWTnXmYK4fi1pfLncqMcAAAAQsNMIZy+o1S9p7Gi\nXGfgeYq2kZ9dZGcb+dlFdu7ljfQETkhtrYZ5Nmvv+cMjPRMAAAB0ESauCDevKBeTwI1ynYFeKdvI\nzy6ys4387CI79zJRCLOiHAAAAELNRCHMinKdi14p28jPLrKzjfzsIjv3MlEIe0tL5c/j0WkAAAAI\nnagvhOvswe/FAAAZ8UlEQVT31cqzZav8I0ZEeiquQa+UbeRnF9nZRn52kZ17RX0hvPnl9fooZpQU\nx41yAAAACJ2oL4RZUa7z0StlG/nZRXa2kZ9dZOdeUV8Ix68tlT+PG+UAAAAQWlFfCKdVlqr3NArh\nzkSvlG3kZxfZ2UZ+dpGde0X1ynIHq2qVUb9Zn5+dHempAAAAoIuJ6kL487+v06Heo5SayI1ynYle\nKdvIzy6ys4387CI794rqQjirqkSxF+boYKQnAgAAgC4nqnuEY8vK1JibG+lpuA69UraRn11kZxv5\n2UV27hXVhbC3pER+CmEAAACEgcdxHCfUBy0qKlJ+fv7JHaS2VinZ2dpfUcFiGgAAAGhTcXGxCgsL\ng9o3aq8Ix5aXH1lWmSIYAAAAYRB0IXznnXdq9OjRGj16tO66665QzkmStOMv5dp9Cm0RkUCvlG3k\nZxfZ2UZ+dpGdewVVCFdUVOipp55SeXm5SktL9cQTT2jr1q0hndiOP6/R2vjxIT0mAAAA0CyoQjgp\nKUndunXToUOHdOjQIcXFxSk5OTmkExtYWaLe08aG9Jg4MTxP0Tbys4vsbCM/u8jOvYJ6jnBqaqp+\n/OMfKzMzU01NTbr//vuVkpISskkdrKrVoPoKJbGiHAAAAMIkqCvCW7Zs0SOPPKKtW7dq06ZN+q//\n+i/t2LEjZJPyLV2vih6jFMeKchFBr5Rt5GcX2dlGfnaRnXsFdUV41apVmjhxonr16iVJysvLU0lJ\niaZPn94y5rrrrlNWVpYkKTk5WTk5OS3/9ND8B6697a0vv6VeqcOV/sWxvmo826HdLi8vj6r5sE1+\nbLPNNtvh3G4WLfNh+6vzWrFihXw+nyRp9uzZClZQzxH+4IMPNHv2bK1evVp+v1+5ublaunSphg8f\nLunknyN84F/nal9OgQbffXnQxwAAAEDXdzLPEfYGs9OECRN00UUXKS8vT5J09dVXtxTBoZC5s1h9\n7pojf8iOCAAAALQW9HOEb7/9dq1bt07r1q3T/PnzQzej2lrF+HxHFtNARBz7T0WwhfzsIjvbyM8u\nsnOvqFtZjhXlAAAA0BmirhD2lpXJP25cpKfhas1N6bCJ/OwiO9vIzy6yc6+oK4Rjy8rUmMvSygAA\nAAivqCuE971Rpr1D8iI9DVejV8o28rOL7GwjP7vIzr2iqhA+WFWrpL1bFJcXuidQAAAAAG2JqkKY\nFeWiA71StpGfXWRnG/nZRXbuFVWFcM3yNdqVQVsEAAAAwi+qCuG4taXy5/LEiEijV8o28rOL7Gwj\nP7vIzr2iqhBOryxRyjQKYQAAAISfx3EcJ9QHLSoqUn5+fsd2qq1Vr2HZ2rOxgh5hAAAAnJDi4mIV\nFhYGtW/UXBGOLS+XRo+gCAYAAECniJpCmBXloge9UraRn11kZxv52UV27hU1hXBsWZkaKYQBAADQ\nSaKmEPaWlsqfx6PTogHPU7SN/OwiO9vIzy6yc6/oKIRraxWzdav8I0ZEeiYAAABwiagohD99Yb02\ndx8lxXGjXDSgV8o28rOL7GwjP7vIzr28kZ6AJB342xrV9s1Tn0hPBAAAAK4RFVeE49aWyp/HjXLR\ngl4p28jPLrKzjfzsIjv3iopCeGBlqXoXjo30NAAAAOAiES+ED1bVKqN+szLOGR7pqeAL9ErZRn52\nkZ1t5GcX2blXxAvhfy5br4oeo1hRDgAAAJ0q4jfL5dQXy39hjvyRngha0CtlG/nZRXa2kZ9dZOde\nEb8i7F1TpthJ3CgHAACAzhX5Qri0VP7c3EhPA0ehV8o28rOL7GwjP7vIzr0iWwg3ryg3cmREpwEA\nAAD3iWghHFtefmRZZVaUiyr0StlGfnaRnW3kZxfZuVdEC+HGVWVqHEt/MAAAADpfRAvhtU+s1Yq6\n8ZGcAtpAr5Rt5GcX2dlGfnaRnXtFtBAeWFmqlKmsKAcAAIDO53Ecxwn1QYuKipSfn3/cMQeratV7\neLY+91WwmAYAAACCUlxcrMLCwqD2jdgVYd9SVpQDAABA5ESsEK5Zvka7MvIi9fY4DnqlbCM/u8jO\nNvKzi+zcK2KFcOqWUsVM5IkRAAAAiIyI9QgnFRSo9pFH5B/LzXIAAAAIjr0e4eYV5UaMiMjbAwAA\nABEphFlRLrrRK2Ub+dlFdraRn11k514RKYS9ZWXyj6M/GAAAAJETmSvCZWVqpBCOWqy5bhv52UV2\ntpGfXWTnXhEphA+vKFXdGB6dBgAAgMjp9EL4YFWtvNu2qnE4N8pFK3qlbCM/u8jONvKzi+zcq9ML\nYVaUAwAAQDTo9EK4Zvka7cqkLSKa0StlG/nZRXa2kZ9dZOdenV4Ix60tlT83t7PfFgAAAGil0wvh\n9MoS9Z7GanLRjF4p28jPLrKzjfzsIjv38nbmmzk1tcryV2j/2dmd+bYAAABAgE69IuxdW67YsSO4\nUS7K0StlG/nZRXa2kZ9dZOdenVsIs6IcAAAAokTQhfCqVas0duxYjRo1St/+9rdPaJ/YsjI1cqNc\n1KNXyjbys4vsbCM/u8jOvYLqEW5qatKsWbP0+OOPq6CgQHv27DmxNysp0eFrrgnmLQEAAICQCuqK\n8Icffqh+/fqpoKBAkpSamvrVO9XWKsbnk38EK8pFO3qlbCM/u8jONvKzi+zcK6hC2OfzKTk5WdOn\nT1d+fr4efvjhr9xn+6vrVNVvpBTHjXIAAACIvKAK4bq6Or377rt69NFHtXz5ci1atEgVFRXH3eez\nP5drXUJ+UJNE56JXyjbys4vsbCM/u8jOvYLqEU5LS9OoUaOUkZEhSRo/frw+/vhjDRkypGXMdddd\np6ysLElScnKyxn9QLP+UMyV9+Qeu+Z8i2I6u7fLy8qiaD9vkxzbbbLMdzu1m0TIftr86rxUrVsjn\n80mSZs+erWB5HMdxOrrTgQMHNHr0aJWXl6tnz54aP368XnrpJWVnH1koo6ioSPn5ra/+7k47Q9WL\nH9HQfxsT9GQBAACAoxUXF6uwsDCofb3B7JScnKxFixZp6tSpamho0OWXX95SBLflYFWtBtVXKIkV\n5QAAABAlgn6O8CWXXKKSkhKtXbtWt95663HH+pauV0WPUawoZ8Sx/1QEW8jPLrKzjfzsIjv3CuqK\ncEedsqdEh89gRTkAAABEj04phFO3lKrx3K+pvjPeDCetuSkdNpGfXWRnG/nZRXbuFXRrREd4S0rk\nZ2llAAAARJHwF8KsKGcOvVK2kZ9dZGcb+dlFdu4V9taI2PLyI0UwK8oBAIAw2rNnjw4fPtzh/VJT\nU1VZWRmGGSFU4uPjlZqaGvLjhr0Q9paVyT+OG+UsoVfKNvKzi+xsI7/IqqmpkSSlp6d3eN9g9kHn\n2rNnj2pqapSYmBjS44a9NWLdU2v1UU+WVgYAAOFz4MAB9enTJ9LTQJj06dNHBw4cCPlxw14Ip2zk\nRjlr6JWyjfzsIjvbyC+yPB6PPB5PpKeBMAlXvmFtjWBFOQAAAESrsF4RZkU5m+hzs4387CI728gP\nx/PYY4/ptNNOU1ZWlt55552W12+88Ub993//d6uxN998s7KystS3b18tX768s6fqKmEthGuWr9Gu\nzLxwvgUAAEBUa2ho0O23365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- "text": [ - "" - ] - } - ], - "prompt_number": 22 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we can see the effects of ignoring the signal. We not only filter out noise, but legitimate changes in the signal as well. \n", - "\n", - "Maybe we need a 'Godilocks' filter, where is not too large, not too small, but just right? Well, not exactly. As alluded to earlier, different filters choose g and h in different ways depending on the mathematical properties of the problem. For example, the Benedict-Bordner filter was invented to minimize the transient error in this example, where $\\dot{x}$ makes a step jump. We will not discuss this filter in this book, but here are two plots chosen with different allowable pairs of g and h. This filter design minimizes transient errors for step jumps in $\\dot{x}$ at the cost of not being optimal for other types of changes in $\\dot{x}$." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "zs = [5,6,7,8,9,9,9,9,9,10,11,12,13,14,15,16,16,16,16,16,16,16,16,16,16,16]\n", - " \n", - "data = g_h_filter (data=zs, x0=4., dx=1., dt=1.,g=.302, h=0.054)\n", - "plot_g_h_results (zs, data, 'g = 0.302, h = 0.054')\n", - "\n", - "data = g_h_filter (data=zs, x0=4., dx=1., dt=1.,g=.546, h=0.205)\n", - "plot_g_h_results (zs, data, 'g = 0.546, h = 0.205')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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RkY/YuVODgwc1OHmyZCX3xAk1/P1FbNyYh1atKv7q/ZNPKlnGJKcLDgZGjy6s9vlFRcDl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\n7rdo0SJmzJiBw+Fg0aJF7mtPPvkkMTExADz88MM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SFWExznIKNzk5WQkJCUpKSip2+ZNPPimHw6Fx48aVuM0nn3yijh07VqgQAED5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\nMhYtkiIifF0SAABAjUbz6yM/b0qW7ep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- "text": [ - "" - ] - } - ], - "prompt_number": 23 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Varying h" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's leave g unchanged and investigate the effect of modifying h. We know that h affects how much of we favor the measurement of $\\dot{x}$ vs our predictiton. But what does this *mean*? If our signal is changing a lot (quickly relative to the time step of our filter), then a large $h$ will cause us to react to those transient changes rapidly. A smaller $h$ will cause us to react more slowly.\n", - "\n", - "We will look at three examples. We have a noiseless measurement that slowly goes from 0 to 1 in 50 steps. Our first filter uses a nearly correct initial value for $\\dot{x}$ and a small $h$. You can see from the output that the filter output is very close to the signal. The second filter uses the very incorrect guess of $\\dot{x}=2$. Here we see the filter 'ringing' until it settles down and finds the signal. The third filter uses the same conditions but it now sets $h=0.5$. If you look at the amplitude of the ringing you can see that it is much smaller than in the second chart, but the frequency is greater. It also settles down a bit quicker than the second filter, though not by much." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "zs = np.linspace(0,1,50)\n", - "\n", - "data = g_h_filter (data=zs, x0=0, dx=0., dt=1.,g=.2, h=0.05)\n", - "plot_g_h_results (zs, data, 'dx=0, h = 0.05')\n", - "\n", - "data = g_h_filter (data=zs, x0=0, dx=2., dt=1.,g=.2, h=0.05)\n", - "plt.ylim([-1,5])\n", - "plot_g_h_results (zs, data, 'dx=2, h = 0.05')\n", - "\n", - "data = g_h_filter (data=zs, x0=0, dx=2., dt=1.,g=.2, h=0.5)\n", - "plt.ylim([-1,5])\n", - "plot_g_h_results (zs, data, 'dx=2, h = 0.5')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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Hl+rEdT3U8OUJMnXvrDN79ip32rRy2xRLrBgDAACgiMw//6yMfyQqZMVS1a7dUTnPPqfa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\nsM672vXOnPlR06ad0uDBbbz/fDgu9eN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4xw42OEDT2WxsjwwAAAIvoA3w/fez/AN8HS67ddppbu3YYdcXXziCNCKEAzJ9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\nrbLl6FCzooGRna3qbt1ktG2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L0vW/vWn69rtI3XlnqU45pe61IRedadOMtoYc1d1KmVau3KPk5G5+/TGg9SED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\n7NkunXlmaDfAZLdghrqAGeoCZqgLWIE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- "text": [ - "" - ] - } - ], - "prompt_number": 24 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Summary" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "I encourage you to experiment with this filter to develop your understanding of how it reacts. It shouldn't take too many attempts to come to the realization that ad-hoc choices for $g$ and $h$ do not perform very well. A particular choice might perform well in one situation, but very poorly in another. Even when you understand the effect of $g$ and $h$ it can be difficult to choose proper values. In fact, it is extremely unlikely that you will choose values for $g$ and $h$ that is optimal for any given problem. Filters are *designed*, not selected *ad hoc*. \n", - "\n", - "In some ways I do not want to end the chapter here, as there is a significant amount that we can say about selecting $g$ and $h$. But the g-h filter in this form is not the purpose of this book. Designing the Kalman filter requires you to specify a number of parameters - indirectly they do relate to choosing $g$ and $h$, but you will never refer to them direcly when designing Kalman filters. Furthermore, due to your choices $g$ and $h$ will vary at every time step in a very non-obvious manner. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There is another feature of these filters we have barely touched upon - Bayesian statistics. You will note that the term 'Bayesian' is in the title of this book; this is not a coincidence! For the time being we will leave $g$ and $h$ behind, largely unexplored, and develop a very powerful form of probabilistic reasoning about filtering. Yet suddenly this same g-h filter algorithm will appear, this time with a formal mathematical edifice that allows us to create filters from multiple sensors, to accurately estimate the amount of error in our solution, and to control robots." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "missing: properties - recursive - markov - random variable\n", - "should we fit bayes into here, or just leave to the next chapter\n", - "chart showing recursive loop\n" - ] - }, - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "Discrete Bayes Filter" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Kalman filter belongs to a family of filters called *bayesian filters*. Without going into\n", - "\n", - "blah blah" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Tracking a Dog" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us begin with a simple problem. We have a dog friendly workspace, and so people bring their dogs to work. However, occasionally the dogs wander out of your office and down the halls. We want to be able to track them. So during a hackathon somebody created a little sonar sensor to attach to the dog's collar. It emits a signal, listens for the echo, and based on how quickly an echo comes back we can tell whether the dog is in front of an open doorway or not. It also senses when the dog walks, and reports in which direction the dog has moved. It connects to our network via wifi and sends an update once a second.\n", - "\n", - "I want to track my dog Simon, so I attach the device to his collar and then fire up Python, ready to try to write code to track him through the building. At first blush this may appear impossible. If I start listening to the sensor of Simon's collar I might read 'door', 'hall', 'hall', and so on. How can I use that information to determine where Simon is?\n", - "\n", - "To keep the problem small, we will assume that there are only 10 positions in a single hallway to consider, which we will number 0 to 9, where 1 is to the right of 0, 2 is to the right of 1, and so on. For reasons that will be clear later, we will also assume that the hallway is circular or rectangular. If you move right from position 9, you will be at position 0. \n", - "\n", - "When I begin listening to the sensor I have no reason to believe that Simon is at any particular position in the hallway. He is equally likely to be in any position. The probability that he is in each position is therefore $1/10$ \n", - "\n", - "Let us represent our belief of his position at any time in a numpy array." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "\n", - "pos = np.array([.1, .1, .1, .1, .1, .1, .1, .1, .1, .1])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's create a map of the hallway in another list. Suppose rehere are first two doors close together, and then another door quite a bit further down the hallway. We will use 1 to denote a door, and 0 to denote a wall:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "hallway = np.array([1, 1, 0, 0, 0, 0, 0, 0, 1, 0])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So I start listening to Simon's transmissions on the network, and the first data I get from the sesnor is \"door\". From this I conclude that he is in front of a door, but which one? I have no idea. I have no reason to believe is in front of the first, second, or third door. But what I can do is assign a probability to each door. All doors are equally likely, so I assign a probability of 1/3 to each door. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from __future__ import print_function, division\n", - "import matplotlib.pyplot as plt\n", - "import bar_plot\n", - "import numpy as np\n", - "\n", - "pos = np.array([0.333, 0.333, 0., 0., 0., 0., 0., 0., 0.333, 0.])\n", - "bar_plot.plot (pos)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We call this a multimodal distribution because we have multiple beliefs about the position of our dog. Of course we are not saying that we think he is simultaneously in three different locations, merely that so far we have narrowed down our knowledge in his position to these locations. \n", - "\n", - "I hand coded the pos array in the code above. How would we implement this in code? Well, hallway represents each door as a 1, and wall as 0, so we will multiply the hallway variable by the percentage, like so;" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pos = hallway * 0.3\n", - "print('pos =', pos)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "pos = [ 0.3 0.3 0. 0. 0. 0. 0. 0. 0.3 0. ]\n" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Extracting Information from Multiple Sensor Readings" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's put Python aside and think about the problem a bit. Suppose we were to read the following from Simon's sensor:\n", - "\n", - " * door\n", - " * move right\n", - " * door\n", - " \n", - "\n", - "Can we deduce where Simon is at the end of that sequence? Of course! Given the hallway's layout there is only one place where you can be in front of a door, move once to the right, and be in front of another door, and that is at the left end. Therefore we can confidently state that Simon is in front of the second doorway. If this is not clear, suppose Simon had started at the second or third door. After moving to the right, his sensor would have returned 'wall'. Therefore the only possibility is that he is now in front of the second door. We denote this in Python with:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pos = np.array([0,1,0,0,0,0,0,0,0,0])\n", - "print(pos)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[0 1 0 0 0 0 0 0 0 0]\n" - ] - } - ], - "prompt_number": 6 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Obviously I carefully constructed the hallway layout and sensor readings to give us an exact answer quickly. Real problems will not be so clear cut. But this should trigger your intuition - the first sensor reading only gave us very low probabilities (0.333) for Simon's location, but after a position update and another sensor reading we knew much more about where he is. You might suspect, correctly, that if you had a very long hallway with a large number of doors that after several sensor readings and positions updates we would either be able to know where Simon was, or have the possibilities narrowed down to a small number of possibilities. For example, suppose we had a long sequence of \"door, right, door, right, wall, right, wall, right, door, right, door, right, wall, right, wall, right, wall, right, wall, right, door\". Simon could only be located where we had a sequence of [1,1,0,0,1,1,0,0,0,0,1] in the hallway. There might be only one match for that, or at most a few. Either way we will be far more certain about his position then when we started.\n", - "\n", - "We could work through the code to implement this solution, but instead let us consider a real world complication to the problem." - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Noisy Sensors" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Unfortunately I have yet to come across a perfect sensor. Perhaps the sensor would not detect a door if Simon sat in front of it while scratching himself, or it might report there is a door if he is facing towards the wall, not down the hallway. So in practice when I get a report 'door' I cannot assign 1/3 as the probability for each door. I have to assign something less than 1/3 to each door, and then assign a small probability to each blank wall position. At this point it doesn't matter exactly what numbers we assign; let us say that the probably of 'door' being correct is 0.6, and the probability of being incorrect is 0.2, which is another way of saying it is about 3 times more likely to be right than wrong. How would we do this?\n", - "\n", - "At first this may seem like an insurmountable problem. If the sensor is noisy it casts doubt on every piece of data. How can we conclude anything if we are always unsure?\n", - "\n", - "The key, as with the problem above, is probabilities. We are already comfortable with assigning a probabilistic belief about the location of the dog; now we just have to incorporate the additional uncertainty caused by the sensor noise. Say we think there is a 50% chance that our dog is in front of a specific door and we get a reading of 'door'. Well, we think that is only likely to be true 0.6 of the time, so we multiply: $0.5 * 0.6= 0.3$. Likewise, if we think the chances that our dog is in front of a wall is 0.1, and the reading is 'door', we would multiply the probability by the chances of a miss: $0.1 * 0.2 = 0.02$.\n", - "\n", - "However, we more or less chose 0.6 and 0.2 at random; if we multiply the pos array by these values the end result will no longer represent a true probability distribution. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def measure (pos, measure, p_hit, p_miss):\n", - " q = np.array(pos, dtype=float)\n", - " for i in range(len(hallway)):\n", - " if hallway[i] == measure:\n", - " q[i] = pos[i] * p_hit\n", - " else:\n", - " q[i] = pos[i] * p_miss\n", - " return q\n", - "\n", - "pos = np.array([0.2]*10)\n", - "reading = 1 # 1 is 'door'\n", - "pos = measure (pos, 1, .6, .2)\n", - "\n", - "print(pos)\n", - "print('sum =', sum(pos))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[ 0.12 0.12 0.04 0.04 0.04 0.04 0.04 0.04 0.12 0.04]\n", - "sum = 0.64\n" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that this is not a probability distribution because it does not sum to 1.0. But we can see that the code is doing mostly the right thing - the doors are assigned a number (0.12) that is 3 times higher than the walls (0.04). So we can write a bit of code to normalize the result so that the probabilities correctly sum to 1.0." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def normalize (p):\n", - " s = sum(p)\n", - " for i in range (len(p)):\n", - " p[i] = p[i] / s\n", - " \n", - "def sense (pos, measure, p_hit, p_miss):\n", - " q = np.array(pos, dtype=float)\n", - " for i in range(len(hallway)):\n", - " if hallway[i] == measure:\n", - " q[i] = pos[i] * p_hit\n", - " else:\n", - " q[i] = pos[i] * p_miss\n", - " normalize(q)\n", - " return q\n", - "\n", - "\n", - "pos = np.array([0.2]*10)\n", - "reading = 1 # 1 is 'door'\n", - "pos = sense (pos, 1, .6, .2)\n", - "\n", - "print('sum =', sum(pos))\n", - "print('probability of door =', pos[0])\n", - "print('probability of wall =', pos[2])" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "sum = 1.0\n", - "probability of door = 0.1875\n", - "probability of wall = 0.0625\n" - ] - } - ], - "prompt_number": 8 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Normalization is done by dividing each element by the sum of all elements in the list. If this is not clear you should spend a few minutes proving it to yourself algebraically. We can see from the output that the sum is now 1.0, and that the probability of a door vs wall is still three times larger. The result also fits our intuitiion that the probability of a door must be less than 0.333, and that the probability of a wall must be greater than 0.0. Finally, it should fit our intuition that we have not yet been given any information that would allow us to distinguish between any given door or wall position, so all door positions should have the same value, and the same should be true for wall positions. " - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Incorporating Movement Data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Recall how quickly we were able to find an exact solution to our dog's position when we incorporated a series of measurements and movement updates. However, that occured in a fictional world of perfect sensors. Might we be able to find an exact solution even in the presense of noisy sensors?\n", - "\n", - "Unfortunately, the answer is no. Even if the sensor readings perfectly match an extremely complicated hallway map we could not say that we are 100% sure that the dog is in a specific position - there is, after all, the possibility that every sensor reading was wrong! Naturally, in a more typical situation most sensor readings will be correct, and we might be close to 100% sure of our answer, but never 100% sure. This may seem head-spinningly complicated, but lets just go ahead and program the math, which as we have seen is quite simple.\n", - "\n", - "First let's deal with the simple case - assume the movement sensor is perfect, and it reports that the dog has moved one space to the right. How would we alter our pos array?\n", - "\n", - "I hope after a moment's thought it is clear that we should just shift all the values one space to the right. If we previously thought there was a 50% chance of simon being at position 3, then after the move to the right we should believe that there is a 50% chance he is at position 4. So let's implement that. Recall that the hallway is circular, so we will use modulo arithmetic to perform the shift correctly" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy\n", - "def perfect_update(pos, move):\n", - " \"\"\" move the position by 'move' spaces, where positive is to the right, and negative\n", - " is to the left\n", - " \"\"\"\n", - " n = len(pos)\n", - " result = np.array(pos, dtype=float)\n", - " for i in range(n):\n", - " result[i] = pos[(i-move) % n]\n", - " return result\n", - " \n", - "pos = np.array([.4, .1, .2, .3])\n", - "print('pos before update =', pos)\n", - "pos = perfect_update(pos, 1)\n", - "print('pos after update =', pos)\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "pos before update = [ 0.4 0.1 0.2 0.3]\n", - "pos after update = [ 0.3 0.4 0.1 0.2]\n" - ] - } - ], - "prompt_number": 9 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that we correctly shifted all values one position to the right, wrapping from the end of the array back to the beginning." - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Adding Noise to the Update" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We want to solve real world problems, and we have already stated that all sensors have noise. Therefore the code above must be wrong. What if the sensor reported that our dog moved one space, but he actually moved two spaces, or zero? Once again this may initially sound like an insummountable problem, but let's just model it in math. Since this is just an example, we will create a pretty simple noise model for the sensor - later in the book we will handle far more sophisticated errors.\n", - "\n", - "We will say that when the sensor sends a movement update, it is 80% likely to be right, and it is 10% likely to overshoot one position to the right, and 10% likely to undershoot to the left. That is, if we say the movement was 4 (meaning 4 spaces to the right), the dog is 80% likely to have moved 4 spaces to the right, 10% to have moved 3 spaces, and 10% to have moved 5 spaces.\n", - "\n", - "This is slightly harder than the math we have done so far, but it is still tractable. Each result in the array now needs to incorporate probabilities for 3 different situations. For example, consider position 9 for the case where the reported movement is 2. It should be clear that after the move we need to incorporate the probability that was at position 7 (9-2). However, there is a small chance that our dog actually moved from either 1 or 3 spaces away due to the sensor noise, so we also need to use positions 6 and 8. How much? Well, we have the probabilities, so we can just multiply and add. It would be 80% of position 7 plus 10% of position 6 and 10% of position 8! Let's try coding that:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def update (pos, move, p_correct, p_under, p_over):\n", - " n = len(pos)\n", - " result = np.array(pos, dtype=float)\n", - " for i in range(n):\n", - " result[i] = \\\n", - " pos[(i-move) % n] * p_correct + \\\n", - " pos[(i-move-1) % n] * p_over + \\\n", - " pos[(i-move+1) % n] * p_under \n", - " return result\n", - "\n", - "p = np.array([0,0,0,1,0,0,0,0])\n", - "res = update (p, 2, .8, .1, .1)\n", - "print(res)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[ 0. 0. 0. 0. 0.1 0.8 0.1 0. ]\n" - ] - } - ], - "prompt_number": 10 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The simple test case that we ran appears to work correctly. We initially believed that the dog was in position 3 with 100% certainty; after the movement update we now give an 80% probability to the dog being in position 5, and a 10% chance to undershooting to position 4, and a 10% chance of overshooting to position 6. Let us look at a case where we have multiple beliefs:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "p = np.array([0, 0, .4, .6, 0, 0, 0, 0])\n", - "res = update (p, 2, .8, .1, .1)\n", - "print(res)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[ 0. 0. 0. 0.04 0.38 0.52 0.06 0. ]\n" - ] - } - ], - "prompt_number": 11 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here the results are more complicated, but you should still be able to work it out in your head. The 0.04 is due to the possibility that the 0.4 belief undershot by 1. The 0.38 is due to the following: the 80% chance that we moved 2 positions $(.4 * .8)$ and the 10% chance that we undershot $(.6*.1)$. Overshooting plays no role here because if we overshot both .4 and .6 would be past this position. **I strongly suggest working some examples until all of this is very clear, as so much of what follows depends on understanding this step.**\n", - "\n", - "If you look at the probabilities after performing the update you probably feel dismay. In the example above we started with probabilitys of .4 and .6 in two fields; after performing the update the probabilities are not only lowered, but they are strewn out across the map." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "bar_plot.plot (res)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 12 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is not a coincidence, or the result of a carefully chosen example - it is always true of the update step. This is inevitable; if our sensor is noisy we will lose a bit of information on every update. Suppose we were to perform the update an infinite number of times - what would the result be? If we lose information on every step, we must eventually end up with no information at all, and our probabilities will be equally distributed across the pos array. Let's try this with say 500 iterations.\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pos = [1.0,0,0,0,0,0,0,0,0,0]\n", - "for i in range (500):\n", - " pos = update(pos, 1, .8, .1, .1)\n", - "print(pos)\n", - "bar_plot.plot(pos)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[ 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]\n" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 13 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After 500 iterations we have lost all information, even though we were 100% sure that we started in position 1. Feel free to play with the numbers to see the effect of different number of updates. For example, after 100 updates we have a small amount of information left.\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pos = [1.0,0,0,0,0,0,0,0,0,0]\n", - "for i in range (100):\n", - " pos = update(pos, 1, .8, .1, .1)\n", - "print(pos)\n", - "bar_plot.plot(pos)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[ 0.10407069 0.10329322 0.10125784 0.09874205 0.09670682 0.09592945\n", - " 0.09670682 0.09874205 0.10125784 0.10329322]\n" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 14 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Integrating Measurements and Movement Updates" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The problem of loosing information during an update may make it seem as if our system would quickly devolve into no knowledge. However, recall that our process is not an endless series of updates, but of *measure->update->measure->update->measure->update...* The output of the measure step is fed into the update. The update step, with a degraded certainty, is then fed into the measure step. \n", - "\n", - "Let's think about this intuitively. After the first measure->update round we have degraded the knowledge we gained by the measurement by a small amount. But now we take another measurement. When we try to incorporate that new measurement into our belief, do we become more certain, less certain, or equally certain. Consider a simple case - you are sitting in your office. A co-worker asks another co-worker where you are, and they report \"in his office\". You keep sitting there while they ask and answer \"has he moved\"? \"No\" \"Where is he\" \"In his office\". Eventually you get up and move, and lets say the person didn't see you move. At that time the questions will go \"Has he moved\" \"no\" (but you have!) \"Where is he\" \"In the kitchen\". Wow! At that moment the statement that you haven't moved conflicts strongly with the next measurement that you are in the kitchen. If we were modelling these with probabilities the probability that you are in your office would lowever, and the probability that you are in the kitchen would go up a little bit. But now imagine the subsequent conversation: \"has he moved\" \"no\" \"where is he\" \"in the kitchen\". Pretty quickly the belief that you are in your office would fade away, and the belief that you are in the kitchen would increase to near certainty. The belief that you are in the office will never go to zero, nor will the belief that you are in the kitchen ever go to 1.0 because of the chances of error, but in practice your co-workers would be correct to be quite confident in their system.\n", - "\n", - "That is what intuition tells us. What does the math tell us?\n", - "\n", - "Well, we have already programmed the measure step, and we have programmed the update step. All we need to do is feed the result of one into the other, and we will have programmed our dog tracker!!! Let's see how it performs. We will input data as if the dog started at position 0 and moved right at each update. However, as in a real world application, we will start with no knowledge and assign equal probability to all positions. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "p = np.array([.1]*10)\n", - "p = sense(p, 1, .6, .2)\n", - "print(p)\n", - "p = update(p, 1, .8, .1, .1)\n", - "print(p)\n", - "bar_plot.plot(p)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[ 0.1875 0.1875 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.1875\n", - " 0.0625]\n", - "[ 0.0875 0.175 0.175 0.075 0.0625 0.0625 0.0625 0.0625 0.075\n", - " 0.1625]\n" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 15 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So after the first sense we have assigned a high probability to each door position, and a low probability to each wall position. The update step shifted these probabilities to the right, smearing them about a bit. Now lets look at what happens at the next sense." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "p = sense(p, 1, .6, .2)\n", - "print(p)\n", - "bar_plot.plot(p)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[ 0.15671642 0.31343284 0.10447761 0.04477612 0.03731343 0.03731343\n", - " 0.03731343 0.03731343 0.13432836 0.09701493]\n" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 16 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice the tall bar at position 1. This corresponds with the (correct) case of starting at position 0, sensing a door, shifting 1 to the right, and sensing another door. No other positions make this set of observations as likely. Now lets add an update and then sense the wall." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "p = update(p, 1, .8, .1, .1)\n", - "p = sense(p, 0, .6, .2)\n", - "bar_plot.plot(p)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 17 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is exciting! We have a very prominent bar at position 2 with a value of around 35%. It is over twice the value of any other bar in the plot, and is about 4% larger than our last plot, where the tallest bar was around 31%. Let's see one more sense->update cycle." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "p = update(p, 1, .8, .1, .1)\n", - "p = sense(p, 0, .6, .2)\n", - "bar_plot.plot(p)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 18 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here things have degraded a bit due to the long string of wall positions in the map. We cannot be as sure where we are when there is an undifferentiated line of wall positions, so naturally our probabilities spread out a bit." - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "The Effect of Bad Sensor Data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You may be suspicious of the results above because I always passed correct sensor data into the functions. However, we are claiming that this code implements a *filter* - it should filter out bad sensor measurements. Does it do that?\n", - "\n", - "To make this easy to program and visualize I will change the layout of the hallway to mostly alternating doors and hallways:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "hallway = [1,0,1,0,0,1,0,1,0,0]\n", - "pos = np.array([.1]*10)\n", - "measurements = [1,0,1,0,0]\n", - "\n", - "for m in measurements:\n", - " pos = sense(pos, m, .6, .2)\n", - " pos = update(pos, 1, .8, .1, .1)\n", - "bar_plot.plot(pos)\n", - "print(pos)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[ 0.2245871 0.06288015 0.06109133 0.0581008 0.09334062 0.2245871\n", - " 0.06288015 0.06109133 0.0581008 0.09334062]\n" - ] - } - ], - "prompt_number": 19 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "At this point we have correctly identified the likely cases, we either started at position 0 or 5, because we saw the following sequence of doors and walls 1,0,1,0,0. But now lets inject a bad measurement, and see what happens:\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pos = sense(pos, m, .6, .2)\n", - "pos = update(pos, 1, .8, .1, .1)\n", - "bar_plot.plot(pos)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 20 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That one bad measurment appears to have significantly eroded our knowledge. However, note that our highest probabilities are still at 0 and 5, which is correct. Now let's continue with a series of correct measurements" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "measurements = [0,1,0,1,0,0]\n", - "\n", - "for m in measurements:\n", - " pos = sense(pos, m, .6, .2)\n", - " pos = update(pos, 1, .8, .1, .1)\n", - "bar_plot.plot(pos)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 21 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As you can see we quickly filtered out the bad sensor reading and converged on the most likely positions for our dog." - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Drawbacks and Limitations to the Discrete Bayesian Filter" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Do not be mislead by the simplicity of the examples I chose. This is a robust and complete implementation of a histogram filter, and you may use the code in real world solutions. If you need a multimodal, discrete filter, this filter works.\n", - "\n", - "With that said, while this filter is used in industry, it is not used often because it has several limitations. Getting around those limitations is the motivation behind the chapters in the rest of this book.\n", - "\n", - "The first problem is scaling. Our dog tracking problem used only one variable, $pos$, to denote the dog's position. Most interesting problems will want to track several things in a large space. Realistically, at a minimum we would want to track our dogs $(x,y)$ coordinate, and probably his velocity $(\\dot{x},\\dot{y})$ as well. We have not covered the multidimensional case, but instead of a histogram we use a multidimensional grid to store the probabilities at each discrete location. Each *sense()* and *update()* step requires updating all values in the grid, so a simple four variable problem would require $O(n^4)$ running time *per time step*. Realistic filters have 10 or more variables to track, leading to exhorbinant computation requirements.\n", - "\n", - "The second problem is that the histogram is discrete, but we live in a continuous world. The histogram requires that you model the output of your filter as a set of discrete points. In our dog in the hallway example, we used 10 positions, which is obviously far too few positions for anything but a toy problem. For example, for a 100 meter hallway you would need 10,000 positions to model the hallway to 1cm accuracy. So each sense and update operation would entail performing calculations for 10,000 different probabilities. It gets exponentially worse as we add dimensions. If our dog was roaming in a $100x100 m^2$ courtyard, we would need 100,000,000 bins ($10,000^2$) to get 1cm accuracy.\n", - "\n", - "A third problem is that the histogram is multimodal. This is not always a problem - an entire class of filters, the particle filters, are multimodal and are often used because of this property. But imagine if the GPS in your car reported to you that it is 40% sure that you are on D street, but 30% sure you are on Willow Avenue. I doubt that you would find that useful. Also, GPSs report their error - they might report that you are at $(109.878W, 38.326N)$ with an error of $9m$. There is no clear mathematical way to extract error information from a histogram. Heuristics suggest themselves to be sure, but there is no exact determination. You may or may not care about that while driving, but you surely do care if you are trying to send a rocket to Mars or track and hit an oncoming missle.\n", - "\n", - "This difficulty is related to the fact that the filter often does not represent what is physically occuring in the world. Consider this distribution for our dog:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "p = [0.2245871, 0.06288015, 0.06109133, 0.0581008, 0.09334062, 0.2245871,\n", - " 0.06288015, 0.06109133, 0.0581008, 0.09334062]\n", - "bar_plot.plot(p) " - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 22 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " The largest probabilities are in position 0 and position 5. This does not fit our physical intuition at all. A dog cannot be in two places at once (my dog Simon certainly tries - his food bowl and my lap often have equal allure to him). We would have to use heuristics to decide how to interpret this distribution, and there is usually no satisfactory answer. This is not always a weakness - a considerable amount of literature has been written on *Multi-Hypothesis Tracking (MHT)*. We cannot always distill our knowledge to one conclusion, and MHT uses various techniques to maintain multiple story lines at once, using backtracking schemes to go *back in time* to correct hypothesis once more information is known. This will be the subject of later chapters. In other cases we truly have a multimodal situation - we may be optically tracking pedistrians on the street and need to represent all of their positions. \n", - " \n", - "In practice it is the exponential increase in computation time that leads to this filter being the least frequently used of all filters in this book. Many problems are best formulated as discrete or multimodal, but we have other filter choices with better performance. With that said, if I had a small problem that this technique could handle I would choose to use it; it is trivial to implement, debug, and understand, all virtues in my book." - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Generalizing to Multiple Dimensions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Summary" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The code is very small, but the result is huge! We will go into the math more later, but we have implemented a form of a Bayesian filter. It is commonly called a Histogram filter. The Kalman filter is also a Bayesian filter, and uses this same logic to produce it's results. The math is a bit more complicated, but not by much. For now, we will just explain that Bayesian statistics compute the liklihood of the present based on the past. If we know there are two doors in a row, and the sensor reported two doors in a row, it is likely that we are positioned near those doors. Bayesian statistics just formalizes that example, and Bayesian filters formalize filtering data based on that math by implementing the sense->update->sense->update process. \n", - "\n", - "We have learned how to start with no information and derive information from noisy sensors. Even though our sensors are very noisey (most sensors are more then 80% accurate, for example) we quickly converge on the most likely position for our dog. We have learned how the update step always degrades our knowledge, but the addition of another measurement, even when it might have noise in it, improves our knowlege, allowing us to converge on the most likely result.\n", - "\n", - "If you followed the math carefully you will realize that all of this math is exact. The bar charts that we are displaying are not an *estimate* or *guess* - they are mathematically exact results that exactly represent our knowledge. The knowledge is probabilistic, to be sure, but it is exact, and correct.\n", - "\n", - "However, we are a long way from tracking an airplane or a car. This code only handles the 1 dimensional case, whereas cars and planes operate in 2 or 3 dimensions. Also, our position vector is *multimodal*. It expresses multiple beliefs at once. Imagine if your GPS told you \"it's 20% likely that you are here, but 10% likely that you are on this other road, and 5% likely that you are at one of 14 other locations. That would not be very useful information. Also, the data is discrete. We split an area into 10 (or whatever) different locations, whereas in most real world applications we want to work with continuous data. We want to be able to represent moving 1 km, 1 meter, 1 mm, or any arbitrary amount, such as 2.347 cm. \n", - "\n", - "Finally, the bar charts may strike you as being a bit less certain than we would want. A 25% certaintly may not give you a lot of confidence in the anwser. Of course, what is important here is the ratio of this probability to the other probabilities in your vector. If the next largest bar is 23% then we are not very knowledgable about our position, whereas if the next largest is 3% we are in fact quite certain. But this is not clear or intuitive. However, there is an extremely important insight that Kalman filters implement that will signficantly improve our accuracy from the same data.\n", - "\n", - "\n", - "**If you can understand this chapter you will be able to understand and implement Kalman filters** I cannot stress this enough. If anything is murky, go back and reread this chapter and play with the code. the rest of this book will build on the algorithms that we use here. If you don't intuitively understand why this histogram filter works, and can at least work through the math, you will have little success with the rest of the material. However, if you grasp the fundamental insight - multiplying probabilities when we measure, and shifting probabilities when we update leads to a converging solution - then you understand everything important you need to grasp the Kalman filter. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "**Author notes:**\n", - " Do I want to go to the multidimensional case? At least describe it, but why not implement it as well" - ] - }, - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "Gaussian Probabilities" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Introduction" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The last chapter ended by discussing some of the drawbacks of the Discrete Bayesian filter. For many tracking and filtering problems our desire is to have a filter that is *unimodal* and *continuous*. That is, we want to model our system using floating point math (continuous) and to have only one belief represented (unimodal). For example, we want to say an aircraft is at (12.34381, -95.54321,2389.5) where that is latitude, longitude, and altidue. We do not want our filter to tell us \"it might be at (1,65,78) or at (34,656,98)\" That doesn't match our physical intuition of how the world works, and as we discussed, it is prohibitively expensive to compute.\n", - "\n", - ">So we desire a unimodal, continuous way to represent probabilities that models how the real world works, and that is very computationally efficient to calculate. As you might guess from the chapter name, Gaussian distributions provide all of these features.\n", - "\n", - "Before we go into the math, lets just look at a graph of the Gaussian distribution to get a sense of what we are talking about." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#ignore this code.\n", - "%matplotlib inline\n", - "from __future__ import division, print_function\n", - "from gaussian_internal import plot_gaussian\n", - "import matplotlib.pylab as pylab\n", - "pylab.rcParams['figure.figsize'] = 10,6\n", - "\n", - "plot_gaussian(mu=100, variance=15*15, xlim=(45,155), xlabel='IQ', ylabel='percent')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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kJUdgoilU6VICxtcmxaK9z4GDtV1Kl0JEMmAzphKc2xePGjJp73Ng+7EmPJxvUboUj/lD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\nYcMGdHR0oLCwEJGRkYiJicGHH36I8PBwREVFob2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- "text": [ - "" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Probably this is immediately recognizable to you as a 'bell curve'. This curve is ubiquitious because under real world conditions most observations are distributed in such a manner. In fact, this is the bell curve for IQ (Intelligence Quotient). You've probably seen this before, and understand it. It tells us that the average IQ is 100, and that the number of people that have IQs higher or lower than that drops off as they get further away from 100. It's hard to see the exact number, but we can see that very few people have an IQ over 150 or under 50, but a lot have an IQ of 90 or 110. \n", - "\n", - "This curve is not unique to IQ distributions - a vast amount of natural phenomena exhibits this sort of distribution, including the sensors that we use in filtering problems. As we will see, it also has all the attributes that we are looking for - it represents a unimodal belief or value as a probabilitiy, it is continuous, and it is computationally efficient. We will soon discover that it also other desirable qualities that we do not yet recognize we need.\n", - "

\n", - "

" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Nomenclature" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A bit of nomenclature before we continue - this chart depicts the probability of of a *random variable* having any value between ($-\\infty..\\infty)$. For example, for this chart the probability of the variable being 100 is roughly 2.7%, whereas the probability of it being 80 is around 1%.\n", - "> *Random variable* will be precisely defined later. For now just think of it as a variable that can 'freely' and 'randomly' vary. A dog's position in a hallway, air temperature, and a drone's height above the ground are all random variables. The position of the North Pole is not, nor is a sin wave (a sin wave is anything but 'free').\n", - "\n", - "You may object that human IQs cannot be less than zero, let alone $-\\infty$. This is true, but this is a common limitation of mathematical modelling. \"The map is not the territory\" is a common expression, and it is true for Bayesian filtering and statistics. The Gaussian distribution above very closely models the distribution of IQ test results, but being a model it is necessarily imperfect. The difference between model and reality will come up again and again in these filters. \n", - "\n", - "You will see these distributions called *Gaussian distributions*, *normal distributions*, and *bell curves*. Bell curve is ambiguous because there are other distributions which also look bell shaped but are not Gaussian distributions, so we will not use it further in this book. But *Gaussian* and *normal* both mean the same thing, and are used interchangeably. I will use both throughout this book as different sources will use either term, and so I want you to be used to seeing both. Finally, as in this paragraph, it is typical to shorten the name and just talk about a *Gaussian* or *normal* - these are both typical shortcut names for the *Gaussian distribution*. " - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Gaussian Distributions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So let us explore how Gaussians work. A Gaussian is a *continuous probability distribution* that is completely described with two parameters, the mean ($\\mu$) and the variance ($\\sigma^2$). It is defined as:\n", - "$$ \n", - "f(x, \\mu, \\sigma) = \\frac{1}{\\sigma\\sqrt{2\\pi}} e^{-\\frac{1}{2}{(x-\\mu)^2}/\\sigma^2 }\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Don't be dissuaded by the equation if you haven't seen it before; you will not need to memorize or manipulate it. The computation of this function is stored in stats.py. \n", - "\n", - "> **Optional:** Let's remind ourselves how to look at a function stored in a file by using the *%load* magic. If you type *%load -s gaussian stats.py* into a code cell and then press CTRL-Enter, the notebook will create a new input cell and load the function into it.\n", - "\n", - " %load -s gaussian stats.py\n", - " \n", - " def gaussian(x, mean, var):\n", - " \"\"\"returns normal distribution for x given a \n", - " gaussian with the specified mean and variance. \n", - " \"\"\"\n", - " return math.exp((-0.5*(x-mean)**2)/var) / \\\n", - " math.sqrt(_two_pi*var)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

We will plot a Gaussian with a mean of 22 $(\\mu=22)$, with a variance of 4 $(\\sigma^2=4)$, and then discuss what this means. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from stats import gaussian\n", - "plot_gaussian(22,4,True,xlabel='$^{\\circ}C$',ylabel=\"Percent\")\n", - "\n", - "print('Probability of 22 is %.2f' % (gaussian(22,22,4)*100))\n", - "print('Probability of 24 is %.2f' % (gaussian(24,22,4)*100))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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YizbYiBFRwJxDI9jb3IcK7qQfUpIk4frSVLx/vFt0KUSkETZiBsW5eSVmoi4YuVTWOLCw\nIAkJ0RbNvzdd2rVT0rC13omhEW9Qvj+PIyVmoo65aIONGBEFRJZlvH+8G9dzWlKItLgolGUn4MM6\np+hSiEgDbMQMinPzSsxEnda5HOscxIhPRll2gqbfl/x3fWla0KYneRwpMRN1zEUbbMSIKCDvH+/G\nipJUSJIkupSIdWV+Etr6h9HocIkuhYjGiY2YQXFuXomZqNMyl2GPD9sanFg+JVWz70mBM5skLC1O\nxcYa7fcU43GkxEzUMRdtsBEjIr/tONmDKelxyIi3ii4l4l07ZbQR8/q4pxiRkbERMyjOzSsxE3Va\n5rKx2o7lk3k1TA8KU2OREmPBIY1vBM7jSImZqGMu2mAjRkR+6R4cwdGOASyelCy6FDrt2imp2FDN\nPcWIjIyNmEFxbl6JmajTKpdNNXYsnpSM2CizJt+Pxm9JsQ07G3sx6NZuTzEeR0rMRB1z0UbIGrHv\nfOc7yM7ORllZ2dnnzGYz5syZgzlz5uCxxx4LVSlEFCBZlrGB05K6Y4uNQll2PLY1cE8xIqOS5BDd\nPXbnzp2wWq247777UFVVBQBITExEX1/fJb+usrISc+fODUWJRHQRNV2DeGpjPX73xekwhcm2Famp\nNtjtDtFljNvWeifeOdKJ/7xpiuhSiAjAvn37sGzZMr9fH7IrYgsXLkRaGnfiJjKiDTV2LJtsC5sm\nLJx8riAJ9fYhtPe5RZdCRGMgdI2Yy+XCvHnzUF5ejq1bt4osxXA4N6/ETNSNNxePT8bmGgeu5d5h\numQ1m3B1kQ2VGu0pxuNIiZmoYy7aENqINTc3Y+/evXjuuedw9913Y3h4WGQ5RKRiz6le5CZFY0Jy\njOhS6CLO7CkWopUmRKQhi8g3z8zMBADMnz8fubm5aGhoQGlpqeJ1Dz/8MAoKCgAAycnJKCsrO7t/\nyZmOnI/5uLy8XFf16OnxGWP5+rXN0bh2VqGu/jx8fP7jxYsXAwBe37gTebE+oeOFjyPn8Znn9FKP\nyPPrtm3b0NjYCAB48MEHEYiQLdYHgIaGBqxcuRJVVVWw2+2IjY1FbGwsGhoaUF5ejurqasTGxp73\nNVysTyRO37AHX3ntU7zypRlIjLaILkdT4bJY/4w/HGhD58AIvrE4X3QpRBFNt4v1H3nkESxatAgn\nTpxAfn4+Vq9ejTlz5mD27Nm4/fbb8dJLLymaMLq4C39yJWZyMePJ5cM6J+bnJYVdExaOlk1OxUd1\nDri9vnF9Hx5HSsxEHXPRRsjOrqtXr8bq1avPe+6HP/xhqN6eiMZgY7UdX74iS3QZ5IfMBCuK0mKx\nq7EXVxWmiC6HiPzEnfUN6tw5ehrFTNSNNZdTPS609g1jXl6SxhVRsCyfPP5bHvE4UmIm6piLNtiI\nEZGqjdV2VBTbYDFx7zCjuKowBVVtA3AMjYguhYj8xEbMoDg3r8RM1I0lF58sY2ONnXuHGUxslBkL\nC5KwpXbsH0LgcaTETNQxF22wESMiharWfiRYzShOixNdCgXo2ilp2FCtzeauRBR8bMQMinPzSsxE\n3Vhy2VhjxzLe4NuQZucmwOnyoMExNKav53GkxEzUMRdtsBEjovO4vT7sONmDJcU20aXQGJgkCRVF\nNmyuCZ890ojCGRsxg+LcvBIzURdoLntO9aLQFouMeGuQKqJgWzrZhk21jjHd8ojHkRIzUcdctMFG\njIjOs7nWwathBleUGosYiwlHOgZEl0JEl8FGzKA4N6/ETNQFksvQiBe7m3pxNTcENTRJklBRbMPm\nMXx6kseREjNRx1y0wUaMiM7acbIHM7MTkBTDWxoZXUWxDR/WOeHxhex2wkQ0BmzEDIpz80rMRF0g\nuWypdWBJEaclw0FOUjQmJEVjX3NvQF/H40iJmahjLtpgI0ZEAIBelwdVbf1YNDFZdCmkkbFOTxJR\n6LARMyjOzSsxE3X+5rK1wYn5eUmIs5qDXBGFytVFKdjV2AuXx+f31/A4UmIm6piLNtiIERGA09OS\n/LRkWLHFRmFqZhx2nuwRXQoRXQQbMYPi3LwSM1HnTy5dA27U2YdwZV5SCCqiUFpanIrNtf7f8ojH\nkRIzUcdctMFGjIjwYZ0TCwuSYbXwlBBuFk1MRlXbAHpdHtGlEJEKnnUNinPzSsxEnT+5bKlzoILT\nkmEpzmrG/LxEfFTv9Ov1PI6UmIk65qINNmJEEa65Zxgd/W5ckZsouhQKktHpSX56kkiP2IgZFOfm\nlZiJusvlsqXOgasLU2A2SSGqiEJtfl4iTjqG0NHvvuxreRwpMRN1zEUbbMSIIpgsy7y3ZASIMptQ\nXpiCLbwqRqQ7bMQMinPzSsxE3aVyqbMPYdjjw/TM+BBWRCIsLbZhkx+NGI8jJWaijrlog40YUQQb\nvaVRCiSJ05LhbmZ2AnqHPWhwDIkuhYjOwUbMoDg3r8RM1F0sF1mWsaXOyWnJCGGSJFQU2bC55tJX\nxXgcKTETdcxFG2zEiCLUkY4BRFtMKEqNFV0KhcjSyaPTk7Isiy6FiE5jI2ZQnJtXYibqLpbLmVsa\ncVoychSlxiLaYsLRjsGLvobHkRIzUcdctMFGjCgCeX0yPqp3oqKI05KRRJIkLCm2cU8xIh1hI2ZQ\nnJtXYibq1HI50NKHzAQrJiRHC6iIRKoosuGjege8PvXpSR5HSsxEHXPRBhsxogi0pc6BJbwaFpEm\nJEcjI96KAy19okshIrARMyzOzSsxE3UX5uL2+rDjZA8bsQi2pNiGLXXq05M8jpSYiTrmog02YkQR\nZndTL4pSY5EWHyW6FBJkSVEKdpzsgdvrE10KUcRjI2ZQnJtXYibqLsxlC29pFPHS460otMVid1Ov\n4vd4HCkxE3XMRRtsxIgiyKDbi92nenHVpBTRpZBgS4ptvPckkQ6wETMozs0rMRN15+ay42QPyrIT\nkBRjEVgR6cHVhSnYfaoXQyPe857ncaTETNQxF22wESOKIFvqOC1Jo5JiLJiZnYAdJ3tEl0IU0diI\nGRTn5pWYibozufS6PDjc1o9FE5MFV0R6saRIOT3J40iJmahjLtpgI0YUIT6qd2JBXhJio8yiSyGd\nWDQxGVVt/eh1eUSXQhSx2IgZFOfmlZiJujO5bKl1oGIypyXpM3FWM+bnJWFrg/PsczyOlJiJOuai\nDTZiRBGga8CNescQ5ucliS6FdIafniQSi42YQXFuXomZqNu2bRu21DmxaGIyrGYe8nS+K/OSUGcf\nQteAGwCPIzXMRB1z0QbPykQRYEutAxX8tCSpsFpMWFiQjA/rnJd/MRFpjo2YQXFuXomZqCssm4/O\nATdm5ySKLoV06tx7T/I4UmIm6piLNtiIEYW5zXVOXF1og9kkiS6FdGpObiLa+9xo7hkWXQpRxGEj\nZlCcm1diJkqyLOMvVc2clqRLMpskXF2Ugi11Dh5HKpiJOuaiDTZiRGGszj4EjwxMy4wTXQrpXMXp\nzV1lWXQlRJGFjZhBcW5eiZkoba51YMX0HEgSpyXp0qZlxWPI48WE6fNEl6I7PLeoYy7aYCNGFKZ8\nsowtdQ5UFHFaki7PJElYUmTD5jruKUYUSmzEDIpz80rM5HxH2wcQG2VG85G9okshg6gotmH9kVbI\nnJ88D88t6piLNtiIEYWpzaevhnFWkvxVlBoLiwQc7RgUXQpRxGAjZlCcm1diJp/x+mR8VOfEkmIb\ncyG/SZKEG2fmYjNveXQeHkPqmIs22IgRhaH9LX3ISrQiNyladClkMBXFNnxU74DXx+lJolBgI2ZQ\nnJtXYiafOfeWRsyFAlFftQfp8VE42NonuhTd4DGkjrlog40YUZhxe3zY2diDawr5aUkam4oiG6cn\niUKEjZhBcW5eiZmM+uRUL4pSY5EWHwWAuVBgysvLcU2xDTtO9sDt9YkuRxd4DKljLtpgI0YUZs6d\nliQai4x4KybZYrHnVK/oUojCHhsxg+LcvBIzAQbcXuw51YvySSlnn2MuFIgz46WimNOTZ/AYUsdc\ntMFGjCiM7DzZg7LsBCTFWESXQgZ3VWEKdjf1YmjEK7oUorDGRsygODevxExG7y154bQkc6FAnBkv\nyTEWzMhKwM6TPYIrEo/HkDrmog2/GrHf/e53qs//4Q9/0LQYIhq7HpcHRzoGsHBisuhSKExwepIo\n+PxqxCorK1Wf//DDDzUthvzHuXmlSM9ka70TC/ISERtlPu/5SM+FAnPueFk0MRlVbf3odXkEViQe\njyF1zEUbl1xI0t7eDlmWIcsy2tvbz/u9xsZGSLyJHZFubK514I6yTNFlUBiJs5oxPy8J2xqcuHFq\nuuhyiMKSJMvyRe9j8cUvfvGiX5iSkoIvf/nLWLJkSTDqOquyshJz584N6nsQGV3ngBtfe/MY/nD3\nTFjNXPrpj9RUG+x2TrtdzrYGJ97+tBP/edMU0aUQGcK+ffuwbNkyv19/yStir7/+OmRZxt/+7d/i\n5ZdfHndxRBQcH9Y6sHhiCpsw0tyVeUl4dmsjugdGzm4STETauexZW5IkTJ06NRS1UAA4N68UyZls\nrrv4Jq6RnAsF7sLxYrWYsLAgGR/WR+7VQx5D6piLNvz68fn73/9+sOsgojFq7nGhe2AEs3ISRJdC\nYWoJPz1JFDSXXCN2rpqaGrS2tmJkZOS855cuXRqUws7gGjGiS/v9vlb0Dnvx8MI80aUYCteI+c/r\nk/HlVw/juVtKkJsULbocIl3TdI3YGb/85S/x8ccfo6CgABbL+V8S7EaMiC5OlmVsqnXgu9dMFF0K\nhTGzScLVRSnYUuvA3XOyRZdDFFb8asQ+/vhjPPPMM0hP58eX9WLbtm3c1fgCkZhJTfcQfLKM0oy4\ni74mEnOhsbvYeKkosuG57U0R2YjxGFLHXLTh1xqxiRMncs8wIh3aVGNHRXEqj08KumlZ8Rga8aLe\nPiS6FKKw4tcasV/+8pc4dOgQ5s+fj/j4+M++WJIuudeYFrhGjEid1yfjb177FD+5YTIKbDGiyzEc\nrhEL3IufNMMkSfjqglzRpRDpVqBrxPy6Iubz+TBz5ky4XC7Y7XbY7XZ0d3eju7t7zIUS0fhUtfXD\nFmthE0YhU1Fsw5Y6B/z8jBcR+cGvNWKPPPJIsOugAHFuXinSMtlce/G9w84VabnQ+FxqvBSlxiLK\nJOFY5yCmZcarviYc8RhSx1y04fc23HV1dXjttdewZs0aAMDJkydRX18ftMKI6OLcXh+2NTixxI9G\njEgrkiShotiGTTWc0iXSil+NWGVlJX76059iaGgI27dvBwC4XC789re/DWZtdAn8KUQpkjLZc6oX\nRamxyIi3Xva1kZQLjd/lxktFsQ0f1Tvg9UXO9CSPIXXMRRt+NWJvvfUWnnrqKdx///0wm80AgOLi\nYjQ2Nga1OCJSt7nGv2lJIq1NSI5BenwUDrb2iS6FKCz41YgNDw8jNTX1vOc8Hg+s1sv/NE7BwXt8\nKUVKJgNuL/Y096F8Uopfr4+UXEgb/oyXiqLIuuURjyF1zEUbfjViZWVlePHFFzEwMABgdDfvP/7x\nj5g1a5bfb/Sd73wH2dnZKCsrO/vcG2+8gZKSEpSWlmLdunUBlk4UmXacdGJWdgKSYvz6rA2R5q4p\ntmHHyR64vT7RpRAZnl/7iPX39+P555/HgQMHAABWqxXTp0/H3//93yMhwb8bDe/cuRNWqxX33Xcf\nqqqq4Ha7MXXqVOzatQsulwsVFRWoqalRfB33ESM63/ffr8F1U9K4UH+cuI/Y+Hx7XTW+UJaBRRP9\nuzJLFCmCcq/JhIQE/NM//RMcDge6u7uRlpYGmy2wfwQWLlyIhoaGs4937dqFGTNmICMjAwCQn5+P\ngwcPYvbs2QF9X6JI4hgcwbGOQfzz8iLRpVCEqygenZ5kI0Y0Pn5vXwEANpsNkydPDrgJU9PW1oac\nnBysWbMGa9euRXZ2NlpbW8f9fSMF5+aVIiGTj+qd+HxBEmIs/h+6kZALacff8XJVYQp2N/ViaMQb\n5IrE4zGkjrlow68rYr/4xS9wxRVXnPdR1R07dmDfvn149NFHx1XAQw89BAB48803L3q/vIcffhgF\nBQUAgOTkZJSVlZ2t5cxAiLTHZ+ilHj4OzeO3D5zE1WkjACb5/fVVVVW6qZ+P9f/Y3/GSHGNBrtWN\n337wCb6rhLIbAAAgAElEQVR+00Ld1B+Mx2fopR69PK6qqtJVPSLHx7Zt287uJPHggw8iEH6tEbv/\n/vuxZs2a8z4l6Xa78dBDD+E3v/mN32/W0NCAlStXoqqqCtu3b8fTTz+Nd999FwBQUVGBn//854oP\nAHCNGNGo1t5h/MM7J/Dq3TNhMfEm3+PFNWLjt7Hajg/rHPjXFcWiSyHSjaDcazImJgZut/u854aH\nhxEdHR1YdedYsGABPv30U3R2dqKpqQmnTp0K6FOYRJFmS50DVxelsAkj3Vg0MRlVbf3odXlEl0Jk\nWH41YgsWLMDzzz+PU6dOYXh4GE1NTfjFL36BK6+80u83euSRR7Bo0SIcP34c+fn5WL9+PZ5++mks\nXrwYy5Ytw3PPPTfmP0QkuvCSOYV3JrIsY9MYN3EN51xIe4GMlzirGfPykrCtwRnEisTjMaSOuWjD\n4s+L7rnnHvzud7/D9773PXg8HlgsFlxzzTW45557/H6j1atXY/Xq1Yrn77rrLv+rJYpQdfYhuDw+\nTI+gGy2TMVQU2fD2kU7cODVddClEhuTXGrEzfD4fent7kZSUBJMpoA9cjhnXiBEB/7OrGWaThK8u\nyBVdStjgGjFtuD0+fPkPh/Gr26f6de9TonAXlDViR44cQUdHB0wmE1JSUkLWhBER4PXJ2FTrwPLJ\nqZd/MVGIWS0mlE9KweYaNrVEY+FXR/XMM8/wvpI6w7l5pXDN5EBLH9LiolBgixnT14drLhQcYxkv\nyyanYkONHQFMsBgKjyF1zEUbfl/a8vdWRkSkrY01diyfwqthpF8zs+PhGvGhtntIdClEhuNXI7Zi\nxQq8+eabYfvTjhGd2VCOPhOOmQyNePFxYy+WFI39NjLhmAsFz1jGi0mSsGyyDRtr7EGoSDweQ+qY\nizb8+tRkVVUVGhsbsWHDBmRmZsJsNgMAJEnCU089FdQCiSLZtgYnyrLjkRIbJboUoktaPiUV315X\njb+7cgLM3OuOyG9+NWKBrP6n0Ni2bRt/GrlAOGaysdqBm6amjet7hGMuFDxjHS95yTHISrBib3Mv\nrsxPDkJl4vAYUsdctOFXI7ZkyZIgl0FEF+occKOmexCfLygSXQqRX5ZPScXGanvYNWJEwcR9KAyK\nP4UohVsmm2scKJ+UAqtlfIdpuOVCwTWe8bKkyIbdp/ow4PZqWJF4PIbUMRdt+HWG93q9WLduHX74\nwx/iscceAwAcOnSIH10lChJZlrGhxo5r+WlJMpCkGAtm5yRga3143/KISEt+NWIvv/wy9u/fj5Ur\nV8LhGN20Lz09HW+99VZQi6OLYxOsFE6Z1HYPYdjjw4ys8d/SKJxyoeAb73hZPjkVlWH26UkeQ+qY\nizb8asR27NiBf/zHf8SVV155dlf9nJwcdHV1BbU4oki1ocaO5ZNTIUn89BkZy5UFSai3D6G9zy26\nFCJD8KsRs1qtGBo6f6M+p9OJpKSkoBRFl8e5eaVwycTrk7Gl1oFlk22afL9wyYVCY7zjxWo24epC\nGzbVhs9VMR5D6piLNvxqxJYsWYKf/OQn2LNnD3w+H6qrq7F69Wpcc801wa6PKOLsbe5FTmI0JiSP\n7ZZGRKItn5KKDdXhe8sjIi351Yh94QtfwKJFi/DKK6/A6/Vi9erVKCsrw+233x7s+ugiODevFC6Z\nbKy2a3Y1DAifXCg0tBgv0zLj4JOBE12DGlQkHo8hdcxFG5fcR8zn86GyshJNTU0oLCzEc889xzUr\nREE04PZi96k+PLooX3QpRGMmSRKWT7ZhY7UdpRnj/8AJUTi75BWx3//+91i7di2cTideffVVvPHG\nG6Gqiy6Dc/NK4ZDJ1nonrshJQFKMX3st+yUccqHQ0Wq8LJucii11Tox4fZp8P5F4DKljLtq4ZCO2\nY8cOPPnkk/jWt76FH/7wh7wMSRRkG6vtWM69wygM5CRFIz85GntO9YkuhUjXLtmIDQ4OIjc3FwBQ\nUFCA/v7+kBRFl8emWMnombT3uXHS6cKV+dp+GtnouVBoaTlelk1JxcYw2FOMx5A65qKNy64RO3z4\nMIDRnb69Xu/Zx2fMnDkzeNURRZANNXZcXZiCKDPvPEbh4ZrCFLz4SQt6XR5Np9uJwokkX+LzxY88\n8shlv8Hq1as1LehClZWVmDt3blDfg0g0nyzjvjeO4IllhShJjxNdTkRITbXBbneILiPs/cfmBkzL\njMdtMzJEl0IUEvv27cOyZcv8fv0lf0QJdpNFRKMOtvYjLsqEKWmxoksh0tT1JWlYs6sZt05P56fu\niVRwDsSgODevZORM1h/vxoqStKD8Q2XkXCj0tB4vs3MTMOD2oqZ76PIv1ikeQ+qYizbYiBEJ1j/s\nwa6mXiybzE9LUvgxSRJWlKTi/ePdoksh0iU2YgbF/VuUjJrJploH5k9IDNpiZqPmQmIEY7xcV5KG\nLXUODHuMuacYjyF1zEUbbMSIBFt/ohsrStNEl0EUNJkJVpSkx2F7g1N0KUS6w0bMoDg3r2TETGq7\nB+Ec8mBObmLQ3sOIuZA4wRov15em4f0Txpye5DGkjrlog40YkUDrT9ixoiQNZhM/TUbhbeHEZNTb\nXWjtGxZdCpGusBEzKM7NKxktE7fXh821DlxbEtxF+kbLhcQK1nixmk2oKLbhgxPG22mfx5A65qIN\nNmJEguw82YOi1BjkJEaLLoUoJFaUpOKDE93w+i66jzhRxGEjZlCcm1cyWibvH+/G9SFYpG+0XEis\nYI6X4rQ4pMRasL/FWDcC5zGkjrlog40YkQDtfW5Udw1i0cQU0aUQhdT1JWncU4zoHGzEDIpz80pG\nymRDdTeWFNsQbQn+IWikXEi8YI+XimIb9jb3ocflCer7aInHkDrmog02YkQh5pPls5+WJIo0CdEW\nfC4/CZtqjLdonygY2IgZFOfmlYySycGWfiREmzElPS4k72eUXEgfQjFeri8dnZ6UZWMs2ucxpI65\naIONGFGIvX+im1fDKKLNyknAkMeH6i7j3gicSCtsxAyKc/NKRsikb9iDT5p6sbTYFrL3NEIupB+h\nGC+jNwI3zqJ9HkPqmIs22IgRhVBljQPz84J3g28io7h2Sio+rHdgaMQruhQiodiIGRTn5pX0noks\ny3jvWBdumpoe0vfVey6kL6EaL5kJVkzPjMeHdfq/ETiPIXXMRRtsxIhC5Ej7ALw+GbNzEkSXQqQL\nN09Lx3vHukSXQSQUGzGD4ty8kt4zWXf6apgkhfYG33rPhfQllONlfl4SHEMjqOkaDNl7jgWPIXXM\nRRtsxIhCoNflwa7GXlw7Jbg3+CYyErNJwg2lvCpGkY2NmEFxbl5Jz5l8UG3H5wuShCzS13MupD+h\nHi/Xl6ThwzonBt36XbTPY0gdc9EGGzGiIJNlGX8RsEifyAjS4qNwRW4CNtU6RJdCJAQbMYPi3LyS\nXjM52NoPi0nC9Kx4Ie+v11xIn0SMlxunpmPd0S7d7rTPY0gdc9EGGzGiIHvvaBdunhb6RfpERjF3\nQiKGRrw41qnvRftEwcBGzKA4N6+kx0y6B0ewt7kPyyaLW6Svx1xIv0SMF5Mk4aap6Xj3qD4X7fMY\nUsdctMFGjCiI/nqsC0uKbIi3mkWXQqRr15em4eOTPXAOjYguhSik2IgZFOfmlfSWiccn471j3Vg5\nXewifb3lQvomarwkxViwaGIy3j+hv/tP8hhSx1y0wUaMKEh2NDgxISkahamxokshMoRbZmRg3dEu\neH36XLRPFAxsxAyKc/NKesvk7SNduEXw1TBAf7mQvokcLyXpcUiLi8Kuph5hNajhMaSOuWiDjRhR\nENTbh9DSO4xFk1JEl0JkKLdMz8Dbn+pz0T5RMLARMyjOzSvpKZN3jnTipqlpsJjEb1mhp1xI/0SP\nl6sKU9DgGEKj0yW0jnOJzkSvmIs22IgRaax/2IMP65y4kTvpEwXMajbh+tI0vHuEV8UoMrARMyjO\nzSvpJZP1J+xYkJ+E1Lgo0aUA0E8uZAx6GC83TU3Hplo7BnRy/0k9ZKJHzEUbbMSINOT1yfjzp51Y\nNSNDdClEhpWZYMXc3ER8oMOtLIi0xkbMoDg3r6SHTHY29iAtLgpTM8XcV1KNHnIh49DLeLm9LBN/\n/rRTF1tZ6CUTvWEu2mAjRqShtw53YtVMXg0jGq9pmfFIjrHobisLIq2xETMozs0ric6kumsQbX3D\nKNfZlhWicyFj0dN4WTUzE28d7hRdhq4y0RPmog02YkQaeetwB26dngGzDrasIAoHVxWmoLlnGLXd\ng6JLIQoaNmIGxbl5JZGZdA+O4OPGXtwwNU1YDRfDsUKB0NN4sZgk3DIjHW8Kviqmp0z0hLlog40Y\nkQbWHe1CRbENidEW0aUQhZUbS9Ox82QP7IMjokshCgo2YgbFuXklUZm4PD68d7QLt+l0ywqOFQqE\n3sZLUowF1xSl4N2j4jZ41VsmesFctMFGjGicNpzoxrSseOSnxIguhSgsfaEsE+uOdmFoRB8bvBJp\niY2YQXFuXklEJl6fjD8d7sBdZZkhf29/caxQIPQ4XvKSYzAzKx7rT9iFvL8eM9ED5qINNmJE47D9\npBMpMVGYkZ0guhSisHbX7Cz8qapDFxu8EmmJjZhBcW5eKdSZyLKMtYc6cOcs/V4NAzhWKDB6HS/T\nMuORER+Fj+qdIX9vvWYiGnPRBhsxojGqauvHgNuLhROTRZdCFBHunJWFtYfaIcu8Kkbhg42YQXFu\nXinUmbxxqAN3lGXCJOl7A1eOFQqEnsfL5wqSMOzx4UBLf0jfV8+ZiMRctMFGjGgMGhxDqOkaxPLJ\nqaJLIYoYJknCHbOy8MahdtGlEGmGjZhBcW5eKZSZvHagHbfOyIDVov9DiGOFAqH38bJssg0nHS6c\n6ArdbY/0nokozEUb+v9XhEhnWnqHsbe5D7dM1+cGrkThzGo24Y5ZmXjtQJvoUog0wUbMoDg3rxSq\nTF4/2I6bp6Uj3moOyfuNF8cKBcII4+WG0jQcbhvAScdQSN7PCJmIwFy0wUaMKACdA25sa3BilU5v\nZ0QUCWKjzFg1MwOvHeRaMTI+4Y2Y2WzGnDlzMGfOHDz22GOiyzEMzs0rhSKTtYc6sKIkDUkxxrm5\nN8cKBcIo4+WW6RnY3dSL1t7hoL+XUTIJNeaiDeH/msTFxWH//v2iyyC6LMfQCCpr7Pj1F6aJLoUo\n4sVbzbh5WjpeP9SOx8oLRJdDNGbCr4jR2HBuXinYmbx5uBPXFNmQFhcV1PfRGscKBcJI42XVzExs\nrXeic8Ad1PcxUiahxFy0IbwRc7lcmDdvHsrLy7F161bR5RCp6nF58JdjXbhL57czIookyTEWrChJ\nwxtcK0YGJrwRa25uxt69e/Hcc8/h7rvvxvBw8Of7wwHn5pWCmckfqzpwdWEKshOjg/YewcKxQoEw\n2ni5c1YmNtU6gnpVzGiZhApz0YbwNWKZmaNXGObPn4/c3Fw0NDSgtLT0vNc8/PDDKCgYXQOQnJyM\nsrKys5dEzwyESHt8hl7qCefHAx7gL01J+OWqqbqoJ9DHVVVVuqqHj/X92Ijj5cbSQvzhQDvmyieD\n8v3P0MufVy+Pq6qqdFWPyH+Pt23bhsbGRgDAgw8+iEBIssC7pzocDsTExCA2NhYNDQ0oLy9HdXU1\nYmNjz76msrISc+fOFVUiEX69qxlurw+PLsoXXQppKDXVBrvdIboM0kCPy4Ovrj2CF26biqxEq+hy\nKMLt27cPy5Yt8/v1Qqcmjx07hjlz5mD27Nm4/fbb8dJLL53XhBGJZh8cwfoT3fjS7CzRpRDRRSTH\nWHDz1HS8yt32yYCENmILFy7EsWPHcPDgQezbtw8rVqwQWY6hXHjJnIKTyRuH2rF8cirS4437UzbH\nCgXCqOPlC2WZ2N7gDMq+YkbNJNiYizaEL9Yn0qvugRFsqLbji7waRqR7STEW3DI9A/+3n1fFyFjY\niBnUmcWC9BmtM/m//W1YUZKGVIPtG3YhjhUKhJHHy+0zM7CrqReNDpem39fImQQTc9EGGzEiFad6\nXNja4OTaMCIDSYi24M5ZmfjNnhbRpRD5jY2YQXFuXknLTH67pxW3z8ww1D0lL4ZjhQJh9PFy6/QM\nHO8axNGOAc2+p9EzCRbmog02YkQXONE5iE/bB7BqJnfRJzKaaIsJX5mbgxc/aYHA3ZmI/MZGzKA4\nN6+kRSayLOPF3c34m7nZiLGEx+HBsUKBCIfxct2UVPS4PNh9qleT7xcOmQQDc9FGePxLQ6SRvc19\n6BoYwfUlaaJLIaIxMpsk3D8/B/+7uwU+XhUjnWMjZlCcm1cabyZen4yXdrfgvvk5MJskjaoSj2OF\nAhEu42XRxGTEWMyorLGP+3uFSyZaYy7aYCNGdNqGajtiLCZcNSlFdClENE6SJOH/fW4CfrO7FUMj\nXtHlEF0UGzGD4ty80ngyGXR78du9Lfja5ydAksLnahjAsUKBCafxMj0rHmU5CVh7qGNc3yecMtES\nc9EGGzEiAK8fbMfc3ESUZsSLLoWINPTAgly8faQTHf1u0aUQqWIjZlCcm1caayZtfcNYd6wL9y/I\n1bgifeBYoUCE23jJTLBi5bR0/O/usW/yGm6ZaIW5aIONGEW8l3a34LYZGcgw8I29iejivjg7C4da\n+zXd5JVIK2zEDIpz80pjyeRwWz8+bR/AHWXhu3krxwoFIhzHS2yUGffNz8GvPj41pu0swjETLTAX\nbbARo4jl9cn4xY4m/L8rJyA2yiy6HCIKouVTUgEAH5wY/3YWRFpiI2ZQnJtXCjSTt490IjkmCtcU\nhfd2FRwrFIhwHS8mScKji/Lxmz0t6HV5AvracM1kvJiLNtiIUUTqHhjBq/vb8OiivLDbroKI1E1J\nj8NVhSn4zZ6xL9wn0hobMYPi3LxSIJn8+pNm3Dg1HfkpMUGsSB84VigQ4T5e7puXg50ne3C80/+F\n++GeyVgxF22wEaOIs7+lD0faB/DlK7JEl0JEIZYQbcEDV+biv7c3wevjfShJPDZiBsW5eSV/MnF7\nfHh+exO+9vnIWaDPsUKBiITxsnxyKqItJqw72uXX6yMhk7FgLtpgI0YR5f8OtGGSLQaLeT9Joogl\nSRIeKy/AK/ta0d7HHfdJLEmWx7CpSghVVlZi7ty5osugMFDbPYjH/1qLX90+FWlxUaLLIcFSU22w\n2x2iyyCBXt3fhsPt/fj3FcX80A5pZt++fVi2bJnfr+cVMYoIXp+Mn21txAMLctmEEREA4K7ZWbAP\njqCyhg05icNGzKA4N690qUz+dLgDCVYzVpSkhrAifeBYoUBE0nixmCR866qJ+PWuZjiGRi76ukjK\nJBDMRRtsxCjsNfe48MbBdjxWXsDpByI6T0lGHK6dkooXdp4SXQpFKK4Ro7Dm9cn41roTWFJkw6qZ\n4Xs/SQoc14jRGcMeH77+1jHcOzcHS4ptosshg+MaMaJzvH6wHTEWE26dkSG6FCLSqWiLCd9bMhEv\n7DyFrgF+ipJCi42YQXFuXunCTKq7BvHWp5349tUTYYrgKUmOFQpEpI6X0ox4rJyejp9tbcSFE0WR\nmsnlMBdtsBGjsOT2+PDTLSfxtc9PQGaCVXQ5RGQAX74iG33DXrzr50avRFrgGjEKS7/8+BS6B0bw\ng6WTuECfVHGNGKlpcrrwzXdP4NmVJRFxL1rSHteIUcT7uLEH2xuc+MbifDZhRBSQ/JQY3Dc/F/++\nqQFuj090ORQB2IgZFOfmlbZt24aOfjee3dqIf1oyCUkxFtEl6QLHCgWC4wW4aWoa8pOj8atdzQCY\nycUwF22wEaOw4ZOB/9jcgFUzMzAjO0F0OURkUJIk4bGrCrCvuRcf1nH6moKLjZhBlZeXiy5Bd2pi\nihAbZcJds7JEl6IrHCsUCI6XUfFWM76/tBC/2HEKRbMWiC5HlzhWtMFGjMLCJ0092Fhtxz9eE9lb\nVRCRdkrS43D3FVn4t8p6DHO9GAUJGzGD4tz8Z5p7XPjPDxtxc3ovbLG8ofeFOFYoEBwv57ttRgZi\nRvrw823K/cUiHceKNtiIkaENur14ckM9/nZeDgri+BMrEWlLkiSszB5Gnd2FP3/aKbocCkP8WJlB\ncW4e8Mky/vPDk5ieFY+bpqZBkpiJGo4VCgTHi1LF1eWY1jeMf3jnBApTY3FFbqLoknSBY0UbvCJG\nhvXqgXY4hjx4ZFEe9wsjoqDKTozG40sm4enNDWjrGxZdDoURNmIGFelz85tr7Xj/eBd+uKwQVvPo\nMI70TC6GuVAgOF6UzmQyZ0IivnRFNn64vg79wx7BVYnHsaINNmJkOFVt/XhhZzP+9bpipMVzcT4R\nhc5tMzIwd0IiflRZjxEv16XS+PFek2Qop3pc+Pa6anz3momYl5ckuhwyMN5rksbK65Pxo8p6JFjN\n+M7VBVwaQefhvSYpbDmGRvDE+lrcNz+XTRgRCWM2SXh8yUScdLjwyr420eWQwbERM6hIm5vvH/bg\n++/XYmlxKm4oTVN9TaRl4i/mQoHgeFF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- "text": [ - "" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Probability of 22 is 19.95\n", - "Probability of 24 is 12.10\n" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So what does this curve *mean*? Assume for a moment that we have a themometer, which reads 22$\\,^{\\circ}C$. No thermometer is perfectly accurate, and so we normally expect that thermometer will read $\\pm$ that temperature by some amount each time we read it. Furthermore, a theorem called **Central Limit Theorem** states that if we make many measurements that the measurements will be normally distributed. If that is true, then this chart can be interpreted as a continuous curve depicting our belief that the temperature is any given temperature. In this curve, we assign a probability of the temperature being exactly 22$^{\\circ}C$ is $19.95\\%$. Looking to the right, we assign the probability that the temperature is 24$^{\\circ}C$ is $12.10\\%$. Because of the curve's symmetry, the probability of 20$^{\\circ}$C is also $12.10\\%$.\n", - "\n", - "So the mean ($\\mu$) is what it sounds like - the average of all possible probabilities. Because of the symmetric shape of the curve it is also the tallest part of the curve. The thermometer reads $22^{\\circ}C$, so that is what we used for the mean. \n", - "\n", - "> *Important*: I will repeat what I wrote at the top of this section: \"A Gaussian...is completely described with two parameters\"\n", - "\n", - "The standard notation for a normal distribution for a random variable $X$ is $X \\sim\\ \\mathcal{N}(\\mu,\\sigma^2)$. This means I can express the temperature reading of our thermometer as\n", - "\n", - "$$temp = \\mathcal{N}(22,4)$$\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 probability of the temperature being $22\\,^{\\circ}C$, $20\\,^{\\circ}C$, $87.34\\,^{\\circ}C$, or any other arbitrary value.\n", - "\n", - "###### The Variance\n", - "\n", - "Since this is a probability distribution it is required that the area under the curve always equals one. This should be intuitively clear - the area under the curve represents all possible occurances, which must sum to one.\n", - "\n", - "This leads to an important insight. If the variance is small the curve will be narrow. To keep the area equal to 1, the curve must also be tall. On the other hand if the variance is large the curve will be wide, and thus it will also have to be short to make the area equal to 1.\n", - "\n", - "Let's look at that graphically:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "xs = np.arange(15,30,0.1)\n", - "p1, = plt.plot (xs,[gaussian(x, 23, .2) for x in xs],'b')\n", - "p2, = plt.plot (xs,[gaussian(x, 23, 1) for x in xs],'g')\n", - "p3, = plt.plot (xs,[gaussian(x, 23, 5) for x in xs],'r')\n", - "plt.legend([p1,p2,p3], ['var = .2', 'var = 1', 'var = 5'])\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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m3XyDBw/G5MmTMWjQIACWivbaooqIyN2VlEjo1s1sd1EFAOHhJuj1nGNF7mHSpEl4//33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\ngegcgciK67neFobeFoHeGove1huDLQK9NZxIWwQtrM2QVD1mXF9iSiK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- "text": [ - "" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So what is this telling us? The blue gaussian is very narrow. It is saying that we believe $x=23$, and that we are very sure about that $(90%)$. In contrast, the red gaussian also believes that $x=23$, but we are much less sure about that $(18%)$. Our believe that $x=23$ is lower, and so our belief about the likely possible values for $x$ is spread out - we think it is quite likely that $x=20$ or $x=26$, for example. The blue gaussian has almost completely eliminated $22$ or $24$ as possible value - their probability is almost $0\\%$, whereas the red curve considers them nearly as likely as $23$.\n", - "\n", - "If we think back to the thermometer, we can consider these three curves as representing the readings from three different thermometers. The blue curve represents a very accurate thermometer, and the red one represents a fairly inaccurate one. Green of course represents one in between the two others. Note the very powerful property the Gaussian distribution affords us - we can entirely represent both the reading and the error of a thermometer with only two numbers - the mean and the variance.\n", - "\n", - "The standard notation for a normal distribution for a random variable $X$ is just $X \\sim\\ \\mathcal{N}(\\mu,\\sigma^2)$ where $\\mu$ is the mean and $\\sigma^2$ is the variance. It may seem odd to use $\\sigma$ squared - why not just $\\sigma$? We will not go into great detail about the math at this point, but in statistics $\\sigma$ is the *standard deviation* of a normal distribution. *Variance* is defined as the square of the standard deviation, hence $\\sigma^2$.\n", - "\n", - "It is worth spending a few words on standard deviation now. The standard deviation is a measure of how much variation from the mean exists. For Gaussian distributions, 68% of all the data falls within one standard deviation($1\\sigma$) of the mean, 95% falls within two standard deviations ($2\\sigma$), and 99.7% within three ($3\\sigma$). This is often called the 68-95-99.7 rule. So if you were told that the average test score in a class was 71 with a standard deviation of 9.4, you could conclude that 95% of the students received a score between 52.2 and 89.8 if the distribution is normal (that is calculated with $71 \\pm (2 * 9.4)$). " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following graph depicts the relationship between the standard deviation and the normal distribution. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from gaussian_internal import display_stddev_plot\n", - "display_stddev_plot()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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\n", - "

**Sidebar**: An equivalent formation for a Gaussian is $\\mathcal{N}(\\mu,1/\\tau)$ where $\\mu$ is the *mean* and $tau$ the *precision*. Here $1/\\tau = \\sigma^2$; it is the reciprocal of the variance. While we do not use this formulation in this book, it underscores that the variance is a measure of how precise our data is. A small variance yields large precision - our measurement is very precise. Conversely, a large variance yields low precision - our belief is spread out across a large area. You should become comfortable with thinking about Gaussians in these equivelant forms. Gaussians reflect our *belief* about a measurement, they express the *precision* of the measurement, and they express how much *variance* there is in the measurements. These are all different ways of stating the same fact.
" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Interactive Gaussians" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For those that are reading this in IPython Notebook, here is an interactive version of the Gaussian plots. Use the sliders to modify $\\mu$ and $\\sigma^2$. Adjusting $\\mu$ will move the graph to the left and right because you are adjusting the mean, and adjusting $\\sigma^2$ will make the bell curve thicker and thinner." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import math\n", - "from IPython.html.widgets import interact, interactive, fixed\n", - "import IPython.html.widgets as widgets\n", - "\n", - "def plt_g (mu,variance):\n", - " xs = np.arange(2,8,0.1)\n", - " ys = [gaussian (x, mu,variance) for x in xs]\n", - " plt.plot (xs, ys)\n", - " plt.ylim((0,1))\n", - " plt.show()\n", - "\n", - "interact (plt_g, mu=(0,10), variance=widgets.FloatSliderWidget(value=0.6,min=0.2,max=4.5))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 7, - "text": [ - "" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Computational Properties of the Gaussian" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Recall how our discrete Bayesian filter worked. We had a vector implemented as a numpy array representing our belief at a certain moment in time. When we performed another measurement using the *sense()* function we had to multiply probabilities together, and when we performed the motion step using the *update()* function we had to shift and add probabilities. I've promised you that the Kalman filter uses essentially the same process, and that it uses Gaussians instead of histograms, so you might reasonable expect that we will be multipling, adding, and shifting Gaussians in the Kalman filter.\n", - "\n", - "A typical textbook would directly launch into a multipage proof of the behavior of Gaussians under these operations, but I don't see the value in that right now. I think the math will be much more intuitive and clear if we just start developing a Kalman filter using Gaussians. I will provide the equations for multiplying and shifting Gaussians at the appropriate time. You will then be able to develop a physical intuition for what these operations do, rather than be forced to digest a lot of fairly abstract math.\n", - "\n", - "The key point, which I will only assert for now, is that all the operations are very simple, and that they preserve the properties of the Gaussian. This is somewhat remarkable, in that the Gaussian is a nonlinear function, and typically if you multiply a nonlinear equation with itself you end up with a different equation. For example, the shape of $sin(x)sin(x)$ is very different from $sin(x)$. But the result of multiplying two Gaussians is yet another Gaussian. This is a fundamental property, and the key reason why Kalman filters are possible." - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Summary and Key Points" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following points **must** be understood by you before we continue:\n", - "\n", - "* Normal distributions occur throughout nature\n", - "* They express a continuous probability distribution\n", - "* They are completely described by two parameters: the mean ($\\mu$) and variance ($\\sigma^2$)\n", - "* $\\mu$ is the average of all possible values\n", - "* $\\sigma^2$ represents how much our measurements vary from the mean" - ] - }, - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "Kalman Filters" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "One Dimensional Kalman Filters" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we understand the histogram filter and Gaussians we are prepared to implement a 1D Kalman filter. We will do this exactly as we did the histogram filter - rather than going into the theory we will just develop the code step by step. But first, let's set the book style." - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Tracking A Dog" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As in the histogram chapter we will be tracking a dog in a long hallway at work. However, in our latest hackathon someone created an RFID tracker that provides a reasonable accurate position for our dog. Suppose the hallway is 100m long. The sensor returns the distance of the dog from the left end of the hallway. So, 23.4 would mean the dog is 23.4 meters from the left end of the hallway.\n", - "\n", - "Naturally, the sensor is not perfect. A reading of 23.4 could correspond to a real position of 23.7, or 23.0. However, it is very unlikely to correspond to a real position of say 47.6. Testing during the hackathon confirmed this result - the sensor is reasonably accurate, and while it had errors, the errors are small. Futhermore, the errors seemed to be evenly distributed on both sides of the measurement; a true position of 23m would be equally likely to be measured as 22.9 as 23.1.\n", - "\n", - "Implementing and/or robustly modelling an RFID system is beyond the scope of this book, so we will write a very simple model. We will start with a simulation of the dog moving from left to right at a constant speed with some random noise added. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from __future__ import print_function, division\n", - "\n", - "import numpy.random as random\n", - "import math\n", - "\n", - "class DogSensor(object):\n", - " \n", - " def __init__(self, x0=0, velocity=1, noise=0.0):\n", - " \"\"\" x0 - initial position\n", - " velocity - (+=right, -=left)\n", - " noise - scaling factor for noise, 0== no noise\n", - " \"\"\"\n", - " self.x = x0\n", - " self.velocity = velocity\n", - " self.noise = math.sqrt(noise)\n", - "\n", - " def sense(self):\n", - " self.x = self.x + self.velocity\n", - " return self.x + random.randn() * self.noise\n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The constructor *\\_\\_init()\\_\\_* initializes the DogSensor class with an initial position (x0), velocity (vel), and an noise scaling factor. The *sense()* function has the dog move by the set velocity and returns its new position, with noise added. If you look at the code for *sense()* you will see a call to *numpy.random.randn()*. This returns a number sampled from a normal distribution with a mean of 0.0. Let's look at some example output for that.\n", - "\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "for i in range(20):\n", - " print(\"%.4f\" % random.randn(),end='\\t')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "-0.6891\t0.0651\t-1.3072\t-0.3641\t-0.9836\t-0.6302\t-0.1925\t-2.5600\t-0.2098\t0.4368\t0.1303\t-0.5265\t-1.1345\t-1.3365\t-0.1951\t0.6675\t2.3115\t-0.4293\t1.0739\t-2.2391\t" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You should see a sequence of numbers near 0, some negative and some positive. Most are probably between -1 and 1, but a few might lie somewhat outside that range. This is what we expect from a normal distribution - values are clustered around the mean, and there are fewer values the further you get from the mean.\n", - "\n", - "Okay, so lets look at the output of the *DogSensor* class. We will start by setting the noise to 0 to check that the class does what we think it does" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import matplotlib.pyplot as plt\n", - "import matplotlib.pylab as pylab\n", - "\n", - "dog = DogSensor (noise=0.0)\n", - "xs = []\n", - "for i in range(10):\n", - " x = dog.sense()\n", - " xs.append(x)\n", - " print(\"%.4f\" % x, end=' '),\n", - "plt.plot(xs)\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "1.0000 2.0000 3.0000 4.0000 5.0000 6.0000 7.0000 8.0000 9.0000 10.0000 " - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The constructor initialized the dog at position 0 with a velocity of 1 (move 1.0 to the right). So we would expect to see an output of 1..10, and indeed that is what we see. If you thought the correct answer should have been 0..9 recall that *sense()* returns the dog's position *after* updating his position, so the first postion is 0.0 + 1, or 1.0.\n", - "\n", - "Now let's inject some noise in the signal." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def test_sensor(noise_scale):\n", - " dog = DogSensor(noise=noise_scale)\n", - "\n", - " xs = []\n", - " for i in range(100):\n", - " x = dog.sense()\n", - " xs.append(x)\n", - " p1, = plt.plot(xs, c='b')\n", - " p2, = plt.plot([0,99],[1,100], 'r--')\n", - " plt.xlabel('time')\n", - " plt.ylabel('pos')\n", - " plt.ylim([0,100])\n", - " plt.title('noise = ' + str(noise_scale))\n", - " plt.legend([p1, p2], ['sensor', 'actual'], loc=2)\n", - " plt.show()\n", - " \n", - "test_sensor(4.0)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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j4nAMHcZzL1/B99/7sWHDKV5/PZ3atUt3m4gRFdrFJCEhgcjISCIiIvjwww+ZOXMmkDv7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\n3mR2TCAzLQ0jJqbc18nIsDBsWATTp+cQHX1m7/3bdVBFiig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- "text": [ - "" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "> **Note**: numpy uses a random number generator to generate the normal distribution samples. The numbers I see as I write this are unlikely to be the ones that you see. If you run the cell above multiple times, you should get a slightly different result each time. I could use *numpy.random.seed(some_value)* to force the results to be the same each time. This would simplify my explanations in some cases, but would ruin the interactive nature of this chapter. To get a real feel for how normal distributions and Kalman filters work you will probably want to run cells several times, observing what changes, and what stays roughly the same.\n", - "\n", - "So the output of the sensor should be a wavering blue line drawn over a dotted red line. The dotted red line shows the actual position of the dog, and the blue line is the noise signal produced by the simulated RFID sensor. Please note that the red dotted line was manually plotted - we do not yet have a filter that recovers that information! \n", - "\n", - "If you are running this in an interactive IPython Notebook, I strongly urge you to run the script several times in a row. You can do this by putting the cursor in the cell containing the Python code and pressing Ctrl+Enter. Each time it runs you should see a different jagged blue line wavering over the top of the dotted red line.\n", - "\n", - "I also urge you to adjust the noise setting to see the result of various values. However, since you may be reading this in a read only notebook, I will show two extreme examples. The first plot shows the noise set to 100.0, and the second shows noise set to 0.5." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "test_sensor(100.0)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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B+hkzZJ+vTge8/XYQfvvbYDzxRA/WrtXKfqw5oxE4cYJKR9zBrwJtQAi2acAK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i4jXcbnjn3u0sct9Kh6l9cbZvj8vgCtEivkCNtvgNzdaJEeVCjCgX3sm6dy/p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\nvy6/BQCo1xxWFwAAuHZCQkJqPs5dV1dX68SJE9q/f78effT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- "text": [ - "" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You may not have a full understanding of the exact *meaning* of a noise value of 100.0, but as it turns out if you multiply *randn()* with a number $n$, the result is just a normal distribution with $\\sigma = \\sqrt{n}$. So the example with noise = 100 is using the normal distribution $\\mathcal{N}(0,100)$. Recall the notation for a normal distribution is $\\mathcal{N}(\\mu,\\sigma^2)$. If the square root is confusing, recall that normal distributions use $\\sigma^2$ for the variance, and $\\sigma$ is the standard deviation, which we do not use in this book. DogSensor.\\_\\_init__() takes the square root of the noise setting so that the *noise * randn()* call properly computes the normal distribution." - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Math with Gaussians" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's say we believe that our dog is at 23m, and the variance is 5, or $pos_{dog}=\\mathcal{N}(23,5)$). We can represent that in a plot:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import stats\n", - "stats.norm_plot(23, 5)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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GalwoFmbEyS6FiCRz6UgGEZGnVLVcRFO3BTMSdbJLIS+1IC0Wu443w6aM40RE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\nOp2289zpmTlz9kWh9OF0OOViz7T9fpIJ0/8cDr9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- "text": [ - "" - ] - } - ], - "prompt_number": 8 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This corresponds to a fairly inexact belief. While we believe that the dog is at 23, note that roughly 21 to 25 are quite likely as well. Let's assume for the moment our dog is standing still, and we query the sensor again. This time it returns 23.2 as the position. Can we use this additional information to improve our estimate of the dog's position?\n", - "\n", - "Intuition suggests 'yes'. Consider: if we read the sensor 100 times and each time it returned a value between 21 and 25, all centered around 23, we should be very confident that the dog is somewhere very near 23. Of course, a different physical interpertation is possible. Perhaps our dog was randomly wandering back and forth in a way that exactly emulated a normal distribution. But that seems extremely unlikely - I certainly have never seen a dog do that. So the only reasonable assumption is that the dog was mostly standing still at 23.0.\n", - "\n", - "Let's look at 100 sensor readings in a plot:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "dog = DogSensor(23, 0, 5)\n", - "xs = range(100)\n", - "ys = []\n", - "for i in xs:\n", - " ys.append(dog.sense())\n", - " \n", - "plt.plot(xs,ys)\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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i2MR2df26kFQN4cpFb4hlEA2xyyqwT47lcGAkhW19cVKEO4jqdTFTFNEXDy/e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\nbPQhES6D9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- "text": [ - "" - ] - } - ], - "prompt_number": 9 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Eyeballing this confirms our intuition - no dog moves like this. However, noisy sensor data certainly looks like this. So let's proceed and try to solve this mathematically. But how?\n", - "\n", - "\n", - "Recall the histogram code for adding a measurement to a pre-existing belief:\n", - "\n", - " def sense(pos, measure, p_hit, p_miss):\n", - " q = array(pos, dtype=float)\n", - " for i in range(len(hallway)):\n", - " if hallway[i] == measure:\n", - " q[i] = pos[i] * p_hit\n", - " else:\n", - " q[i] = pos[i] * p_miss\n", - " normalize(q)\n", - " return q\n", - " \n", - "Note that the algorithm is essentially computing:\n", - "\n", - " new_belief = old_belief * measurement * sensor_error\n", - " \n", - "The measurement term might not be obvious, but recall that measurement in this case was always 1 or 0, and so it was left out for convience. \n", - " \n", - "If we are implementing this with gaussians, we might expect it to be implemented as:\n", - "\n", - " new_gaussian = measurement * old_gaussian\n", - " \n", - "where measurement is a Gaussian returned from the sensor. But does that make sense? Can we multiply gaussians? If we multiply a Gaussian with a Gaussian is the result another Gaussian, or something else?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It is not particularly difficult to perform the algebra to derive the equation for multiplying two gaussians, but I will just present the result:\n", - "$$\n", - "N(\\mu_1, \\sigma_1^2)*N(\\mu_2, \\sigma_2^2) = N(\\frac{\\sigma_1^2 \\mu_2 + \\sigma_2^2 \\mu_1}{\\sigma_1^2 + \\sigma_2^2},\\frac{1}{\\frac{1}{\\sigma_1^2} + \\frac{1}{\\sigma_2^2}}) $$ \n", - "\n", - "In other words the result is a Gaussian with \n", - "\n", - "$$\\begin{aligned}\n", - "\\mu &=\\frac{\\sigma_1^2 \\mu_2 + \\sigma_2^2 \\mu_1} {\\sigma_1^2 + \\sigma_2^2}, \\\\\n", - "\\sigma &= \\frac{1}{\\frac{1}{\\sigma_1^2} + \\frac{1}{\\sigma_2^2}}\n", - "\\end{aligned}$$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Without doing a deep analysis we can immediately infer some things. First and most importantly the result of multiplying two Gaussians is another Gaussian. The expression for the mean is not particularly illuminating, except that it is a combination of the means and variances of the input. But the variance of the result is merely some combination of the variances of the variances of the input. We conclude from this that the variances are completely unaffected by the values of the mean!\n", - "\n", - "Let's immediately look at some plots of this. First, let's look at the result of multiplying $N(23,5)$ to itself. This corresponds to getting 23.0 as the sensor value twice in a row. But before you look at the result, what do you think the result will look like? What should the new mean be? Will the variance by wider, narrower, or the same?" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "\n", - "def multiply(mu1, sig1, mu2, sig2):\n", - " m = (sig1*mu2 + sig2*mu1) / (sig1+sig2)\n", - " s = 1. / (1./sig1 + 1./ sig2)\n", - " return (m,s)\n", - "\n", - "xs = np.arange(16, 30, 0.1)\n", - "\n", - "\n", - "m1,s1 = 23, 5\n", - "m, s = multiply(m1,s1,m1,s1)\n", - "\n", - "ys = [stats.gaussian(x,m1,s1) for x in xs]\n", - "p1, = plt.plot (xs,ys)\n", - "\n", - "ys = [stats.gaussian(x,m,s) for x in xs]\n", - "p2, = plt.plot (xs,ys)\n", - "\n", - "plt.legend([p1,p2],['original', 'multiply'])\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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K+TdEQCYf/ot+zF+s4fJPmBuMmspWNDd0CqjIfvDaF4v5SwsbZiKyWdXll9HZ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\nZSItykR0iF4uLERGmC+Z2+ujudtFg9VJo9VFg81Jo9VJg9VFk81JYri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- "text": [ - "" - ] - } - ], - "prompt_number": 10 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The result is either amazing or what you would expect, depending on your state of mind. I must admit I vacillate freely between the two! Note that the result of the multiplation is taller and narrow than the original Gaussian but the mean is the same. Does this match your intuition of what the result should have been?\n", - "\n", - "If we think of the Gaussians as two measurements, this makes sense. If I measure twice and get the same value, I should be more confident in my answer than if I just measured once. If I measure twice and get $23m$ each time, I should conclude that the length is close to $23m$. So the mean should be $23$. I am more confident with two measurements than with one, so the variance of the result should be smaller. \n", - "\n", - "\"Measure twice, cut once\" is a useful saying and practice due to this fact! The Gaussian is just a mathematical model of this physical fact, so we should expect the math to follow our physical process. \n", - "\n", - "Now let's multiply two gaussians (or equivelently, two measurements) that are partially separated. In other words, their means will be different, but their variances will be the same. What do you think the result will be? Think about it, and then look at the graph." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "xs = np.arange(16, 30, 0.1)\n", - "\n", - "m1,s1 = 23, 5\n", - "m2,s2 = 25, 5\n", - "m, s = multiply(m1,s1,m2,s2)\n", - "\n", - "ys = [stats.gaussian(x,m1,s1) for x in xs]\n", - "p1, = plt.plot (xs,ys)\n", - "\n", - "ys = [stats.gaussian(x,m2,s2) for x in xs]\n", - "p2, = plt.plot (xs,ys)\n", - "\n", - "ys = [stats.gaussian(x,m,s) for x in xs]\n", - "p3, = plt.plot(xs,ys)\n", - "plt.legend([p1,p2,p3],['measure 1', 'measure 2', 'multiply'])\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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XYnbWnn1tJ/nbFmmYhRCilrh6PJHsrbtoOm2c6lJqlZCJo7hy6CSX9vymuhQh\nhCIywyyEELXE/jF/wqvHPQRPGKa6lFonNW4j5z//hk7rFqPRaFSXI4SoJplhFkKIOujSroPkn04m\n8PGHVZdSK/kP7UPJ1Wtkb5GP0YWoi6RhFoDMUqkk2atVG/I3mUwkvPE+YX9+Cq2DvepyKsVW8tfo\ndITPeoZTby7GZDCoLscsbCX72kryty3SMAshhI3L2vgzxiI9foPvV11KreZ9fxfsG7qRGrdRdSlC\niBomM8xCCGHDjCUl7OzxGC3mPI93r86qy6n1cn89wqFJr3Lvzi/ROTmqLkcIUUUywyyEEHVI6lff\n4eDlgVfPe1SXUic0jInELTKclKWrVJcihKhB0jALQGapVJLs1bLl/A3Xi0j8x8c0f+UZmz1zgy3m\nHz5zEufeW0ZxXr7qUqrFFrOvTSR/2yINsxBC2Kjkj1fSILoVDdq3Vl1KneLSvAne93fl3KJlqksR\nQtQQmWEWQggbVHL1Gts7DaPTN+/jEh6iupw653pqJrt6PU7sjhU4enuoLkcIUUkywyyEEHVA0r/j\n8OrRUZplReo19sFvcB/OLfpCdSlCiBogDbMAZJZKJcleLVvMv/jKVZI/WkmzF59UXUq12WL+N4U+\n/zipX26gKCtHdSlVYsvZ1waSv20pt2GOi4sjPDyc5s2bs2HDhrs+LjU1ldjYWFq3bk379u3ZunVr\npdcQQghRvuR/x+HdqzP1mwapLqVOc/Lzxm/oA5yVWWYhar0yZ5j1ej0tWrQgPj6ewsJCevToQWJi\n4h0fm5WVRWZmJpGRkaSkpNClSxcuXLhQ4TVkhlkIIcpXfOUqP3cezj3f/pv6TQJUl1PnFWZks/O+\nR+m6/QucfLxUlyOEqCCzzjDHx8cTERGBt7c3gYGBBAYGcujQoTs+tlGjRkRGRgIQFBSEXq+nuLi4\nUmsIIYQoW9KSr2h0f1dplq2Ek683/sP7ce49OcosRG1WZsOcmZmJn58fS5YsYeXKlfj6+pKenl7u\nops3b6Z9+/bY29uTkZFRpTVEzZJZKnUke7VsKf/iy3mkfLqKptPGqS7FbGwp/7sJffZR0r7eRGFG\ntupSKqU2ZG/LJH/bUqEv/U2cOJFhw4YBlHty/IyMDKZPn877779/y+Mrs4YQQojbJS35Ep8Hu+Ec\nIkeXrYljI08aD+/H2X99rroUIYSF2JV1p5+f3y1Hg28eLb6bwsJChg0bxvz582nSpEml15g8eTJB\nQTe+xOI86HR1AAAgAElEQVTu7k5kZCSxsbHA/34Tk23LbN+8zVrqqUvbsbGxVlVPXdu2lfxNVwvQ\n/2cNnTd9YhX1mGvbVvIvb9sYE0bxSwsJffYx9p1JUF6PbMu2bN+6ffO/U1JSAJgwYQKVUakv/fXs\n2ZPTp08DMHPmTDQaDXPnzgXAZDIxevRounXrxjPPPFOhNX5PvvQnhBB3d2reEvQXc2n9jxmqSxF3\ncfK19zAWFtHq739SXYoQohxm/dKfg4MD8+bNo2vXrvTq1YsFCxaU3peRkUFGRkbp9s6dO1m1ahUf\nfvgh0dHRREdHk5GRUeYawnr8/jcwUbMke7VsIf/iK1c5/9k3hD73uOpSzM4W8q+oJpNHk75mi82c\nl7k2ZW+LJH/bYlfeA4YPH87w4cNvu33p0qW3bMfGxqLX6yu1hhBCiPKlfPI13r264Bzsr7oUUQZH\nbw/8hj7AuQ9W0OKvz6ouRwhhRmWOZNQkGckQQojblVwr4OeOj9Dxm/dxCQtRXY4ox/XUTHb1epx7\nd8Xh4OGuuhwhxF2YdSRDCCGEWuc/X4tHl3bSLNuIeo198Ol3H8kfrVRdihDCjKRhFoDMUqkk2atl\nzfkbCotIWryC0Bdq3+zyTdacf1U1ee4xUj5dTcnVa6pLKVNtzN6WSP62RRpmIYSwUqlffotb63Dc\nWoerLkVUQv0mAXjd15GUT1epLkUIYSYywyyEEFbIWFzCz52HE7X4NRp2iFRdjqikqyfO8OvwF+ge\n/zU6ZyfV5Qgh/kBmmIUQohZIX70F55DG0izbKNeWTWnQoTXnl69TXYoQwgykYRaAzFKpJNmrZY35\nmwwGzv7rM5pOHau6FIuzxvzNpekLY0l6fznGojufclW12py9LZD8bYs0zEIIYWUyv92OnbsrHl3b\nqy5FVIN725bUDw8h9etNqksRQlSTzDALIYQVMZlM7On3FKHPPYZPv+6qyxHVlPPLfo7PeIfYn5ej\n0coxKiGshcwwCyGEDbu89zDFuVdo9ECs6lKEGXh0bYeuvjNZW+TjdyFsmTTMApBZKpUke7WsLf9z\ni1cQ/PRINDqd6lJqhLXlb24ajYYmk8dwbtEXqku5TW3P3tpJ/rZFGmYhhLAS186eJzf+MI1H9FNd\nijAjn/7dKcrKIffXI6pLEUJUkcwwCyGElTg+4x/YNXAlfMZE1aUIM0v+ZBU5O36l3dJ5qksRQiAz\nzEIIYZP0l66Q/s33BD/5iOpShAUEjOzP5b2HyU9MVl2KEKIKpGEWgMxSqSTZq2Ut+Z//bA2NHuyG\nYyNP1aXUKGvJ39J0zk4EjhtC0uIVqkspVVeyt1aSv22RhlkIIRQzFulJWbqakIkjVZciLCj4iaFk\nrP+Roqwc1aUIISpJGmYBQGysnMJKFcleLWvIP231FlxbNcW1ZVPVpdQ4a8i/pjh4NcR/8P0kf7xS\ndSlA3creGkn+tkUaZiGEUMhkMpG0eAUhk0apLkXUgJBJIzn/+VpK8q+pLkUIUQnSMAtAZqlUkuzV\nUp3/xR/j0eh0eHaLUVqHKqrzr2nOIQF4dm3PheUbVJdS57K3NpK/bbFTXYAQQtRlSYtXEDJxJBqN\n5pbbrxaVkHlVT0a+nsyrerLy9eQVlVBYbKTIYKSw2EhhiRF7nQZHOy1Odloc7bQ42+vwdnHAx8Ue\nHxdHfF0d8HS2R6fV3KUC8UdGo4lrV4u4knudvNzr5F2+8Y++qIRivYHiYiPF+hKMBhN29lrsHXTY\n2euwd9DhVM8etwb1cG9YD7eGN/7tVM/+lj/fJpNHc3DCbIKeGIrWXv4aFsIWyHmYhRBCkbxjp9k/\n5k9E//wViXnFJGQXcCq7gISL17hebMTXxQEfVwd8XBzxcbHHvZ4dTnY6HO00ONnpcLLTUmI0UVhi\noLDkRhNdUGwkO/9/jXZmvp58vYGmHvUI93Ym3MuZ5t7ONHZ3RKuRJtpkMnH5UgEZF6789588stLz\ncHC0w62BU2nj6+ZeD0cnO+wcdNj/tznW6bSUFBsoLjb8t5E2cP1a8Y0GO/c6Vy5f58ql69jZafEN\ncP/fP43dOfLYNAIfHYT/0AdURyBEnVTZ8zDLr7ZCCFHDDEYTJ7OvkfDmJyR16s7CNado5ulMuLcz\nPZs1ZFLnxvi6ONx21LmqrukNJF4sIOFiAXtSrvCf/ekUlRjpEOBKTKA77Ru74uZUd/46KCosJul0\nDudOZXPu1EW0Wg2+jd3xDXCjS6+m+DR2x6mevVn2ZTKZuHql8EYznnqFfb8kkZl6Bc/Atlye9wkl\nbTvQOKQhOp1MSAphzerOO6Qo0y+//CLf2FVEslerpvI3GE3sT81jW2Iu+y7kEVicT5+9++n17ac8\nH+aHnQVHJuo76IjydyXK37X0toyrRfx6Po8fEi+x8JcUQhrWo1toA3qENqShs3maxYqoqfyvF+g5\neSidhKMZZKbm0TikIaHhXnTqHkpDr/oW269Go8GtQT3cGtQjvLUvcGPkI/18LseGbGHXonVkNwgk\nuJkXLdv6ERrujc6uZppnee9RS/K3LdIwCyGEhZhMJs5eus73py/x45lcfF0d6NXMgwkd/bm04GMM\nIx6kVXN/JbX5ujoysJU3A1t5oy8xcig9nx/P5vL5gQxa+9Snd5gHnYPccaih5s0SDCVGziZkc+xg\nKilnLhHa3JuY2CYENfXE3kGnrC6tVkPjYA94+QnSVm1m4GujOXMyi/2/JLFl9VGat/EjItof3wB3\ns33KIISoHplhFkIIM9MbjPx0Jpc1x7LJLzLQO8yDXs0aEuDuBEDJtQK2xwyl88aPcA5urLjaW10v\nNrAz6Qrfn75EYk4BfcI8eDiiET6uDqpLq7D8vEIO7k7h8K/n8fRxISK6MeGtfXB0qrkj5xVh1Bez\nvdMjtP/sbdwimwNw+VIBJ35L49jBNHQ6Le27BtOyrT/29uoafCFqo8rOMEvDLIQQZnKlsIRvT1xk\n3YlsQhrWY0hrbzoEuN325brkj1ZyafdBoj+eq6jSism8qmft8Ww2n8qhnb8rQyIb0bKR5cYXqisz\nLY/9O5M4ezKblm39aNclmIae1lsvwLn3l5N3JIGoD1675XaTycT5s5fYtzOJjPNXaNMxkOh7gqjv\n6qioUiFql8o2zLb7WZswKzkfpDqSvVrmyD+noJhFuy7wRNxx0q8W8fcHmzGvbzM6Brrf1iybDAaS\nPvyKkGes/0IlPq4OPN2pMZ+PiKCVT33m/pDE1HWnOJCah7mOtZgj/wtJucR9tJdvPj+Al48LE6Z3\no9fAVlbfLAMEPvYQF3+KpyAl/ZbbNRoNQU09GfJ4e0Y+3ZHrBXo++b8dbFlzlLzL182yb3nvUUvy\nty0ywyyEEFWUV1jCV4cy2XQqh95hHnz0SEs8yvnCXOZ323Fs5EHDDpE1VGX1OTvoGNy6EYNaebP9\nbC7/2nkBr/r2jOvgR4SPi7K6MlKv8Mv3p7mUlU/nns1oFe1vc2ebsHOtT8CogSR/+CUt35h2x8d4\neLtw/0MRxN4fxq87zvHZv3bRqq0/ne4LlSPOQtQQGckQQohKKtAb+PpIFmuPZ9OtSQNGR/viXb9i\nM767+z9Fk2dG4zugh4WrtByD0cSW05f44mA6IQ3r8UQHP5p6OtfY/i9m5rNz62nSz1+mU/dQImMC\nsbPhLycWpmezs8ej3Lt7JQ4N3cp9/LWrRcRvP8vxg2m0iQmgY/dQs50GT4i6Qs7DLIQQFmIymfjh\nTC4f7U2jjZ8L/3qoOf5uFT/Cl/vrEfQXc/Hp282CVVqeTquhb3NPejVryHcnc5i16Qxdgt15ooO/\nRc/nXFRYzK5tiRw/mEZMt1D6DWuj9GwX5uLk5433/bFcWPYNoc89Xu7j67s60nNASzrEhrD7hzN8\n8s8dxPYJI7J9ABq5oqMQFmG7v5ILs5JZKnUke7Uqmv+ZnAL+tOE0q45k8UqvEGb2CKlUswz/vQz2\nUyPQ6Gy/yQNw0Gl5OMKbjx5piZ1Ww/ivT7DueDYGY8U/uKxI/iajiSP7L/DJ//2CvsjAuKmxdOzW\npFY0yzeFTBpJ8sdfY9QXV/g5bg3q8cCQ1gwZ156j+1NZ9sFu0s9frvDz5b1HLcnftsgRZiGEKMM1\nvYFPfk1jx7nLPN7ej77NPdFV4SheQdIFLu0+SOS7r1igSrVcHe2Y0iWQvs29WLT7At+dzOH5roG0\n8qn+l+4y0/LYuvYYJhM8/Fg7/ALczVCx9XGLCMMlLIT0tVtpPKxvpZ7r29idUU934vhvaXyz7CAh\nYV5079sc5wqOCQkhyiczzEIIcRfxKVdYuPM8HQPdeLKa4wbHZ/0TOxdnwmdNMmOF1sdkMvHT2VyW\n7EnlvqYNGdfBH6cqzBeXlBjZ80Mih369QLcHwmndrnGtHzfI3rqLU/OW0OX7T6t8wZKiwhJ2bTvN\nycMZ9BzQkuaRvmauUojaQU4rJ4QQ1ZRXWMLbPyWxaPcFXuoezNTYoGo1y/rcPNJXbyboyaFmrNI6\naTQaejT1YMnQluReL2HS6hMcTr9aqTXSz1/m8/d2cTEzn7HPdSGyQ92YzfXqeQ/GomIu7dxf5TUc\nnezo0b8lD41py86tp1n7xUGuXS0yY5VC1E3SMAtAZqlUkuzV+mP+u5IvM3H1SVwc7VgypAXR/q7V\n3sf5z7/Bu8+9OPl6V3stW+HuZMfMHiFM7BTAvB+TeW/Xea4XG2573O/zLyk2sH1jAms+P0Dnnk15\n6NFoXNycarBqtTRaLSETR5C0+Mtqr+Uf1JDHn+1CQy9n/vPuTk78lnbbubPlvUctyd+2SMMshBDc\nuCT0/+1IYcmeVGb3DGFy5wDqmeFyxEZ9MSmffE2TSSPNUKXt6RzszpKhLSgoNjLlmwROXyy44+Mu\nZuaz7IPdXL5UwLjnY2nRxq/KYwm2zH/og1z57QT5p5KqvZadvY5uDzRnyNj27P7xDN/FHaaosKT6\nRQpRB8kMsxCizjuTU8DcH5II93bm2S6B1Dfj2RdSv/qOtNWbiflqodnWtFU/nsnl/d0XGBHlw5DW\n3mg1GkwmE4f2nmfn96fp9mBzWrdvXCcb5d87/c5HFGXl0PqdP5ttzWK9gZ++O0lS4kX6D4/CP6iB\n2dYWwhbJeZiFEKKCTCYT3xzLZvlvmUzs1JjeYR5mX//c4hU0f3WKWde1VT2aNqRFI2fe+jGZA6l5\nPNvBn183JXAlt4CRT3fCs5G6qwZak6BxQ9gRO4rwPz+Ng1dDs6xp76Dj/ocjOHU0g28+P0C7rsF0\n7BaKtg7MhgthDjKSIQCZpVJJslcjv6iEv35/lrW/pbBwULjZm2WAnO17wWTC675OZl/bVvm5OjJ/\nQBhNtCaW/msnqVdyGf1MZ2mWf8fR2wPfAfeR8p81Zl87vLUvjz3bhaTTF/no/7ZyLV++EKiKvPfb\nFmmYhRB1ztmc6zy7NgFfV0eeCC6s9AVIKurc4hWETBxZ50cMfs9kMnFk73n0v56nw/1hfO/szurj\n2bd9Ia2uC3l6JCmfrsZQaP6G1tXdieHjO+LSQMuyRZW72IkQdZXMMAsh6pStpy+xJD6VZ+5pTM9m\n5j+qfNPVE2fYN3Ia3fd+jdZRLiABUFxsYOvaY2Sm5vHQmGgaetUnK1/P37adw7u+A9O7BeFci67e\nV137Rv8J3wH3ETB6oMX2kXg8k81rjhHbuxltOgbKL3eizpDzMAshxB0UG4ws2nWeZQczeLtfM4s2\ny3DjMthBTwyRZvm/Ll8qYMXiPRgNJkY/cw8NvW5cBbCRiwPz+4fh5qTjubUJpOQWKq7UejR5ZhRJ\ni7+06NH3Zq18GDWxEwd2p7Bp1VGK73DqPyGENMziv2SWSh3J3vKuFJYwY+MZMq7qee+hcJp41Cu9\nzxL5F2ZeJHPTDgIfH2z2tW1Rytkcli/eQ+v2jek3vA0ODv/7vvkvv/yCg52WqbFBDGvjw5++Pc3e\n81cUVms9PGLbo7HTcfHHeIusf/O17+FVnzGT78FQYuCrf+8lP09+aakJ8t5vW6RhFkLUasm513l+\nbQKtGjnzWp9QXBwtf3KglE++xn9IHxw83C2+L2t3ZN8FNqw4RP/hUbTrElLmR/4PNvfktftD+eeO\nFFYfzarzc80ajYaQSaNIWrzC4vtycLCj/4gomrVsxBcf7CEzLc/i+xTClsgMsxCi1tp3IY+3fkrm\nqY7+9An3rJF9lly7zvaYodzz7YfUbxJQI/u0RkajiZ83JXDmRBaDH2+Hh3fFz4KReVXPX7acoWWj\n+jzXNRC7OnzqM6O+mO0dh9Jh+T9xbdWsRvaZcCSDrWuPcf/DEYS39q2RfQpR02SGWQhR5908v/I7\n25N5tXeTGmuW4caFSjzuiarTzbK+qIRvlh0gMy2P0c/cU6lmGcDH1YEFA8O5VFDMzI2J5NXhq9Np\nHewJevIRzpnhctkV1TzSl6HjOvDDhhPE/3Smzh/pFwKkYRb/JbNU6kj25mU0mVi8J5UNJy6yYGA4\nkb5lN2vmzN9kMJD84ZeETBpltjVtTX5eIV9+GI+LqyOPPNGBes5lf+nxbvk7O+iYc38ozTzrMXX9\nKdKv1t3zBQc+9jBZm3dQmHnRrOuW9dr3DXBnzDOdOXU0k82rj2IwGM26byHv/bam3IY5Li6O8PBw\nmjdvzoYNG8p87PTp0/H19SUyMvKW23U6HdHR0URHRzN16tTqVSyEEHehLzEy94ckEnOu838Dw/Cz\n0PmV7yZz0w7sPRrQICay/AfXQjlZ+SxfEk94pC/3PxyBTle9YzI6rYaJ9wQwqJU3L64/TeLFAjNV\nalscGrrhP6QPKZ98XaP7dXV3YsRTHcm/WsQ3yw6i19fdI/1ClDnDrNfradGiBfHx8RQWFtKjRw8S\nExPvutju3btxcHBg3LhxHDlypPR2V1dXrl69WmYhMsMshKiOq0UlzPn+HA3q2fHn7sE42NX8B2h7\nBk4k5KkR+A7qWeP7Vi01OZe1Xxyk2wPhtG5v/nGUHecu8+7O88y4L5j2AW5mX9/aXTt7nj0DJtL9\n11XY1a9X/hPMyGAwsmXNMXKy8hn8eDvqu9TsL6JCWIJZZ5jj4+OJiIjA29ubwMBAAgMDOXTo0F0f\n37lzZzw9a25WUAghALLy9by44TTNPOsxu2eIkmb58v6jFGVcpFG/bjW+b9VOH8vkm2UH6ftIpEWa\nZYB7mzTg1d5NeOunZLaevmSRfViz+qGBNOwYSdrKjTW+b51Oy4NDWxMS5sWKxfHk5lyr8RqEUK3M\nv1UyMzPx8/NjyZIlrFy5El9fX9LT0yu9k8LCQtq3b09sbCw7duyocrHCcmSWSh3JvnpScguZtv4U\nD4R5MOmexmgreaUyc+V/7oMVBD89HK2d5U9bZ00O7T3PtvXHGTquPU3CvSv9/MrkH+nrwjv9m/Hp\n/jTiDmdWel+2LmTSKJI+/AqT0TzzxJXJXqPREHt/GDH3hvDlh3vJTJVzZVeXvPfblgq9s0+cOBGA\n1atXV+mymampqTRq1Ih9+/YxePBgEhMTcXS8/SOdyZMnExQUBIC7uzuRkZHExsYC/3thybZltm+O\n0FhLPbIt2xXZbtSiHa9uPsO9Da7hm3cZjcZHST0/r1pHwfZ4IhfMsqp8LL3tYPTnUPx5mkXrSDx3\nBN/Glt9/cMN6jPa5wrLfirhWZGBcBz927txpFXlYertr167YudZn+8J/YxcTUe31bqrM86M6BZF8\n/iwr/r2HR8bGENDEw2rysbXtm6ylntq+ffO/U1JSAJgwYQKVUeYM886dO5k3bx7r168HoEePHixc\nuJA2bdrcdcGkpCQGDhx4ywzz73Xq1InPPvuM5s2b33K7zDALISrjcHo+f9t2jqmxgXQNaaC0lhOv\n/B9aRwea/2WK0jpqislkYseWUyQez2LYkzG4ujvVeA2Xrxcza9ONczVP6RJQ6U8WbFX6N9+T8uka\nOn3zvtI6kk5f5NuvDtFveJsqfbIghGpmnWGOiYnh2LFjZGdnc/78eS5cuFDaLM+cOZNZs2aVu4Pc\n3FyuX78O3GimU1NTS48iCyFEVew9f4W/bTvHrB4hypvl4st5pH29ieAJw5XWUVNMRhNb1x4nOTGH\nkU93UtIsAzSoZ887/cM4l3udd7YnU2KsG+cK9unfg+vn07ny2wmldYSEeTH48XZsXHmEhCMZSmsR\noiaU2TA7ODgwb948unbtSq9evViwYEHpfRkZGWRk3Po/yZQpU+jSpQsJCQkEBgayYcMGTp48SXR0\nNFFRUQwZMoSPP/6YevVq9hu+onx//IhI1BzJvnJ+OpPLP7an8HqfUKIbu1Z7vermf/7ztXj37oqT\nX+0/ymYwGPlu5WFysvMZPr4jzvXLPsdyRVQn//oOOuY+2Iy8QgN/23oOfUntP1ew1t6O4AnDSFpS\n/QuZVPe17x/UkEeevHGBkyP7LlS7nrpG3vtti115Dxg+fDjDh99+5GTp0qW33bZo0SIWLVp02+0n\nT56sYnlCCPE/35/O4eNf05jXtxmhnup/8Tbqi0n+5GvaL/uH6lIszlBiZMNXhygpNjB0XAfs7XWq\nSwLAyU7LnPtvnD3jr9+fZc79oTgqOEtKTQoYM4ifFw7lemom9Rr7KK2lkZ8bI5/qSNwnv1JSbCC6\nc7DSeoSwlDJnmGuSzDALIcqyMSGHz/enM69vM4IaqhkD+KPUlRtJi9tIzMp3VZdiUSUlRtYvPwga\nDQNHtcXOChtSg9HE29uTyb1ezGv3h1LPShp6Sznx14VotDpa/PVZ1aUAcPlSASs//pV2XYJp3zVE\ndTlClMusM8xCCGENNpy4yLID6bzd33qaZZPJRNLi2n8Z7JJiA2uXHUCr0zLISptluHFVwJe7B+Nd\n34FXNp+lQG9QXZJFBY8fTuqXGyjJt45zIjfwcGbEUx05uDuFvT+fU12OEGZnne98osbJLJU6kn3Z\n1hzN4qtDmbzTP4wAC3zBrKr55+zYh6m4BK+e95i5IutRrDew5vMDODrZMXBkFDoLNMvmfP3rtBr+\n1C2IAHdHZm06w7Va3DQ7B/nheW8MF5ZvqPIa5n7vcWtQjxFPdeTIvvPs+fGMWdeujeS937ZIwyyE\nsFqrjmSx5lg27/Rvhr+bdV2ON+mDFYRMGlmlc9PbgmK9gdWf7ae+qyP9hrVBq7ONvy60Gg0vxAbS\n1LMeMzYm1uqmOWTSSJL/HYexpER1KaVc3Z0Y+VQnThxKZ9e2RNXlCGE2tvEOKCzu5gm+Rc2T7O9s\nzdEs1h7P5h/9w/B1tVyzXJX8r548S97RU/gN6WOBitQrLr5xZNnV3YkHh0ZatFm2xOtfq9HwbJcA\nwr2cmb3pDNeLa2fT3KBdBI5+3mR993OVnm+p9576ro4MnxDDycPpxG8/a5F91Aby3m9bpGEWQlid\ndcezWX00m3f6hdHIpfqnLjO3pCVfEvTEUHRO1nXU2xxuzizXd3W40SxrbfMIukajYUqXAIIbOvHq\nlrMU1tJTzoVMHMm5JStUl3Gb+i6ODB8fw5F9F9j3S5LqcoSoNmmYBSCzVCpJ9rfacOIicYczebt/\nM3xcLd8sVzb/oqwcMr/bTtDYwRaqSJ2SEiPffHEQp3r29K2hZtmSr3+tRsPzXQPxrm/PnO/P1srz\nNPs8eC/6i7nk/nrnq+uWxdLvPS5uTgwfH8PB3ckc3JNi0X3ZInnvty3SMAshrMbGkxdZ8VsGb/cL\nw8+CYxjVkbJ0FX4P98bBU+0VBs2tpMTIui8O4uBgZ1Mzy+W58UXAYFwddPxt2zmKDbWradbodIQ8\nNYKkxdZ3lBlufBFw2PgY9m4/Kxc3ETZNzsMshLAK35/OYem+dN7p14zGii63XB5DQSHbY4bQaf0S\n6ocGqi7HbAwGI+tX/IZGo2HAyCh0taRZ/r0So4k3tp1DA8zu1QQ7Gx01uZOSawVsjxlK540f4Rzc\nWHU5d5R78RpffbSXex8IJyLaOmsUdYuch1kIYXN+OXeZj/emMe9B622WAVLjvqNBTGStapZNRhOb\nVx3FUGJkwIja2SwD2Gk1zOoZQrHRxFs/JWEwWsWxIrOwq+9MwOiBJP87TnUpd9XQqz7Dnozh502n\nOHk4XXU5QlRa7XxnFJUms1Tq1PXs913I492d53njgaZKLkpS0fxNRiNJH35Vqy5UYjKZ2Lr+OHlX\nrjNoTLRFzrNcnpp8/TvotLzaqwl5hQbm70jBaB0fsJpF8JOPkPb1Joov51X4OTX93uPZyIVHxnXg\nhw0nOH08s0b3bY3q+nu/rZGGWQihzNGMfN76KZm/9m5CMy9n1eWUKWvLL9i7udCwU5TqUszCZDLx\n8+ZTZKbmMfix9tjX8ktJ3+Rgp+W1PqFkXdWz8JfztaZpdvJvhHfvLpxftk51KWXy9nNl6Nj2fL/m\nGGcTslWXI0SFyQyzEEKJUxcLmL3pDDPuC6Z9gJvqcsoV/9AzBD0xFL+He6suxSz2/HSGk4fSGfFU\nR+o5W9+p+yzterGBmRvP0MyrHlM6B9SKC9DkHUlg/+Mv0z3+a7QO9qrLKVP6+cus/uwA/Ye3ISTM\nS3U5og6SGWYhhNVLzr3OXzaf4YXYQJtoli8fOM711Ex8BtynuhSzOLArmaP7UnnkiQ51slkGqGev\n480Hm5KQXcBHe9NUl2MWbpHNqR8aSMb6H1SXUi6/wAY8NCaab786xIWkXNXlCFEuaZgFILNUKtW1\n7NPzipi56QxPdWxMbIj6U7NVJP+kxSsIeWo4Wju7GqjIso4eSOXXHecYNr4DLm7qv2Cp8vVf30HH\nmw80Ze+FPOIO1Y6Z2pCJo0havIKKfHis+r0nIKQh/UdEse6Lg2SlV3z2urZQnb+oHGmYhRA15uI1\nPX/emMjIKB96h3moLqdCClLSydnxKwGjB6oupdpOHc1gx+ZTDHuyA+4NrXtmvKa4Odnx9websv7E\nRTaevKi6nGrz7t0Zw/VCLu06qLqUCgkJ86LXoFas/s9+cnOuqS5HiLuShlkAck17lepK9pevFzNj\n4xBd5rkAACAASURBVBn6t/BiUCtv1eWUKi//pCUrCBgzCDvX+jVUkWWcO5XN1rXHGTq2PR7eLqrL\nKWUNr3+v+g7M69uU/xxIZ8e5y6rLqRaNVkvw0yMrdCETa8geoHmkL517NuPrT/aRn1eoupwaYy35\ni4qRhlkIYXHX9AZmbTpD12B3RkT5qC6nwvQ5l0lftZngCcNUl1ItF85d4ruVR3j4sWga+Vv/zLgK\njd2deKNPU97deZ6DqVdVl1MtjYf15cqBY+QnJqsupcKiOgbSJiaAr5fuo/B6sepyhLiNNMwCkFkq\nlWp79kUlRv6y+QwRPvUZ18FPdTm3KSv/lE9X49PvPpx8reeIeGVlpuWxdvlvDBjRBv+ghqrLuY01\nvf6beTnzl15NmPtjEgnZtjseoKvnSODjg0n+sOwLmVhT9gAdu4cSHObF6v/sR68vUV2OxVlb/qJs\n0jALISzGYDTx9x+T8HZx4BkbO3WXoaCQlKWrCHnGdi9UcvlSAWs+28/9D7UiuJmcuqsi2vi58OK9\nQby65SwpubY7HhD05FDS125Fn2M7IyYajYb7HmxOQy9n1i3/DUOJUXVJQpSShlkAMkulUm3N3mQy\nsWjXBa4XG5jeLQitlTbLd8s/9atvadChNS5hITVbkJkU5OtZtXQfne5rSnhrX9Xl3JU1vv47B7sz\noaM/MzclkpWvV11OlTh6e+DTrzvnP1tz18dYY/YarYYHBrdGp9Oy8evDmGrRJcz/yBrzF3cnDbMQ\nwiKW/5bJiexrvNo7FHudbb3VmAwGzi1eQZPJY1SXUiV6fQmrP9tPeKQv0fcEqS7HJt0f5snQyEbM\n2JjIZRudqQ2ZOJKUpasxFBapLqVStDotA0ZGkX+1iG3rT1ToFHlCWJpt/S0mLEZmqdSpjdlvSshh\nU0IObzzQlPoO1n3J5Tvln/ntdhwbedKwYxsFFVWP0WBkw4pDeDZyIfb+MNXllMuaX/9DWjfi3iYN\nmL35DNf0BtXlVJpri1BcI5qRvub7O95vzdnb2+sY/Fg70s5fZte2RNXlWIQ15y9uJw2zEMKs4lOu\nsHRfGnMfbIqns3VfnvdOTCYTZ99bRpMptnd02WQyseWbY5hMJvoMjrCpmXFrNa69H+Fezsz5/ix6\nG5ypDZk0iqQlX9rkUVpHJ3uGjmvPyUPpHNiVpLocUcdJwywAmaVSqTZlfzLrGv/4OYU594cS2ED9\nVeQq4o/5X9p5AENBAY362N6fy86tiWRnXGXgqLbobGQMxtpf/xqNhme7BOLmZMfb25Mx2ljj6dkt\nBjQacrbvve0+a88eoL6LI4882YG9P5/j5OF01eWYlS3kL/7HNt5RhRBW78KVQuZ8///s3Xd4VFX6\nwPHvTHrvvfeEEiAJIF3pICJVLBTFgj/rrm1trF1RV8WyFtZV7AIigiBdkd4hhUB6771PJpO5vz8Q\nd1kxJGFm7szkfJ7HRwZmzn3zPjd33jnz3nNyeWhMMHHeprvJR94/vyLsnltQKE3r8ph8pJBzKWXM\nWZKItY3pb+FtTCyUCv42LoT6Ng0fHi4xqdlahUJB6LIbyevGRibGysXNnjlLEtn941kKc2rkDkfo\no0zrHUHQG9FLJR9zyH1tawdPbsthSaIfI0Jc5A6nR/47/03p2TSlZ+M/d4qMEfVcVnoFB3/OYd6t\nSTg42sgdTo+Yyvlvbank2UlhnC5tYl1qpdzh9Ij/7Ek0n8ulMS3zor83ldwDePs5c92Ng/jx22Sq\nyk17Y5kLTCn/giiYBUG4Qq3qTp7ensOkKHemxZr2Wr95739FyB3zUdpYyx1Kt5UU1LFjwxlmL0rA\n1cNe7nDMmqONJS9NjWDjmSp2ZdXKHU63KW2sCb1zAXn//EruUK5IcIQHE2bE8f1nJ2isb5M7HKGP\nEQWzAIheKjmZcu47OrW8sDuPKE97Fg4x3rV+u3Ih/21FZVTtPkTQ4lkyR9R9NZXNbPzyFNPnD8Q3\n0LRm9i8wtfPfy8Gal6ZGsOpICSeKG+UOp9uCFs+i+tejtBaU/P53ppZ7gNhBfiSOCuG7T4/T1mqa\na2RfYIr578tEwSwIQq9IksRb+wqxtlDywKggk1+RIf9fawm4cQZWLk5yh9ItzY0q1q8+zrhpMYRF\nm+7W3aYo1M2O5RPDWLGngOzqVrnD6RZLJwcCb5lJ/gem28t8QdLoMMJivPjhi5N0dJjecn+CaRIF\nswCIXio5mWruPzlWSmmjmifGh2KhNN1ief/+/ajrGild+xOhd94gdzjd0q7qYP3qEwwaHkz/hAC5\nw7kipnr+D/R15IFRQSzfkUtZk2lsDBJ65w2U/bCT9qrz7SSmmnuAq6fG4ORiy09rUtCa6G6Appz/\nvkgUzIIg9NgPZ6o4UNDA85PDsbU0/ctI0Wff4zV5DLb+3nKHclkajZYfvjhFYKgbw8aGyR1OnzYm\nzJUbB/nw1LYcGlQaucO5LBtvD3xnTqDwk+/kDuWKKZQKps6LR6Xq4GexG6BgAArJSM6y3bt3k5CQ\nIHcYgiBcxt7cOj48XMKb10Xh62RaKzJcSqeqnV+HzmXo2rdxiouQO5wuSVqJzWuSkSSJGTcORmnC\nM/vm5N/HSkkubeLV6ZHYWRn3zpYtecUcvvZOxh39DktH013+8YJ2VQffrjpKTLwvV11t3L+/gnE5\nefIkEyZM6PbzTX9qSBAEg0kpa+Ldg8W8MCXcLIplgNJ1W3EZFGv8xbIksWfrOVqa2pk+P14Uy0Zk\naZIfga62vPxzPp1G3h7gEBaIx+gkir7cJHcoOnFhN8CUY8WknSiWOxzBjImCWQBEL5WcTCX3ebVt\nvLg7nyfHhxJhJsuXSZ2dpL/5iUlsg318fz75WTXMWpSApZHPYvaEqZz/XVEoFDw0JphOSeKdA0VG\n3x4Qdt9CClatYd8ve+QORSccnW2ZuySRvdszycuskjucbjOHc78vEQWzIAiXVdms5untOfzfiECG\n+JvGKhLdUbFtHwpHe9yuGix3KF1KP13KyYMFzLstCVs7K7nDES7BUqng6fFhZNe08sXJcrnD6ZJL\nfAwOUSFo9p2SOxSd8fB25PpbhvDT2hTKihvkDkcwQ6JgFgCxHqScjD33jSoNT23LYfYAb66JcJM7\nHJ2RJIm8d78g/vG7jXpJvILsavZsOcfcWxNxcrGVOxydM/bzvyfsrS14cXIEu7Nr2XKuWu5wuhR+\n/yIsdxxB6jSfZdkCQtyYMmcAP3xxkrqaFrnDuSxzOvf7AlEwC4Lwp9o1Wp7dmUtSoBPzBhr/ChI9\nUfPrUTQtbfhMGyt3KH+qorSRzWtSmHnzYDx9zGdm35y52Vvx8tQIvjhRxqEC453pdB+ViKWzExVb\nfpU7FJ2K7OfDyPERrP/0BC3NprHcn2AaRMEsAKKXSk7GmvtOrcSKX/LxcrTmzuGmvdbvpeSs/Izw\nBxZx4OBBuUO5pPraVjZ8foJJ1/cjMMxd7nD0xljP/ysR4GLLs5PCeXNfIWcrjXOmU6FQ0D55KDlv\nf2b0Pdc9NWh4MLGD/Pj+sxOo2413uT9zPPfNmSiYBUH4A0mSeP9QMS0dnTw8NhilEbcs9EbdkWRU\npZX4zZ4kdyiX1NqiZv3q4wwfF070ANPccryvi/V24NFxwTy7M5eiepXc4VySRWIcSBJVu4zzQ+OV\nGDUxEi9fJzZ9c5rOTq3c4QhmQBTMAiB6qeRkjLn/NrmCMxUtPDMxHGsL87tM5Lz9OWH3LURpaWl0\n+VerNWz4/ATR/X0ZMiJE7nD0ztjyr0vDglxYOtSfJ7flUNPaIXc4fzBmzBjCH1hMrhnOMisUCibP\n6o9CoWDHhjNG+fOZ87lvjszvnVAQhCuyI7OGn87V8NLUCByszWf5sgsaUjJoOptN4ILpcofyB9pO\nLZu/Tcbdy4HRk6PkDkfQgSnRHkyN8eDp7Tm0qI3vBjvf665BXddI7UHzWTHjAqWFkutuGkRNZTMH\ndmbJHY5g4kTBLACil0pOxpT7o0UN/PtYKS9PjcDD3jyXL8t9+zPC7r4JpY01YDz5lySJnRvT0Wol\nJs8eYNQrd+iSseRfn24e7EOslz3P78qjw4jaA/bv34/CwoLw+xaS+/ZncoejF9bWlsxZnMi51HJO\nHy6UO5yL9IVz35yIglkQBAAyqlp4/ddCnpkYTpCr+S1fBtCcmU/dkWQCF14vdyh/cHB3NpVljcy8\naTAWZtgG05cpFAruGxmErZWSN/YWojWy9gD/eVNpySmk/mS63KHohb2jNfNuTeLQLzlkpVfIHY5g\nosRVWQBEL5WcjCH3JQ3tPLMjl4fGBNPPx0HucPQm953PCbljPpYOdr//nTHkP/lIIWdPlzFnSSLW\nNpZyh2NQxpB/Q7BQKnjimlDKm9R8cqxU7nCA/+ReaWVJ2D23kPv2ankD0iNXD3tmL0pgx/dplBTU\nyR0O0HfOfXMhCmZB6OPqWjt4ans2ixP9GBHiInc4etNaUELVz4cIvm2u3KFcJDu9goM/5zDvtiQc\nHG3kDkfQI1tLJc9PDudgQQMb0irlDucigTdfR8OpszSdzZE7FL3xDXRh+g3xbPzyFDWVzXKHI5iY\nyxbMa9euJTo6mpiYGDZv3tzlcx955BF8fX0ZOHBgr8cQ5CF6qeQjZ+7bOjp5ekcO4yPcmR7rKVsc\nhpD77hcELZ6FlcvFG4DImf+Sgjq2f5/G7EUJuHrYyxaHnPratcfZ1pKXp0awLqWSvbnyznT+d+4t\n7GwIXXYjOW+tli8gAwiL9mLstBjWrz5Oc6O8y/31tXPf1HVZMKvVah5//HEOHDjArl27+Mtf/tLl\nYHPnzmXLli1XNIYgCIah0Uq8sDuPSA97FiWY91q/bUVlVGzZQ+hdN8odyu9qKpvZ+NUppt8Qj2+g\n+c7sC3/k62TDC1PCefdgMSllTXKH87ugW+dQe/AkTedy5Q5FrwYkBBA/LIj1q0/QrjK+5f4E49Rl\nwXzkyBH69++Pl5cXQUFBBAUFkZyc/KfPHzFiBB4eHlc0hiAP0UslHzlyL0kSb+4rxFKp4IFRQWa/\nIkPOO58TtHgW1u5/LEzlyH9zo4r1q48zdmoMYdFeBj++Memr154ID3uevCaUF3fnk1fbJksM/5t7\nSwc7Qu++iZy3PpUlHkMaPi6cgBA3fvjyFBqNPCuX9NVz31R1WTBXVFTg5+fHRx99xLp16/D19aWs\nrKxHB9DFGIIg6NYnx8soaVDx5PgwLJTmXSy3FZVRsfkXQpfdJHcoALSrOli/+gSDhgUxIMH8thwX\num9IgBN3XxXA09tzqGxWyx0OAMG3zaH2wEmaM/LkDkWvFAoF46+Lw9bWim3fpSJpjWvlEsH4dOum\nv2XLljF//nyAXs9E6WIMQX9EL5V8DJ37jWeqOJBfz/OTI7C1NP/7fnPf/YLAhddfcnYZDJt/jUbL\nD1+eIiDUjWHjwg12XGPW16894yPdmdXfi6e259DcrjHosS+Ve0sHe0KXLSC7D8wyK5UKpi+Ip6mh\njV+3ZRj8+H393Dc1Xa5f5Ofnd9FscHl5OX5+fj06QE/GuOeeewgODgbAxcWFgQMH/v6VxYUTSzzW\nz+PU1FSjikc81s9jbUB/1iRXcLNvA6nHD8sej74fJ4ZGUv7jz1i/8Veq9u+XNR5JkqgvdsLWzgob\ntxoOHDgge37EY+N47NuQhS/WPLMzj1emRnD08EGDHP+C//33klh/Wt/9gubMfByjQ2XPjz4fW1lZ\n4BfVwZnDBTg625I02nA/7wXGlA9zfnzhz4WF5zewueOOO+gJhdTFButqtZrY2FiOHDmCSqVi/Pjx\nZGWd317yiSeeQKFQ8PLLL1/0mvz8fK677rrfC7Cuxvhvu3fvJiEhoUfBC4LQfSllzbywO48V0yKI\n6CMrMpx57HUsnR2IefoeuUPhly1nqShpZN5tSVhamd+W48KV0UoSr/ycjxZ4anwoSpm/ic1553Oa\nz+Yw6IPnZI3DUBrr2/jmoyOMmxZDbHzPJgYF03Ty5EkmTJjQ7ed3+X2stbU1K1asYNSoUUyYMIGV\nK1f+/m/l5eWUl5df9Px7772XkSNHkpGRQVBQEJs3b+5yDEEQDCOvto0Xd+fx5DWhfaZYbiupoPzH\n3YTdLX/v8rF9eeRn1TBrUYIoloVLUioUPDouhIY2DR8eLqGLuSyDCFk6l5q9x2jOypc1DkNxdrVj\nzuJEdv94lsKcGrnDEYxQlzPMhiRmmOW1/7++rhYMS9+5r2xW89cfM7ljmD/XRLjr7TjGJv3xf2Dh\nYEfM8nu7fJ6+83/2dCl7t2dy07LhOLvaXf4FfYy49lysuV3DQ5uzmBjlzg3xPno91uVyn/P2ZzRn\n5jHon8/qNQ5jUphTw4/fJnPD0qF4+Tld/gVXQJz78tLpDLMgCKatqV3DU9tzmN3fq08Vy20lFZRt\n3EXY/90saxwF2TX8suUcc5YkimJZ6BZHG0temhrBpvQqdmXVyhpLyNJ51Ow52mdmmQGCIzyYMCOO\n7z8/QWO9PMv9CcZJzDALgplq12h5Yms20V723H1VoNzhGFTao69i5eIka+9yRUkD360+wcybBxMU\n1nc+rAi6kV/XxmNbsvnb1SEkBjrLFkfOO5/TdCaLwR+9IFsMcji+P5+UY0XctGw4dvbWcocj6IGY\nYRYEgU6txMs/5+PtaM1dw/vWWr+t+cVUbNlD2L0LZYuhrqaF7z8/yeRZ/UWxLPRKqJsdyyeGsWJP\nAdnVrbLFEXL7fOoOnaYxLVO2GOSQNDqUsBgvfvjiJB0dnXKHIxgBUTALwB+XuREMR9e5lySJlfsL\n6dBqeXhssOx32xta9j/+Tcjt87F2696snK7z39LUznefHmfkhEii+uu3B9UciGvPnxvo68gDo4JY\nviOXsqZ2nY/fndxbOtgRdv9Csl79l86Pb+yunhqDk4stP61JQauHjU3EuW9aRMEsCGZm9fEy8utU\nLJ8QhpVF3/oVbzqXS/Weo4TetUCW47erOvhu9XEGJAQwaFiQLDEI5mVMmCs3DvLhqW05NKg0ssQQ\ntGgWTenZ1J9Ik+X4clEoFUydF49K1cHPP56VfeUSQV6ih1kQzMiGtEp+PFvNW9dF42JrKXc4Bnfq\n9idxTRxA2D2Gv9lP09HJ+tUn8PBxZMJ1cWJHU0Gn/n2slJSyJl6dHiXLDp1FX26k7IddDPvuXYMf\nW27tqg6+XXWU2Hhfhl8dIXc4go6IHmZB6KN+yallXWolr0yN7JPFckPyOepPpBF821yDH1urldiy\nNgV7R2vGzxDFsqB7S5P8CHC24aXdeWj00B5wOQELrkVVXE7N/uMGP7bcbGytmHtrIslHi0g9USx3\nOIJMRMEsAKKXSk66yP3x4kY+OFTCS1Mi8HHqm3d0Z61YRcSDS7Cws+nR6640/5IksXtTOu0qDdPm\nx6NUimK5J8S1p3sUCgUPjQ0B4M29BWh18OVwT3KvtLIk8tE7yHzloz7ZmuDobMu825LYvyOLrPQK\nnYwpzn3TIgpmQTBxGVUtvLqngL9PDCPMvW+u9Vt3JJmW7AICb5lp8GMf3J1NeXEDsxYOwVKGr8qF\nvsNSqeCpCWGUNan5SIbdAP1mTaSzpY2qnQcNelxj4e7lyOzFCezYcIbCXLEbYF8jepgFwYQV1at4\ndEsWD44OZkSIi9zhyEKSJI7OvpeAG68l8MZrDXrs04cLOX4gn5uWDcfBsWcz24LQW83tGh7ZksXY\nMDduHuJr0GNXbP2V7H98wsidn6JQ9s0PiBd2A5x7ayK+AX3zumsORA+zIPQRNS0dPLkth1uT/Pts\nsQxQtesg6po6/OdNMehxM1LLObwnh3m3JYliWTCo87sBRrI9s4bNZ6sNemzvqWNRWltRtmGnQY9r\nTIIjPJg8uz8bPj9JbVWz3OEIBiIKZgEQvVRy6k3uG1UantyWzbVxHkyN8dBDVKZB6uwk88X3iXn6\nHpSWvbvRsTf5L8iuYfemdOYsScTV3b5XxxXOE9ee3vGwt2LFtEi+OlXOnpy6Xo3Rm9wrFApilt9L\n1opVaNvVvTquOYjq58PoyVF89+lxmhpUvRpDnPumRRTMgmBi2jo6eXp7DgkBTiyI79sbY5Ss2YqV\nmzNek0cb7JilhfVsXpPMdTcPxttPvi2LBcHP2YaXpkTw/qFijhc3Guy47iOH4BgbTsGn6w12TGM0\nMDGQISNCWPfJMdpa++6Hh75C9DALgglRa7Q8vSMHPycb/jI6qE8vX9bZqmLvqAUM+fglXBMHGOSY\nVWVNrPvkGFPnDSQ8xssgxxSEyzlT3syzu/J4fnI4cd4OBjlm07lcjs29jzEHvsXKtW9/cNy7LYPC\n3FpuuH0o1jZ9b0lPUyV6mAXBTGm0Ei/9ko+LrSUPjOrbxTJA/sdrcU0cYLBiua66hfWfHWf8dXGi\nWBaMSn9fRx4dF8wzO3LJr2szyDGdYsPxnjqG3He/MMjxjNmYKdF4+Tqx8atTaDRaucMR9EQUzAIg\neqnk1J3cayWJN/YW0KmVeGxcCBZ9fK1fdU09+R9+Q/STd1/xWN3Jf2N9G+s+OcbICZHExvtd8TGF\n/xDXHt0YFuTC3VcF8OS2HMqb2rv1mivNfeSjd1D89Y+0FZdf0TimTqFQMGlWf6xtLPlpbQrabm4s\nI8590yIKZkEwcpIk8d7BYiqbO3h6QhhWFuLXNmflavyun4hDeJDej9XS3M66T46RMDKE+KH6P54g\n9Nb4SHduiPfh8a051LZ26P14tr5eBC2ZTdZrH+v9WMZOqVRw7YJBqNo62LXxTJ/c3MXciR5mQTBy\nnxwr5URJI69Nj8LB2kLucGTXWlDCoWl3MPrXr7DxctfrsVRtHaz9+Cjhsd6MnhSl12MJgq58eaqc\nvbl1vH5tFC62+u2p1TS1sHfEDSStWYlzf/E7om7XsO6TY/gHu3L19Ng+3zpnzEQPsyCYkTXJFRwq\naODlqZGiWP5N5ssfEnrnDXovltVqDd9/doLAUHdGTYzU67EEQZduGezD8GAXntiaTXO7Rq/HsnRy\nIOIvt5L54vt6PY6psLaxZO6tSRTl1XFgZ5bc4Qg6JApmARC9VHL6s9z/mF7FT+eqWTEtUu+zRKai\n7kgy9cfTCLnrRp2Nean8azRaNn55CjdPB665VswS6ZO49uieQqFgaZIfA3wdeWp7Dq3qzks+T1e5\nD1o8i9bCMqp29c0ts/+XrZ0V825LIiu9ksO/5Pzp88S5b1pEwSwIRmh3di3fnK5gxbRIPBys5A7H\nKEhaLWeXv030U/+HpYOd3o6j7dSyZU0y1jaWTJndH0Ufv8FSME0KhYL/uyqAUDc7ntmZi0qPqzco\nra2IffZ+zj37Dlq1/nunTYG9gzXzlyaRdrKE4/vz5Q5H0AFRMAsAjB5tuI0fhIv9b+4PFtSz6kgJ\nL0+LwM9ZbLl8Qcman1DaWOE3e5JOx/3v/Etaie0b0uhQa7h2wSCU4gZLvRPXHv1RKBQ8MCoITwcr\nnt+Vi7rz4qJZl7n3mjgSuyB/Cvv4Zib/zdHZlhtuH8rJQwWcPlL4h38X575pEe8GgmBEDhc28Na+\nIl6YHEGom/5mUU2NpqmFrBWriHv+Qb21R0haiR0/nKGhto2ZtwzB0lJcHgXTZ6FU8MjYEGwtLXjp\n53w03VzyrKcUCgWxzz1Aztufo67u3Vbd5sjZ1Y4blg7lyJ5c0k6WyB2OcAXEO4IAiF4qOV3I/dGi\nBt7YW8gLk8OJ9rKXOSrjkvP2Z3iMG4bLkH46H3v//v1IksSuTenUVjUzZ0ki1taiZ9xQxLVH/yyU\nCp64JgStVuLVPfl0/lY06zr3jtGh+M+ZJJaZ+x+uHvbMuy2JfdszOZdS9vvfi3PftIiCWRCMwPHi\nRl7/tZDnJoUTa6CtbU1Fa34xxV//SPRTV75JyaVIksTuH89SWdbInCVJYmtbwSxZWShZPiGMRlUn\nb+0rRKunFWUjH7mdip/20JSerZfxTZWHtyPzbk3i581nyT5bKXc4Qi+IglkARC+VnBzCBvHqngKe\nmRhGPx9RLP+vc8+9R+jdN2Hr46nzsSVJoqPek/LiBubdloSNWI3E4MS1x3CsLZU8OymM0sZ23jtY\nzKhRo3R+DCtXZyIfXsrZ5SvF5h3/w8vPiTmLE9n+fRr5WdXi3DcxomAWBBkllzbx8i/5LJ8QxgBf\nR7nDMTo1+47TdCab0LsW6HxsSZLY89M5SgrrfiuWxWokgvmzs7LghSkRZFW3supIiV6K2sBF16Ou\nqafip191Prap8w10YdbCIWxZk0xhTo3c4Qg9IApmARC9VHJIKWvmxZ/zud6riXg/USz/L626g/Qn\n3yT2ufuxsNXtaiGSJLF3WyZFeXUExXViayeKZbmIa4/hOVhb8NKUCA5ml/OhHopmpaUlcS/+lXPP\nvIOmpU2nY5uDgBA3rrt5MN9/cYyCbFE0mwpRMAuCDM6UN/PC7jyevCaUUAf9rY9qyvI/+gb7EH+8\np47V6biSJLF/Rxb52dXMX5qEpbVYZ1noe5xtLVkYpCK9ooX3D+m+aPYYnYjbsHhyVq7W6bjmIjjc\ng+ghdmz+9jT5WdVyhyN0g0Iykiaj3bt3k5CQIHcYgqB36RUtPLMzl79dHUJSoLPc4Ril1sIyDk1d\nyoitH2MfEqDTsQ/syiIrvYIbbh+GvYO1TscWBFPTou7kia3ZRHnac+/IQJQ6XLaxvbKG/VcvYviG\nf+IYE6azcc1JcX4dG786xfT5AwmL9pI7nD7l5MmTTJgwodvPFzPMgmBA5yrPF8uPjgsWxXIXzi1/\ni9C7Fui8WD64O5vMtArmLx0qimVB4Hx7xivTIsmpaePdA0U6XT3DxtuDyIeXcubxf4gbAP9EYKgb\nsxYO4ad1qeRmVMkdjtAFUTALgOgjNISzlS0s35HLQ2OCGRbk8vvfi9xfrHL7PlpyCgn7v5t1NqYk\nSRzcnc25lDJuuH0oDo7/6YkW+ZeXyL98LuTewdqCl6dGUFCn4u39ui2ag2+dTWdLK6XfbdPZvoDX\nmQAAIABJREFUmObiQv4DQtyYvSiBrd+lkiOWnDNaomAWBANIKWvi7zvOzyyPCHG5/Av6KE1LG+lP\nvUW/Vx5BaaObGWBJkti7PZPMtHIW3DEMByex3bgg/C97awtemhpBcUM7b+4t/H1zkyulsLCg/6uP\nkvnC+3TUN+pkTHPkH+zKnCXnl5zLTq+QOxzhEkQPsyDo2bGiRl77tYAnx4cyxN9J7nCMWsZLH6Aq\nLmfQB8/pZDxJK7F781nKiuqZd1sSdvaiDUMQutLW0cnfd+Ti5WDFw2NDsFDqpqf5zN9eB6D/q4/q\nZDxzVV7SwPerTzDx+n5ED/CVOxyzJnqYBcGI7M+v57VfC3h2Upgoli+jOSOP4q9+JObZ+3UynlYr\nsX1DGlVljdxw+1BRLAtCN1xYp7mmtYPXfy3Q2Uxz9BPLqNy6l/qT6ToZz1z5Brgw97Ykdm1KJyO1\nXO5whP8iCmYBEH2E+vBzdi3vHijipakR9Pf583WWRe5B6uwk9aGXiXr0dp3s6NfZqWXLmmQa61XM\nvcymJCL/8hL5l8+f5d7WUsnzkyOoV2l4dU++TopmK1dnYp69n7SHX0Gr7rji8czBn+Xfx9+Zebed\n30b7XEqZgaMS/owomAVBD7Zm1PCvo6WsmBZJtKe93OEYvYJ/f4fSyoqgJbOveCxNRyebvj5Nh7qT\nOYsTsLYW210LQk/ZWCp5flI4LWotL+zOQ9155evF+82ehF2gL7nvfK6DCM2bt9/5ovmXLedIPVEs\ndzgCoodZEHRuQ1ol36VW8ur0SAJdbOUOx+i1FpRwaNodXLV5FQ7hQVc0Voe6kx++PImNrSXX3jAI\nC0sxJyAIV6KjU8uKPQU0t3fy7KQw7Kwsrmg8VVkVByYsYdj6d3GKi9BRlOartrqFdZ8cI2lUKImj\nQuUOx6yIHmZBkNG3yeX8cKaKN2ZEiWK5GyRJIu3hFYTfu/CKi+V2lYb1q4/j4GTDjAWiWBYEXbCy\nUPLkNaF4O1rxxNYcmts1VzSerZ8X0U8uI+2vL6PVXNlYfYG7pwM33TWc04cLObg7W6xnLSPxjiIA\noo/wSkmSxKfHS9mZWcsbM6Lw7cHSZX0598VfbULT1ELIsgVXNE5bq5p1nxzDw9uRaXMHorTo/qWt\nL+ffGIj8y6e7ubdQKvjrmGBivO15ZEs2dW1X1oMceMtMLBztKVi19orGMXXdzb+zqx033jWcrDMV\n7NmaIYpmmYiCWRCukFaS+PBwCUcKG/nHjCg8xQ5y3aIqrSTz5Y8YuPIplJa97zNublSx9uNjBIS6\nMfH6fih0tAyWIAj/oVQouHt4ACNDXHh4cxYVTepej6VQKBjwxuPkvvcFLblFOozSfDk42bDgzmGU\nFtSx/fs0tDroKRd6RvQwC8IV6OjU8o+9hVQ2q3l+cjhONuIGs+6QJImTix/DZVAskY/c3utx6mpa\n+O7T4wxICOCqayJQKESxLAj6tiGtknWplbw0JYIwd7tej5O/ag0VP/3KsO/fQ6EU83fdoW7XsOnr\nU1haWnDtjYOwusKe8r5M9DALgoG0dXTyzM5cVB1aVkyLFMVyD5Ss+Ym24nLCH1jc6zHKSxr4dtVR\nho8LZ8T4SFEsC4KBzB7gzZ3D/PnbT9mklTf3epyQ2+chdXZS8O91OozOvFnbWDJ7USKWVhZ898lx\nVFfYHiN0nyiYBUD0EfZUfVsHj/2UjYe9FX+fGIbNFdxg1tdy31pQQsbz/2TQ+8+itP7z9ZG7UpBd\nzfrVJ5g4sx/xQ6/sZsG+ln9jI/IvnyvJ/TUR7jx2dQjP7crjUEFDr8ZQWFgQ/+5yct76jKazOb2O\nxVT1Nv8WlkquvSEenwBnvl11hOZGlY4jEy5FFMyC0EMVTWoe2pxFgr8TD40J1tnWsX2B1NlJyv0v\nEP7Aol4vKXUupYzNa1KYefNgovr76DhCQRC6KynQmRenhPP2/kK2ZtT0agz70EBinv4/Uu57Hm17\n7/ui+xqFUsE118YSN9ifrz86Qm1V72f6he4RPcyC0AOZ1a08syOXG+K9mT3AW+5wTE7O259Rs+84\nQ9e+3eOeRUmSOL4/n5MHC5izOBEvP7HVuCAYg+IGFU9ty2FCpDuLEnx73B4lSRKnlj6BQ3gQMcvv\n1VOU5ivtRDF7t2cy8+YhBIa6yR2OyRA9zIKgJ0cKG3hqWw73jgwUxXIvNJw+S8GqNcS/s7zHxbJW\nK/Hzj2c5c6qEm5YNF8WyIBiRQBdbVl4XzbHiRl7fW0hHD1dwUCgUDHj9b5Su307twVN6itJ8DUgM\nZPr8eDZ+dUpspa1Hl33XWrt2LdHR0cTExLB58+ZePdfCwoIhQ4YwZMgQ/vKXv1x51ILOiT7Crm0+\nW81b+wp5fnI4o0NddTp2X8h9Z6uKlPueI+6lv2Lr37MPG2q1ho1fnaKmqoWb7hqOs2vv78q/lL6Q\nf2Mm8i8fXebezd6K16ZH0tLeyVPbc2hRd/bo9daebgz4x+OkPPACHY19o71Al/kPjfJk/tIkft2a\nwdG9eWKtZj3osmBWq9U8/vjjHDhwgF27dnVZ7Hb1XHt7e06dOsWpU6dYuXKl7qIXBD3TShL/PlrC\n+tRK3pgRTZy3g9whmaSM59/DeVAsfrMm9eh1LU3trP34GLZ2lsxdkoiNbe9uEhQEQf/srCz4+8Qw\ngl1t+euPmVQ296wn2WviSLwnjiT9iX/oKULz5u3nzE3LhpN+uoRdm9LFWs061mXBfOTIEfr374+X\nlxdBQUEEBQWRnJzc7eempKToJWhB90aPHi13CEZHpdHy0s/5pJa3sHJmNAEu3d+9ryfMPfflm3+h\navch+r38cI9eV1XexFcfHiYs2pOpcwfqbatrc8+/sRP5l48+cm+hVHDviEAmR7nz4KZMzlW29Oj1\nMX+/j6bULIq/6fobbXOgj/w7u9px013Dqa9pZcMXJ2lXiWXndKXLd6CKigr8/Pz46KOPWLduHb6+\nvpSVXbo/pqvnqlQqEhMTGT16NPv27dP9TyEIOlbdoubhzZlYWyh4bXokLrZijeXeaM0vJv1vrzN4\n1QtYuXS/7zjnXCVrPz7KmElRjJoYJdZYFgQTolAomBfvw/2jAlm+I5c9OXXdfq2FvS2D//UiGS+8\n3yeXmtMFG1sr5ixJxNnNjq8/PEJ9bavcIZmFbk3ZLFu2jPnz5wNc9o3rv597QUlJCSdOnGDlypXc\nfPPNtLe39zJcQV9EH+F/ZFa38sCmTEaHuvLYuBCs9TSzeYG55r5T1c7pO58m4qGluAzp163XnF8J\nI48dG84we3ECcYP99Ryl+ebfVIj8y0ffuR8Z4sqKaRF8fKyEL06Wdbuv1jEmjNjn7uf0nU+hae7Z\nDLUp0Wf+LSyUTJzZj0HDg/jmoyMU53f/Q4twaV1Om/n5+V00o1xeXo6fn1+Pn+vtff4mn6SkJPz9\n/cnPzycmJuYPY9xzzz0EBwcD4OLiwsCBA3//yuLCiSUe6+dxamqqUcUj12MpYADvHChiskczQc31\nKBS+RhWfKT1W/WsDnqGBBC+d263na7USbdVulBc3EJ1kQW7hGfyDjefnEY/FY3N7fIE+jxfhYc9C\n3wbWpKsorFfxyNgQjh0+ePnX+znhNmwQZx59jcabJ6JQKGTPlynmP2FECKXleXy3+igTZw5gQEKA\n0fz8cuR7//79FBYWAnDHHXfQE12uw6xWq4mNjeXIkSOoVCrGjx9PVlYWAE888QQKhYKXX365y+fW\n1dVha2uLnZ0d+fn5jB49mqysLOzsLr7TXazDLMhJK0l8cbKcHZk1PDspnChPe7lDMmmlG3aQ/drH\njNj+CVbOjpd9fktTO5u+Po2tvRXX3hCPtdhmXBDMSrtGy5v7CiluUPHMxHC8Ha0v+5rOtnYOX3sn\nQbfOIXjxLANEab5qKpvZ8PlJIuK8GDc1BqWFWFW4p+swd/muZG1tzYoVKxg1ahTARStclJeXX9Se\n8WfPPXv2LEuXLsXGxgYLCwv+/e9//6FYFgQ5tag7WfFLPi0dnbx3fQxu9mIlhivRnF3A2adWMnTt\nym4Vy2VF9Wz6+jQDEgMYOT4Shdg5URDMjo2lksevDuG71Eoe2JjBk+NDib/MeuoWdjYM/teLHL7u\nblwGx+ES/8dvpoXu8fB25JZ7rmLLmhS++/Q4M24ajL3D5T+0CP8hdvoTgPNfU1z4+qIvKaxX8ezO\nXBICnFg2PAArGT51m1PuNU0tHJ5xFyF3zCdo0eVnhFJPFLN3awaT5wwgqp8821ybU/5Nkci/fOTK\n/YniRl7dU8AtQ3yZ2c/zsvdGlf2wi8yXP2TE1o+x9tDtOvhykiP/Wq3E/p2ZnEspZ9YtQ/D2dzbo\n8Y2J2OlPELrpUEEDD2/OYsEgH+4bGSRLsWxOJK2W5Hufw234IAIXXt/lczs1WnZvSufor7nceNdw\n2YplQRAMLzHQmbdnRvPTuWre2FtIu6br9YL9Zk3E97prOH3n02g7NAaK0jwplQrGTolh7JRo1n16\nnLOnS+UOyWSIGWahz+nUSqw+XsrPOXU8PSFMbEaiI5mvfEjdkRSGrn0bpfWft7U01rex+dtk7Oyt\nmH5DvNiMRBD6qLaOTt7aV0hRQzvLJ4Th7/zna91LnZ2cXPwYdkF+9FvxiAGjNF9VZU1s/OoUYdGe\njJsei6WeV4QyNmKGWRC6UNPawWM/ZZNd08b7s2NFsawjpRt2UPb9ToZ8/FKXxXJ+VjVffXCYiDhv\nZi1MEMWyIPRhdlYWPHFNKNNiPHhwUyYH8uv/9LkKCwviP3iOmgMnKPxsg+GCNGNefk4sum8EzY3t\nfLvqCA11bXKHZNREwSwAf1zmxhwllzZx3w8ZDPF35MUpEUazGYmp574h+Rxnn1pJwmevYu3pdsnn\naLUSB3ZlsW19KjMWDGL4uHCjubnP1PNv6kT+5WMMuVcoFMzs58ULk8P58HAJq46UoNFe+otvK2dH\nEj57jezXP6b24CkDR6p7xpB/G1srZt4ymJiBvnz1wSFyM6rkDsloiYJZMHudWolvTpfzyi/5PDI2\nmIUJflgYSbFm6torazi19An6v/4YTv0iL/mclqZ21q8+TlFeLQvvGUFQuLuBoxQEwdjFejvwz1kx\nFNSpeGxLFpXN6ks+zyE8iPh/PsPpZctpLRD9t7qgUCgYOiaMmTcPYecPZ9i3PZPOzq77yvsi0cMs\nmLXqFjWv7ilAK8Hj14TgJZbR0RlNcwtH59yH99SxRD502yWfk5dZxbb1aQxMDGDkhEix9qcgCF3S\nShJrUyr4PrWKB0YHMTr00qtiFHy8jsLPvmf4xg+xdncxcJTmq6W5na3fpdLe1sG1Cwbh6m6+exKI\nHmZB+M3hwgbu/SGDQf5OvDY9UhTLOqRVd3Dqjqdwjo8h4q+3/uHfOzVa9mw9x44NZ7h2QTyjJ0eL\nYlkQhMtSKhTcOMiX5yaHs+pICe8cKLrkKhohd8zHa8JITi55jM62dhkiNU8OjjbMXZz4W4vGYc6l\nlF3+RX2EeAcTAOPopdIVtUbL+4eKee9gEcsnhLFwiK9Rt2CYWu4lSSLtoVdQWlvTb8Ujf1hDta6m\nha8/OkxdVQuL7htJcLiHTJF2j6nl39yI/MvHmHMf5+3AB7NjaWrXcP/GDPJq/3hDWszf78UuyI/k\n//s7Wo3pLTdnrPlXKBUkjQ5j7pJE9u/MYvv3aajVppdfXRMFs2BWcmpauW9jBtUtHXwwO5YBvpff\naU7omcyXPqA1v5jBHz6P0vI/N05KkkTy0SK+/uAw/YcEMGtRgthJShCEXnOwtuDJa0KZM8Cbx37K\nZn1qJdr/6iJVKJUMXPkUna0qzj75JkbSYWo2fANdWHzfSDo7tXzx7kFKC/98FZO+QPQwC2ahU/tb\n31taFXcN92dipPtld48Seq7g43UUrl7P8E0fXdQ32NyoYvuGM7Q2tTNtfjyePuKDiiAIulPa2M5r\newqwslDw6LgQvB3/82Fc09TCkdn34Hvt1UT89dL3UwhXJiO1nN0/phM/NIgR4yOwMIMWO9HDLPQ5\npY3tPLw5i5MlTfxzVgyTojxEsawHpeu3k/vPL0n8+q2LiuXMtHI+f+8gPv7O3Hz3VaJYFgRB5/yd\nbXhjRhSJgU7c+0MGO7Nqfp9RtnRyIOnrNyn+ZguFq7+XOVLzFDPQl8X3jaSitJGvPzhMdUWz3CEZ\nnCiYBcB4e6m6opUkNqVX8eCmTMaGu/Lq9MiLZh1MhSnkvuyHXWQ89x5J37yFfbAfAK0tarasSWbf\n9kxmLUxg9KQoLExwpyhTyL85E/mXj6nl3kJ5/obAFdMiWJdSyXO78qhp7QDAxtuDoWtXkvvuFxR9\ntUnmSLvH1PLv6GzLnMUJxA8LYs2/jnB0bx7aPrT8nHHs3CAIPVTcoOLNfYVotfDGjCiCXW3lDsls\nlW/Zw9nlKxm6ZiVOseFIkkRGajm/bDlHbLwvi+4fibW1uJQIgmAYER72vDcrhq9PlXP39+e4c5g/\nk6LcsQ8NZOi6dzg69z6UlpYELJgud6hmR6FQMGhYECGRHuz4Po2M1DKmzh2Il6+T3KHpnehhFkxK\np1ZifWola1MquGWILzP7eRn1ChimrnL7PtIeeZWkr9/AeWAMzY0qdm1Kp7aqhalzB+IffOk1UgVB\nEAwhu7qVN/YV4mZnyYOjgvFxsqY5K59j8x8gZvm9+M+dIneIZkuSJFKPF7NveyaDrwrmqqsjTOpb\nxp72MIuCWTAZWdWtvL2/CHtrJX8dHYyfs43cIZm1ql0HSf3LSyR+db5YTj1RzL4dWQwaGshV4yOx\nNKELoyAI5kujlViXUsH61MrfJ1LasvI5dsODxD73AH6zJsodollralCx84czNNa3MWlWfwJC3OQO\nqVvETX9CrxhzL1WLupMPDhXz1LYcZsR58uq0SLMqlo0x9xVbfyX1wRdJ+Pw11D4BfLPqCKnHi5l/\nWxKjJ0ebVbFsjPnvS0T+5WMuubdUKrhpsC9vzojmQH4D92/MoNTdh6Rv3+Lc39+mZN1WuUO8JHPJ\nv5OLLbMXJ3DV1RFs+vo0Ozak0dZ66a3NTZloPBSMliRJ7Muv58NDJSQGOvGveXG42IpTVt+Kv9lM\n1isfMejz10musCRt6zFGT4wkfmgQCtH+IgiCkQp2s+X1ayPZlV3L8h05jA1zZf7Xb5G+5FE6GpoI\nveMGuUM0WwqFgthBfoRGe7J/Zxar3z7A2KnR9BvsbzarVomWDMEoFdWr+OBwMVXNHTwwOoiBYgMS\ng8j78BsKPl6Hx/NPcSiticBQd8ZNi8HByXxm9AVBMH+NKg3/PlbK0aJGbguywPZvz+I3axKRj95u\nNgWcMSsrbmDXD2ewtrFk/Iw4vPyM76ZA0cMsmLQWdSdfnixjV3YdCwb5cH0/T6zMYIF0YydJElmv\nfETpj79QOXcpbdaOXDMj1ui3tRYEQejK2coW3j9UjG1TI9M+fgffkUOIe/EvKJTifUXftFqJlKNF\nHNydTfQAX0ZNisTO3niWfhU9zEKvyN1LpZUktmbUcPu6dFrUWlbNiWXeQO8+USzLnvsODSkPrSB3\n46+cm7CQ6NFxLLp3RJ8pluXOf18n8i+fvpD7OG8H3p4ZzaRh4Xy66H7OHkrj2LJn0LbL32Nr7vlX\nKhUMviqY2/46GoUCPnlrPycPFZjs2s2iIVSQ3YniRj4+VoqNhZLnp0QQ7Wkvd0h9RltVPQdufoyG\nxnZcHn6UJTMGGNUMgCAIwpVSKhRMjvZgVKgr30Q/TcULb1IxdRljv34dFz9PucMze3b21kyY2Y/4\nYUH8svkspw8XMnZqDBGxXibVHiNaMgTZ5NS08q+jpZQ3qbl9qD+jQ11M6pfHlGm1EilbTlD02PMo\nBg7kqrcfw9PP5fIvFARBMHGl9W1sf/I9HPfsxf4fzzB1aqJYz99AJEkiN6OKvdsysbO3Yty0GPyC\n5FnPv6ctGWKGWTC4sqZ2vjhRxomSJm4Z4sv0WE8sxcXKICRJIi+zmsOrfsL1h68IfeA2Bj54k9xh\nCYIgGIy/qx23vf8oJz+Lo/jBp3jmpiVMXTqdUSFi0kbfFAoFEbHehEV7ceZkCRu/OoV/sCujJ0Xh\n7mXcN/ebf4Oo0C2G6KWqbFazcn8h9/2QgY+TDZ/M78fMfl59vlg2VB9bYU4N33x4mKMvforXT2sY\n/vkroljG/PsIjZ3Iv3z6eu4TlsxgzLdvMGbDNxx55d/ct+EsR4saMNQX7305/0qlgoFJgdz+0Fh8\n/J355qMjbP0uhfraVrlD+1NihlnQu5rWDr49XcHPObVMj/Hgk/n9xHrKBlScX8eBnVk0V9UTdXoH\nFtWVDP5pFQ5hgXKHJgiCICu3xAGM3fYxzsuWM/DLQj5rXMhX7s4sSfRjiL+TmHHWMytrC4ZfHcHg\nq4I5vj+fL/95iOgBPlx1TQTOrnZyh3cR0cMs6E1ls5p1KZX8nFPLpCh3FsT74GZvJXdYfYIkSRTl\n1nL4lxzq69pIDFHS8ua7eIxNIvb5B7GwFesqC4IgXKDt0JD50geU//gznX9/hK/UbrjYWnLTYB+G\nBjqLwtlA2lrVHN+XT/LRIqIH+DB0bBhuHg56OZZYh1mQXVG9irUpFRwsaGBqtAdzB3rjLgplg5Ak\nidxzVRzek0N7m4ahY0NxSj1GzusfE/fSX/CfPVnuEAVBEIxW5fZ9pD28gpB7bqFg8lTWJFegVCq4\naZAPo0Jdxc2BBtLaoubkwQKSjxQSGuXJsHHhePnqdvMTUTALvbJ//35Gjx59RWOcrWxhfWolyWXN\nXN/Pk5n9vHAWrReXpYvcd2q0nEsp49j+PJRKJcPHhRPsbcW5J/5Ba34Jg1a9gGNkiI4iNi+6yL/Q\neyL/8hG5v7TWwjKSly3HytWJfv94nGSNLd+cLqdZ3cm8gd5MiHTHxvLKbwET+b+8dpWG00cKOXEg\nH78gV5JGhxIY6qaTGX+xSoZgUJ1aiYMFDaxPraSmtYPZA7x4eGwwdlYWcofWJ7S1qkk+WsTpw4V4\neDsybmoMoVGelG/6mUM3v0XAjdcy6IPnUNqItZUFQRC6wz7Yj+GbPiT33S84NOk2Yv5+LytvmEZK\n+flJodXHy5gR58l1cZ6izVDPbGwtGT4unISRIZw5UcKODWlY21iSNCqU6IG+WBhwczMxwyz0SlO7\nhh2ZtWxMr8Ldzoq5A70ZGeIivq4ykKryJk4fLiQjtZzIft4kjgrFy9cJdU096U+8QVN6FgPfWY5r\nQn+5QxUEQTBZjWmZpD74ErZ+XvT/x9+w9fWisF7FD2lV7MmtY1SoC9f38yJSbLhlEJL2/DrOx/fn\nU1/byuCrghmYGIi9Y88nhURLhqBXWdWt/Jhezf78eoYGOTOrvxdx3vppyBcu1qnRkpVewenDhdTV\ntBI/NJDBw4NxcLJBkiTK1m8n44X38ZszmajH7sTCTtzYJwiCcKW06g5yVn5G0WffE/XEMgJvvg6F\nUkmDSsNP56rZfLYaTwcrrovzYmyYK9Y6aNcQLq+ipIFThwvJOlNBRJw3g4cH4xfU/bW0RcEs9EpX\nvVRtHZ3szatny9lqats6uDbWk6kxHrjZia+idOFyfWz1ta2kHS8m9UQJ7p4ODL4qmMh+3r9/FdWU\nnk36k2/Q2aqi34pHxKxyD4k+QnmJ/MtH5L5nGtMySX/iDaQODf1eeRiXIf2A862JR4sa2ZReRXZN\nG5Oj3JkS40Gwq22X44n860Zbq5q0EyUkHynC2taSQUMDiR3kh41t1zWK6GEWdEKSJNIrWtiWWcOB\n/AYG+jpy02BfhgU5i7YLA+hQd5J1poLU48VUVzQRN9if+UuH4unzn52QOhqayH79Y8o27CTysTsJ\nWjgThYXoHRcEQdAH5wHRDN/4AaXrtnFyyd/wmjyK6CfuxtrDlREhLowIcaGkQcVP52p4dEsW/s42\nTIn2YGyYK/bW4tqsL3b21gwdE0bSqFDys6tJPV7M3u2ZRMR6MyApgKBQdxQ6qFvEDLNwkbKmdn7J\nrmNXdi0AU6M9mBjlLpaFMwBJK1GcX8fZ5FIy0yrwC3JhQGIgEXHeWP7XV3xadQdFX2wk9+3PLrpg\nC4IgCIbx3xMWYfcuJPi2uRe1wWm0EseKGtmWWUNqWTMjQlwYH+HGYH8nMelkAK3Nas4ml5J6vJiO\njk5mL0rA0+fiZelES4bQYw0qDXtz69idXUdJYztjw1wZH+lGP28HsVi7AVSVNZGeXMq55DJs7ayI\nG+xH3CB/nFwu/jpP6uykbMNOsl77GIfIEKKfXIbzgGiZohYEQRCaM/PJenUVDafSiXh4KQELpqO0\nvPjL+9rWDvbk1rE7u5aa1g6uDndjfKQ7UR524j1WzyRJorykEU8fR6z+Z/UuUTAL3VLf1sHBggb2\n5dVztrKFMFs1C0ZEkxTojKX49KtXkiRRXd5MRlo5mWnlNDe1MnhYKHGD/S+5MLuk1VK5Yz9Zr/4L\nSwc7op/8P9xHDpEhcvMk+gjlJfIvH5F73ak/eYbMlz5AVV5N1KN34HvdNZdskSusV/FLTh0/Z9fS\nrlIxqZ8fY8JcRfEsA9HDLPypymY1hwsb2J9fT1Z1G0mBTkyL9eDvE8M4ceQQVwW7yB2i2ZK0EuUl\nDWSnV5KZVk6nViJ6gA/T5g0kOz+VMWNi/vAabYeGsg07yXvvS5S21kT97U68p4wRF1VBEAQj45rQ\nn6HfvUvN3mNkv/4xWa+uIuyem/GfPw0L2/+0agS72rIk0Y/FCb58t/sQTcDLP+fTqZUYE+bKyBAX\n4rwdRNuGERIzzGZMK0lkVbdyuLCRw4UNVDWrGRbkzMhQV4YGOutkpyLhz6nVGgqza8g+W0luRhV2\n9tZExHkR3d8XnwDnPy18NS1tFH/zI/kffIN9WCDh9y/CY+xQUSgLgiCYAEmSqDuSTN67X9CYlkXI\nnTcQtHgWVs6Of/r8vFoV+/LrOVTQQE1rB0ODnLkq2JmkAGdxw6CeiJaMPq6urYMTxU1zRgh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LTvY7keac8nbRiEzDDx//kvuP8f/1AOZi2Yu7q6uHTp0tT60NAQXV1dcx7b3d3N+Pj4nI5h2zY9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\nELOQglkIIYQQQohZSMEshBBCCCHELP4fMK5kG7vKF9gAAAAASUVORK5CYII=\n", - "text": [ - "" - ] - } - ], - "prompt_number": 11 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Another beautiful result! If I handed you a measuring tape and asked you to measure the distance from table to a wall, and you got 23m, and then a friend make the same measurement and got 25m, your best guess must be 24m. \n", - "\n", - "That is fairly counter-intuitive, so let's consider it further. Perhaps a more reasonable assumption would be that either you or your coworker just made a mistake, and the true distance is either 23 or 25, but certainly not 24. Surely that is possible. However, suppose the two measurements you reported as 24.01 and 23.99. In that case you would agree that in this case the best guess for the correct value is 24? Which interpretation we choose depends on the properties of the sensors we are using. Humans make galling mistakes, physical sensors do not. \n", - "\n", - "This topic is fairly deep, and I will explore it once we have completed our Kalman filter. For now I will merely say that the Kalman filter requires the interpretation that measurements are accurate, with Gaussian noise, and that a large error caused by misreading a measuring tape is not Gaussian noise.\n", - "\n", - "For now I ask that you trust me. The math is correct, so we have no choice but to accept it and use it. We will see how the Kalman filter deals with movements vs error very soon. In the meantime, accept that 24 is the correct answer to this problem.\n", - "\n", - "One final test of your intuition. What if the two measurements are widely separated? " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "xs = np.arange(0, 60, 0.1)\n", - "\n", - "m1,s1 = 10, 5\n", - "m2,s2 = 50, 5\n", - "m, s = multiply(m1,s1,m2,s2)\n", - "\n", - "ys = [stats.gaussian(x,m1,s1) for x in xs]\n", - "p1, = plt.plot (xs,ys)\n", - "\n", - "ys = [stats.gaussian(x,m2,s2) for x in xs]\n", - "p2, = plt.plot (xs,ys)\n", - "\n", - "ys = [stats.gaussian(x,m,s) for x in xs]\n", - "p3, = plt.plot(xs,ys)\n", - "plt.legend([p1,p2,p3],['measure 1', 'measure 2', 'multiply'])\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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0i7lTj2RaMuLNnzE3BwZLDsLnB5MJjSTAsadtzF9mMcZz0SOPPAIAePXVV6HT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\ncLUJCQY1YgP5P9iP17VcABAfpEaUzheHq2fmUcM8dzddug7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- "text": [ - "" - ] - } - ], - "prompt_number": 12 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This result bothered me quite a bit when I first learned it. If my first measurement was 10, and the next one was 50, why would I choose 30 as a result? And why would I be *more* confident? Doesn't it make sense that either one of the measurements is wrong, or that I am measuring a moving object? Shouldn't the result be nearer 50? And, shouldn't the variance be larger, not smaller?\n", - "\n", - "Well, no. Recall the g-h filter chapter. In that chapter we agreed that if I weighed myself on two scales, and the first read 160lbs while the second read 170lbs, and both were equally accurate, the best estimate was 165lbs. Futhermore I should be a bit more confident about 165lbs vs 160lbs or 170lbs because I know have two readings, both near this estimate, increasing my confidence that neither is wildly wrong. \n", - "\n", - "Let's look at the math again to convince ourselves that the physical interpretation of the Gaussian equations makes sense.\n", - "\n", - "$$\n", - "\\mu=\\frac{\\sigma_1^2 \\mu_2 + \\sigma_2^2 \\mu_1} {\\sigma_1^2 + \\sigma_2^2}\n", - "$$\n", - "\n", - "If both scales have the same accuracy, then $\\sigma_1^2 = \\sigma_2^2$, and the resulting equation is\n", - "\n", - "$$\\mu=\\frac{\\mu_1 + \\mu_2}{2}$$\n", - "\n", - "which is just the average of the two weighings. If we look at the extreme cases, assume the first scale is very much more accurate than than the second one. At the limit, we can set \n", - "$\\sigma_1^2=0$, yielding\n", - "\n", - "$$\n", - "\\begin{aligned}\n", - "\\mu&=\\frac{0*\\mu_2 + \\sigma_2^2 \\mu_1} { \\sigma_2^2}, \\\\\n", - "\\text{or just}\\\\\n", - "\\mu&=\\mu_1\n", - "\\end{aligned}\n", - "$$\n", - "\n", - "Finally, if we set $\\sigma_1^2 = 9\\sigma_2^2$, then the resulting equation is\n", - "\n", - "$$\n", - "\\begin{aligned}\n", - "\\mu&=\\frac{9 \\sigma_2^2 \\mu_2 + \\sigma_2^2 \\mu_1} {9 \\sigma_2^2 + \\sigma_2^2} \\\\\n", - "\\text{or just}\\\\\n", - "\\mu&= \\frac{1}{10} \\mu_1 + \\frac{9}{10} \\mu_2\n", - "\\end{aligned}\n", - "$$\n", - "\n", - "This again fits our physical intuition of favoring the second, accurate scale over the first, inaccurate scale." - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Implementing the Sensing Step" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Recall the histogram filter uses a numpy array to encode our belief about the position of our dog at any time. That array stored our belief of our dog's position in the hallway using 10 discrete positions. This was very crude, because with a 100m hallway that corresponded to positions 10m apart. It would have been trivial to expand the number of positions to say 1,000, and that is what we would do if using it for a real problem. But the problem remains that the distribution is discrete and multimodal - it can express strong belief that the dog is in two positions at the same time.\n", - "\n", - "Therefore, we will use a single Gaussian to reflect our current belief of the dog's position. In other words, we will use $dog_{pos} = \\mathcal{N}(\\mu,\\sigma^2)$. Gaussians extend to infinity on both sides of the mean, so the single Gaussian will cover the entire hallway. They are unimodal, and seem to reflect the behavior of real-world sensors - most errors are small and clustered around the mean. Here is the entire implementation of the sense function for a Kalman filter:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def sense(mu, sigma, measurement, measurement_sigma):\n", - " return multiply(mu, sigma, measurement, measurement_sigma)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 13 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Kalman filters are supposed to be hard! But this is very short and straightforward. All we are doing is multiplying the Gaussian that reflects our belief of where the dog was with the new measurement. Perhaps this would be clearer if we used more specific names:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def sense_dog(dog_pos, dog_sigma, measurement, measurement_sigma):\n", - " return multiply(dog_pos, dog_sigma, measurement, measurement_sigma)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 14 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That is less abstract, which perhaps helps with comprehension, but it is poor coding practice. We are writing a Kalman filter that works for any problem, not just tracking dogs in a hallway, so we don't use variable names with 'dog' in them. Still, the *sense_dog()* function should make what we are doing very clear. \n", - "\n", - "Let's look at an example. We will suppose that our current belief for the dog's position is $N(2,5)$. Don't worry about where that number came from. It may appear that we have a chicken and egg problem, in that how do we know the position before we sense it, but we will resolve that shortly. We will create a *DogSensor* object initialized to be at position 0.0, and with no velocity, and modest noise. This corresponds to the dog standing still at the far left side of the hallway. Note that we mistakenly believe the dog is at postion 2.0, not 0.0." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "dog = DogSensor(velocity=0, noise=1)\n", - "\n", - "pos,s = 2, 5\n", - "for i in range(20):\n", - " pos,s = sense(pos, s, dog.sense(), 5)\n", - " print('time:', i, '\\tposition =', \"%.3f\" % pos, '\\tvariance =', \"%.3f\" % s)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "time: 0 \tposition = 0.260 \tvariance = 2.500\n", - "time: 1 \tposition = 0.381 \tvariance = 1.667\n", - "time: 2 \tposition = 0.401 \tvariance = 1.250\n", - "time: 3 \tposition = 0.308 \tvariance = 1.000\n", - "time: 4 \tposition = 0.626 \tvariance = 0.833\n", - "time: 5 \tposition = 0.456 \tvariance = 0.714\n", - "time: 6 \tposition = 0.540 \tvariance = 0.625\n", - "time: 7 \tposition = 0.225 \tvariance = 0.556\n", - "time: 8 \tposition = 0.043 \tvariance = 0.500\n", - "time: 9 \tposition = -0.006 \tvariance = 0.455\n", - "time: 10 \tposition = -0.087 \tvariance = 0.417\n", - "time: 11 \tposition = 0.044 \tvariance = 0.385\n", - "time: 12 \tposition = 0.027 \tvariance = 0.357\n", - "time: 13 \tposition = 0.051 \tvariance = 0.333\n", - "time: 14 \tposition = 0.059 \tvariance = 0.312\n", - "time: 15 \tposition = 0.023 \tvariance = 0.294\n", - "time: 16 \tposition = 0.068 \tvariance = 0.278\n", - "time: 17 \tposition = 0.096 \tvariance = 0.263\n", - "time: 18 \tposition = 0.054 \tvariance = 0.250\n", - "time: 19 \tposition = 0.065 \tvariance = 0.238\n" - ] - } - ], - "prompt_number": 15 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Because of the random numbers I do not know the exact values that you see, but the position should have converged very quickly to almost 0 despite the initial error of believing that the position was 2.0. Furthermore, the variance should have quickly converged from the intial value of 5.0 to 0.238.\n", - "\n", - "By now the fact that we converged to a position of 0.0 should not be terribly suprising. All we are doing is computing $new\\_position = old\\_position * measurement$, and the measurement is a normal distribution around 0, so we should get very close to 0 after 20 iterations. But the truly amazing part of this code is how the variance became 0.238 despite every measurement having a variance of 5.0. \n", - "\n", - "If we think about the physical interpretation of this is should be clear that this is what should happen. If you sent 20 people into the hall with a tape measure to physically measure the position of the dog you would be very confident in the result after 20 measurements - more confident than after 1 or 2 measurements. So it makes sense that as we make more measurements the variance gets smaller.\n", - "\n", - "Mathematically it makes sense as well. Recall the computation for the variance after the multiplication: $\\sigma^2 = 1/(\\frac{1}{{\\sigma}_1} + \\frac{1}{{\\sigma}_2})$. We take the reciprocals of the sigma from the measurement and prior belief, add them, and take the reciprocal of the result. Think about that for a moment, and you will see that this will always result in smaller numbers as we proceed." - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Implementing Updates" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That is a beautiful result, but it is not yet a filter. We assumed that the dog was sitting still, an extremely dubious assumption. Certainly it is a useless one - who would need to write a filter to track nonmoving objects? The histogram used a loop of sense and update functions, and we must do the same to accomodate movement.\n", - "\n", - "How how do we perform the update function with gaussians? Recall the histogram method:\n", - "\n", - " def update(pos, move, p_correct, p_under, p_over):\n", - " n = len(pos)\n", - " result = array(pos, dtype=float)\n", - " for i in range(n):\n", - " result[i] = \\\n", - " pos[(i-move) % n] * p_correct + \\\n", - " pos[(i-move-1) % n] * p_over + \\\n", - " pos[(i-move+1) % n] * p_under \n", - " return result\n", - " \n", - " \n", - "In a nutshell, we shift the probability vector by the amount we believe the animal moved, and adjust the probability. How do we do that with gaussians?\n", - "\n", - "It turns out that we just add gaussians. Think of the case without gaussians. I think my dog is at 7.3m, and he moves 2.6m to right, where is he now? Obviously, $7.3+2.6=9.9$. He is at 9.9m. Abstractly, the algorithm is $new\\_pos = old\\_pos + dist\\_moved$. It does not matter if we use floating point numbers or gaussians for these values, the algorithm must be the same. \n", - "\n", - "How is addition for gaussians performed? It turns out to be very simple:\n", - "$$ N({\\mu}_1, {{\\sigma}_1}^2)+N({\\mu}_2, {{\\sigma}_2}^2) = N({\\mu}_1 + {\\mu}_2, {{\\sigma}_1}^2 + {{\\sigma}_2}^2)$$\n", - "\n", - "All we do is add the means and the variance separately! Does that make sense? Think of the physical representation of this abstract equation.\n", - "${\\mu}_1$ is the old position, and ${\\mu}_2$ is the distance moved. Surely it makes sense that our new position is ${\\mu}_1 + {\\mu}_2$. What about the variance? It is perhaps harder to form an intuition about this. However, recall that with the *update()* function for the histogram filter we always lost information - our confidence after the update was lower than our confidence before the update. Perhaps this makes sense - we don't really know where the dog is moving, so perhaps the confidence should get smaller (variance gets larger). I assure you that the equation for gaussian addition is correct, and derived by basic algebra. Therefore it is reasonable to expect that if we are using gaussians to model physical events, the results must correctly describe those events.\n", - "\n", - "I recognize the amount of hand waving in that argument. Now is a good time to either work through the algebra to convince yourself of the mathematical correctness of the algorithm, or to work through some examples and see that it behaves reasonably. This book will do the latter.\n", - "\n", - "So, here is our implementation of the update function:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def update(pos, sigma, movement, movement_sigma):\n", - " return (pos + movement, sigma + movement_sigma)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 16 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What is left? Just calling these functions. The histogram did nothing more than loop over the *sense()* and *update()* functions, so let's do the same. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# assume dog is always moving 1m to the right\n", - "movement = 1\n", - "movement_error = 2\n", - "sensor_error = 10\n", - "pos = (0, 500) # gaussian N(0,50)\n", - "\n", - "dog = DogSensor(pos[0], velocity=movement, noise=sensor_error)\n", - "\n", - "zs = []\n", - "ps = []\n", - "\n", - "for i in range(10):\n", - " pos = update(pos[0], pos[1], movement, movement_error)\n", - " print('UPDATE: %.4f,\\t%.4f' % (pos[0], pos[1]))\n", - " \n", - " Z = dog.sense()\n", - " zs.append(Z)\n", - " \n", - " pos = sense(pos[0], pos[1], Z, sensor_error)\n", - " ps.append(pos[0])\n", - " \n", - " print('SENSE: %.4f,\\t%.4f' % (pos[0], pos[1]))\n", - " print()\n", - " \n", - "p1, = plt.plot(zs,c='r', linestyle='dashed')\n", - "p2, = plt.plot(ps, c='b')\n", - "plt.legend([p1,p2], ['measurement', 'filter'], 2)\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "UPDATE: 1.0000,\t502.0000\n", - "SENSE: -4.7569,\t9.8047\n", - "\n", - "UPDATE: -3.7569,\t11.8047\n", - "SENSE: -0.2966,\t5.4138\n", - "\n", - "UPDATE: 0.7034,\t7.4138\n", - "SENSE: 3.9704,\t4.2574\n", - "\n", - "UPDATE: 4.9704,\t6.2574\n", - "SENSE: 5.5456,\t3.8490\n", - "\n", - "UPDATE: 6.5456,\t5.8490\n", - "SENSE: 6.3932,\t3.6904\n", - "\n", - "UPDATE: 7.3932,\t5.6904\n", - "SENSE: 6.2941,\t3.6267\n", - "\n", - "UPDATE: 7.2941,\t5.6267\n", - "SENSE: 7.2177,\t3.6007\n", - "\n", - "UPDATE: 8.2177,\t5.6007\n", - "SENSE: 7.4951,\t3.5900\n", - "\n", - "UPDATE: 8.4951,\t5.5900\n", - "SENSE: 7.8431,\t3.5856\n", - "\n", - "UPDATE: 8.8431,\t5.5856\n", - "SENSE: 8.2236,\t3.5838\n", - "\n" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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Bo1s3eOrUgaduXchVqmhqhYNgxpleUoRl2jS4b7sNzoceUruU0OPxoFK7dnC1\naoX8UaO8Z9MSaYTL5V1GrGBcYc8eAxo2dKNDByfatXOhRQtXaYsXkJ+ZFi6EccsWGNLSIFss3neT\n2rSB48knoZnfPLTC6YR04QLkGjWK3WTYtg0RTz0Fd926hY2su25duBMS4G7ZUoViQ5PITC+bXio3\n3aFDiHzwQWRt3gy5hDNDyf+kCxdgmTYNpmXL4Hj8cXhuvx3uBg0095YmBT+PB/jlF33huML27UbE\nx7vRvr33aG7r1k7tnVfjcnnXw+3QQe1K1CHL0B06BMO2bTDs34+8f/4zpI88ShcvwrxwYeFRW93R\no94TBNu0Qc4XXxR/QEEbFcKZaQFPZAsQAT3bJMuwjhoF28sv+73hDeic/EyOjkb+22/D1r8/MqZM\nQd2DByFdvlxi06vfsweW6dPhqVEDcvXq8FSvDrlGDbjr1YPnjjtUqF4dbrf3NdWhA19TN3Oj7z1Z\nBg4f1hWOK6SmGhAVJaN9exd69XLgww/zULWqf1dYKA8pIwPh/foBFgtykpNR3ku0BeTPKUmCp0ED\nOBo0gKOUu+gOHULYpElwXjm3wNOgQbmaPFVystu9Kx8cPQr9sWOQLl2C7eWXi99PkiBlZcGVlATP\nI4/AU7cuPLVqlX4hIT82uwH5etIwNr1ULqaVKyFdvAj73/6mdikEQK5dG4eeeAI1bvBD0lO7Nhw9\ne0J39iykM2dg2L0b0pkz8DRsiPw33yx2f/3evTCtWuVtjq9pkj2xsQF1Rnh2NvD99wZs327Ajh0G\n7N1rQH5+d+h03msDmEwyzObS/zabZZhMKHbbzR5b0u3X7stsLnpbICzMceqUVDiusGWLER6Pdxmx\nzp2dePvtPNSurd0m91qGrVsR3r8/7H36wDZyZLkb3mAmV6sG5733wpCWBsuMGZDy8+Fq3RqORx+F\ns0cPtcvzKu1yt3l5iGrZEtLZs/DExl4dQSjl4gty5col/iykwMfxBvKZdOkSKrVujZylS+Fu1kzt\ncshPdH/+CePXX0N35gykM2egO3MGusxMONu3R/6kScXuX3DWeGFzXL06PDVqAJGRFfr237lzErZv\nv9rk/v67Ho0bu9C6tQutWnlnSSMjvTOndjvgcEiw2wG7XSqyfe3fNtu12977Fvxd8NjrP+5wADZb\nyR8v6XEGQ/GG2dsgF//7Rs14SQ11WZtxk8nbhF+4ICE11TuusHWrEefOSUhOdl0ZWXDi9tsrbhkx\nRXg8sHxVePifAAAgAElEQVTwAcxz5yJ31iy4OnVSu6KAI504AWNaGmSrVZWm17BhA/TXnDimO3oU\n+vR0XPr99xKvcKc7dsx7tJazykGL4w3kX/n5yH/lFTa8Qc5z222wDxki/gC7HbqjR2HYubNIk2x/\n+ukSm2TdwYPQ//HH1Sa5WrUyXwBAloHjx3WFTe727QZkZOjQooW3yZ04MR9JSSWfMGU0FrxrWfD7\nv3pHKWUZcDpRSpMtCTXnBR/PzQUuXND53Hw7HN59Go3ehrhVKxfatXPi2Wdz0aiResuIKUG6eBH6\nPXuQtXEj5Nq11S4nIMm1a8PRq1ept1umTIF+797CpRbdiYliDacse39uXBlBcHTvXuLKNObly+Gp\nXBmeOnXgatXKe+S2Tp1SL+nsqVNH+HOj4MUjvRrAmR0xzEmMZnNyu0t8+9jw7bcwL1oEXWZmYZMs\nWyzeSy8PHVrs/rrjxyGfv4hfsuOw/bdq2L7LjO3bDXC5vI1Z69bePwkJ7pu+W63ZrDRClr1HtHfs\nSEOHDjdfI5r4miognTkDQ1oaDNu2wZiaCt2pU3C1bIn8N9+EOyGhWE7W4cNh2LkTuvR0yFZr4QhC\n3sSJkKtXV/EzURdfT+L8eqR3586d+Nvf/gaXy4XExEQsX77c110RUSgopQN1dekCV5cuVz8gy5Au\nX756RjS8jdf+/Xps327Ars8jsePXeNyCi2jn+QIPhO/B2zV+R9yIh+B86sli+5cuXAAkCXLlyjy7\nuowkyTvqoNcHxowuaYdcvTqcjzwC5yOPIB+AdO4cDNu2wVOlSon3t/fqBXu/fnDHx3tHoYj8wKcj\nvR6PBw0bNsQnn3yCNm3a4Pz586hy3QuZR3qJyFc5OcAPPxQ96axuXXfhPG6rVi7ExMiA2w3p/Hno\nzpyBp3LlEt+qtrz/PswzZ0LKy4NcrRo8V+aM7X37wnXffSp8dlShcnO9b3nzJDWioOa3I727d+9G\ntWrV0KZNGwAo1vASEZXF+fMSduy42uQePKhHo0ZutG7txEsv2dCihRtRUSX8fq7XQ65eHe4bvP1p\nGznSe2a+zeZdsSIz09sk163rv0+INEH322+IeO455I8bB2f37mqXQ0Qq8+lUhPT0dERFReHBBx9E\n06ZNMXv2bKXrCimpqalqlyBGlmGZOhXSuXOqPH3A5KSyQMjp+HEdVqwwYfhwK1q1qoSmTaPwySdm\n3HKLjAkT8vH775ewbl023njDhs6dXSU3vGVlscATFwf3PffA2bUrPLffHhBZaUEg5mT8/HNE9ugB\n25AhFdrwBmJWamBOYpiTsnw60muz2ZCWloaffvoJUVFRuOeee/DAAw+gXr16StdHGmL8+muYVqyA\n7aWX1C6FAogsAwcP6rBjhwHbtnmv0GW3Xz3prE8fOxo1cmtiJSH9gQOwjhqF/FGjvMtYcQY48Njt\nCHvtNRg3b0bO6tVwN2qkdkVEpBE+/TdTs2ZN3HXXXah9ZX6uWbNm+O2334o1vYMGDUJ8fDwAICoq\nComJiYVnIRb89sLtZCQnJ2uqnpK2t3/7LToOH468BQsAs1m1egqonYeWt9V+PTmdwJIlP+Hnn6OR\nmVkfO3YYYDTm4667LuDhh6MxapQNGRlbIEnayKvIduvWsP3tb8CIEUBYGAzvvAPXvfciNS1NG/Wp\ntF3wMa3Uc6PtsDffxIXffsP+d95BqysNbyh9/wXSdgGt1KPFbb6ebvz6SU1NRXp6OgCgX79+uBmf\nTmS7fPkyEhIScODAAYSHh6NZs2ZYtWoVGjRoUHgfnsgWXMLGjYN04QLyZs1SuxTSmLy8oied7d5t\nQHz81ZPOWrd2ITY2wM7+d7thXLMGYe+9Bzk8HLmzZnkvu0ral5MDhIfzKD1RiBE5kc2nmd6oqChM\nnz4dnTp1QtOmTfH0008XaXipbK7/rVdr9AcOwLRyJfInTFC1Dq3npBX+zuniRQnr1hnxxhth6Nw5\nEg0aVMbEiWHIz5cwYIAdP/54Gamp2XjvvXw8+qhT0w1vqVnp9XD27ImstDTYXnoJcrVqFVuYxgTU\n915EhKoNb0BlpSLmJIY5Kcvg6wMfe+wxPPbYY0rWQhql/+kn5L/1FuSqVdUuhVRw4kTBygpGbN9u\nwIkTOtxzj/cI7ptv5qNpU1dJF0wKDjodnH/9q9pVEBGRAnhFNiIqJMvA77/rCkcVtm83IC9PKjKq\nkJiojZPO1KbfsQO6y5fh7NKFb6WrwJCWBtPixcibM4f5E5F/r8hGRIHP5QIOHPBe6ayg0Q0Plwsv\n5TtihA3163vYU5RAcjpheecdWCZPhm3UKDgfeIDNV0XweGCeMQOWuXORO2sWMyciYT7N9JKyOLMj\nhjmJuVFO+flAaqoB771nQc+eEbj11sp46aVwHD6sw1//6sD//peFffuyMHt2Hvr0caBBg+BueMvz\nmnK1a4fszZthGzECln/8A5EdO8L49ddFLp8cLLTyvSddvIjwZ56Bad06ZG3cCFfHjmqXVIxWstI6\n5iSGOSmLR3qJgtilSxJ27jQUHsn9+Wc97rrLu7LCiy/aMX9+Lm65JfiatAqj08HZvTucXbvCuG4d\nzAsXwtmpk/eyt6Qo6cQJRHbvDme3bshdvBgwGtUuiYgCDGd6qTibDYatW+Hq3FntSqiM8vKAb74x\nFja56el6NGt2dR63WTMXwsPVrpLIB243DNu2wdWundqVEJEGcaaXfGKZNg36X39l0xtATp+WMH++\nGYsXm9G4sRsdOjjx5JN5uPtuNw+IaYB08iTkmBhAx4kyn+n1bHiJqFz4E1gDtDSzozt0COb585H3\nj3+oXUoxWspJK378UY+BA61o27YSsrMlrFuXjaFDv8FLL9nRrBkb3pupqNeU9fXXEdmhA4xffgl4\nPBXynEri9544ZiWGOYlhTspi00tXyTKso0bB9vLLkGvVUrsaKoXHA6xbZ8RDD0Xg6acj0LChG3v2\nZGHy5HzcdlvgNVShIHf+fOS//josH3yASu3awfif/wRk81tRjKtXQ8rIULsMIgoynOmlQqYVK2Ce\nNQvZGzeCC7FqT24u8O9/mzF3rhmVKskYNMiGhx5y8mhuIJFlGDZuRNjkyfDUqoXcRYvUrkhb7HaE\nvfYajJs3I2fpUnjuuEPtiogoQHCml8TJMkwLFyJv6lQ2vBpz8qSEefMsWLrUhNatXfjXv3LRsqU7\nqJcSC1qSBFfnzsi+7z5IZ8+qXY2m6NLTEf788/DUro2sTZuASpXULomIggzHGzRAEzM7koSctWvh\nbtZM7UpKpYmcKtDevXq8+KIV7dpVgs0GbNiQjcWLc9Gq1Y0b3lDLqTxUy0qSIFevrs5z+8DfORnX\nr0dk585wPPYYchcuDOiGl99/YpiTGOakLB7So6v0erUrCHlut3ded/ZsM44f1+HFF+14//28QO4B\nqCzy8xHxxBOwP/ccnH/9a8h8T+qOHkXO4sVwt2ypdilEFMQ400ukAdnZwLJl3nndqlW987rduzs5\naRJqZBmG775D2OTJkC5dQv6oUXA+8kjINL9ERL4SmenleAORik6ckPDGG2Fo0iQKO3YYMGdOLr79\nNht//Ssb3pAkSXB16oTsb75B3qRJsMybh0pt2sDw3XdqV0ZEFPDY9GqAWjM70tmz3kt4BYhgmm36\n4Qc9XnghHB06VILHA3z3XTY++SQXLVq4y73vYMrJ3zSblSTB1bEjstetQ97kyZBVnm9RLCePB7pj\nx5TZl0Zp9jWlMcxJDHNSFo8lhSpZhvXvf4erXTvYBw1Su5qQ4HIBKSlGzJ5twZkzEvr3t2P69FzO\n61LpJAmuv/xF7SoUIV28COugQYBej9ylS9Uuh4hCEGd6Q5QxJQVhb7+NrC1bALNZ7XKCWlYWsGSJ\nGR99ZEZsrHdet2tXJ8c0qVykc+dg3LgRjsce0/wyg/q9exH+/PNwdu2K/LfeAkwmtUsioiDDmV4q\nWXY2rGPGeNfkZcPrN8eO6fDqq2FISorCvn0GfPJJLtaty0aPHmx4qfykS5dg+vRTVGrVCqZ//9v7\nVoLWyDJMCxYg4oknkD9+PPLffZcNLxGphk2vBlT0zE7YpElwdugAV3JyhT5veQXCbJMsAzt26NGn\nTzjuvTcSJhOweXMWPv44F02bln9eV0Qg5KQVgZyV5/bbkbN2LfKmT4fp3/9GpZYtYVq2zC/Nr685\n6X79FebFi5G9bh2cDz+scFXaFMivqYrEnMQwJ2Vp+z0xUpzu6FGYPv8cWWlpapcSVJxO4MsvvfO6\nly5553VnzsxFRITalVGwcyUnIyc5GYa0NFimTIGrSRN47rpL7bIAAJ677kL2f/8L6Hh8hYjUx5ne\nECSdPg05JkbtMoLC5csSFi0y4eOPLahb142BA+24/36OLxAREVUkkZleHukNQWx4y+/wYR3mzjVj\n5UoTunRxYunSHDRuXDHjC0RlJV26BDk8HDAa/fckHg+P6BKRpvEnlAZwZkeM2jnJMrBtmwG9e4fj\n/vsjEREhIzU1C3Pm5Gmq4VU7p0ASKlmZlixBpebNYVq0CHA4yvz4m+WkS09H5P33Q79/v68lBo1Q\neU2VF3MSw5yUxaaX6CYcDmDlShM6dYrEsGFWdOrkxL59l/H66zbExvplOohIUfYhQ5A7Zw5Ma9Z4\nm9+FC31qfktiXL8ekZ07w/HII3Dffbci+yQi8gfO9IYCp9O/b2sGqYsXr87r1q/vndft3NnJd3Ap\noOl37kTYlCnQpad7T2j1dQkxlwuWf/wD5uXLkTNvHtytWilbKBFRGXCmlwCbDZU6dULO0qXw3Hqr\n2tUEhD/+0GHOHDO++MKEBx90YvnyHDRqpJ3xBaLycLdsiZxVq6BLTy/Xmrnh/ftDungRWf/7H+Sq\nVRWskIjIP3jMSgP8ObNjmTYN7vr1g6Lh9WdOsgxs3WrAU0+Fo2vXSERHy9i+PQszZ+YFXMPLGTBx\noZyVJz5e+L4l5ZQ/bhxyVq5kw3udUH5NlQVzEsOclMUjvUFMd+gQzAsWIGvzZrVL0Sy7HfjiCxNm\nzzbD4ZAwcKANCxbkIixM7cqI1GEdPhzuRo1g7937hlds9NSrV4FVERGVH2d6g5UsI+Lhh+Hs2hX2\nAQPUrkZzzp+X8MknZixYYEbDhm4MHGhDp04uzutSyNPv2QPLlCkw/PQTbMOGeZtfi0XtsoiIbkhk\nppf/xQcp04oVkLKyYO/XT+1SNOXgQR2GD7finnsqIT1dh88/z8aqVTm47z42vEQA4G7aFLmffYac\nJUtg2LQJUU2bwjx9utplERGVG/+b1wB/zOy42rRB7uzZgCF4Jlh8zUmWge++M+CJJyLw8MORqFnT\ng127sjBjRh7uusujcJXq4wyYOGZVOndSEnL//W/kLFuG/Vz9RRhfU2KYkxjmpKzg6YioCE9cnNol\nqM5mAz7/3ITZsy2QZWDQIBsWL3bwnVqiMnA3aYJzOTlql0FEVG6c6aWgc/ashAULzPjkEzPuvts7\nr/uXv7ggSWpXRkRERP7g95ne7OxsxMbGYurUqeXZDZEifvlFhyFDrGjRohIyMnRYsyYbK1bkoGNH\nNrxEREShrlxN78SJE3HPPfdAYkdRLpzZEVNSTh4PsGGDAT17RuCxxyJRp44HP/yQhWnT8nDHHcE3\nryuCrydxzEoMcxLHrMQwJzHMSVk+z/QePHgQZ8+eRbNmzeCnCQkqA93x4wh76y3kzpuHUDismZ8P\nLF9uwpw5FphMMgYOtKNnT8eNlhUlIiKiEObzTG/Pnj3xwQcfYMGCBYiIiMDLL79c5HbO9FYgWUb4\n00/D3bw5bCNGqF2NX2VmSpg3z4xFi8xo1syFQYPsSE7m+AIREVEoE5np9elI79q1a9GgQQPExcXx\nKK8GGL/6CvojR5C7aJHapfjNkSM6vP++BV9/bcSjjzrw1VfZqF8/NMcXiIiIqOx8anp37dqFVatW\nYc2aNTh37hx0Oh1iY2Px1FNPFbnfoEGDEH/l+u5RUVFITExEcnIygKtzKtxOLjKzU+bHN24M69ix\n2DF4MM7v2qWJz0fp7cxMCQ88YERi4i7s3p2I6GgZqampyMzURn1a2y7X6ynEtq/PTO16tLo9e/Zs\n/vwW3Ob3n9j2gQMHMHDgQM3Uo9Vtvp5u/PM7NTUV6enpAIB+AhfjKveSZePHj0dkZCRGXPe2Oscb\nxKWmphZ+Mcsq7LXXIF26hLyZMxWuShtsNqBHj0h07uxEmzYbfc4plJTn9RRqmJUY5iSOWYlhTmKY\nkzi/jTeQssrzgvbUqgXH8OEKVqMdsgz8/e9WxMd7MGqUDZLEb3wR/AEpjlmJYU7imJUY5iSGOSmr\n3E3vm2++qUQd5CP7oEFql+A306ZZ8OefeqSkZPNENSIiIiqXcq3TS8q4dj6FvNauNWLBAjOWLs1B\nWJj3Y8xJDHMSx6zEMCdxzEoMcxLDnJTF8QbSnB9/1GPECCtWrsxBTAxXByEiIqLyK/eJbKXhiWzk\ni8xMCZ07R2LChHz89a9OtcshIiKiACByIhvHGwKMYetWGDZsULsMv7DZgN69I9C7t4MNLxERESmK\nTa8GCM/s5OfDOmyYf4tRyfUrNZSEs01imJM4ZiWGOYljVmKYkxjmpCzO9AYQy7RpcCcmwtW5s9ql\nKI4rNRAREZE/caY3QOh+/x2R3boha/NmyLGxapejqLVrjRg71ooNG7J44hoRERGVGS9OESxkGdaR\nI2EbOTLoGl6u1EBEREQVgTO9GnCzmR3pzBnI1avDLnBd6UCSmSmhd+9wvPdeHpo0cd/0/pxtEsOc\nxDErMcxJHLMSw5zEMCdl8UhvAJBr1EDuvHlql6GogpUannmGKzUQERGR/3GmlyqcLAMDBljhckmY\nNy+XJ64RERFRuXCmlzRp2jQL/viDKzUQERFRxeFMrwaE0sxOSooRCxaYsXRpDsLCyvbYUMqpPJiT\nOGYlhjmJY1ZimJMY5qQsNr0aZdiyBcjJUbsMRf34ox7Dh1uxZAlXaiAiIqKKxZleDdKlpyOyUydk\nb9wIT926apejiMxMCZ07R2LChHyeuEZERESKEpnp5ZFerZFlhI0eDfugQUHT8HKlBiIiIlIbm14N\nuHZmx5iSAv3Ro7C99JKKFSlHloGhQ62Ij/dg9GhbufbF2SYxzEkcsxLDnMQxKzHMSQxzUhZXb9CS\n7GxYx45F7kcfASaT2tUogis1EBERkRZwpldDTMuXw7B1K/I+/FDtUhSRkmLEmDFWbNiQxRPXiIiI\nyG+4Tm+AcfTqBcejj6pdhiIKVmpYsYIrNRAREZH6ONOrAUVmdgyB/3tIZqaE3r3D8d57eUhKciu2\nX842iWFO4piVGOYkjlmJYU5imJOy2PSSorhSAxEREWkRZ3pJMbIMDBhghdMpYf78XJ64RkRERBWC\nM70BQDpxAnLt2mqXoYiClRrWruVKDURERKQtHG9Qkf7AAUR264bUzZvVLqXcUlKMWLDAjKVLc2C1\n+uc5ONskhjmJY1ZimJM4ZiWGOYlhTsrikV4VWaZPh71fP0CvV7uUcuFKDURERKR1nOlVie7PPxH5\nwAO4vGcPEBmpdjk+y8yU0LlzJMaPz8cjj/DENSIiIqp4IjO9HG9QiWXGDNhfeCGgG95rV2pgw0tE\nRERaxqZXBdLJkzCuXQt7//4AAnNmR5aBoUOtiIvzYPRoW4U8ZyDmpAbmJI5ZiWFO4piVGOYkhjkp\nizO9ajCZkDdjBuToaLUr8RlXaiAiIqJAwpleKrOUFCPGjLFiw4YsnrhGREREquM6vaQ4rtRARERE\ngYgzvRoQKDM7mZkSevcOx5QpeUhKclf48wdKTmpjTuKYlRjmJI5ZiWFOYpiTsnxuek+ePInk5GQ0\natQIzZo1w8aNG5WsizSGKzUQERFRIPN5pvfMmTPIzMxEYmIi0tPT0aZNG5w4caLwds70Xsduh/7H\nH+Fu3lztSspMloEBA6xwOiXMn5/LE9eIiIhIU/w601u9enVUr14dABAfHw+HwwGn0wmj0ejrLoOa\n6bPPYEpJQc7KlWqXUmbTp3OlBiIiIgpsisz0rl+/Hs2aNWPDWxqXC5YZM2AbPrzEm7U8s5OSYsT8\n+WYsWZIDq1XdWrSck5YwJ3HMSgxzEsesxDAnMcxJWeVevSEjIwMjR47El19+Wey2QYMGIT4+HgAQ\nFRWFxMREJCcnA7j6hQyFbeOaNbhssSDN7UbylWy0VF9p24cPV8Lbb7fDihU5OHx4Kw4fVreeAwcO\naCofbgf+dgGt1KPV7QMHDmiqHm4H/jZ/nnNbiZ/fqampSE9PBwD069cPN1OudXptNhs6d+6M119/\nHV26dClyG2d6r5BlRHbogPxx4+C6LiMty8yU0LlzJMaPz+eJa0RERKRpIjO9Po83yLKM559/Hk8/\n/XSxhpeuMmzcCMgyXJ07q12KMK7UQERERMHG56Y3LS0Nq1atwkcffYSkpCQkJSUhIyNDydqCgrtF\nC+R+9BFudAbY9W+1qkmWgaFDrYiL82D0aJva5RShpZy0jDmJY1ZimJM4ZiWGOYlhTsoy+PrA5ORk\nOBwOJWsJSnJUFOSoKLXLEMaVGoiIiCgYlWum90Y40xt4UlKMGDPGim+/zUJsLC8xTERERIHBr+v0\nUnA5cECP4cOtWLEihw0vERERBR1F1uml8lF7ZiczU8Izz4RjypQ8JCW5Va3lRtTOKVAwJ3HMSgxz\nEsesxDAnMcxJWWx6/UB39CgMGzaoXYYQrtRAREREoYAzvX5gHToUnpo1YRs7Vu1SbkiWgQEDrHA6\nJcyfn8sT14iIiCggcaZXBdKpUzCuXYusH35Qu5Sb4koNREREFCo43qAwy6xZcDz5JOToaOHHqDGz\nk5JixLx5ZixZkgOrtcKf3iecbRLDnMQxKzHMSRyzEsOcxDAnZfFIr4KkCxdgWrYMWVu3ql3KDXGl\nBiIiIgo1nOlVkPmDD6D/80/kzZihdimlysyU0LlzJMaPz+eJa0RERBQUONNbwewDB0LKzVW7jFIV\nrNTw9NNcqYGIiIhCC2d6lWQyQb7lljI/rCJmdmQZGDrUirg4D0aPtvn9+fyBs01imJM4ZiWGOYlj\nVmKYkxjmpCwe6Q0R167UoOOvOkRERBRiONMbAlJSjHjlFSs2bMjiiWtEREQUdDjTS1ypgYiIiAic\n6S03w4YNMH7zTbn24a+ZncxMCc88E44pU/KQlOT2y3NUJM42iWFO4piVGOYkjlmJYU5imJOy2PSW\nhywj7O23IWtwSJYrNRARERFdxZnecjBs2ICwCROQvWULtHQdX1kGBgywwumUMG9eLk9cIyIioqDG\nmV4/s0ybBtuwYZpqeAGu1EBERER0PbZEPjJs3w5dRgacDz9c7n0pObOTkmLEvHlmLFmSA6tVsd1q\nAmebxDAnccxKDHMSx6zEMCcxzElZPNLrI+Pq1bD9/e+AQTsRFqzUsHw5V2ogIiIiuhZnen0ly4Db\nrZmmNzNTQufOkRg/Pp8nrhEREVFIEZnp5XiDryRJMw2vzQb83/9xpQYiIiKi0rDp1YDyzOzIMjB0\nqBW1ankwerRNwaq0h7NNYpiTOGYlhjmJY1ZimJMY5qQsbRyqJJ9Nn27BoUN6pKRwpQYiIiKi0nCm\nN4ClpBjxyitWbNiQxRPXiIiIKGRxpldhlvfeg+G//1W7DABXV2pYsoQrNRARERHdDJteQdKFCzDP\nng33HXcovu+yzuxkZkp45plwTJmSh6ZN3YrXo1WcbRLDnMQxKzHMSRyzEsOcxDAnZbHpFWT+6CM4\nu3eHXKuWqnVwpQYiIiKisuNMr4jsbEQ1bYrsdevguf121cqQZWDAACscDgnz5+fyxDUiIiIiiM30\ncvUGAeZFi+BKTla14QW4UgMRERGRr9g6CdD/8gtsw4b5bf8iMzspKUbMm2fG0qU5sFr9VoqmcbZJ\nDHMSx6zEMCdxzEoMcxLDnJTFI70C8mbNUvX5C1ZqWL6cKzUQERER+cLnmd4VK1Zg3LhxkCQJU6dO\nRffu3YvcHlQzvSrKzJTQuXMk3norHz178sQ1IiIiouv5babX4XBgzJgx2LlzJ2w2Gzp27Fis6aXy\nu3alBja8RERERL7zaaZ3586dSEhIQLVq1RAXF4e4uDjs379f6dpCRkkzO7IMDB1qRa1aHowebVOh\nKu3hbJMY5iSOWYlhTuKYlRjmJIY5KcunI72ZmZmIiYnB3LlzER0djZo1a+L06dNo3Lix0vWpR5YB\nSVLt6blSAxEREZFyytVO9e/fH48//jgAQFKxQfSHiEcfhX7v3gp5ruTk5CLbXKmhZNfnRCVjTuKY\nlRjmJI5ZiWFOYpiTsnw60hsTE4PTp08XbmdkZCAmJqbY/QYNGoT4+HgAQFRUFBITEwu/gAWH7LW4\nrd+xA47ffsOWrCy0vfK5VNTzR0V1wPDhVrz6aioOH76M2Fj18+A2t7nNbW5zm9vc1tJ2wb/T09MB\nAP369cPN+LR6g8PhwJ133ll4IlunTp1w6NChIvcJ5NUbInr1guPBB+F47rkKeb7U1FQkJydzpYab\nKMiJbow5iWNWYpiTOGYlhjmJYU7i/LZ6g8lkwqRJk9C2rfc46PTp033ZjSbpDxyA/qef4Fi8uEKf\nlys1EBEREfmPz+v03kygHukN79sXriZNYB8ypMKeU5aBAQOscDgkzJ+fyxPXiIiIiMrAb0d6g5bH\nA7lyZdgraKyhAFdqICIiIvIvtljX0umQN3UqEBlZYU+ZkmLErFngSg0Crh1ep9IxJ3HMSgxzEses\nxDAnMcxJWWx6VfTLL7orKzV8j9hYv0yZEBERERE406uaS5ck3HtvJEaPtqFXL4fa5RAREREFLJGZ\nXh7pVYHbDbz4Yji6dHGy4SUiIiKqAGx6ASArq0KfbtIkC/LzgQkT8gFwZkcUcxLDnMQxKzHMSRyz\nEsOcxDAnZbHpzclBVIsWkDIzK+Tp1q41YvlyExYsyIXRWCFPSURERBTyQn6m1zxzJgy7dyN3wQK/\nPw9YdEkAABcBSURBVNdvv+nQo0ckli/PQdOmbr8/HxEREVEo4Dq9N2O3wzJrFnL+/W+/P1VWlveK\na+PH57PhJSIiIqpgIT3eYPrsM7jvugvuu+/26/N4PED//uHo2NGJp58ufuIaZ3bEMCcxzEkcsxLD\nnMQxKzHMSQxzUlboHul1u2H517+QN2OG359qyhQLLl+WsGhRvt+fi4iIiIiKC92Z3txcmJcsgb1/\nf0CS/PY069YZMWqUFZs2ZaFGDV6AgoiIiEhpnOm9kfBw2AcM8OtT/P67Dn//uxXLluWw4SUiIiJS\nUUjP9PpTwYlrr7+ej+bNb3ziGmd2xDAnMcxJHLMSw5zEMSsxzEkMc1IWm14/8HiAQYPC0batC88+\nyyuuEREREaktdGd6/ei99yzYtMmIL7/MhsmkdjVEREREwU1kpjfkjvTqfvnFr/tfv96IhQvNWLgw\nhw0vERERkUaEVNOrP3AAkY8/Djj8M3Lwxx86DBlixYIFOahZU/wAOmd2xDAnMcxJHLMSw5zEMSsx\nzEkMc1JWSDW9lunTYRswAP44BJud7T1x7dVX89GyJa+4RkRERKQlITPTq/vzT0Q+8AAu79kDREYq\num9ZBvr0Ccctt8iYPj3Pn8v+EhEREdF1uE7vNSwzZsD+wguKN7wAMH26BadP6/Dxx9lseImIiIg0\nKCTGG6RTp2Bcu9Z79TWFbdxowMcfm7FoUQ7MZt/2wZkdMcxJDHMSx6zEMCdxzEoMcxLDnJQVEkd6\n5UqVkLtwIeToaEX3e+SIDoMHh2PhwlzExvKKa0RERERaFTIzvUrLyQHuv78Snn/ejn797GqXQ0RE\nRBSyuE6vn8gy8Pe/h6NJExf69mXDS0RERKR1bHp98K9/mXHsmA5TpyqzUgNndsQwJzHMSRyzEsOc\nxDErMcxJDHNSVkjM9Crpu+8MmD3bgg0bsmCxqF0NEREREYkI3pleux3GjRvh7NZNsV0eO6ZDly6R\nmD8/F8nJLsX2S0RERES+C+mZXtNnn8G8cKFi+8vLA/7v/8IxfLiNDS8RERFRgAnOptflgmXGDNiG\nD1dkd7IMDB0ajoQEN/r3V/7ENc7siGFOYpiTOGYlhjmJY1ZimJMY5qSsoJzpNa5ZA7laNbhat1Zk\nf7NmmXHokA7r1vGKa0RERESBKPhmemUZkR06IH/cOLi6dCn37rZsMeDFF8OxYUM24uI8ChRIRERE\nREoKyZlew3//C8gyXJ07l3tfx4/r8OKL4fjoo1w2vEREREQBrMxN78mTJ5GcnIxGjRqhWbNm2Lhx\noz/q8pmrfXvkfvopyjuHkJ/vPXFtyBAb2rf374lrnNkRw5zEMCdxzEoMcxLHrMQwJzHMSVllnuk1\nGo2YPXs2EhMTkZ6ejjZt2uDEiRP+qM03RiM88fHl2oUsA8OHW1G/vgeDBvGKa0RERESBrtwzvdWr\nV8fJkydhNBqLfFz1dXrLYe5cMz791IRvvsmG1ap2NURERER0IyIzveVavWH9+vVo1qxZsYY3kKWl\nGfDPf1rw7bdseImIiIiCxQ1neqdPn47ExMQif9544w0AQEZGBkaOHIlZs2ZVSKEV4cQJCf36hWPO\nnFzUqVNxJ65xZkcMcxLDnMQxKzHMSRyzEsOcxDAnZd3wSO+wYcMwbNiwYh+32Wx4/PHHMXXqVNSr\nV6/Uxw8aNAjxV+Zro6KikJiYiOTkZABXv5BKbOsOH8bhJUtw4t57fd7ff/+7DWPGtMXAgTZ07OhS\ntD5uK7N94MABTdXD7cDfLqCVerS6feDAAU3Vw+3A3+bPc24r8fM7NTUV6enpAIB+/frhZso80yvL\nMp5++mm0b98eAwcOLPV+FTnTax06FJ6aNWEbO9anx8sy8NJLVuTlSViwIJcXoCAiIiIKIH6Z6U1L\nS8OqVavw22+/4aOPPgIArFu3DjVr1vStynKSTp2Cce1aZP3wg8/7WLDAjH37DFi/PosNLxEREVEQ\nKvM6vcnJyXA4HNi7d2/hH7UaXgCwzJoFx5NPQo6O9unxO3boMWWKBUuW5CAiQuHiBF3/ViuVjDmJ\nYU7imJUY5iSOWYlhTmKYk7LKfKRXS6QLF2BatgxZW7f69PhTpyS88EIEPvwwF7feyiuuEREREQWr\ncq/TW5qKmOk1z5sH/Y8/Im/GjDI/1m4HunePxIMPOjFihM0P1RERERFRRfD7Or1qs/ft671ecBnJ\nMjBqlBWxsR4MH86Gl4iIiCjYlXmmV1MkCb5cQWLRIhO+/96ADz/UxkoNnNkRw5zEMCdxzEoMcxLH\nrMQwJzHMSVkBfaTXFzt36vHuu2FYty4bkZFqV0NEREREFSGgZ3rL6vRpCffdVwnTpuWiSxeX2uUQ\nERERkQJEZnoDe7yhDBwO4LnnIvDcc3Y2vEREREQ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- "text": [ - "" - ] - } - ], - "prompt_number": 17 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There is a fair bit of arbitrary constants code above, but don't worry about it. What does require explanation are the first few lines:\n", - "\n", - " movement = 1 \n", - " movement_error = 2\n", - " \n", - "For the moment we are assuming that we have some other sensor that detects how the dog is moving. For example, there could be an inertial sensor clipped onto the dog's collar, and it reports how far the dog moved each time it is triggered. The details don't matter. The upshot is that we have a sensor, it has noise, and so we represent it with a Gaussian. Later we will learn what to do if we do not have a sensor for the *update()* step.\n", - "\n", - "For now let's walk through the code and output bit by bit.\n", - "\n", - " movement = 1\n", - " movement_error = 2\n", - " sensor_error = 10\n", - " pos = (0, 500) # gaussian N(0,500)\n", - " \n", - " \n", - "The first lines just set up the initial conditions for our filter. We are assuming that the dog moves steadily to the right 1m at a time. We have a relatively low error of 2 for the movement sensor, and a higher error of 10 for the RFID position sensor. Finally, we set our belief of the dog's initial position as $N(0,500)$. Why those numbers. Well, 0 is as good as any number if we don't know where the dog is. But we set the variance to 500 to denote that we have no confidence in this value at all. 100m is almost as likely as 0 with this value for the variance. \n", - "\n", - "Next we initialize the RFID simulator with\n", - " dog = DogSensor(pos[0], velocity=movement, noise=sensor_error)\n", - "\n", - "It may seem very 'convienent' to set the simulator to the same position as our guess, and it is. Do not fret. In the next example we will see the effect of a wildly inaccurate guess for the dog's initial position.\n", - "\n", - "The next code allocates an array to store the output of the measurements and filtered positions. \n", - "\n", - " zs = []\n", - " ps = []\n", - " \n", - "This is the first time that I am introducing standard nomenclature used by the Kalman filtering literature. It is traditional to call our measurement $Z$, and so I follow that convention here. As an aside, I find the nomenclature used by the literature very obscure. However, if you wish to read the literature you will have to become used to it, so I will not use a much more readable variable name such as $m$ or $measure$.\n", - " \n", - " \n", - "Now we just enter our *sense()->update()* loop.\n", - "\n", - " for i in range(10):\n", - " pos = update(pos[0], pos[1], movement, sensor_error)\n", - " print 'UPDATE:', \"%.4f\" %pos[0], \", %.4f\" %pos[1]\n", - "\n", - "Wait, why *update()* before sense? It turns out the order does not matter once, but the first call to DogSensor.sense() assumes that the dog has already moved, so we start with the update step. In practice you will order these calls based on the details of your sensor, and you will very typically do the *sense()* first.\n", - "\n", - "So we call the update function with the gaussian representing our current belief about our position, the another gaussian representing our belief as to where the dog is moving, and then print the output. Your output will differ, but when writing this I get this as output:\n", - "\n", - " UPDATE: 1.000 502.000\n", - "\n", - "What is this saying? After the update, we believe that we are at 1.0, and the variance is now 502.0. Recall we started at 500.0. The variance got worse, which is always what happens during the update step.\n", - "\n", - " Z = dog.sense()\n", - " zs.append(Z)\n", - " \n", - "Here we sense the dog's position, and store it in our array so we can plot the results later.\n", - "\n", - "Finally we call the sense function of our filter, save the result in our *ps* array, and print the updated position belief:\n", - "\n", - " pos = sense(pos[0], pos[1], Z, movement_error)\n", - " ps.append(pos[0])\n", - " print 'SENSE:', \"%.4f\" %pos[0], \", %.4f\" %pos[1]\n", - " \n", - "Your result will be different, but I get\n", - "\n", - " SENSE: 1.6279 , 9.8047\n", - " \n", - "as the result. What is happening? Well, at this point the dog is really at 1.0, however the predicted position is 1.6279. What is happening is the RFID sensor has a fair amount of noise, and so we compute the position as 1.6279. That is pretty far off from 1, but this is just are first time through the loop. Intuition tells us that the results will get better as we make more measurements, so let's hope that this is true for our filter as well. Now look at the variance: 9.8047. It has dropped tremendously from 502.0. Why? Well, the RFID has a reasonably small variance of 2.0, so we trust it far more than our previous belief. At this point there is no way to know for sure that the RFID is outputting reliable data, so the variance is not 2.0, but is has gotten much better.\n", - "\n", - "Now the software just loops, calling *update()* and *sense()* in turn. Because of the random sampling I do not know exactly what numbers you are seeing, but the final position is probably between 9 and 11, and the final variance is probably around 3.5. After several runs I did see the final position nearer 7, which would have been the result of several measurements with relatively large errors.\n", - "\n", - "Now look at the plot. The noisy measurements are plotted in with a dotted red line, and the filter results are in the solid blue line. Both are quite noisy, but notice how much noisier the measurements (red line) are. This is your first Kalman filter shown to work!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this example I only plotted 10 data points so the output from the print statements would not overwhelm us. Now let's look at the filter's performance with more data. This time we will plot both the output of the filter and the variance." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%precision 2\n", - "# assume dog is always moving 1m to the right\n", - "movement = 1\n", - "movement_error = 2\n", - "sensor_error = 4.5\n", - "pos = (0, 100) # gaussian N(0,50)\n", - "\n", - "dog = DogSensor(pos[0], velocity=movement, noise=sensor_error)\n", - "\n", - "zs = []\n", - "ps = []\n", - "vs = []\n", - "\n", - "for i in range(50):\n", - " pos = update(pos[0], pos[1], movement, movement_error) \n", - " Z = dog.sense()\n", - " zs.append(Z)\n", - " vs.append(pos[1])\n", - " \n", - " pos = sense(pos[0], pos[1], Z, sensor_error)\n", - " ps.append(pos[0])\n", - " \n", - "#plt.subplot(121) \n", - "p1, = plt.plot(zs,c='r', linestyle='dashed')\n", - "p2, = plt.plot(ps, c='b')\n", - "plt.legend([p1,p2], ['measurement', 'filter'], 2)\n", - "plt.show()\n", - "\n", - "plt.plot(vs)\n", - "plt.title('Variance')\n", - "plt.show()\n", - "print ([float(\"%0.4f\" % v) for v in vs])" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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8YqWiKlYBnJmZSa1atZg6dSoACxcuJDExkaSkJFasWFGqAYqIiEjZdvCghRdf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\nLu6S5jcnB4YNc3HNNX7++lc1vyIiInI5NcASUsG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- "text": [ - "" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[102.0, 6.3099, 4.6267, 4.2812, 4.1939, 4.1708, 4.1646, 4.1629, 4.1624, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623]\n" - ] - } - ], - "prompt_number": 18 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we can see that the variance converges very quickly to roughly 4.1623 in 10 steps. We interpret this as meaning that we become very confident in our position estimate very quickly. The first few measurements are unsure due to our uncertainty in our guess at the initial position, but the filter is able to quickly determine an accurate estimate." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> Before I go on, I want to emphasize that this code fully implements a 1D Kalman filter. If you have tried to read the literatue, you are perhaps surprised, because this looks nothing like the complex, endless pages of math in those books. To be fair, the math gets a bit more complicated in multiple dimensions, but not by much. So long as we worry about *using* the equations rather than *deriving* them we can create Kalman filters without a lot of effort. Moreover, I hope you'll agree that you have a decent intuitive grasp of what is happening. We represent our beliefs with Gaussians, and our beliefs get better over time because more measurement means more data to work with. \"Measure twice, cut once!\"" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Relationship to the g-h Filter" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the first chapter I stated that the Kalman filter is a form of g-h filter. However, we have been reasoning about the probability of Gaussians, and not used any of the reasoning or equations of the first chapter. A trivial amount of algebra will reveal the relationship, so let's do that now. It's not particularly illuminating algebra, so feel free to skip to the bottom to see the final equation that relates *g* to the variances.\n", - "\n", - "The equation for our estimate is:\n", - "\n", - "$$\n", - "\\mu_{x'}=\\frac{\\sigma_1^2 \\mu_2 + \\sigma_2^2 \\mu_1} {\\sigma_1^2 + \\sigma_2^2}\n", - "$$\n", - "\n", - "which I will make more friendly for our eyes as:\n", - "\n", - "$$\n", - "\\mu_{x'}=\\frac{ya + xb} {a+b}\n", - "$$\n", - "\n", - "We can easily put this into the g-h form with the following algebra\n", - "\n", - "$$\n", - "\\begin{aligned}\n", - "\\mu_{x'}&=(x-x) + \\frac{ya + xb} {a+b} \\\\\n", - "\\mu_{x'}&=x-\\frac{a+b}{a+b}x + \\frac{ya + xb} {a+b} \\\\ \n", - "\\mu_{x'}&=x +\\frac{-x(a+b) + xb+ya}{a+b} \\\\\n", - "\\mu_{x'}&=x+ \\frac{-xa+ya}{a+b} \\\\\n", - "\\mu_{x'}&=x+ \\frac{a}{a+b}(y-x)\\\\\n", - "\\end{aligned}\n", - "$$\n", - "\n", - "We are almost done, but recall that the variance of estimate is given by \n", - "\n", - "$${\\sigma_{x'}^2} = \\frac{1}{ \\frac{1}{\\sigma_1^2} + \\frac{1}{\\sigma_2^2}}\\\\\n", - "= \\frac{1}{ \\frac{1}{a} + \\frac{1}{b}}\n", - "$$\n", - "\n", - "We can incorporate that term into our equation above by observing that\n", - "$$ \n", - "\\begin{aligned}\n", - "\\frac{a}{a+b} &= \\frac{a/a}{(a+b)/a} = \\frac{1}{(a+b)/a}\\\\\n", - " &= \\frac{1}{1 + \\frac{b}{a}} = \\frac{1}{\\frac{b}{b} + \\frac{b}{a}}\\\\\n", - " &= \\frac{1}{b}\\frac{1}{\\frac{1}{b} + \\frac{1}{a}} \\\\\n", - " &= \\frac{\\sigma^2_{x'}}{b}\n", - " \\end{aligned}\n", - "$$\n", - "\n", - "We can tie all of this together with\n", - "\n", - "$$\n", - "\\begin{aligned}\n", - "\\mu_{x'}&=x+ \\frac{a}{a+b}(y-x)\\\\\n", - "&= x + \\frac{\\sigma^2_{x'}}{b}(y-x) \\\\\n", - "&= x + g_n(y-x)\\\\\n", - "\\blacksquare\n", - "\\end{aligned}\n", - "$$\n", - "\n", - "where\n", - "\n", - "$$g_n = \\frac{\\sigma^2_{x'}}{\\sigma^2_{y}}$$\n", - "\n", - "The end result is multipying the residual of the two measurements by a constant and adding to our previous value, which is the *g* equation for the g-h filter. *g* is the variance of the new estimate divided by the variance of the measurement. Of course in this case g is not truly a constant, as it varies with each time step as the variance changes, but it is truly the same formula. We can also derive the formula for *h* in the same way but I don't find this a particularly interesting derivation. The end result is\n", - "\n", - "$$h_n = \\frac{COV (x,\\dot{x})}{\\sigma^2_{y}}$$\n", - "\n", - "The takeaway point is that *g* and *h* are specified fully by the variance and covariances of the measurement and preditions at time *n*. In other words, we are just picking a point between the measurement and prediction by a scale factor determined by the quality of each of those two inputs. That is all the Kalman filter is. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#####Excercise:\n", - "Modify the values of *movement_error* and *sensor_error* and note the effect on the filter and on the variance. Which has a larger effect on the value that variance converges to. For example, which results in a smaller variance:\n", - "\n", - " movement_error = 40\n", - " sensor_error = 2\n", - " \n", - "or:\n", - "\n", - " movement_error = 2\n", - " sensor_error = 40 " - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Introduction to Designing a Filter" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So far we have developed our filter based on the dog sensors introduced in the Discrete Bayesian filter chapter. We are used to this problem by now, and may feel ill-equiped to implement a Kalman filter for a different problem. To be honest, there is still quite a bit of information missing from this presentation. The next chapter will fill in the gaps. Still, lets get a feel for it by designing and implementing a Kalman filter for a thermometer. The sensor for the thermometer outputs a voltage that corresponds to the temperature that is being measured. We have read the manufacturer's specifications for the sensor, and it tells us that the sensor exhibits white noise with a standard deviation of 2.13.\n", - "\n", - "We do not have a real sensor to read, so we will simulate the sensor with the following function. We have hard-coded the voltage to 16.3 - obviously the voltage will differ based on the temperature, but that is not important to our filter design." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "temp_variance = 2.13**2\n", - "def volt():\n", - " return random.randn()*temp_variance + 16.3" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 19 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We generate white noise with a given variance using the equation *random.randn() * variance*. The specification gives us the standard deviation of the noise, not the variance, but recall that variance is just the square of the standard deviation. Hence we raise 2.13 to the second power.\n", - "> **Sidebar**: spec sheets are just what they sound like - specificiations. Any individual sensor will exhibit different performance based on normal manufacturing variations. Numbers given are often maximums - the spec is a guarantee that the performace will be at least that good. So, our sensor might have standard deviation of 1.8. If you buy an expensive piece of equipment it often comes with a sheet of paper displaying the test results of your specific item; this is usually very trustworthy. On the other hand, if this is a cheap sensor it is likely it received little to no testing prior to being sold. Manufacturers typically test a small subset of their output to verify that everything falls within the desired performance range. If you have a critical application you will need to read the specification sheet carefully to figure out exactly what they mean by their ranges. Do they guarantee their number is a maximum, or is it, say, the $3\\sigma$ error rate? Is every item tested? Is the variance normal, or some other distribution. Finally, manufacturing is not perfect. Your part might be defective and not match the performance on the sheet.\n", - "\n", - "> For example, I just randomly looked up a data sheet for an airflow sensor. There is a field \"Repeatability\", with the value \"$\\pm0.50\\%$ Reading\". Is this a Gaussian? Is there a bias? For example, perhaps the repeatibility is nearly $0.0\\%$ at low temperatures, and always nearly $+0.50$ at high temperatures. Data sheets for electrical components often contain a section of \"Typical Performance Characteristics\". These are used to capture information that cannot be easily conveyed in a table. For example, I am looking at a chart showing output voltage vs current for a LM555 timer. There are three curves showing the performance at different temperatures. The response is ideally linear, but all three lines are curved. This clarifies that errors in voltage outputs are probably not Gaussian - in this chip's case higher temperatures leads to lower voltage output, and the voltage output is quite nonlinear if the input current is very high. \n", - "\n", - "> As you might guess, modeling the performance of your sensors is one of the harder parts of creating good Kalman filter. \n", - "\n", - "Now we need to write the Kalman filter processing loop. As with our previous problem, we need to perform a cycle of sensing and updating. The sensing step probably seems clear - call *volt()* to get the measurement, pass the result into *sense()* function, but what about the update step? We do not have a sensor to detect 'movement' in the voltage, and for any small duration we expect the voltage to remain constant. How shall we handle this?\n", - "\n", - "As always, we will trust in the math. We have no movement, and no error associated with them, so we will just set both to zero. Let's see what happens. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "sensor_error = temp_variance\n", - "movement_error = 0\n", - "movement = 0\n", - "voltage = (25,1000) #who knows what the first value is?\n", - "\n", - "zs = []\n", - "ps = []\n", - "vs = []\n", - "N=50\n", - "\n", - "for i in range(N):\n", - " Z = volt()\n", - " zs.append(Z)\n", - " \n", - " voltage = sense(voltage[0], voltage[1], Z, sensor_error)\n", - " ps.append(voltage[0])\n", - " vs.append(voltage[1])\n", - "\n", - " voltage = update(voltage[0], voltage[1], movement, movement_error)\n", - "\n", - "plt.scatter(range(N), zs, marker='+')\n", - "p1, = plt.plot(ps, c='g')\n", - "plt.legend([p1], ['filter'], 3)\n", - "plt.xlim((0,N));plt.ylim((0,30))\n", - "plt.show()\n", - "plt.plot(vs)\n", - "plt.title('Variance')\n", - "plt.show()\n", - "print('Variance converges to',vs[-1])\n", - "print('Last voltage is',voltage[0])" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - 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D6dCp5lM6de6Uqpu7vjVo72zXyGEjlR6XroxhGUqPS1d6XLryM/Ld3yK0Olt1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\n7XlJK6auUER4xJAHCwAAAJg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LLldeKGXMVsGEXMCEXMCEXMAJWR1d4Fq6AAAAcEsWiy5ndEsZs1UwIRcwIRcwIRdwQnZn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\nuL0UAAAA5FhRF92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- "text": [ - "" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Variance converges to 0.0907297673624\n", - "Last voltage is 16.2989678826\n" - ] - } - ], - "prompt_number": 20 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The first plot shows the individual sensor measurements marked with '+'s vs the filter output. Despite a lot of noise in the sensor we quickly discover the approximate voltage of the sensor. In the run I just completed at the time of authorship, the last voltage output from the filter is $16.213$, which is quite close to the $16.4$ used by the *volt()* function. On other runs I have gotten up to around $16.9$ as an output and also as low as 15.5 or so.\n", - "\n", - "The second plot shows how the variance converges over time. Compare this plot to the variance plot for the dog sensor. While this does converge to a very small value, it is much slower than the dog problem. The next section **Explaining the Results - Multi-Sensor Fusion** explains why this happens.\n", - "\n", - "##### Exercise(optional):\n", - "Write a function that runs the Kalman filter many times and record what value the voltage converges to each time. Plot this as a histogram. After 10,000 runs do the results look normally distributed? Does this match your intuition of what should happen?\n", - "\n", - "> use plt.hist(data,bins=100) to plot the histogram. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#Your code here" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 21 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "######Solution\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "sensor_error = temp_variance\n", - "\n", - "def VKF():\n", - " voltage=(14,1000)\n", - " for i in range(N):\n", - " Z = volt()\n", - " voltage = sense(voltage[0], voltage[1], Z, sensor_error)\n", - " return voltage[0]\n", - "\n", - "vs = []\n", - "for i in range (10000):\n", - " vs.append (VKF())\n", - "plt.hist(vs, bins=100) \n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 22 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "######Discussion\n", - "The results do in fact look like a normal distribution. Each voltage is Gaussian, and the **Central Limit Theorem** guarantees that a large number of Gaussians is normally distributed. We will discuss this more in a subsequent math chapter." - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Explaining the Results - Multi-Sensor Fusion" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So how does the Kalman filter do so well? I have glossed over one aspect of the filter as it becomes confusing to address too many points at the same time. We will return to the dog tracking problem. We used two sensors to track the dog - the RFID sensor that detects position, and the inertial tracker that tracked movement. However, we have focussed all of our attention on the position sensor. Let's change focus and see how the filter performs if the intertial tracker is also noisy. This will provide us with an vital insight into the performance of Kalman filters." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "sensor_error = 30\n", - "movement_sensor = 30\n", - "pos = (0,500)\n", - "\n", - "dog = DogSensor(0, velocity=movement, noise=sensor_error)\n", - "\n", - "zs = []\n", - "ps = []\n", - "vs = []\n", - "\n", - "for i in range(100):\n", - " Z = dog.sense()\n", - " zs.append(Z)\n", - " \n", - " pos = sense(pos[0], pos[1], Z, sensor_error)\n", - " ps.append(pos[0])\n", - " vs.append(pos[1])\n", - "\n", - " pos = update(pos[0], pos[1], movement+ random.randn(), movement_error)\n", - "\n", - "p1, = plt.plot(zs,c='r', linestyle='dashed')\n", - "p2, = plt.plot(ps, c='b')\n", - "plt.legend([p1,p2], ['measurement', 'filter'], 2)\n", - "plt.show()\n", - "plt.plot(vs)\n", - "plt.title('Variance')\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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dF3zPnmh86qljd2RlaSKlJO2/kLUxFCBDvNzC9+4tJu0T2RlWroQlDVdFU83w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L48R/PFz6A7deRXQgmgQ9V/sW1WFirMKBdwDkQQJiEA+EBW4N7Eli0LtmxZ6eMhd5UrTOVcco9t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\nbIPvUjWM9oqQi3eBU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hjVfALp2FFEQYGIuXMt+PJLZdPjkoWYnw/+3DkIrVsD8KcJCe3a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\nfEoIKHeMSKN5QaTQvCBSaF4QJVGATAghhBBCSAMcY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lYblulk9DDL1dMCEXMCEXMCEXcFJalk9jRhgAAABuw8NySCt6u2BCLmBCLmBCLuAk1hEG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\nlOux5PNa8sVf90Rfj2/7PNG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- "text": [ - "" - ] - } - ], - "prompt_number": 23 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This result is worse than the example where only the measurement sensor was noisy. Instead of being mostly straight, this time the filter's output is distintly jagged. But, it still mostly tracks the dog. What is happening here?\n", - "\n", - "This illustrates the effects of *multi-sensor fusion*. Suppose we get a position reading of -28.78 followed by 31.43. From that information alone it is impossible to tell if the dog is standing still during very noisy measurements, or perhaps sprinting from -29 to 31 and being accurately measured. But we have a second source of information, his velocity. Even when the velocity is also noisy, it constrains what our beliefs might be. For example, suppose that with the 31.43 position reading we get a velocity reading of 59. That matches the difference between the two positions quite well, so this will lead us to believe the RFID sensor and the velocity sensor. Now suppose we got a velocity reading of 1.7. This doesn't match our RFID reading very well - it suggests that the dog is standing still or moving slowly.\n", - "\n", - "When sensors measure different aspects of the system and they all agree we have strong evidence that the sensors are accurate. And when they do not agree it is a strong indication that one or more of them are inaccurate. \n", - "\n", - "We will formalize this mathematically in the next chapter; for now trust this intuitive explanation. We use this sort of reasoning every day in our lives. If one person tells us something that seems far fetched we are inclined to doubt them. But if several people independently relay the same information we attach higher credence to the data. If one person disagrees with several other people, we tend to distrust the outlier. If we know the people that might alter our belief. If a friend is inclined to practical jokes and tall tales we may put very little trust in what they say. If one lawyer and three lay people opine on some fact of law, and the lawyer disagees with the three you'll probably lend more credence to what the lawyer says because of her expertise. In the next chapter we will learn how to mathematicall model this sort of reasoning." - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "More examples" - ] - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Example: Extreme Amounts of Noise" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So I didn't put a lot of noise in the signal, and I also 'correctly guessed' that the dog was at position 0. How does the filter perform in real world conditions? Let's explore and find out. I will start by injecting a lot of noise in the RFID sensor. I will inject an extreme amount of noise - noise that apparently swamps the actual measurement. What does your intution tell about how the filter will perform if the noise is allowed to be anywhere from -300 or 300. In other words, an actual position of 1.0 might be reported as 287.9, or -189.6, or any other number in that range. Think about it before you scroll down." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "sensor_error = 30000\n", - "movement_error = 2\n", - "pos = (0,500)\n", - "\n", - "dog = DogSensor(pos[0], velocity=movement, noise=sensor_error)\n", - "\n", - "zs = []\n", - "ps = []\n", - "\n", - "for i in range(1000):\n", - " pos = update(pos[0], pos[1], movement, movement_error)\n", - " \n", - " Z = dog.sense()\n", - " zs.append(Z)\n", - " \n", - " pos = sense(pos[0], pos[1], Z, sensor_error)\n", - " ps.append(pos[0])\n", - "\n", - "\n", - "p1, = plt.plot(zs,c='r', linestyle='dashed')\n", - "p2, = plt.plot(ps, c='b')\n", - "plt.legend([p1,p2], ['measurement', 'filter'], 2)\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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tWxtqGdeKae1aMJWV8RbDUJiiIlhee01bY5UPx0TzfRXy8uBSUnDF5YLFwGw2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\nj+gbb3jVD06F4/dnEtkDGZddRo2V4JD9oCiQ0mnqeDp6tPjbEAtYjfPO8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U2b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\ndi6/EyTPYa5B8aty882mWQfPdc4ym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LMx8WgqZFH8pYLo2laYULt2RZzLHgo0+yZu347Q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\nCqAMtj17wBw/nvTnwSFD4B8/HtyXX2p+RlIsDkY8f/gDgoMHW2pDO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- "text": [ - "" - ] - } - ], - "prompt_number": 24 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this example the noise is extreme yet the filter still outputs a nearly straight line! This is an astonishing result! What do you think might be the cause of this performance? If you are not sure, don't worry, we will discuss it latter." - ] - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Example: Bad Initial Estimate" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "Now let's lets look at the results when we make a bad initial estimate of position. To avoid obscuring the results I'll reduce the sensor variance to 30, but set the initial position to 1000m. Can the filter recover from a 1000m initial error?" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "sensor_error = 30\n", - "movement_error = 2\n", - "pos = (1000,500)\n", - "\n", - "dog = DogSensor(0, velocity=movement, noise=sensor_error)\n", - "\n", - "zs = []\n", - "ps = []\n", - "\n", - "for i in range(100):\n", - " pos = update(pos[0], pos[1], movement, movement_error)\n", - " \n", - " Z = dog.sense()\n", - " zs.append(Z)\n", - " \n", - " pos = sense(pos[0], pos[1], Z, sensor_error)\n", - " ps.append(pos[0])\n", - "\n", - "\n", - "p1, = plt.plot(zs,c='r', linestyle='dashed')\n", - "p2, = plt.plot(ps, c='b')\n", - "plt.legend([p1,p2], ['measurement', 'filter'], 2)\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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FKUnzBXL+DDIRERERUUWoFAUy10Ku+tg7RiLMBYkwFyTCXJCSNF95RkY6cPSo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- "text": [ - "" - ] - } - ], - "prompt_number": 25 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Again the answer is yes! Because we are relatively sure about our belief in the sensor ($\\sigma=30$) even after the first step we have changed our belief in the first position from 1000 to somewhere around 60.0 or so. After another 5-10 measurements we have converged to the correct value! So this is how we get around the chicken and egg problem of initial guesses. In practice we would probably just assign the first measurement from the sensor as the initial value, but you can see it doesn't matter much if we wildly guess at the initial conditions - the Kalman filter still converges very quickly." - ] - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Example: Large Noise and Bad Initial Estimate" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What about the worst of both worlds, large noise and a bad initial estimate?" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "sensor_error = 30000\n", - "movement_error = 2\n", - "pos = (1000,500)\n", - "\n", - "\n", - "dog = DogSensor(0, velocity=movement, noise=sensor_error) \n", - "zs = []\n", - "ps = []\n", - "\n", - "for i in range(1000):\n", - " pos = update (pos[0], pos[1], movement, movement_error)\n", - " \n", - " Z = dog.sense()\n", - " zs.append(Z)\n", - " \n", - " pos = sense (pos[0], pos[1], Z, sensor_error)\n", - " ps.append(pos[0])\n", - "\n", - "\n", - "p1, = plt.plot(zs,c='r', linestyle='dashed')\n", - "p2, = plt.plot(ps, c='b')\n", - "plt.legend([p1,p2], ['measurement', 'filter'], 2)\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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//op2MQgh5UhQQfT06dPRoUMHMAwDs9mMSZMmYdeuXdi6dSvGjRsHAD6Xh4PI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\nbtsWgdmzoVx2GSkjZ8Mb7xWMZuNE6L8Nvvii2ShkN693hM2uyTJprnn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jR7KjMbRlaIxz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KJAl6rVpwz54N7wsvEMHv558z5i2hL76A2r07PFOmwDd2LMSLF6F26UKbKEVB9PnnodmUf/i1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- "text": [ - "" - ] - } - ], - "prompt_number": 26 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This time the filter does struggle. Notice that the previous example only computed 100 updates, whereas this example uses 1000. By my eye it takes the filter 400 or so iterations to become reasonable accurate, but maybe over 600 before the results are good. Kalman filters are good, but we cannot expect miracles. If we have extremely noisy data and extremely bad initial conditions, this is as good as it gets.\n", - "\n", - "Finally, let's make the suggest change of making our initial position guess just be the first sensor measurement." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "sensor_error = 30000\n", - "movement_error = 2\n", - "pos = None\n", - "\n", - "dog = DogSensor(0, velocity=movement, noise=sensor_error)\n", - "\n", - "zs = []\n", - "ps = []\n", - "\n", - "for i in range(1000):\n", - " Z = dog.sense()\n", - " zs.append(Z)\n", - " if pos == None:\n", - " pos = (Z, 500)\n", - " \n", - " pos = sense (pos[0], pos[1], Z, sensor_error)\n", - " ps.append(pos[0])\n", - "\n", - " pos = update (pos[0], pos[1], movement, movement_error)\n", - "\n", - "p1, = plt.plot(zs,c='r', linestyle='dashed')\n", - "p2, = plt.plot(ps, c='b')\n", - "plt.legend([p1,p2], ['measurement', 'filter'], 2)\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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ZM/6Dt56NYwRtJLHhQGjfHi4VccTpP/6Q/hBF1L/9NlxXX62xotin/aYaGmBe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Z29YfuEE5296Sa2uPKiBGGHRxMJPxm6tcC4xAo2b0bFoEGgLVuCtmkDs1UraMcd599FhplAN2Wn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\nyMLxdVN4zjkUxGzf7q6JnIdwch36dJoyF08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kfMrKEKikUI2VlpKutVA1UeSjwBjJdS1f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KDSayPDoXrOHEKBKyy03U905Eir9oOmQdyyBZExY8h7btgQ4Q8+gDJ3LuTvvw/W5xZF/yJf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- "text": [ - "" - ] - } - ], - "prompt_number": 27 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This simple change significantly improves the results. On some runs it takes 200 iterations or so to settle to a good solution, but other runs it converges very rapidly. This all depends on whether the initial measurement $Z$ had a small amount or large amount of noise. \n", - "\n", - "200 iterations may seem like a lot, but the amount of noise we are injecting is truly huge. In the real world we use sensors like thermometers, laser rangefinders, GPS satellites, computer vision, and so on. None have the enormous error as shown here. A reasonable value for the variance for a cheap thermometer might be 10, for example, and our code is using 30,000 for the variance. " - ] - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Exercise: Interactive Plots" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Implement the Kalman filter using IPython Notebook's animation features to allow you to modify the various constants in real time using sliders. Refer to the section **Interactive Gaussians** in the Gaussian chapter to see how to do this. You will use the *interact()* function to call a calculation and plotting function. Each parameter passed into *interact()* automatically gets a slider created for it. I have built the boilerplate for this; just fill in the required code." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.html.widgets import interact, interactive, fixed\n", - "import IPython.html.widgets as widgets\n", - "def plot_kalman_filter(start_pos, sensor_noise, movement, movement_noise, noise_scale):\n", - " # your code goes here\n", - " pass\n", - "\n", - "interact(plot_kalman_filter,\n", - " start_pos=(-10,10), \n", - " sensor_noise=widgets.IntSliderWidget(value=5,min=0,max=100), \n", - " movement=widgets.FloatSliderWidget(value=1,min=-2.,max=2.), \n", - " movement_noise=widgets.FloatSliderWidget(value=5,min=0,max=100.),\n", - " noise_scale=widgets.FloatSliderWidget(value=1,min=0,max=2.))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 28, - "text": [ - "" - ] - } - ], - "prompt_number": 28 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "######Solution\n", - "One possible solution follows." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "\n", - "zs = np.zeros(100)\n", - "ps = np.zeros(100)\n", - "def plot_kalman_filter(start_pos, sensor_noise, movement, movement_noise,noise_scale):\n", - " dog = DogSensor(start_pos, velocity=movement, noise=sensor_noise)\n", - " random.seed(303)\n", - " pos = (0,100)\n", - "\n", - " for i in range(100):\n", - " Z = dog.sense() + random.randn()*noise_scale\n", - " zs[i] = Z\n", - "\n", - " pos = sense(pos[0], pos[1], Z, sensor_error)\n", - " ps[i] = pos[0]\n", - "\n", - " pos = update(pos[0], pos[1], movement + random.randn()*movement_noise, movement_noise)\n", - "\n", - " p1, = plt.plot(zs,c='r', linestyle='dashed')\n", - " p2, = plt.plot(ps, c='b')\n", - " plt.legend([p1,p2], ['measurement', 'filter'], 2)\n", - " plt.show()\n", - "\n", - "interact(plot_kalman_filter,\n", - " start_pos=(-10,10), \n", - " sensor_noise=widgets.IntSliderWidget(value=5,min=0,max=100), \n", - " movement=widgets.FloatSliderWidget(value=1,min=-2.,max=2.), \n", - " movement_noise=widgets.FloatSliderWidget(value=2,min=0,max=100.),\n", - " noise_scale=widgets.FloatSliderWidget(value=1,min=0,max=20.))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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XroRPnIjtl19wDR5MoF69EoxQpPQ88kgk//ynm4svDpT4vTSCLCFBvWNiRnkh\nZipKXlh+/x3nggW4hgwp1HmeXr2wHjhA9uTJuO+9lxIfagsRZZ0XBw9a+PjjEphvrJD27LHi9ZZ1\nFMH38ccOdu+2MXKkq1TupxFkERGREGRUrkz6559jVK9euPOqViV927YSikrMHD5s4ZZbYsjIsJCS\n4uX557NxOkvv/idOWPjoIwdz5oTxyy9W4uIMxozJoUsXb7n6+cjvh4wMC5mZkJ5uITPT8se2hSee\niOT117MK82FKsWgeZBERERO7dllp0CAQrMkj5Dx17JiFrl1j6N7dw+DBLv71ryh+/dXKzJmZxMeX\nXIllGLBxo53Zs52sXOkgKclHv35uOnTw8dlndp55JgKnE55+Ooe2bX0lFkewpKVZuf76GNxuiImB\n6GiDmBgj7+sVV/gYNqzwK+ZpHmQREZEgWbvWTo8e0Qwf7uaZZ3LKOhwJUcePW+jePZouXTw89FDu\nR/+zZ2cxaVI4HTvGMnNmJq1b+4N+348+cjBhQgR2O/Tv72bcuByqVv2zGE9O9tGhQwYLFzoYPjyS\nBg0CPPlkDo0bBz+WYPB6YdCgKEaMcDF8ePCXjS4K/VwsIaGse8ckNCkvxExJ58W+fRb+9a8oZs3K\nYtUqB6+9Vkqf6UqxlPb7RXo69OgRTfv2Ph577M++WKsVHn7YxeTJ2fTtG8177wWv1yIQgGeeieDZ\nZyN45ZVsNmxIZ+hQ92nF8alx9Ojh5csv00lO9tK9ezRDhkTy/fehV/o9/3w4lSsbDB0aGsUxaARZ\nREQkj9sNd90VzdChLrp08dKsmY8bb4whLs7gH/8o2XlXpfzIyICePWNo1crH2LE5pn2+N9zgZcWK\nDPr1i2b7djv3359zcuFCbDaw2QxsNnA6wVGAZ/syMmDw4CgyMix8+mkGVaoUrH0jLAwGD3bTu7eb\nt98Op3v3GC6/3M+IES7atvXl26NsGPDttzbWrrXTpo2PVq1KZvR57Vo78+aFsW5deki1M6kHWURE\n5A8PPRTBoUNW3nsvK69w2LXLyi23xPDaa1lcf30J93Lm5BDxwgvkjBkTtJXzJLiys6FXr2guuSTA\nlCnZ5/xrSk+H+++P4ssv7QQCuSvB+f3g81nytq+/3suQIW7atDEvWPfssdKnTzRXXulj4sTiPQDo\ncsG8eU6mTQsnJsZg+HAXXbt6sdtz/2zr1ztYtSr3V0SEwbXX+li1ykFSkpennsohLi54ZeORIxY6\ndIjljTe3Mvd6AAAgAElEQVSyaN++ZP5tqQdZRESkGObPd7J2rYM1a9JPK1IaNQowe3YmfftG88EH\nmUUbSUtPJ+L55/H06IG/Zct8DwubMwfr//6n4jhEZWVBv37RJCYWrDgGiI2Fd9/NOus1581z8sAD\nkUREGAwZ4qZbN0/ebA1ffGFn4MAoHnzQxaBB7mLPShEeDnfe6aF/fw8rVzp49dVwxo6NoH79AF9+\naadZMx+dOnlZvNhF/fq58w2np8OLL0bQpk0so0a5GDjQjb2YFWQgAP/6VxR9+7pLrDguDo0gS0hI\nTU3VKkhyBuWFmCmJvNi5M3eUePHizHwfZFq1ysHIkZEsXZpBgwaFWKjA4yH22mvxN2qE/YsvyBk7\nFk/v3ubHtWpF1owZZy2ixVxJv18cO2bh9tujadTIz8svZ2OzBff6gQCkpNh5441wdu2yMWCAm9hY\ngylTwpk+PYsOHUquiNy82caBA1aSknxUqpR/WfjDD1ZGj47kyBErEydm065d0WN65ZUwPv7YyfLl\nGcUuts9GI8giIiJFkJ4Od94ZzfjxZ3/K/+9/9/Lkkzn07BnNypUZ1Kx5ZiERCOSuy3HaKJ/TSebs\n2QTq18e6axfR/fph276dnHHjTms+dc6fT+Dii1Uch6A9e6z06BFNt24eHnvMVSJzC1utcP31Pq6/\nPpOdO61Mnx7O//5nY8WKDC65pGRXjrviCj9w7k9GGjYMsHBhJsuWORg6NJJWrfyMGZNDvXqFi2/L\nFhuvvRZOSkrJFsfFoRFkERGpsAwD7rwzimrVAkyaVLDp3F5+OYxJkyJwOg18Pssf/aS5vwIBC/Hx\nAa691su11/q49lovtWuf/t+s5fffCZs2Ddcjj5BXHfh8xF51FdlTp+LTpyYh5euvbfTpE82oUTnc\nfbce1DwpOxumTQtn+vQwbrnFy4MP5pj+0PhX6enQvn0sY8fm0LVryS/5V9QRZBXIIiJSYb32WhiL\nFztZsSKjUCt0/fqrBcuBAzizfsdyUSL2SlHY7bmjgHv2WPnsMzuff+5g/Xo7lSsbecVyUpKX2Ngz\nr2fbtImIcePIXLaswiwNXRq2brUxaVI4iYkBmjTx06SJn0aN/AX+u16zxs7gwVG89FI2Xbqch+s3\nB8Fvv1l4+eVw5sxx0revh5EjXWfMsOF2w6ZNdtats/Pxx07atfMW+AfS4lKBLOWaek3FjPJCzAQr\nL77+2kavXtF8+mkGiYmF/wjbsWgREZMmYd2zByM2Fn+9ehi1auHu1Qvf9dcDuS0XO3fa+OwzO2vX\nOti82U67dl66dvXSubOXypVP+S/Y6y3YfF9i6tS8MAx4660wJk8O59FHc8jOtrBjh40dO+z88ouV\niy/+s1iuVy9AvXoB6tb1Ex395/XmzXPy5JMRzJqVyVVXheYCG6Hk4EELkyeHs3ixk3vucfP3v3tJ\nTbWzbl1u3jds6KdDBy8dOvi46ipf0Hu486MeZBERkQLKyoJ//jOK55/PLlJxDODt1g1vt24QCGA5\neBDbL79g/eknjLi4vGOsVrjsMj+XXeZn2DA3J05YWLXKwbJlDh55JJIrrvDRtauH5GQfhhFGdjZk\nZ1vIybHk/b5RIz8NG5ZsD+r5JD0dRoyI4v/+z8rq1RnUrXv6987lgu+/t7Fjh41du2x8+aWdX36x\nkZZmJTraoG7dAFWrBtixw8aSJRk0aqTvfUHUqGEwaVIOw4a5mTgxnMGDo2jXzsedd7p5552s038Y\nLAc0giwiIhXOyJGR+HwwbVr2OY+1f/45GAa+9u2DGkNmJvz3vw6WLnXy5Zd2HA6DiAiIijKIjMz9\nfUSEwZdf2nnooeBM8XW+277dxt13R9Gxo5dx43IK1TYTCMChQxb27Mktltu391KjRvkq6uRMGkEW\nEZGQZhjwyy9WPv88tz933z4r06Zl5c21WlqWLHH88dFv+tkPdLmIGDcO55IlZL3+etDjiI6Gbt28\ndOt29t7Wn3+2MmhQFOvX23nllexyNxJXGgwDZsxwMmFCBBMnZp/ze2rGaoWaNQ1q1vTRpk0JBCnl\nigpkCQnqNRUzyovy7+hRC2vXOvIeWvP74dprvVx3nZfMTAu33BLDvHmZNGlS8B7P4uTFvn0WHn44\nkrlzM4mJyecgtxvnRx8R/vLL+P/2N9LXr8e44IIi3S8YLroowMqVGTzzTATt28fw9ttZf0zLdf7J\nysp9mOvwYStHj1o4cuTPr8eO5baeOBzgdBp5X51OyMy0cPx4FitXZnDxxWqJkOJTgSwiIiVi82Yb\n/fvnLo977bU+Ro7MXZnr1DaB6tUD9LjRxoe9P6L5uM4U6jPxQvL7c1fuGjLETcuW+ReY0bffDlYr\n2c8/jy8pKSRmlQgLg+eey+Gaa3z07x/N0KEuRoxwl8qCex4PrF9vJyfHgtWaO9JqsYDVamCxwMUX\nBwo9D+6pfD74/HM7CxY4WbnSwWWX+aldO0BcnEG1agEuu8wgLi5AtWoGEREGXi94vRY8nj+/+nwA\nqVx8sYZ+JTjUgywiIkG3bp2de+6J4vXXs7j++rOvtrVm5iGGPFKD2ReOoN2EjnhvvrlIRenevVZq\n1Qrku/DAlCnhrF1rZ/HizLM/QZ+ZyWnTGYSYffss3HNPNFFRBm++mUXVqgX/b3zePCcrVjjo1Cl3\nFo2/Tsd1qp9+svLee2F8+KGTiy7KfXAtEOCPXxYCgdwfOr77zsbGjelceGHB4zAM+OYbG/PnO1m4\n0EmtWgF69vTQrZuHatXUQiLBo2neREQkJCxf7uCBByKZNSuLq68+szg+OS2aceGFefu+/NLGHbeH\n8Ublh7m1xiayX3wR/2WXFeh++/dbeOyxSD77zI7VCu3b++jY0UvHjn8+ZLVli42+faNJSUnPW7jD\n8vvvGJUrB+FPXPp8PnjuuXD+8x8nM2dm0aLF2VsuAgEYPz6cRYuc3Huvi3XrHKxd6+Dyy3106eLl\npps81K5t4Hbn/v29914Yu3bZ6N3bQ//+7rO2LTzySASBALz4YsHmtXW74ZZbYjh82ELPnh569vSU\neh+6VBxFLZBL4cMZkXNLTU0t6xAkBCkvyp8PP3Ty0EORLFiQaVocA0SMGYNj+fLT9l11lZ8FS9wM\nd73Ev+s+Rdhbb+V7j5N54fPBm2+G0b59LA0b+vn++xNs2JDO9dd7WbPGQdu2sVx7bQzPPBPB4MFR\nvPhiNrVrG9i2biWqd2+ib701dyizHLLb4cknXTz3XA633x7N7NnOfI/NzoYBA6LYuNHO6tUZ3HWX\nh5kzs9i163eGDXPzzTc2OnSIpUOHGJo0qcScOWEMGOBmx44TPP10zjl7ekePdrF0qZNvvy3YxLYT\nJ4YTFxdg27Z0HnvMFbTiWO8XEkxF7kHetGkT99xzDz6fj8svv5wPP/yQ+fPn88QTT2CxWJg8eTJd\nunQJZqwiIhLC3norjFdfDWfJkgwaNDAveixHjmBfv56sadPOeK1pUz9LlmZw221/Z+tNSSSv9tK6\ntZ8LLjiziN261caDD0ZSqZLBxx//eb+ICIM+fTz06ePB58s9LiXFwYABbrpVW0/EbS9i+9//cI0c\nibtfv5DoLy6OLl28NGiQQf/+0WzbZmfChOzT2rgPHbLQt2809ev7WbQo67TXIiLghhu83HCDF58P\nNm+2Ex8f4KKLClewXnCBwaOP5vDIIxEsX5551m/p1q023n8/jPXr08v7t17Oc0VqsQgEAlx66aXM\nmDGDNm3a8OuvvxITE0OjRo3YtGkTLpeLpKQkdu/efca5arEQETm/GAZMnhzOhx86Wbgw86wLb4S9\n9hq2778n26RAPmnfPguzZoWxZYudrVvt1KwZ4IorfFxxhY+mTf3Mnu1k2TInzzyTQ8+engIVWhFP\nPIFjxQpc992Hp3dvcOY/4loeZWTAsGFRHDhgZdasTGrVMtixw0afPtHcdZebBx5wlWhB6vdDcnIM\n997r4rbbzKdYc7mgfftYHnkkh+7dtWyzlI5SnQd569atxMXF0eaPiQKrVKnC+vXrady4MXF/rCCU\nkJDA9u3badq0aVFuISIiJWTvXitOpxGURRCysuCxxyLZutXGihUZVK9+lmsaBmHvv0/2lClnvWbt\n2gaPP+4Cctsovv/exubNdtavtzN1ajjXXONj48b0Qs0H7Bo2jJynnjpvl3KOiYFZs7J4+eUwrrsu\nlsGDXbz+ejgvvJDNrbeWfDFqs8HEidkMGhTNDTecICrqzGMmTIigUSN/keYoFiltRepBTktLo1Kl\nSnTu3JkWLVrwxhtvcPjwYWrUqMH06dNZsGAB8fHxHDx4MNjxynlKvWNiRnkRXMePWxg9OoKOHWNo\n1y6W558PJyur6Nf75hsbycmxuN3w8cfnKI4B29at4PXiu+qqAt/DbocmTfwMHOhm+vRstmxJ57bb\nVhd6sQyjRo3ztjg+yWKB++5z8/rrWSxf7uSDDzJLpTg+6aqr/LRp42Xq1PAzXvvqKxsffuhk0qTs\nEhvJ1vuFBFORRpBdLhdffPEF3377LZUqVaJVq1YMHDgQgMGDBwOwcOFCLPn8Kxg6dCiJiYkAVKpU\niSZNmuRN+n4ywbVdsbZPCpV4tB0a2zt27AipeMrr9lVXtWPmzDDGj7dx9dUH2bixMm43DB+eSbNm\nVXj22dwptjZsKNj12rRpxxtvhPHiizYGDdrOE0/UK1A8W3bvJqpvXxr88X9DQeO/NjERIyyM9T/+\nyKlC5fsbattJSe1ISsogNTWV1NTSvf9NN4Xz4IMd6dPHw/79nwPQsmU7hg+P4q67tvHDDweJi9P7\nhbZLtp5ITU0lLS0NgEGDBlEURepBTklJYcyYMWzYsAGAPn36cOmll7J582aWLVsGQFJSEi+//DKX\nX375GeeqB1lEpHSkpNh54olIqlcPMH58Do0bnz4d2KZNNh5/PBKA557LPucKbYcOWRg2LIqMDAtv\nv51FnTolPz1X+Pjx2H76iax//7vE7yXF99JL4WzZYuP993M/nnjyyQj27bPy738X4+MKkSIq1R7k\nVq1akZaWxvHjx4mKimLHjh08+uijzJgxg6NHj+Jyudi3b98ZxbGIiJSs48ct/Pijld27bSxd6mD3\nbhtjx+bQubPX9KPtK6/0s3p1Bv/5j5O7746mdWsfrVv7qFzZoFIl45SvAb75xs4DD0Ryxx1uHn7Y\nle+CHMHmuv9+Ytu1w/7pp/iuu+7cJ2RkEP7yy7gefZSzrwgiJWHoUBdt2sTy6ad2YmIMFixwsn59\nelmHJVIoRXp7q1SpElOnTiU5ORmv10vfvn1p0qQJEyZMoG3btgBMnTo1qIHK+S01NTXvYxKRk5QX\nZxcIwHvvOdm2zc7u3blFsctloX59P5dc4ue663zMmpV1ztWbrVbo1cvDTTd5mD07jL17rXz3nYXf\nf7dw4sTJr1YiIw1mzDBf/KNERUaS/cILRD70EOlffEHqtm3554VhEHXffRgxMSqOy8jJZbEffzwS\nw8h9eK8wq/0Vld4vJJiK/PN/jx496NGjx2n7evXqRa9evYodlIiInJ1hwOjREWzbZqdfPze9egW4\n5BI/1asbRX4IKioKhgxxFz6QzMzcaRRKkO+66/A3b0745MmQlJTvcWHvvot1924yVq0q0Xjk7Dp1\n8vLvf4cRE2Nw882atULKHy01LSJSzhgGPP10BKmpdhYtyiA2tuxiCZs2DfvWrWfvD87KwnTer0Ky\nHDpETLdupK9dC+FnzpRg27qV6N69yVi1ikC9esW+nxSP2507iF9arTgiZkq1B1lERMx5PLB1qx2X\nC6KjDWJijD++5m4H41P/F14IJyXFzrJlmWVaHNu2biX85ZfJ+PTTM19MT4fYWPD5qHTllWSsXEkg\nIaFY9zPi48n84APz4njHDqJvu43s115TcRwiztXaIxLKVCBLSFDvmJgpD3lhGPDjj1bWrnWwdq2d\nDRsc1K/vJzbWICPDQkaGhczM3F9ZWVC9usH772fSrNnZZ4vIzyuvhLFwoZNlyzJMl2AuLZYTJ4ga\nNIjsyZMJ/DFtZx7DIKZXL3xNmuBr04ZAjRrFLo5PCtSta5oX/osuImPNGgIXXRSU+0j5Ux7eL6T8\nUIEsIlJIx45Z+PxzO+vWOVi7NnfxieRkL7ff7uH117O58ELzwjUQgI8/dvCPf0Qzb17hi+R33glj\n5swwli/PoFq1siuOMQwi770Xb6dOeLt2PfN1i4XMefOIeOwxogcOJOscK+cFRVSUimMRCRr1IIuI\nnEN2NmzcaOezzxx89pmdPXtstG3rpX17H8nJXi65JFCoB+M+/tjB/fdHFqpInjPHycSJESxfnlEq\ncw+fjX3DBiIef5yMTz455+fotq1b8TdpAk5nKUUnIvIn9SCLiATZ3r1W7rsvki1b7DRp4qN9ex8v\nvJBNixb+Yq1afOONXiC7QCPJPh/MmhXGlCnhLFlS9sUxgK9NGzJWrChQk6m/ZctSiEhEJLisZR2A\nCJy+RKTISWWZFwcOWLj11miSkrzs3Pk7H3+cySOPuLjyyuIVxyfdeKOXl17KLZK//vrMJ/fcbpg5\n00nr1rEsXuzgo48yuOSS4BTHjlWrsK9eXbyLREYGJZai0PuFmFFeSDBpBFlE5C+OHrXQrVsMAwa4\nuffeQs4LXAhmI8lZWbkjxtOmhdO4sZ833sjiqquK9kCfKa+XiIcewpKdTfbEiXhvuy141xYROU+o\nQJaQoCePxUxZ5MXvv1u47bZobr7ZU6LF8UmnFsl9+niYO9fJVVf5mDs3k6ZNg1gY/8H6f/+H75pr\ncA0bRkyPHuS43Xj69Dn7SYZBkVcfKQF6vxAzygsJJrVYiIj8ISMDevaMpl07H4895iq1+954o5dX\nX80iIwOWLs1g1qysEimOAQIXXUT2tGkE/vY3MhYvxrlgQW4/Rz5sO3YQk5yM5ciREolHRCQUqUCW\nkKDeMTFTmnmRkwN9+0bzt7/5GT8+p9QHTDt18jFpUg4NG5beQ3iBBg3IXLTI/GE7wyDs3XeJ7t4d\n17BhGNWqlVpc56L3CzGjvJBgUouFiFR4Hg/ceWc08fEBpkzJDqVugrKRnk7Uvfdi/eWX3BXwLrmk\nrCMSESlVGkGWkKDeMTFT0nlx/LiFhQtzF+5wOg2mTcsOylLQ5ZrPR+wNNxCIiyNj1aqQLI71fiFm\nlBcSTBpBFpEKIxCA7dttfPqpg08/dfD99zbatPHSpYuXfv3cQZm+LWQFAmAtwJiI3U7mvHlBWxpa\nRKQ80giyhAT1jomZYOXFDz9YeeihCC69tBJDhkTx++8WRo/O4X//+50PP8xi4EB3Qda8KLcs+/cT\n0749+Av24F+oF8d6vxAzygsJJo0gi8h5ye+H//7XwVtvhfH99zbuuMPN6tWhsRLd2TgWLcJ7001B\nXZo5/K238LVrh/pHREQKxmIYhlGaN0xJSaFFixaleUsRqUBOnLDw/vtO3nknjAsuMBg82M0tt3jK\nxwhxIEDUHXdgxMSQ/frrwZl7OD2dSs2bk7F2LYHExOJfT0SkHNm2bRsdO3Ys9HkaQRaRcsXthkmT\nwklLs5KTYyEry0JODmRnW8jOtnDkiIXrr/cxfXoWrVr5Q3tGiuxsbN99h79169xtq5Wst94i5uab\nCX/+eVyPPVbsW4S99x6+pCQVxyIihaACWUJCamqqnkCWM/w1L7KyoF+/aKKjDbp08RIRYRAZaRAZ\nyR9fDeLiDC64oFQ/GCsawyBy1CgAsk8WyACRkWR+8AExf/87gYQEPP37F/0eXi/hb75J5pw5xQw2\ntOj9QswoLySYVCCLSLlw4oSFXr2iadDAz9SpITAdm99frJ5e58yZ2LdvJ3316jNeM+LiyJw/n5gu\nXQjUqoUvOblI97AcPoyna1f8zZoVOU4RkYpIPcgiEvKOHrVw2225S0A/+2xOgWYrK0n2L74gfMoU\nMj/6qEjn27ZuJbp379xFOC6+OP/jtmzBiIsjUKdOUUMVEanQ1IMsIuelffssdO8eQ/fuHh55xFX2\nPcXZ2USOHEnOuHFFOt3y669EDRhA9ksvnbU4BvC3amW63zlzJs6lS7GcOIHlxAnw+bD4/eSMGYOn\nV68ixSUiIn9SgSwhQb1jYmb+/P/H+PHtGTzYzdCh7rIOB4CI55/H37w53s6dT9tvOXECy6FDBBo2\nPOv51p9/xnPXXblTuRWRv2VLXAkJGJUrY8TG5k4JZ7USqFy5yNcsT/R+IWaUFxJMKpBFJCR9+62N\nxx67mqeectG/v6eswwFyWx6cCxaQbrIgge2rr4gaPpzMuXPxn6WNzN+69Z+zVhSRv0mTYp0vIiJn\np5X0JCTop345yTBg1iwn3bpFM2mSP2SKYzweokaMIPu55zCqVj3jZd9115H90ktE33479g0bsOzf\nj/Wnn8og0POf3i/EjPJCgkkjyCISMtLT4b77ovjf/6wsX55Bw4YhtOqdw0H2Cy/krkiXD2/nzmRF\nRhLVvz8YBjlPP43nHH3GIiISejSCLCEh1eQja6lYvvrKRvv2sVSpEuC//80tjkMqLywWfNdcc87V\n7Xzt25P+xRec+OYbPHfcUUrBVSwhlRcSMpQXEkwaQRaRMhUIwKuvhjFtWjhTpmTTpYu3rEMqNiM+\nvqxDEBGRYlCBLCFBvWPnP8OAjAw4ftzK8eMWfvvNwvHjFubODSMry0JKSgYJCae3VCgvxIzyQswo\nLySYVCCLSFD9/ruFXbusfP+9jV27bHz/vY3du20cO2YhPBwqVw5w4YW5y0FfcIFB+/Zehg51Yw+1\ndyOfD9s335x1RgoRETk/hdp/SVJBaf7K8skw4KefrKxZ42DtWjvffGMnI8NCw4Z+Lr3UT6NGfm64\nwUuDBn7i4gyczsJdv6zywv7550SOHo2/Xj2y5sw5Z9+xlC69X4gZ5YUEkwpkESmU9HRYv97BmjUO\nUlLseL0WkpO99Orl4YUXcqhdO1Bu60lrWhoRY8Zg276dnHHj8HbpouJYRKQCshiGYZTmDVNSUmih\njyxFQlp2NqSlWdm718aePVb27LGyd6+VPXts7NtnpVUrH8nJXpKTvVx6afktiE/l+OgjIh9+GPeQ\nIbiGD4eIiLIOSUREimnbtm107Nix0OdpBFlEAPj1VwvLlztYvNjJ5s12atcOUKdOgLp1/dSpE6Bd\nOx916gS46CI/kZFlHe25WX/4gYjx47EePozlyBGsR45gycnB2749mYsWnXG8v1UrMtatI5CQUAbR\niohIKFGBLCFBvWNl4/ffLaxY4WDRIidffWWnY0cvd9/tZu7czJAYQC1OXhhVquDp0YNAtWoY1aoR\nqFYNoqJy55UzEahTpzihSinS+4WYUV5IMKlAFqmAMjLgwQcjWbXKSfv2Xvr0cTNrViZRUWUdWeHZ\n16zB36QJRlzcafuNqlXx3nzzmSfYbKUUmYiIlFcqkCUk6Kf+0rNvn4Xbb4+mdWs/3377OzExZR1R\n/s6WF9Yff8x9oG73brLefRf/XwpkOX/p/ULMKC8kmFQgi1Qg27bZ6N8/mqFDXQwd6i53D9dZjh3D\nuWQJtm+/xbFsGa6RI8maNQvCwso6NBEROY9YyzoAEcjtHZOiOXAgt484I+Psxy1Z4uD226OZNCmb\nYcPKR3F8Rl64XNh27sRfpw7pGzbgHjFCxXEFpPcLMaO8kGAqVoGckZFBzZo1mTx5MgDz58+nQYMG\nNGzYkOXLlwclQJHyyO2GL76w5/c8WLEZBmzaZGPgwCjato1l+vQwLrusMnfdFcWyZQ5crtOPfeml\ncB5/PJL//CeTzp29JRNUEFmOHsWxcuUZ+43atcmePBn3ffdhVKtWBpGJiEhFUKwWi/Hjx9OqVSss\nFgsej4fRo0ezadMmXC4XSUlJdOnSJVhxynnufOkdy8qCmTPDeP31cHw+6NHDw7PP5gRttNblgkWL\nnLz1Vhjp6RbuucfNSy9lERsLx49bWLrUwTvvhDFyZCSdO3vp3t3DwoVOvvvOxurV6dSsWarTnhdZ\n+OuvQ1YW7V54oaxDkRB0vrxfSHApLySYijyC/MMPP3D06FFatmyJYRhs3ryZxo0bExcXR0JCAgkJ\nCWzfvj2YsYqErBMnLLz4YjjNm1fiq6/sfPBBJl9+mc6aNQ5efbX4LQC//mph/PhwmjatxEcfOXn0\n0Ry++iqdIUPcxMbmHnPBBQZ33ulhyZJMvvgincsu8/P88xG43RZWrMgou+I4EMD+xRe5Q9kFYDl+\nHOd77+G6994SDkxERMRckQvkRx99lKeffjpv+9ChQ9SoUYPp06ezYMEC4uPjOXjwYDBilAqgvPaO\nHTliYezYcFq0iGXPHivLl2cwc2YWl1/u54ILDBYsyODtt8OZN89ZpOsfPmxhzJgIWreO5dix3Ov/\n5z+ZdOrkw3qWf701ahj8619uPv00g3feySq76dvS04nq25foW27BsXhxgU4Jmz4d7403YtSuXW7z\nQkqW8kLMKC8kmIrUYrFs2TIaNGhAQkICf12pevDgwQAsXLgQSz6fKw8dOpTExEQAKlWqRJMmTfI+\nGjmZ4NquWNsnhUo8Z9s2DHA42vPuu+F88glce+0B1q69gMTEAKmpqRw58ufxe/as59FHo3nyyfZU\nqRIgPHxdge5Xt+41vPpqOB98YKVDh32sX38htWoZpKamcvhwaH0/8tu2pqVhu/FG9jdrRtWlS4ka\nOJDV4eF4Y2LyPf/L1atJfvNNXCkpAOzYsSNk/jzaDp3tk0IlHm2HxrbeL7R9UmpqKmlpaQAMGjSI\norAYf61wC2DMmDF8+OGH2O12jh07htVqZdiwYXz11VcsW7YMgKSkJF5++WUuv/zy085NSUmhRYsW\nRQpWpCxlZMCCBU7efTccrxfuvtvN7bd7qFz53P+ENm2y0a9fNB9+mEnLlv58j9u928q0aeEsWeKg\nb18Pw4e7qF69fPQNnyE7G0dKCt6uXQFwvvce3k6dMOLj8z0lbPp07Fu2kPX226UVpYiInMe2bdtG\nx/umi4sAACAASURBVI4dC31ekQrkUz3zzDPExMQwYsQIGjZsmPeQXnJyMj/++OMZx6tAlvLm118t\nPP98OAsXOrnmGh8DB7q55hpfoR+8++QTB/ffH8nSpRnUr//n9Bb79llYtMjJwoVODh600revm3/9\ny03VquW0MC4OrxdLRgbGhReWdSQiInIeKGqBbA9WAA6HgwkTJtC2bVsApk6dGqxLSwWQmpqa9zFJ\nqJkyJZzffrOSmlq8WSBuuMHL0aM59OwZzZw5WXz5pZ2FCx388IONm27y8vTTObRt68MetH+V5ZDD\ncVpxHMp5IWVHeSFmlBcSTMX+r/ipp57K+32vXr3o1atXcS8pEjKys2HePCdr1gRnFoj+/T0cO2bl\nppti+PvfPYwc6SYpyYuzaM/wlb2cHBwpKThWrCBnwgSMSpXKOiIREZFiK3aLRWGpxULKkzlznKxY\n4eCDD7KCel3DoFysZGcqIwPHf/+Lc9kyHGvW4GveHM/NN+Pp2RNiYso6OhERkTxFbbHQUtMi+TAM\nePfdMO6+2x30a5dWcWzZvz/o14x86inCPvgAb1ISJ7ZuJXPxYjx331244jgjg4hRo8Ab+qv6iYhI\nxaMCWULCX6dvCgXbttn4/XcLHTv6yjqUInF++CHRffsWeIGOgsqePJnMBQvw3HEHRtWqRbtIdDS2\ntDTCX3kFx4oVOD75xPSwUMwLKXvKCzGjvJBgqsiPA4mc1b//HcaAAe6zLsgRqqw//kjEmDFkLl4c\n/OHqYFzPYiFryhRiO3TAiIwk+9VXi39NERGRIFEPsoiJ336z0LJlLFu2pFOlSjmbbi0nh5hOnXAP\nHIjnrrvKOpqzcs6YgXPRIjKXLCnHTdkiIhKq1IMsEkTvv++kc2dv+SuO/3979x0dVZ33cfwzmclM\nJg2khw1BUQGNFMnyuGJEqS6CWAFFBUE0IIg8gLtIOcuKIsLjgqAU6YKNIhrAPQiIhWJQWTl2iroI\nUoUw6dPu80ckBrkgSW4mQ/J+neORm7nlN4fPmXz5zff+riT3uHEKNm4sb9++p78QDMq2f//5n8jr\nlXv8eNmOH7d2gMUv0a8fxTEAIOxQICMshFPvWDAoLVxYPjfnlTfb/v1ybN+unKlTzyg67Z99priu\nXQsfCfhH8vIUe//9itizR0Z0dDmN9lfnKI7DKRcIH+QCZsgFrESBDPzOxo0OVatmnPOR0BUh8q23\nZP/kk3PedGckJipr0yYpPv6M1wKtW8vftq3cTz55zuvY9u9XbM+eClavrpyFC6WoqDKPHQCACwkF\nMsJCOD39aMEClx58sCD8vvV3uRTzyCOK/8tf5Jo2Tbaffzbfz24/6ynynnpKznfekWPLFtPXo55+\nWvE33CB/aqpyZ82SIiOtGHmphVMuED7IBcyQC1iJAhkoZt++CH3yiUN33OGt6KGcwdelizzbtytn\n+nTZf/xR8ampir3rLtkyM8/7HEa1asr9v/9T9NChhY8J/J1g48byfPSR8v/+d12Qy3cAAGABfgMi\nLIRL79iiRU716uVVubbdejyK/Pe/S3eszabANdcod9o0nfzySxX07Vvixzv7unSRPyVFrgULznjN\n26OHjPr1Sze2chAuuUB4IRcwQy5gJdZBBn5VUCC98opLa9eex01sZeBaulTuiRPl2bJFwYYNS3+i\n6Gj5brmlVIfmPvec5HaX/toAAFRizCAjLIRD71h6ulPJyQFddlmwXK/jfPttZS9ceO7i2DBk37Gj\n/AYRFyc5wv/fx+GQC4QfcgEz5AJWokAGfjV/fuHNeeXJtn+/Ivbskf/GG8+5n2v+/MI+YZ+vXMcD\nAADORIGMsFDRvWMffODQkSM23XRT+RakzvR0+W6++ZyrQzi2blXUlCnKWbKkwleRqGgVnQuEJ3IB\nM+QCVgr/71iBcub3S6NHR+uf/8wr964D27Fj8t55p+lrkevWyblihRxbtihn5kwFL7mkfAcDAABM\n2QzjHE8dKAcbN25Uq1atQnlJ4Jzmz3cpPT1Sb72VXbFrH+fkKHrUKAWuukoFaWkVOBAAACqHHTt2\nqEOHDiU+jhlkVGknTtg0eXKU3nyzgotjSYqJUe6MGRU8CAAAQA8ywkJF9Y49+2yUunXzKTk5vB4r\njUL0FMIMuYAZcgErMYOMKuvbbyO0cqVT27Z5KnooAAAgjNCDjCrJMKQePWLVoYNPgwaV79JuAACg\nYpS2B5kWC1RJ69c79NNPERowIDTFcdQzz8h27FhIrgUAAMqGAhlhIZS9Y16vNHZstJ56Kjckywzb\n9u+Xa948GdWqlf/FKhl6CmGGXMAMuYCVKJBR5cyd69LFFwfVqZM/JNc7n4eDAACA8MFNeggLqamp\nIbnO0aM2TZsWpbVrs0JyPUlyvvWW8v72t5BdrzIJVS5wYSEXMEMuYCUKZFQJhw7ZtG2bQ0uWuNSj\nh1eNGwdDcl3b/v2K2LtX/htuCMn1AABA2dFigbBgZe+YYUjffx+hV15xasiQaP35z/Fq0yZey5c7\n1a6dT6NH51l2LQWDsu/cedaXnenp8nXpQntFKdFTCDPkAmbIBazEDDIqjb17I7RihVMrVzqVk2PT\ntdf6de21fj3ySL6aNg0qIuhXTFqabPcfV6BlS/lbtpT/+utl1KhR6mtG7N+v2N695evYUXnjx8u4\n6KLTXi+4/37ZsrPL+tYAAEAIMYOMsFDa3rHDh22aPduljh3j1LVrnE6csGnWrBx9+eVJzZuXowcf\nLNCVVwYVESFFTZ4s2/Hjyn/kERlut5xvvCH7N9+UadzBpCSd3LZNRlSU4tu0kfONNwqnsE+Ji5OR\nkFCma1Rl9BTCDLmAGXIBKzGDjAtSRoZdkye79dlndnXp4tMTT+Tphhv8cpwt0YYh24kTypk9W0bd\nuvJ36nTO87vHjVOgUSP527dXsGFDKSdHjv/8R36zD+D4eOU9+6y8vXopevhwOV97TTnz58uoWbPs\nbxQAAIQcM8gIC+fbO7Z/v00PPRSj/v1jdccdXn399UnNmpWrDh3OURxLks2mvClTZNSte17X8bds\nKcf27Yrr3FnxrVur2jXXFM4On0OgVStlbdggb8+erHlsEXoKYYZcwAy5gJWYQcYFITdXeuGFKM2Z\n41L//gWaOjVHsbHldz3fnXfKd+edhTfhffmlFAwq0LLlHx/ocMjbu3f5DQwAAJQ7CmRUqEBAGjbI\nrq2f/FVXJBu64oqAmjYN6IorArr88qAcDmnVqkiNH+9WSkpAmzZlKSkpNEu0SZIiIhRo3jx018Np\n6CmEGXIBM+QCVqJARoUJBqXHHovWz5u+0SrX37Wz3Rx9dbSu0tOdmjzZrp9+ilCNGoZq1gxq9uxc\ntWkTmiffAQCAqo0eZFQIw5CeeMKtPXvsev2JDEV2rq97n79eo+/6UosX5ygjw6O9ezO1YkWW3nsv\n67fi2DAUNXGi7F99deZJvd6iP9oyMxVz331SnoVrHiPk6CmEGXIBM+QCVqJARsgZhvTkk25t3+7Q\nsmVZcvbvqb133KH8v/1Ncd27K+LrryVJbrfUtGlQdvuvBwYCin7sMUVu2qRg/fpnnDe2Z09FDxyo\niF27FD10qIKJiYUnAQAAKAGbYRRftLX8bdy4Ua1atQrlJRFmpkyJ0qpVTq1enaWaNU+PX+TKlbJl\nZ8vbt+/pBxUUKObhh2XzeJS9ZIlM79DzeBQ1d65cs2cr+Kc/KWvdOsnlKsd3AgAAwtmOHTvUoUOH\nEh9HDzJC6sUXXVq2zKk1a84sjqXC1SPOkJ2t2D59ZMTFKfv1189e9MbHK3/ECOUPHFh49x/FMQAA\nKIVStVgcOHBAqampuuqqq5SSkqINGzZIkpYtW6bGjRurSZMmWrNmjaUDxYVv0SKn5s51adWqLNWt\ne3pxfK7eMcfWrQo2aKCc+fPPr+iNiZHi48s6XIQBegphhlzADLmAlUo1gxwZGalZs2apWbNm2rdv\nn9q0aaMffvhBo0aNUkZGhvLz89WuXTt169bN6vEijHm90ujRbv34o115eVJenk25ubaiPzud0urV\nWUpMNBTx448KJiScV8Hr79xZ/s6dQ/AOAAAASlkg16lTR3Xq1JEkJSUlyev1atu2bUpOTlbt2rUl\nSQ0aNNDOnTvVokUL60aLsPbUU4XF8cCB+YqOltxuQ263oehoKSrKUPXqhpxOSYahmH79lDd6dNEj\nn1m/EmbIBcyQC5ghF7BSmXuQ161bp5SUFB05ckQJCQmaM2eOatSooXr16ungwYMUyFXEu+86tGqV\nUx984FGNGue+79Oxdatsubnyl6JpHgAAoLyVaZm3Q4cOaeTIkZo5c2bRz9LS0tSjRw9Jks1mK9vo\ncEE4cMCmoUNjNHdutmoax/5wf9fMmcofNEiK+C1+9I7BDLmAGXIBM+QCVir1DHJ+fr569Oih5557\nTpdccol+/vlnHTx4sOj1Q4cOKSEhwfTYRx55RElJSZKkatWqqVmzZkVfjZwKONthuv3RR7pywQLV\nnDRJRt26+uCDLRoz5lqlpRXoL38JyJHcVkdbtVL1uXOlqKgzjv/PG2/ouq1b5Z0797Tzn1Lh74/t\nsNr+4osvwmo8bIfH9inhMh62w2Obzwu2T9m8ebP27dsnSRowYIBKo1TrIBuGod69e6tt27YaNGiQ\nJMnr9app06ZFN+m1b99eu3fvPuNY1kG+sLlmzJBzxQplrVkjxcXp6aej9NlnDq1Yka2ICMl28qSi\nH3tMEd9/r5z58xW8/PLTjnc//riM6tWVP2ZMBb0DAABQVYR0HeQtW7Zo5cqV+vbbb/XSSy/JZrNp\n7dq1mjRpkq677jpJ0rRp00pzaoSxyHXrFDV7tjzr1klxcdq0yaFXX3Vp0yZPUbeEUa2achYulHPx\nYsV16aK8J5+U9557pF/bbXydOyvQvHkFvgsAAIBz40l6kDweKS6uqIg1E/H114q79VZlv/qqAq1b\n6/Bhm9q1i9fs2Tlq29Z/1mNiBg9W9pIlMhITzzmEzZs3F31NApxCLmCGXMAMuYCZ0s4gl+kmPVQO\n7gkTFNOvn5Sdbb5Dbq5i771XeU8/rUDr1goEpLS0GN1/f8FZi2NJCl55pbLee+8Pi2MAAIBwQoFc\nyX31lV3XXBOvXr1itXev+V933oQJMuLiFN+5syK+//7MHaKjlbN4sbw9eyoz06YRI6IVDEp/+1v+\nHw/gPFcy4V/9MEMuYIZcwAy5gJUokCuxt9+O1G23xWr48Hylpvp0001xeuqpKOXk/G7HqCjlTp+u\n/AEDFNelixzr159xrqzLmuv5511q3brwEc4LF+bIbg/BmwAAAAgxCuQ/4PNJ69ZF6sEHY7RiRWRF\nD+e8BIPS009Hadw4t1asyFavXl49+miBPvzQo30/2nRdY5/SF3h0Wve5zSZv//7KXrxYMY89Jse2\nbZIK3/+iRU61bl1Nn3/u0DvvZGnatFzVrGlt6/rvl28CJHIBc+QCZsgFrFSqVSyqgq++suu115xa\nscKphg2Duvlmr554Ilr/8z9ZSkoKVvTwzsrjkQYOjNHJkzZt3Jil2rV/K2Tr1ze0uPmz2rb3uB6d\n/5wWrjY0YUKeEhODCgYLC+tgo2tlLN+sQPUa+vjNSD3zjFuJiUEtWZKtVq0CFfjOAAAAQoMCuZj8\nfGnxYpdee82pX36J0N13F2jNmixddllhQWyzSY8+Gq1Vq7KLPwQubOzZE6H77otVaqpPixblyek8\n/fWIPXsUNX26/rxxo96vn6X5813q0SNWeXmFD7X77b94RURISUlBTZmSqxtvPPuNeFahdwxmyAXM\nkAuYIRewEsu8FTN6tFtffmnXiBH5uv56/xlFcCAg3XxznO6806uHHy4I+fiOHbNp7lyXcnNtCgQK\nZ3z9fikQsMnvl959N1KjR+epb1/vmQcHg4rt1k2+7t1VMHBgyMcOAAAQaizzVkbbt9u1apVTixbl\n6IYbziyOJclul158MUdTpkSddUWI8rJ1q0M33BCvo0cjVLt2UImJQV1ySVBNmwbVooVfrVv7tXx5\ntnlxLMm1YIFsgYAKHnoopOM+X/SOwQy5gBlyATPkAlaixUJSQYE0dGiMnnkmVzVqnHtC/bLLgho5\nMl+DB8do7dqscl/JIRiUpk6N0ty5Ls2YkaNOnUrX7hC49FLlzJghlp4AAAA4N1osVLjiw3ff2bV4\ncc55LdsbDEq33Rarjh19Gjq0/Fotjh61KS0tRgUF0ty5OapfP6R/VQAAABe00rZYVPkZ5C++sGvx\nYpc+/NBzvs+0UESENGNGrjp2jFPnzj41bWr9qhabNzuUlhaje+4p0KhR+XKY/E053ntPzpUrFWjV\nSv5WrSS7XY7331cgOVn+UoQBAAAAVbwH2ecrXJVi/Pg81atXstnZhg2DGjMmT4MHx8jns3Zc06e7\n9NBDMZoxI0djx/5aHOef+dS6QOPG8rduLfvOnYp+9FHFPPSQIg4ckFGjhrUDCgF6x2CGXMAMuYAZ\ncgErVekZ5BdeiFKtWobuucf8xrY/0revV2vWODVtWpQef/w8Hrt8HubNc2npUpfee8+jhITCot2e\nkaGY//1feT78UMWnko3ERHkfeEDeBx6w5NoAAACopDPI773nUEpKvDp2jNOqVZHym9zXtmtXhGbO\ndGnq1Nzzbq34PZtNev75HM2d69LixU6VtZv7nXciNXVqlJYvzy4qjm0HDyq2f3/ljh8v0z6LSoL1\nK2GGXMAMuYAZcgErVaoC+ZdfbBo4MFrDh0dr0qRcDR+er3nzXEpJidfMmS55PIX7BQKFq1b8/e/5\natCgbP3Df/qTofT0wodu9O0bo+PHS1dtf/KJXcOGRWvp0mw1bPjrmLxexfbrp4IHHpC/c+cyjRMA\nAADnp1IUyIYhvfGGU9ddF69atQxt2eJRp05+3XyzT2vXZmvhwhzt2OHQ1VdX09ixbj37bJTsdkP9\n+1uzAkXTpkGtX5+lhg2Duv76eL3/fslmevfujVCfPrF68cUcXX31b49zdo8erWDNmsofMcKScYYz\nesdghlzADLmAGXIBK13w39n/978RGj48WseO2fTaa9mnFZintGoV0Lx5Ofrppwi99JJLy5Y5tWKF\ntY+LdrmkCRPy1L69T4MHx+jOO70aMyZPLte5jzt61KaePWM1alTeaWscR3zzjSK3bJFn3TqF5XOt\nAQAAKqkLeh3kpUudGj/eraFD8zVoUIEiIy05bZn98otNjz0WrZ9+itCLL+YqOTlg2ueckyPdemuc\n2rf3afRok5v8Cgr0hxU2AAAATFWpdZD9fmncOLc2bIjUO+9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- "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 29, - "text": [ - "" - ] - } - ], - "prompt_number": 29 - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Exercise - Nonlinear Systems" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our equations are linear: \n", - "$$\\begin{aligned}new\\_pos&=old\\_pos+dist\\_moved\\\\\n", - "new\\_position&=old\\_position*measurement\\end{aligned}$$\n", - "\n", - "Do you suppose that this filter works well or poorly with nonlinear systems?\n", - "\n", - "Implement a Kalman filter that uses the following *sin()* to generate the measurement value for i in range(100):\n", - "\n", - " Z = math.sin(i/3.) * 2\n", - " \n", - "Adust the variance and initial positions to see the effect. What is, for example, the result of a very bad initial guess?" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#enter your code here." - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 30 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###### Solution:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "sensor_error = 30\n", - "movement_error = 2\n", - "pos = (100,500)\n", - "\n", - "zs = []\n", - "ps = []\n", - "\n", - "\n", - "for i in range(100):\n", - " pos = update(pos[0], pos[1], movement, movement_error)\n", - "\n", - " Z = math.sin(i/3.)*2\n", - " zs.append(Z)\n", - " \n", - " pos = sense(pos[0], pos[1], Z, sensor_error)\n", - " ps.append(pos[0])\n", - "\n", - "\n", - "p1, = plt.plot(zs,c='r', linestyle='dashed')\n", - "p2, = plt.plot(ps, c='b')\n", - "plt.legend([p1,p2], ['measurement', 'filter'], 2)\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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xQVIYFySFcUFyCngizFkjiIiIiEiLAj5rBOcRpnuxtoukMC5ICuOCpDAuSE4BTYRDQgCX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\nvaccRs25wgOeCOv++guC1ZqnVwv5TeiYMTCtXu3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A9C+/QHfvntvj0tMp7NpFo1s3wSr4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- "text": [ - "" - ] - } - ], - "prompt_number": 31 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "######Discussion\n", - "\n", - "Here we set a bad initial guess of 100. We can see that the filter never 'acquires' the signal. Note now the peak of the filter output always lags the peak of the signal by a small amount. More clearely we can see the large gap in height between the measurement and filter. \n", - "**REWriTE - not seeing heigh gap now**\n", - "\n", - "Maybe we just didn't adjust things 'quite right'. After all, the output looks like a sin wave, it is just offset in $x$ and $y$. Let's test this assumption." - ] - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Exercise - Noisy Nonlinear Systems" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Implement the same system, but add noise to the measurement." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#enter your code here" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 32 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "######Solution" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "sensor_error = 30\n", - "movement_error = 2\n", - "pos = (100,500)\n", - "\n", - "zs = []\n", - "ps = []\n", - "\n", - "\n", - "for i in range(100):\n", - " pos = update(pos[0], pos[1], movement, movement_error)\n", - "\n", - " Z = math.sin(i/3.)*2 + random.randn()*1.2\n", - " zs.append(Z)\n", - " \n", - " pos = sense(pos[0], pos[1], Z, sensor_error)\n", - " ps.append(pos[0])\n", - "\n", - "\n", - "p1, = plt.plot(zs,c='r', linestyle='dashed')\n", - "p2, = plt.plot(ps, c='b')\n", - "plt.legend([p1,p2], ['measurement', 'filter'], 3)\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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qBfMTT3js/e/fZ7BpkwYvvWTA779n4eRJHi+8EAhThQhaGS7FuIsXZdMkiG9o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\n7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- "text": [ - "" - ] - } - ], - "prompt_number": 33 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###### Discussion\n", - "This is terrible! The output is not at all like a sin wave, except in the grossest way. With linear systems we could add extreme amounts of noise to our signal and still extract a very accurate result, but here even modest noise creates a very bad result.\n", - "\n", - "Very shortly after practioners began implementing Kalman filters they realized the poor performance of them for nonlinear systems and began devising ways of dealing with it. Much of this book is devoted to this problem and its various solutions." - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Summary" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This information in this chapter takes some time to assimulate. To truly understand this you will probably have to work through this chapter several times. I encourage you to change the various constants and observe the results. Convince yourself that Gaussians are a good representation of a unimodal belief of something like the position of a dog in a hallway. Then convince yourself that multiplying Gaussians truly does compute a new belief from your prior belief and the new measurement. Finally, convince yourself that if you are measuring movement, that adding the Gaussians correctly updates your belief. That is all the Kalman filter does. Even now I alternate between complacency and amazement at the results. \n", - "\n", - "If you understand this, you will be able to understand multidimensional Kalman filters and the various extensions that have been make on them. If you do not fully understand this, I strongly suggest rereading this chapter. Try implementing the filter from scratch, just by looking at the equations and reading the text. Change the constants. Maybe try to implement a different tracking problem, like tracking stock prices. Experimentation will build your intuition and understanding of how these marvelous filters work." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 33 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def volt():\n", - " return random.randn()*4. + 14.4\n", - "\n", - "sensor_error = 30\n", - "movement_error = 2\n", - "pos = (12,500)\n", - "\n", - "zs = []\n", - "ps = []\n", - "vs = []\n", - "\n", - "\n", - "for i in range(100):\n", - " Z = volt()\n", - " zs.append(Z)\n", - " \n", - " pos = sense(pos[0], pos[1], Z, sensor_error)\n", - " ps.append(pos[0])\n", - " vs.append(pos[1])\n", - " #pos = update(pos[0], pos[1], 0, movement_error)\n", - "\n", - "\n", - "p1, = plt.plot(zs,c='r', linestyle='dashed')\n", - "p2, = plt.plot(ps, c='b')\n", - "plt.legend([p1,p2], ['measurement', 'filter'], 3)\n", - "plt.show()\n", - "plt.plot(vs)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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wLheYzSb7mJiXF/T/YEc9MUeYO3bseMURxmB9/nmknHUW+J9/1ndiHQijRsEQ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\nx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RGgEAAABPynprxAgzwp5GbxdMyAVMyAVMyAWcxIwwAAAAPCn7hTAzwp5GbxdMyAVMyAVM\nyAWclINVIyiEAQAAkHtZLYRDAZ/CYyyf5mX0dsGEXMCEXMCEXMBJWZ4RZvk0AAAAuEPWe4RHeFjO\n0+jtggm5gAm5gAm5gJN4WA4AAACexMNyyCp6u2BCLmBCLmBCLuCkKxbCjzzyiKqrq1VfXz++z+/3\nq7GxUY2Njdq+fftV3yzEjDAAAABcInClA+6++2594Qtf0P333z++r7CwUG+99daUb1aQxwdqeB29\nXTAhFzAhFzAhF3DSFWeE165dq4qKCkduVhDwaSTK8mkAAADIvWn1CIfDYTU3N6ulpUWvvvrqVZ9X\nEPAzI+xx9HbBhFzAhFzAhFzASVdsjTDp6upSVVWVDhw4oDvvvFNHjx5Vfn7+Rcc9+OCDWrx4sSSp\ntLRU1cvXKBwtl3QhyKl/4mDbG9spbhkP2+7Ybmtrc9V42HbHdopbxsO2O7b5+4LttrY2DQ0NSZI6\nOzu1detWTZdl27Z9pYM6Ojq0adOm8fCl++3f/m09/fTTqqurm7B/3759ampqmrCv72xE2/ce0TNf\nWD3tAQMAAAApra2t2rBhw7TOnXJrxMDAgEZGRiQlCuSurq7xWd8rKQj4NMqqEQAAAHCBKxbCDz30\nkNatW6cjR46otrZWjz/+uBobG9XQ0KC77rpLTz31lEKh0FXdrCCPT5bzusn/5AlI5AJm5AIm5AJO\nClzpgMcff1yPP/74hH2PPfbYtG6W57MUt21F47YCPmta1wAAAACckNVPlrMsS8VBv86MRrN5W7hI\nqtkdSEcuYEIuYEIu4KSsFsKStLAkqL6zkWzfFgAAAJgg+4Vwcb56zlAIexW9XTAhFzAhFzAhF3BS\n1gvh6pKgeimEAQAAkGM5mBEOqofWCM+itwsm5AIm5AIm5AJOysmMcM+Z0WzfFgAAAJggJw/L0Rrh\nXfR2wYRcwIRcwIRcwEk5aY3oOxvRVXyyMwAAAJAxWS+EQ3l+FeT5NTjCWsJeRG8XTMgFTMgFTMgF\nnJT1QlhK9gnzwBwAAAByKCeF8MLiIGsJexS9XTAhFzAhFzAhF3BSzgrh3rOsHAEAAIDcyV1rBDPC\nnkRvF0zIBUzIBUzIBZyUmxlhllADAABAjuVmRrg4X708LOdJ9HbBhFzAhFzAhFzASTkphKtKEmsJ\nx1lLGAAAADmSk0K4IOBTUdCv0+dZS9hr6O2CCbmACbmACbmAk3JSCEvJJdRYOQIAAAA5krtCmAfm\nPIneLpiQC5iQC5iQCzgpZ4VwdUk+S6gBAAAgZ3LaGsHKEd5DbxdMyAVMyAVMyAWclMMZYT5UAwAA\nALnDjDCyit4umJALmJALmJALOCmnhfDJcxHF4qwlDAAAgOzLWSEcDPhUku/XwMhYroaAHKC3Cybk\nAibkAibkAk7KWSEsJT9qmT5hAAAA5EBOC+GFPDDnOfR2wYRcwIRcwIRcwEk5nhEOqocH5gAAAJAD\nOZ8R7j3Dxyx7Cb1dMCEXMCEXMCEXcFJuC2GWUAMAAECO5LY1go9Z9hx6u2BCLmBCLmBCLuCknBbC\nC4rz1H9ujLWEAQAAkHU5LYSDfp9KCwLqP89awl5BbxdMyAVMyAVMyAWclNNCWEotocYDcwAAAMiu\nnBfC1awl7Cn0dsGEXMCEXMCEXMBJOS+EWTkCAAAAuZD7QriEj1n2Enq7YEIuYEIuYEIu4KScF8K0\nRgAAACAXcl8I0xrhKfR2wYRcwIRcwIRcwEk5L4Qri/I0cJ61hAEAAJBdOS+E8/w+lYUC6jvHrLAX\n0NsFE3IBE3IBE3IBJ+W8EJYSH7XMA3MAAADIJlcUwgtL6BP2Cnq7YEIuYEIuYEIu4CRXFMLVxUFm\nhAEAAJBV7iiE+Zhlz6C3CybkAibkAibkAk5yRSG8sDioHlojAAAAkEVXLIQfeeQRVVdXq76+fnzf\nnj17tHz5ctXV1emFF16Y8SAWltAa4RX0dsGEXMCEXMCEXMBJVyyE7777br344ovj25FIRI8++qhe\nf/11vfLKK9q+ffuMB7GgKKjBkajGYvEZXwsAAAC4GlcshNeuXauKiorx7f3792vVqlVasGCBamtr\nVVtbq4MHD85oEH6fpfmFeTp5bmxG14H70dsFE3IBE3IBE3IBJwWmekJPT49qamq0e/duzZ8/X9XV\n1eru7lZDQ8OMBlKdbI9YNC9/RtcBAAAArsa0H5bbtm2bNm/eLEmyLGvGA+GBOW+gtwsm5AIm5AIm\n5AJOmvKM8KJFi9Td3T2+nZohNnnwwQe1ePFiSVJpaanq6+vH/0kjFeTU9ujpHv1yQPpMXYXxfbbn\nxnaKW8bDtju229raXDUett2xneKW8bDtjm3+vmC7ra1NQ0NDkqTOzk5t3bpV02XZtm1f6aCOjg5t\n2rRJbW1tikQiWrFihfbv369wOKxbb71V7e3tF52zb98+NTU1XfVAXm7v15vHz+jRW66byvgBAADg\nYa2trdqwYcO0zg1c6YCHHnpIf/3Xf61Tp06ptrZWTzzxhHbs2KH169dLknbt2jWtG0+2aF6+/mbo\npCPXAgAAAK7kij3Cjz/+uE6cOKFIJKIPP/xQmzZt0pYtW3TkyBEdOXJEd9xxhyMDubGiUB8Ojupc\nJObI9eBOk//JE5DIBczIBUzIBZzkik+Wk6RgwKe6BYV6p/dsrocCAAAAD3BNISxJa2qK9XY3hfBc\nlmp2B9KRC5iQC5iQCzjJXYVwdbEOUggDAAAgC1xVCK+sKtIHp8M6T5/wnEVvF0zIBUzIBUzIBZzk\nqkI4GPBpeWWh3uk9l+uhAAAAYI5zVSEspfqEz+R6GMgQertgQi5gQi5gQi7gJHcWwj30CQMAACCz\nXFcIr6wq0vsDYY2M0Sc8F9HbBRNyARNyARNyASe5rhDOD/h0Q2WIPmEAAABklOsKYUlqqClhPeE5\nit4umJALmJALmJALOMmVhfCaaj5YAwAAAJnlykJ45cIiHRsYoU94DqK3CybkAibkAibkAk5yZSFc\nEPDpxoqQfkOfMAAAADLElYWwJNXX0B4xF9HbBRNyARNyARNyASe5thBuYD1hAAAAZJBrC+GVVUU6\n1k+f8FxDbxdMyAVMyAVMyAWc5NpCOJTn17L5Ib3bR58wAAAAnOfaQlhKtkfQJzyn0NsFE3IBE3IB\nE3IBJ7m6EF5DIQwAAIAMcXUh/I8WFulo/4jC0XiuhwKH0NsFE3IBE3IBE3IBJ7m6EKZPGAAAAJni\n6kJYoj1irqG3CybkAibkAibkAk6iEAYAAIAnub4QXrWwSO2nzmuUPuE5gd4umJALmJALmJALOMn1\nhXAoz6/rygt0iD5hAAAAOMj1hbAkNV1Top91DuV6GHAAvV0wIRcwIRcwIRdw0qwohG9fXqF97QO0\nRwAAAMAxs6IQrpmXr7oFRfp/753O9VAwQ/R2wYRcwIRcwIRcwEmzohCWpI0rK7X33VO5HgYAAADm\niFlTCP9W7TydHhlT+6nzuR4KZoDeLpiQC5iQC5iQCzhp1hTCfp+lz9ZV6gVmhQEAAOCAWVMIS9Kn\n6yr06vuDOjsazfVQME30dsGEXMCEXMCEXMBJs6oQnl+Yp+ZrS/Ry+0CuhwIAAIBZblYVwpK0aWWl\nXjzUL9u2cz0UTAO9XTAhFzAhFzAhF3DSrCuE66uLZUl6u/tsrocCAACAWWzWFcKWZWnjSh6am63o\n7YIJuYAJuYAJuYCTZl0hLEmfvHG+3uw6o4HzY7keCgAAAGapWVkIFwX9+vjSMv3fw/25HgqmiN4u\nmJALmJALmJALOGlWFsJS6qG5U4rFeWgOAAAAUzdrC+EbKgtVWZSnX3w4nOuhYAro7YIJuYAJuYAJ\nuYCTZm0hLEkbV1Zq77sncz0MAAAAzEKzuhD+naXlaj81oo7TI7keCq4SvV0wIRcwIRcwIRdw0qwu\nhIMBn+5rqta3Xz/OB2wAAABgSmZ1ISxJd6yo1Gg0zscuzxL0dsGEXMCEXMCEXMBJs74Q9vssfXV9\nrZ765QkNh6O5Hg4AAABmiVlfCEvS8gWF+vjSMv3lgRO5HgqugN4umJALmJALmJALOGlOFMKSdH9z\njX7eOaR3+87leigAAACYBaZdCPv9fjU2NqqxsVHbt293ckzTUpwf0Fd+6xr919c+5EM2XIzeLpiQ\nC5iQC5iQCzgpMN0TCwsL9dZbbzk5lhm75fpyvXSkX//nNyd11+qqXA8HAAAALjZnWiMkybIsPbyu\nVs+81aNT5yK5Hg4M6O2CCbmACbmACbmAk6ZdCIfDYTU3N6ulpUWvvvqqk2OakdqyAm1cWan//vOu\nXA8FAAAALjbtQrirq0tvvvmmdu3apXvvvVejo6NOjmtGvvCRarWfOq8Dx4dzPRRMQm8XTMgFTMgF\nTMgFnDTtHuGqqkQP7k033aRFixapo6NDdXV1E4558MEHtXjxYklSaWmp6uvrx/9JIxXkTGznB3z6\nROmw/vMr7Xryd9eoPJSX0fuxffXbKW4ZD9vu2G5ra3PVeNh2x3aKW8bDtju2+fuC7ba2Ng0NDUmS\nOjs7tXXrVk2XZU/js4lPnz6tgoIChUIhdXR0qKWlRe3t7QqFQuPH7Nu3T01NTdMemBO+e+CEDp44\nq//y2RsUDMypdmgAAABIam1t1YYNG6Z17rSqw0OHDqmxsVENDQ2666679NRTT00ogt3iy801WlCc\npz/9hw8Un3q9DwAAgDlsWoXw2rVrdejQIR08eFCtra26/fbbnR6XI3yWpa/fvEQnz47pf73Znevh\nQBf/kycgkQuYkQuYkAs4ac73CwQDPn3jU0v102On9eMj/bkeDgAAAFxizhfCklQWytMf3Xa9vvOL\nEzp44kyuh+NpqWZ3IB25gAm5gAm5gJM8UQhL0uLyAv2bW6/Tf/r7Dn04GM71cAAAAJBjnimEJalx\nUYn++UcX6bEfH9PgyFiuh+NJ9HbBhFzAhFzAhFzASZ4qhCXp03UV+p1l5fqDvz2qk3wMMwAAgGdN\nax3hq+GGdYQvxbZt/bCtT3/zzkn90W3Xa1mF+5Z+AwAAwJVlfR3h2c6yLG1es1C//1vX6A/+7qje\n6uIBOgAAAK/xZCGc8onry/XYhqX6k5906OV2llbLBnq7YEIuYEIuYEIu4CRPF8KStKamWH92x416\n+s0eff+tHmWoUwQAAAAu48keYZP+82N67KVjurGyUP9ifa0CPivXQwIAAMAV0CPsgIrCPO3ceKNO\nj4zpX/7osD44PZLrIQEAACCDKITThPL8+uanlumzKyr1yItH9cO3exWL0yrhJHq7YEIuYEIuYEIu\n4CQK4Uksy9IdKyr1F59brp91Dunrf9uu7uHRXA8LAAAADqNH+DJicVvP/7pPzx7s1T/76CJ9tq5C\nlkXvMAAAgFvQI5whfl9iveE/23ijXnz3lP7dS8fUORjO9bAAAADgAArhq3BdeUh/8Y/r9JFFJfpX\nL7TrL177UAPnx3I9rFmJ3i6YkAuYkAuYkAs4iUL4KgV8lrasWainPr9SwYCl33/uXX2vtVsjY7Fc\nDw0AAADTQI/wNHUPj+q7B06oreec7muq1u3LK+Rn7WEAAICsmkmPcMDhsXhGzbx8/dtbl+pQ3zl9\n5xcntOftPt21eoFuW16hggAT7QAAAG5HxTZDK6qK9Kd33KCvfXyx3uw6o/v+9zv6nwdO0EN8CfR2\nwYRcwIRcwIRcwEnMCDvAsiytqSnWmppiHR8K6/m2k9r6w3e1/rpS3V1fpevKQ7keIgAAACahRzhD\nhsJR7X33lPb+5qRqSwt02/L5+vjSMoXy/LkeGgAAwJxBj7ALlRYE9E8bq7VlTZV+0TmsH7f367/9\nvEvrlpTqthvnq76mWD4+nAMAACBn6BHOsKDfp5alZfoPt12vv/z8Si2bH9ITbxzXl5/9jZ5+s1vv\nD4woQ5PyrkRvF0zIBUzIBUzIBZzEjHAWlRfm6e76Kt21eoGO9Y/o5aMDeuzHxxTwWVq/pEzrryvT\niqpCZooBAACygB7hHLNtW0f7R/R6x6Be/2BIZ0ajWre4TGuXlKq+ppil2AAAAC6DHuFZzLIs3VhZ\nqBsrC3X/TYt0fCis1zuG9MyvevTe349oxYJCNV8zT83Xlmjp/BCzxQAAAA5hutFlri0t0O82LNS3\nNi3XM1+D/O6rAAAOEElEQVRYrX+yqkp95yL6j/s6dM/3f60dP+nQ3x3u14eD4VnZW0xvF0zIBUzI\nBUzIBZzEjLCLFQX9WrukVGuXlEqSes6MqrXrjA6eOKPvv9Wt0ait1QuLtLq6WKuri3RDRSEf8wwA\nAHCV6BGexfrORvTrnrP6de85/brnrHrPRnR9RUh1lYVavqBIyysLtWheUBbtFAAAYI6iR9ijqoqD\nuvWG+br1hvmSpDOjUbWfOq/DJ8/rH947rf/xiy6NRuO6sbJQyysLtWx+SMsqQrpmXj4zxwAAwPMo\nhOeQkvyAmq6Zp6Zr5o3vGzg/psMnz6v91Hn99L3T+u6BExoYieq68gItLU8UxkvLC7S4vEBlBYGM\nzx6/9tpramlpyeg9MPuQC5iQC5iQCziJQniOm1+YN6HPWJLORWLqGBjRe8mvf3jvtD4YDEuSlpQV\nqLasQEvKC7S4rEDXlOarqijIDDIAAJhz6BGGpMR6xoMjUXUOhvXBYFgfJl+PD41qKBxVdXFQ15Ym\nCuNF8/J1TWm+qkuCFMkAACCn6BHGjFmWpfLCPJUX5qlhUcmE98LRuLqHR9U1NKqu4VEdPnlOPzl2\nWj1nRjU4ElVFUZ5qSoKqLkkUx9UlQVUVJ77mh/IolAEAgCtRCOOKCgI+LZ0f0tL5oYvei8TiOnk2\nou4zEfWciajnzKhe7xhS39mI+s5FdCYc0/zCvGRhnKfw6V411i1TZVGeKguDqizKU1kowAeFeBw9\nfzAhFzAhF3AShTBmJOj36ZrSAl1TWmB8PxKLq//cmHrPRtR3NqI3B3vVMRDWgePDOnVuTCfPjelc\nJKbyUEAVhXman/yqGH8NaH4oT+WhPJWGAgowuwwAABxCjzByLhKLq//8mAbOj2ngfDT5OqaBkbHk\n/qgGR8Y0FI6qKOhXeSgxi1weCqgslKfSgoBKCwIqKwioLBQY3y7O9zPTDADAHEePMGa1oN+nmpJ8\n1ZTkX/a4uG1rOBzV6ZGoBkeiGkgWx4MjifWTB8NRDY1EE/vCUY2MxVSSH9C8fL9KCwIqKQioND+g\neQX+8f0l+QGVpF6T+/P9Fh9CAgCAB1AII6tm0tvlsyyVhfJUFsq7quOjcVtnwlENj0Y1FI5pOBzV\n0Gg08RqO6vhQWGdGY8mvxHFnR2Oybak436+ioF8l+X4VBxOzy8XBxFdR8r3iYOJ1/CvPr8KgTwUB\nH4X0FNHzBxNyARNyASdRCGPOCvgurIQxFZFoXGciMZ0bjelMJFEcnxmN6Vwk8TU4ElXX0KjORWI6\nG7mw/3wkpnNjcY3F4ioK+lWY51dR0KfCPL8Kg34V5vkUyvMn30t8H0q+Fk56Tez3KT/go70DAIAM\noUcYcFg0bieL4kRxfH4snnxN/z6u82MxjUTiGknuH4nGNDKW2E68xjUajSs/4BsvjAsCPhUE/CrI\n8ykU8CVfE9uJ9xLFc2o7P3D5V5a2AwDMdvQIAy4S8FmaVxDQvIKZ/98rbtsajcbHC+ORsZjC0Xji\nK1k8J14TRfNQODr+/mjacaOxC/tG097zWZaCfksFAZ+CaQVy0O9TfsBKvvqUP2k7mPw+/bjJ3+f5\nk9+P70u8UnwDANyCQhhZRW/X1PgsK9kq4Xf82rZtayxuKxKNazRqazQ2sVBObCcK8UjyvUgssX1u\nNKaBWFSj0UQryGgscZ1ILHFM6nUsFlckee3UtiTlpRXGQb+lsdGwykqKxovnPL+lPF/ivTy/pbzx\nfWnfJ4+51P6A31LQZyngtxRIve9LvJ/aDvgS31OcuxN/X8CEXMBJFMKAR1nJ2eCg36fiyy/Y4ahY\nfGKhPBq1tf/AAdU3rFQkFtdYspBOvCaOGYvbGkt9H0sU8GdHYxqLRyfsj6aOiyePG9934RrR+IV9\nqeMtS+NFcXqBnCqax78mb/t8CvikgN+ngGV635pQbKdfw29N3O/36aJj/Vbaecn9F20nx84DmgAw\ndfQIA/C8WDxRXMfSC+S4rWiqmLYT3yeK6Ph4MR1NK6wnfBn2xwzHxdL2x+yLj43FNeG8mD3pnLit\nmJ3oS/dZShbGE4tk//j3Fwpu34RjEvv8aYX3hHOticf5JuxLHONLXSO532dNvLfPSrznG7++ksdY\n4+NOneNLv1fqGN+F8y98r/HrjV8/OSZ+KQC8hR5hAJiBVMGW3MrpWKbDtm3FbSUL4/QiW+PF88RC\nOlE8x1PbdlrhndyXeE8XFd9xO22fLcWTM/yxSWOIp5+b9t7k68YnvDfp/snrx2yNjzX9OvHJ+5P7\nLClRKKcX2GnFs29SIT7+ni/92InHTzx24vHW5OukF+qWJctwncnnWFbqWhfuYbp2+nGWEr8wWEr+\nTMnX1H6fZSX/LCafmxpT2ntK7POnvZd+b8tKXj9tjNakcfk08T1L/FIC95t2Ibxnzx794R/+oSzL\n0s6dO7Vx40Ynx4U5it4umJCLmUkVOHOt13k6ubDTCuT018nF83jhHE9tpxXWyQL8wrGmfYnz7fRj\n4pKt5DHJAt6efN0J51zYjsbjGk07fsK141JcE8+zL7rehXHYE86/xD018dz0+0oXnzfh3rp4HLJt\nxRMv48fbShbUSiuOxwvvicV06peX9CLd0sRfGpQ8Z+T8eRUXFV64ljWx6LdS+3ThWppcxEsXxqOJ\n45owzvSx6eJrW2njlCXDtayL76kL173oerr457UmXyvt+wvXNPyshvtMPE7jy3Omfr4LP4PhOpP+\nfFJ/Lokj0/88Jt1X5rGO70t7X2l/VtKF/72Sbyk/A6sdTasQjkQievTRR7V//36Fw2HdcsstFMK4\nKj09PbkeAlyIXMBkOrkY/6VAzv7HElNnTyqcJxfJsXiq6E6+l36OfaH4n7zvxz9+WZ/8xKfGf+mY\nWKRPKu6V+KUk1QM6ofhPni87/RcNSZr4S0qyzh+/Xup+iesl9tuTvrcnjyX5s6V+zng8nrzvhZ9v\nfHzp90uemz5GO23s6dedfA877We5+LiJ40sdp9TPp8SBdtrPO/nP8KJrpo37wj3SxjDpZ9Sk+6T/\nGSjt/PTr/dFty9SwqGS6kTSaViG8f/9+rVq1SgsWLJAk1dbW6uDBg2poaHB0cJh78vOz+FQWZg1y\nARNyMbulfilJbjl23ZpCS8sqQo5dD942rUK4t7dXNTU12r17t+bPn6/q6mp1d3dTCAMAAGDWmNHD\nctu2bZMkPf/88zTE46p0dnbmeghwIXIBE3IBE3IBJ02rEK6pqVF3d/f4dk9Pj2pqaiYcEw6H1dra\nOrPRYc5Zu3YtucBFyAVMyAVMyAUmC4fD0z53WusIRyIRrVixYvxhuVtvvVXt7e3THgQAAACQbdOa\nEQ4Gg9qxY4fWr18vSdq1a5ejgwIAAAAyLWOfLAcAAAC4mS/XAwAAAABygUIYAAAAnjSj5dMu5Wc/\n+5meffZZSdKXvvQlNTc3Z+I2cLGBgQF961vf0vnz5xUIBPTFL35Ra9asIRuQJI2MjGj79u3auHGj\nNm3aRC6g9vZ27d69W7FYTEuWLNH27dvJBfSDH/xAb7zxhiRp3bp1+vznP08uPOjpp5/Wq6++qnnz\n5mnnzp2SLl1rTjkftsPGxsb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- "text": [ - "" - ] - } - ], - "prompt_number": 34 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "author notes:\n", - " clean up the code - same stuff duplicated over and over - write a 'clean implemntation' at the end.\n", - " \n", - " " - ] - }, - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "Multidimensional Kalman Filters" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Introduction" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The techniques in the last chapter are very powerful, but they only work in one dimension. The gaussians represent a mean and variance that are scalars - real numbers. They provide no way to represent multidimensional data, such as the position of a dog in a field. You may retort that you could use two Kalman filters for that case, one tracks the x coordinate and the other tracks the y coordinate. That does work in some cases, but put that thought aside, because soon you will see some enormous benefits to implementing the multidimensional case." - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Multivariate Normal Distributions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What might a *multivariate normal distribution* look like? In this context, multivariate just means multiple variables. Our goal is to be able to represent a normal distribution across multiple dimensions. Consider the 2 dimensional case. Let's say we believe that $x = 2$ and $y = 7$. Therefore we can see that for $N$ dimensions, we need $N$ means, like so:\n", - "\n", - "$$\n", - "\\mu = \\begin{bmatrix}{\\mu}_1\\\\{\\mu}_2\\\\ \\vdots \\\\{\\mu}_n\\end{bmatrix}\n", - "$$\n", - "\n", - "Therefore for this example we would have\n", - "\n", - "$$\n", - "\\mu = \\begin{bmatrix}2\\\\7\\end{bmatrix} \n", - "$$\n", - "\n", - "The next step is representing our variances. At first blush we might think we would also need N variances for N dimensions. We might want to say the variance for x is 10 and the variance for y is 8, like so. \n", - "\n", - "$$\\sigma^2 = \\begin{bmatrix}10\\\\8\\end{bmatrix}$$ \n", - "\n", - "While this is possible, it does not consider the more general case. For example, suppose we were tracking house prices vs total $m^2$ of the floor plan. These numbers are *correlated*. It is not an exact correlation, but in general houses in the same neighborhood are more expensive if they have a larger floor plan. We want a way to express not only what we think the variance is in the price and the $m^2$, but also the degree to which they are correlated. It turns out that we use the following matrix to denote *covariances* with multivariate normal distributions. You might guess, correctly, that *covariance* is short for *correlated variances*." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "$$\n", - "\\Sigma = \\begin{pmatrix}\n", - " \\sigma_1^2 & p\\sigma_1\\sigma_2 & \\cdots & p\\sigma_1\\sigma_n \\\\\n", - " p\\sigma_2\\sigma_1 &\\sigma_2^2 & \\cdots & p\\sigma_2\\sigma_n \\\\\n", - " \\vdots & \\vdots & \\ddots & \\vdots \\\\\n", - " p\\sigma_n\\sigma_1 & p\\sigma_n\\sigma_2 & \\cdots & \\sigma_n^2\n", - " \\end{pmatrix}\n", - "$$\n", - "\n", - "If you haven't seen this before it is probably a bit confusing at the moment. Rather than explain the math right now, we will take our usual tactic of building our intuition first with various physical models. At this point, note that the diagonal contains the variance for each state variable, and that all off-diagonal elements are a product of the $\\sigma$ corresponding to the $i$th (row) and $j$th (column) state variable multiplied by a constant $p$." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, without explanation, here is the full equation for the multivarate normal distribution in $n$ dimensions.\n", - "\n", - "$$\\mathcal{N}(\\mu,\\,\\Sigma) = (2\\pi)^{-\\frac{n}{2}}|\\Sigma|^{-\\frac{1}{2}}\\, e^{ -\\frac{1}{2}(\\mathbf{x}-\\mu)'\\Sigma^{-1}(\\mathbf{x}-\\mu) }$$\n", - "\n", - "I urge you to not try to remember this function. We will program it in a Python function and then call it when we need to compute a specific value. However, if you look at it briefly you will note that it looks quite similar to the *univarate normal distribution* except it uses matrices instead of scalar values, and the root of $\\pi$ is scaled by $n$. Here is the *univariate* equation for reference:\n", - "\n", - "$$ \n", - "f(x, \\mu, \\sigma) = \\frac{1}{\\sigma\\sqrt{2\\pi}} e^{{-\\frac{1}{2}}{(x-\\mu)^2}/\\sigma^2 }\n", - "$$\n", - "\n", - "If you are reasonably well-versed in linear algebra this equation should look quite managable; if not, don't worry! If you want to learn the math we will cover it in detail in the next optional chapter. If you choose to skip that chapter the rest of this book should still be managable for you\n", - "\n", - "I have programmed it and saved it in the file *stats.py* with the function name *multivariate_gaussian*. I am not showing the code here because I have taken advantage of the linear algebra solving apparatus of numpy to efficiently compute a solution - the code does not correspond to the equation in a one to one manner. If you wish to view the code, I urge you to either load it in an editor, or load it into this worksheet by putting *%load -s multivariate_gaussian stats.py* in the next cell and executing it with ctrl-enter. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - ">As of version 0.14 scipy.stats has implemented the multivariate normal equation with the function **multivariate_normal()**. It is superior to my function in several ways. First, it is implemented in Fortran, and is therefore faster than mine. Second, it implements a 'frozen' form where you set the mean and covariance once, and then calculate the probability for any number of values for x over any arbitrary number of calls. This is much more efficient then recomputing everything in each call. So, if you have version 0.14 or later you may want to substitute my function for the built in version. Use **scipy.version.version** to get the version number. I deliberately named my function **multivariate_gaussian()** to ensure it is never confused with the built in version.\n", - "\n", - "> If you intend to use Python for Kalman filters, you will want to read the tutorial for the scipy.stats module, which explains 'freezing' distributions and other very useful features. As of this date, it includes an example of using the multivariate_normal function, which does work a bit differently from my function." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from stats import gaussian, multivariate_gaussian" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's use it to compute a few values just to make sure we know how to call and use the function, and then move on to more interesting things.\n", - "\n", - "First, let's find the probability for our dog being at (2.5, 7.3) if we believe he is at (2,7) with a variance of 8 for $x$ and a variance of 10 for $y$. This function requires us to pass everything in as numpy arrays (we will soon provide a more robust version that works with numpy matrices, numpy arrays, and/or scalars in any combinations. That code contains a lot of boilerplate which obscures the algorithm).\n", - "\n", - "Start by setting $x$ to (2.5,7.3):" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "x = np.array([2.5, 7.3])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we set the mean of our belief:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "mu = np.array([2,7])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we have to define our covariance matrix. In the problem statement we did not mention any correlation between $x$ and $y$, and we will assume there is none. This makes sense; a dog can choose to independently wander in either the $x$ direction or $y$ direction without affecting the other. If there is no correlation between the values you just fill in the diagonal of the covariance matrix with the variances. I will use the seemingly arbitrary name $P$ for the covariance matrix. The Kalman filters use the name $P$ for this matrix, so I will introduce the terminology now to avoid explaining why I change the name later. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "P = np.array([[8.,0],[0,10.]])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now just call the function" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print(multivariate_gaussian(x,mu,P))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "0.0174395374407\n" - ] - } - ], - "prompt_number": 6 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's check the probability for the dog being at exactly (2,7)" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from __future__ import print_function\n", - "\n", - "x = np.array([2,7])\n", - "print(\"Probability dog is at (2,7) is %.2f%%\" % (multivariate_gaussian(x,mu,P) * 100.))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Probability dog is at (2,7) is 1.78%\n" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These numbers are not easy to interpret. Let's plot this in 3D, with the $z$ (up) coordinate being the probability." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%matplotlib inline\n", - "import matplotlib.pylab as pylab\n", - "from matplotlib import cm\n", - "from mpl_toolkits.mplot3d import Axes3D\n", - "import numpy as np\n", - "\n", - "pylab.rcParams['figure.figsize'] = 12,6\n", - "pylab.rcParams['axes.color_cycle'] = '348ABD, 7A68A6, A60628, 467821, CF4457, 188487, E24A33'\n", - "\n", - "P = np.array([[8.,0],[0,10.]])\n", - "mu = np.array([2,7])\n", - "\n", - "xs, ys = np.arange(-8, 13, .5), np.arange(-8, 20, .5)\n", - "xv, yv = np.meshgrid (xs, ys)\n", - "\n", - "zs = np.array([100.* multivariate_gaussian(np.array([x,y]),mu,P) \\\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=-9, cmap=cm.autumn)\n", - "ax.contour(xv, yv, zv, zdir='y', offset=20, cmap=cm.BuGn)\n", - "plt.xlim((-10,15))\n", - "plt.ylim((-10,20))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 8, - "text": [ - "(-10, 20)" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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hEJqmlXZ8jo2+/nskTvrMuFAlX7TZs5YSP/c7hO96F9JYb83tB0q3XzAtcH+P7u/T/aco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Gg3EbwWgt704IsvVgx18OWm2AZICs6XfoX37FuQStfg2PohAILtx3firSjLX0WqLcNOzUas2RO2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s6g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du8GTStlCk9cgRj+XLUFSsI3367GMhGjkTbu7c0yiuhWY2v+9X9+/E9/7y0XSVW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\nZfdr3743TZ4My5NPsgUvUYQiCOCvX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- "text": [ - "" - ] - } - ], - "prompt_number": 8 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The result is clearly a 3D bell shaped curve. We can see that the gaussian is centered around (2,7), and that the probability quickly drops away in all directions. On the sides of the plot I have drawn the Gaussians for $x$ in greens and for $y$ in orange.\n", - "\n", - "As beautiful as this is, it is perhaps a bit hard to get useful information. For example, it is not easy to tell if $x$ and $y$ both have the same variance or not. So for most of the rest of this book we will display multidimensional Gaussian using contour plots. I will use some helper functions in *gaussian.py* to plot them. If you are interested in linear algebra go ahead and look at the code used to produce these contours, otherwise feel free to ignore it." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import stats\n", - "\n", - "P = np.array([[2,0],[0,2]])\n", - "plt.subplot(131)\n", - "stats.plot_covariance_ellipse(P, x=2, y=7, title='|2 0|\\n|0 2|')\n", - "\n", - "plt.subplot(132)\n", - "P = np.array([[2,0],[0,9]])\n", - "stats.plot_covariance_ellipse(P, x=2, y=7, title='|2 0|\\n|0 9|')\n", - "\n", - "plt.subplot(133)\n", - "P = np.array([[2,1.2],[1.2,2]])\n", - "stats.plot_covariance_ellipse(P, x=2, y=7, title='|2 1.2|\\n|1.2 2|')\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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AxMbGIikpCSkpKQD+LBPga+2/9vuBr7/ejv374/DHH7WwZYsFR44AZrMPlSqZcOONPgDn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- "text": [ - "" - ] - } - ], - "prompt_number": 9 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From a mathematical perspective these display the values that the multivariate gaussian takes for a specific sigma (in this case $\\sigma^2=1$. Think of it as taking a horizontal slice through the 3D surface plot we did above. However, thinking about the physical interpretation of these plots clarifies their meaning.\n", - "\n", - "The first plot uses the mean and covariance matrices of\n", - "$$\n", - "\\begin{aligned}\n", - "\\mu &= \\begin{bmatrix}2\\\\7\\end{bmatrix} \\\\\n", - "\\sigma^2 &= \\begin{bmatrix}2&0\\\\0&2\\end{bmatrix}\n", - "\\end{aligned}\n", - "$$\n", - "\n", - "Let this be our current belief about the position of our dog in a field. In other words, we believe that he is positioned at (2,7) with a variance of $\\sigma^2=2$ for both x and y. The contour plot shows where we believe the dog is located with the '+' in the center of the ellipse. The ellipse shows the boundary for the $1\\sigma^2$ probability - points where the dog is quite likely to be based on our current knowledge. Of course, the dog might be very far from this point, as Gaussians allow the mean to be any value. For example, the dog could be at (3234.76,189989.62), but that has vanishing low probability of being true. Generally speaking displaying the $1\\sigma^2$ to $2\\sigma^2$ contour captures the most likely values for the distribution. An equivelent way of thinking about this is the circle/ellipse shows us the amount of error in our belief. A tiny circle would indicate that we have a very small error, and a very large circle indicates a lot of error in our belief. We will use this throughout the rest of the book to display and evaluate the accuracy of our filters at any point in time. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The second plot uses the mean and covariance matrices of\n", - "\n", - "$$\n", - "\\begin{aligned}\n", - "\\mu &=\\begin{bmatrix}2\\\\7\\end{bmatrix} \\\\\n", - "\\sigma^2 &= \\begin{bmatrix}2&0\\\\0&9\\end{bmatrix}\n", - "\\end{aligned}\n", - "$$\n", - "\n", - "This time we use a different variance for $x$ (2) vs $y$ (9). The result is an ellipse. When we look at it we can immediately tell that we have a lot more uncertainty in the $y$ value vs the $x$ value. Our belief that the value is (2,7) is the same in both cases, but errors are different. This sort of thing happens naturally as we track objects in the world - one sensor has a better view of the object, or is closer, than another sensor, and so we end up with different error rates in the different axis." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The third plot uses the mean and covariance matrices of:\n", - "$$\n", - "\\begin{aligned}\n", - "\\mu &=\\begin{bmatrix}2\\\\7\\end{bmatrix} \\\\\n", - "\\sigma^2 &= \\begin{bmatrix}2&1.2\\\\1.2&2\\end{bmatrix}\n", - "\\end{aligned}\n", - "$$\n", - "\n", - "This is the first contour that has values in the off-diagonal elements of $cov$, and this is the first contour plot with a slanted ellipse. This is not a coincidence. The two facts are telling use the same thing. A slanted ellipse tells us that the $x$ and $y$ values are somehow **correlated**. We denote that in the covariance matrix with values off the diagonal. What does this mean in physical terms? Think of trying to park your car in a parking spot. You can not pull up beside the spot and then move sideways into the space because most cars cannot go purely sideways. $x$ and $y$ are not independent. This is a consequence of the steering system in a car. When your tires are turned the car rotates around its rear axle while moving forward. Or think of a horse attached to a pivoting exercise bar in a corral. The horse can only walk in circles, he cannot vary $x$ and $y$ independently, which means he cannot walk straight forward to to the side. If $x$ changes, $y$ must also change in a defined way. \n", - "\n", - "So when we see this ellipse we know that $x$ and $y$ are correlated, and that the correlation is \"strong\". The size of the ellipse shows how much error we have in each axis, and the slant shows how strongly correlated the values are.\n", - "\n", - "A word about **correlation** and **independence**. If variables are **independent** they can vary separately. If you walk in an open field, you can move in the $x$ direction (east-west), the $y$ direction(north-south), or any combination thereof. Independent variables are always also **uncorrelated**. Except in special cases, the reverse does not hold true. Variables can be uncorrelated, but dependent. For example, consider the pair$(x,y)$ where $y=x^2$. Correlation is a linear measurement, so $x$ and $y$ are uncorrelated. However, they are obviously dependent on each other. \n", - "\n", - "** wikipedia article 'correlation and dependence' claims multivariate normals are a special case, where the correlation coeff $p$ completely defines the dependence. FIGURE THIS OUT!**" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Unobserved Variables" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's say we are tracking an aircraft and we get the following data for the $x$ coordinate at time $t$=1,2, and 3 seconds. What does your intuition tell you the value of $x$ will be at time $t$=4 seconds?" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import mkf_internal\n", - "mkf_internal.show_position_chart()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 10 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It appears that the aircraft is flying in a straight line because we can draw a line between the three points, and we know that aircraft cannot turn on a dime. The most reasonable guess is that $x$=4 at $t$=4. I will depict that with a green arrow." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "mkf_internal.show_position_prediction_chart()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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Lli05/PDDc+WVV+YFL3hBkuTqq6/OT3/602zdujWf/vSnd54+L1myJP/0T/+U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\nRU5ErkJggg==\n", - "text": [ - "" - ] - } - ], - "prompt_number": 11 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If this is data from a Kalman filter, then each point has both a mean and variance. Let's try to show that by showing the approximate error for each point. Don't worry about why I am using a covariance matrix to depict the variance at this point, it will become clear in a few paragraphs. The intent at this point is to show that while we have $x$=1,2,3 that there is a lot of error associated with each measurement." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "mkf_internal.show_x_error_char()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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yNzUlzzAPD7vn7RMlu3aqOsxlZSamprhGKTv4l5zoDX19Pixfvrgd5uZmA/39\n4qlVRMmMj2soLTURUIxPPnbMh8su875e4wWzai3W1JgsmCkpVSTDGoF4cTvMZWXA9PSi/BNEKWPB\nTPSGvj49pYLZS4a5osI6OIIbVShVo6Pq7jIAdHXpWL9eHsmQrc36eqv4PndOvhZraxnJoORUBfPI\niPcOs5cM8/CwuAmVkQzKJl4did6QasHs1bJlBs6c4a8apWZsTEN1tbxgnp62CpRUIkSA+3HtjGSQ\nF1NTGsrK7OtydhaIROTTM9JRVAQEAmI3mZEMyib+FSd6w5kzOpYv976JykuGGbAK5rNn+atGqXHL\nLx8/7sPq1TH4FJFk1dpcs8bAyZPytchIBnkRDIoFc3z8odcJQ16unTU1hnDHo6yMHWbKHv4VJwIQ\njQJDQ4s3RzSRVTDzIk+pGR3VUV0t7yCfOqVj9erU74asXh3D8eOqDjMjGZScrMNsjT9Mvh6HhzX8\n+78X4Pz55NfD2lrxA1x5OTvMlD28OhLBOta1rk69wUrGS4YZYIeZ0uMWyTh1yuowq6jW5tq1Bk6e\nVB1ewkgGJRcMWptRE42MuOftAWs9v/Od5Xj88QK8/e0FGBtzX2uyOx7sMFM28a84EYD+fh1Llix+\nfhmwJmWwYKZUuW36s45wT329rlkTU0Yyqqp4iholNz1tTatI5BYfivv614vwtrdF8eMfT2P79gHc\nd1+R6+traxnJoNzCv+JEAM6d07F0aWoFSCoZZm76o1Ql6zCvWqVer6q12dJifXiLSZrT1dUmxsdZ\njJCaaco7zG7xIcDaFPiDHxTg3ntnAQBf+Uo1HnmkAFNT6n9L1mEuLQXm5qwIHVGm8a84EayC+WJ1\nmJcsMXD+PH/VKDWjozpqauRrsrtbx8qV3jeoxhUWWoVIf7+4HquqzKS3ySm/hUKA3w8huub24Q4A\nnn46gC1bYmhutl6zbJmJ7dtj+K//UmfgrEy9fT1qmnWMO49wp2zgX3EixCMZqW3485phbmw0MDDA\nw0soNWNjGqqqxEUzPQ1MT2toalIvKLe1uWJFDKdPi5d+dpgpmelpsbsMeCuY3/Oe8IXHHR0duOmm\nMJ56qkD5PdXVJsbGxHVaWspYBmUHC2YiWIc5pBrJ8Co+m9Tt9iORkyoX2teno7nZ8DzCy6m11ZAW\nzOXlJoJBTRrXIALkI+UA97y9aQLPPx/A7t32HMXu3RG88IIfhuKyW1VlSO94lJaamJlhwUyZx4KZ\nCOlt+vNCTo8wAAAgAElEQVSaYdY0oKmJsQxKzcSEvMN85oxVMLtxW5stLQZ6esS1qOtARQU3/pGa\n1WEWnx8bU8eHTpzQUVhoorV1/uvt7e1objZRXm7i6FH1JlTZHY+SEhbMlB38C04EYGBAR2Ojt4LZ\nMIDOTl9KI7jisQwir8bHNVRWyjvMqZ7wl6ilxUBfn7pIYY6ZVKanoYxkyD7cAcDevX7s2CG/bXHN\nNVHs3euXfk0VESottTrdRJnGv+BEsApmt0xo3MwM8J73lOHuu0uxdWsJnn9efrF3amoy2WEmzwzD\nOiCiokJWMPuSHuHulmF2mwvOHDO5kU3IAKwPd6qC+bXX/Ni+3R7HiK/PLVti2LdPfg116zAHg6m+\nc6KFy/pf8D179mDz5s1oa2vD+9///my/HcpDwaA1pkhWnDh99rMlaGkx8Mork/jsZ1/BRz9aiuHh\n5AVGU5OBc+dYiJA309PWNAC/pJbo7dWTFsxu3OaCV1ayw0xqqgyzKj4EAPv3+3DFFfI5cFu2RPH6\n6/KDdNQdZpMdZsoKb+2xi8QwDNxxxx347ne/i507d2JkZCSbb4fy1NCQjoaG5Juojh3T8fOfB/Dq\nqxPQdeBP/qQNp0+Hcd99RbjvvlnX721sZIaZvBsf11FVJS+Kz55dWIZ56VID587pMAwrt5yIHWZy\nMzurobjYXhibplUwy+JD0Shw9KgPbW32SEZ8fba1xXDihA/RqPjhsKLCOgbbuU656Y+yJat/wV97\n7TXU19dj586dAIDa2tpsvh3KUwMDGhoakneX//mfi3D33SFUVMw/9/GPz+HRRwswOen+vZzFTKlQ\nFSDAwie6FBdbJ6bJ7oxUVpqYnGQxQnIzM9adj0TBIFBQYP3HqadHR329YbtmJiopsa6NstMnfT5r\nnTo3obLDTNmS1b/gvb29qKysxI033oitW7figQceyObboTw1OJh8w9/sLPDEEwF86EOhC891dHRg\nyRITb397FD/5iXqeKADU18sLFCIZVcFsGPG8ffoZZsCKZchOn7SmZPCDHckFg2KH2e3DXVeXD+vX\nixv+EtdnW1sMR47IYxmyD3A8uISyJatXxrm5Obz44ot46KGH8Otf/xpf//rX0d3dnc23RHlocFBP\n2mH+xS+sk6pkh5vcemsYTzyRrGA2MDjIQoS8UW2iGh7WUF5uorBwYT9/6VJDetpfRQU7zKQ2Oytu\n+nMrmI8d82H9evcPd2vWxHD8uLxglo05tDb9cY1S5mU1w9zU1IS2tjY0NzcDALZt24ajR49i5cqV\nF15zzz33oKWlBQBQWVmJTZs2Xcg/xT+l8jEfL+Tx+fPXo6HBcH39008H0NZ2BB0dp2350I6ODlx3\nXTv+9E9L8fOf70F5eUT6/fX1Jvr7Y+jo6Mj6/14+zv3HExMa5uYG0NHxuu3rJ05UYunSnUm/v729\n3fXrTU0mfvvbU6iqOm37+tBQK0Kh9Vn/38/Hufm4q+sybNjQbPu6z/d2VFaa0td3dFyJ972vRvh5\nietzzZrr8MILfun3m+ZOTE4W2L6/tHQ3Bgb0nPj/g48v/cfx/97b2wsAuOuuu6CimWb2DuydmJjA\nxo0bceDAAZSWlmLbtm147LHHsG7dOgDAs88+i61bt2br7VGe+MQnSrBlSxQf+UhY+nXDAC67rBLP\nPDOlnH/7wQ+W4r//9zBuvTUi/XosBixZUoWzZ8cRCCzaW6c3qfvvL8SZMzr+/u/tm0l//vMAvv/9\nAvzoRwubq3XffUWIRIDPfW7O9vxPfhLAL35RgG9/m3O7SPSZzxSjtdXAxz42H037xS8C+N735Gvy\nXe8qx9/8zSx27owqf+aePT587nMleOYZ8SjUD36wFB/8YBjvfvf8dfXf/q0Qhw/78I//yFwGLb7O\nzk7s3r1b+rWs3iOurKzE17/+dVx33XXYunUr/vAP//BCsUyUKUNDGurr1Z8bDxzwobraFIrlxE+o\nb31rFB0d6krY5wNqasyUDjuh/DUxIZ/BfO6cJo0FOSWuTRnVyZOMZJCbmRkNJSX29Tc5qY5k9PTo\nWLnSPcO8apWB7m55KSKLZBQXm5h1H0pEdFH4vb4wHA7j6NGjGB8fx7XXXotgMAhN01Di3DKbottu\nuw233Xbbgn4G0UIMD+uorVXn7H77Wz/a29UdEgBob4/iO99xD5bW1RkYGtLR1CQ/9YoobnJSk85a\nTuVESjeqqS0smMnN7KwmTMmYmrJy9U5TU9bXkh0IVVdnIhzWMDkJYZqGbNNfcTHHylF2eOowHzt2\nDH/+53+Ohx9+GA899BAA4ODBg5xqQW8KIyMa6urUF/U9e/y4+mqxYE7MMre1xTAyouH8efWFvL7e\nxOAgL/SUnOqUPy8TXQD72pRpbDSla1XW0SOKs8bKiR3m8nLxtX191gE7svn2ietT06zj2k+fFjf+\nlZfLp2TMzQkvJbroPBXM//Zv/4Y/+ZM/wT/8wz/A/8Z08SuuuAJHjhy5qG+OKBOGhnRlJMM0gVde\n8WPHDvcOs64DW7fG0NmpvmnT0GBgeJiTMig5VdducNDbzPBkGMmgdMgiGVNT8lNSvRzhHtfaGsPp\n06oxh7JIBtcoZZ6nv96Dg4O48sorbc8FAgEYxsJvDRJlUyhk/Ud1LPbZsxqiUaC1VVzrzpzoli1R\n7NsnH48EWLce2WEmL6yunbgmBwasUymTSZZhrq21CpGo43Ng/HQ1IpmZGXEOs+rDXbzDLONcn25z\nwZ3rsaiIkQzKDk8Fc2trK5577jnbc6+88opt/BvRpWh4WENtrak8Fnv/fj82b44lPTYbsDrMr72m\n7jDX1xsYHWWHmZJTRzI0NDYuvMPs81nHYDsP0ykrs267OwtpIsA6wMmZYZ6clK/VM2fUBbNTc7OB\ns2fFa2NZmYnpaTGSwU1/lA2e/nrfeeedeOSRR/DpT38aoVAIX/rSl/C9730Pf/RHf3Sx3x/RRTUy\n4r7hb/9+HzZvllcPzpzoli1RvP66D6pBjdXVnJJB3si6dqZpZZjr6xeeYQasD3DOiJCuy4sUIgCY\nm9NQVOStw9zfrz7C3bk+ly2TF8zl5WKHmZEMyhZPUzJaWlrwjW98A6+99hpGRkZQV1eHrVu3ori4\n+GK/P6KLamjIfcPfwYM+3HabfD6zU2OjCb/fGv21dKn4M2trTYyO8kJPycmKkIkJDYWFwGJddlWb\nUMvKgOlpoKpqcf4devOYndWE9acumHVlwey0bJk8khFfi4mKi03MzfE6Spnn+f5wIBBAW1sbdu3a\nhfXr1yMYDGJ4ePhivjeii25kRHctmA8c8GHTJvkYOFlOtK0thsOH5Tnm2loTIyOMZFByskjG8LCG\nujpvBUiyDDNgdZiHhuS3wbnxj2Tm5iDtMJeVyWaG61iyxFuGeckS+dSW8nJ5JGOGZ5ZQFnjqMP/r\nv/4rfvOb36CsrAy6br/Afutb37oob4woE6wMs/yiPjVlzWhescL75tYNG6yC+frrxRhHTY3BDjMl\nFY1aGc3SUvvzVsG8eAez1tebGBryVqQQAfJIxvS0WDCbpnvB7NTYaGBwUIdpwrZfpKxMvumPkQzK\nBk8F86uvvooHH3xwwYeUEOWasTENNTXyIuT4cR/WrInBpxh8IcuJtrXF8Nvfyn+trA4zL/TkLl6A\nODeaDg/rnjvMXjLMDQ3qDjMLZnIyTeuDXFGR/flgUCyYJyY0BALih7445/osKrLmO4+OWpuw42Rr\nsbAQiESsD5Z+z0evES2cp+V23XXX4Utf+hKWLFkidJjvueeei/LGiDJhdFTHhg3yyMWxYz6sW5fa\n6MT162P43vfkJ/5VVVm3umMxKItwIisTKj4fn+iyWGprTRw/Lu8wc7QcOUUiVvc3ELA/HwxqKC21\nr8uBAS3lEymbmkycP6+jtnb+eiwrmDXNyvHPzkL6e0J0sXgKVD733HPYuHEj2trahP8QXcpGRzVU\nV8sv7F1dPqxfrz7GWpYTXbvWwIkT8kkZfr9VjPAkNXIzNQVpJjSVDrOXDHNNjXwTKjvMJDM3J244\nNQwrT+zsJA8Ous8Ll61PK5bhjF8AsRgQduy7LiqyjtMmyiRPHeaVK1fi8ssvR2Njo63DrHkZTkuU\nw8bHNVRXqyIZOn7/971NyIirrjYRCJjKebnxWIYqBkIk69gBVoe5pWXxDouqqTGkm1CZYSaZ2Vkx\nvzwzYxXRjhvPac0Ll21C1TQrqjEzo6GgYP7nFRbyeGzKPE8F85kzZ/Dggw9Kv8ZNf3QpGx1VF6+n\nTvmwerW6QFHlRNesMXDypA+NjeLGv/gs5rVr03u/9OY3PS0f0zUyomPLFvUdDwA4f17DH/5hGYaG\nbsK3vz2Nq69Wv1415pAdZpIJheQb/mQf7gYG3OeFy66dqjGHpaXimMOiIhOhkAaAjQfKHE8FM4ti\nerNSbfozDOD0aR0rVrgXKDKrV8dw/LiOnTvFr9XUGBgf1wGk/nMpP6g6zNaHO/cO88c/XorduyO4\n8soY7rqrDC+/PKHceKXahFpWBsaGSDA7K0YyVGt1ZCT1iS5uYw6to7DZYabsWtBQ2Pvvv3+x3gdR\nVoyN6dIM87lz1hzcsjL196pyoqtWGejulu/qq6oyMTbGYoTUZFMHAPf4EADs2+fD0aM6/vIv51BZ\n+TyuvDKKRx4pUL6+stLa3Oc8BtuKZKT99ulNSjZSTrVWh4bc8/aya6dqzGFJiYlgUBwtZ3WYiTJn\nQQXzyy+/vFjvgyjjQiFrM4msKO7u9mHlyvS6wCtWxHD6tPxXiwUzJWPd5hafd4sPAcB//EcB7rgj\njII3auT/8T9CePhh+cQWwJrUUlkprkfZ7Fui2Vmrs5soGNQgmzabToe5rk5+sFNpabzDPK+w0Lp+\nE2WSMpLx2GOP4dZbbwUA/OhHP4KmaTDf2Pof/+9RZ2uC6BIyNmZ17GR7V0+d0rFypfvtb1WGuaXF\nQG+vumAeH2cxQmrBIKS3ud1mhhsG8NOfFuCpp6YAWGszFovi7FkdfX06li+Xr+V4LKO+fv7nygoU\nonBY1mG2OsBOySa6yK6dqoOdSkqsfydRURGPx6bMU3aYR0dHL/z3xx9/HCMjIxgdHcXo6ChGRkYw\nMjJyoYAmuhRZI+Xka7ivT097IsGKFQZ6euS/WtXVLJjJnezktGjU6uY5j8uOO3jQh4oKE6tWza9Z\nnw945zsj+OUv1VtVKivF9Si7BU4UCokd5tlZTVowJ7sbIqPahFpaKq7HwkJGMijzlFfSu++++8J/\nDwQC0gNK9uzZc3HeFVEGTEzoqKpSF8zXXut+B6Wjo0PaKamrs2aETk4CFRX2r7HDTMkEgxqWLrV/\nWBsb01BZaQrju+J+/Ws/3v72yIXH8bXZ3h7FL38ZwJ13yscjVlebmJiwb0KVFShEoZCGwkLnWLn0\nCmbZtVO1CTU+Vi4RN/1RNnjKMH/iE5+QPr9u3bpFfTNEmTQ+rqGqSt5F7u1V38ZORtOA5csNnD4t\nbvyrrjYwNragrQP0JifrMLvFMQDglVf8uPpq8QPerl1RvPSSX3qQDgBUVRlChrm01JqvS5QoFMKF\nfHycbHKGYQCTk5qyGaFSUWEVxpGI/fmyMvEDXHExO8yUea5/uT/1qU8BALZt2yb9+l//9V8v/jsi\nypCJCatrJ9PX50sayVBlmAGgudnA2bPir5fsFjhRItnkgbExdQFimlbBvGPHfJc4vjaXLzdgGMDZ\ns/I1J4sIscNMMvIMs9hhnpiwRs35XYbWyq6dmia/PhYXi91kdpgpG1wL5qGhoUy9D6KMszrMYhES\niVgnVTlvi6dCVTAzw0zJWJv+7M+5dezOn7dGwzU3i+tV04AtW6LYt09evcimZMhugRPJOswzM+Ic\n5mR3Q9xUV4vrsahIXI/c9EfZ4Fowm6aJgYEB1/8QXaomJuSbqM6f11FXZyIQcP9+1RxmAFi2zJB2\n9ThWjpKR5ULHx3Xl3ZCDB33YtClmm/aSuDY3bYph/375XHArw8wOMyUnyzDLIhnx6UNuVNdO1SbU\n2Vn7c4GANRKUKJNcT/oLh8P4+Mc/7voDHnnkkUV9Q0SZMjGhYdkysSt39qz8+VQsW2bguefEX6+q\nKqtAMU1Ix9kRBYMaiovF29yVlfI1eeCAH5dfrp4Z3tYWw2OPyQ8wkX2AKymxMsyGAeUmQ8o/sikZ\nMzMaGhrs63J8XB11S0b2Aa642CrMExUWWhuriTLJtWAuLCzED37wg0y9F6KMmpjQsHGjeGHv79ex\nZEnygtktw2x1mMVqIxCwbmsGg/IDU4hmZ8Xb3Kr4EAB0del429vsG/4S12ZbWwx/93fqkyeto9rn\n+XxAUZFVpKiO1ab8o5qS4cw1u+0NiVNdO61NqPb1KItksMNM2cD+AeUt1YX93Dl9QfllQF0wA9Zt\nx8lJdkdIbmZGvM2tig8BwLFjPqxfr+4wr15t4Nw5XbpJqqJCvhaZYyancFjMMM/NQTjpb2Ii9QkZ\ncfE7cIlKSsS8cmEhC2bKPNeC+bLLLsvU+yDKONWtw3PnvHWY3TLMTU0GBgZ06Tiv8nLxjwJRnDzD\nLF+rhgEcP+7D2rX2gjlxbfr91umTp07Jp7bICmbmmMlJ1mGemxOf89JhVl07Kyq8RTICAUYyKPNc\nC+a/+qu/ytT7IMo4VSekv19fcIa5pMS6lSibiMEOM7mRnZ6mWqvnz1sj6JwH5DitWRPDyZNiLENW\noADzOWaiONVJf868vdsG1WRkdzxkkQx2mCkbGMmgvKW6zW11mJNf8N0yzADQ2Gji3DmxGFHdBicy\nTatQdd7mnpyUr9Xubh9WrhQ/3DnX5urVBk6eFC/3jGSQV+GwhoIC+xoMhay8e6KpKXV8KE517ZR9\ngJNPyWCHmTKPBTPlrclJ+a3DgQENTU0L6zADwJIlBs6f916kEM3NWRuafI5msKoIOXVKx6pV6vxy\nXGur/OTJ8nITMzNAzPEjios555bsZBnm2Vlx05/qw50X8g6zVZgnYoeZsoEFM+Ulw5AfQWyawOCg\nLoxKknHLMANWjllWMDOSQSqyW9yAVTCXl4vPnz6to7VVXKvOtdnaGkNPj7gWdd2ahDE1JRYpztwo\n5bdIBMJs+rk5cYPq1BSkazVRKhnmwkLxw1tBATvMlHksmCkvBYPWhd55fOv0tDUfeTFGvjU2mhgY\nYIeZvJPFMQB1166vT8fy5ck/3FkdZtXUFkNYj8XFjGSQXSSiIRAQN/05O8xeIhkqZWUmpqeTd5gL\nCthhpsxjwUx5SdWx89pdBrxkmA0MDMgzzJySQTLBoDiDGVCv195eH1pakmeYly830N+vw5AsbVVu\nlJEMShSJyCMZzjsiqrWayC3D7CyYCwtNhELODjOE54guNhbMlJdUF/WBAR2NjQvPLwNAQ4OBoSFG\nMsg7WSY0ErG6abLOs9cOc1GRVYwMDorrrqwM0q4eIxmUSLXpzzk5Y2pKjLp5Za1F+3NFRRBmiFuR\njLT+CaK0sWCmvDQ5qSqYNTQ0eLvYJ8sw19WZGBrilAzyTp4Jtdaq8yj1aBQYGtKkM8Nla7O5WX6Y\nTnm5KRQpjGSQUyQiRthks5lle0PiDh/WceedpXj44f3Sr1trURwrJ+sws2CmTGPBTHlpMSIZydTX\nGxgcFH/FZDk9IiC1W9wDAxpqakxhI5aK6vTJsjJT2PTHSAY5yaZkyDrMwaC6YP74x0sRiwHf+MaV\n0kOd4msx8WuFhdYHycTn/H4TkQjXJ2UWC2bKS6oiZHhYQ12dtw5zsgxzQ4OJ4WHZLXCxQCEC1Juo\nZJtQ3Y5wl63NpUutHLOTbD3KTlej/Obc9GcY1nOJRXQ0ahXWztnMgNVdHhjQ8Z3vBGEYZTh4UBxz\nGAhYXezECIbPZ/0nGrW/LvExUSawYKa8pJo6MDyso65ucTrM1dVW9CISsT8vu+1IBFhFqrNjNz0t\nH9Pl9Qj3uKYm+dQW1W1wRjIokXPTn9VdtkeFrE2rEOJDAPDMMwG8611h+HzADTdE8Mwz8lsjskNz\n4l3muEAAwnWV6GJjwUx5SdVhHhnx3mFOlmH2+YCaGhMjI/aLPyMZpBIKiZGM6Wn55Izz53XlATuy\ntWnNBZff8XCuR9npapTfnJv+wmExvzw1BWUcY88eP3butNrC1dUH8NJLfunrSktlBbM9IsRIBmUD\nC2bKS4sRyfCirk6clMFIBqnMzYm3s1WbqAYHNTQ2el+rjY3yg3Rk65FTMsjJuelvbk68GzIzI/9w\nBwCvv+7Htm3WkZLr1o3j9dfFSAZgTYNxbkJ1bvJjJIOygQUz5SXV6KPhYR21tYszhxkAamtNjI7a\nixFGMkhFNlZOtYlqYEC9QVW2NlUnT8pm38omE1B+c276szrO9tfMzGgoKRHX6uiohmAQF0Ygvve9\n2xGNQjHm0EQwKJ7sl9hRZiSDsoEFM+WlYDAzHWZZJKO01OrOxGKL9s/Qm8TcnBjJcCuYm5q8r9X6\nevmYw9JS+Rxm5+lqlN8iEQ1+//x6C4UgfLhTFcxdXT6sW2dcyDZrGnDZZTEcOyZ2mWUZZuugkvnH\nfr+JaJQf6CizWDBTXgoGrUIhUTRqdZ6rq92LkKEhDf/wD0X41reOJv13amsNjI7af800LX7bkRd8\nspudlUcyZLe5Bwc11Nd7zzDX1FibUJ23sq3MqP25wkJmmMkuFoNthGEoJOswi3PEAeDkSR1r1853\nCDo6OrB6tYETJ8QSpKSEHWbKTSyYKS/JcqGjoxqqqkzoLr8V4TDw+79fhlOndHzta1vwwgvyjStx\nNTViJAOwYhlTU2m9dXoTk42VCwYhHSs3NKSjvt57hzm+CdU56lDW0WOHmZyiUXuGORyGsOlPlWHu\n7taxYoX9w93KlQZ6esQOs2ykYWGhfT2yYKZsYMFMeckaf2S/sI+NWQdBuPnhDwtQU2Pi/vtn8M1v\nRvFXf1UsHcAfJ8swA5yUQXKyTX+ytWqa1kQXVd5ela+XbUItLRXXYmEhM8xkJyuYnSf/yQ7eAYDe\nXh9aWubXant7O1pbYzh9Wt5hdh6a4yyQGcmgbGDBTHlJ1mEeG0sex3jooSL8z/85B00DbrwxgmhU\nU45HAqxIxsiIfPYtJ2WQk6zDLItkTE1ZxUpJSWo/X5Zjlo3xKi62z70likY1+Hym7XHimDlAHcno\n69MvbPiLW7bMwJkz4rWxuFiMAzk/wLHDTNnAgpnykqwIGR3VUV2tnpBx+LCOqSlg1y4rBPriix34\ngz8I4f/+X/XZxNXV6tP+nDk9Imsjlf052UaqkRH3aS6qGeE1NSbGxpyRDCv2kYgdZnKKRu0Z5nAY\nwrHssg98ANDfr9lOpezo6EBzs/zkSVkkIxAQx8qFw5rr3T2ixcaCmfKSbPJAskjGf/1XAO9+d8SW\ncX73uyN46qkC5YW7psbExIRYeMhyo0TWRiqxa+fcoJruNBfZHQ9Zh7moSLwtTvnNGclwnvwHxKe8\n2J8zDGBwUDxkp7HRutvhnBZUVCR2mAsK7B/gfD5A101OGqKMYsFMeWl6WtxIFd/0p/L88wFcd938\nfcD29nasXWtA1yHd7Q0AVVViRw+wunrOyQREsg6zLBea7MOdKsMsG3OoOoqYm/4ozjAAw9BszYJw\nWEMgYF+D1pQXca2Wlpq2dd3e3o5AwLo+OiNCJSXyDrMzguH3czQnZRYLZso7ppn6pr9IBOjs9OOa\na+wzuTQNeOtbI3jxRXmOubpaXjDLNloRyTrMwaAYyUgWH1KRbUKN55UTiw/nUcSU36zusnlhjjKg\njmQ4P9wNDGjKaS4NDQaGh+1liCwOZJ3sZ3/O5+Npf5RZLJgp78zNWd0J58V+bExdhOzf70NrawwV\nFfPPxXOiO3ZE8cor8oK5osLKKjs7IYxkkIw1qsv+3OysWDAn26CqzjCLkQxdF7t6HCtHiZz5Zes5\n8cOdbMrL8LB4ImV8fdbXm8Jpf9Ypk/afEQiYQofZ52OHmTKLBTPlHVnHDgDGx9WRjM5OP7Ztk1+d\nt2yJYd8+ecGs69ZEDGeOmZv+SCYU0iSzbcVpGKOjySe6yFRWmhgfF9edMzdqdfRYkJAlGrUK1ESq\nDrNz/Q4Naaitla/VujpT0mGGkGH2+8VIhs9nIhbjNZQyJ+sF89TUFJYuXYp//Md/zPZboTyhGq4/\nMaGhslJ+YT9wwIcrrrDf/4vnRNvaYujt1YVJA3FVVWKRYmWYebEnu1BI7DDLpmQk6zCrMszV1fJN\nqM5RXppmvQ+OliPA6iYnHosNWAWss2CWZfBHR3XU1dk7zPH1aZ2EKuswJ49kMMNMmZb1gvlLX/oS\ntm/fDk1j8UCZEQzK59e6FcwHD/pw+eXyq3MgAKxeHUNXl3hqFSDPMZeWmsoCm/JXOCze5pZt+puY\n0NPqMKs2oRYXi5tQnccRU/5yTsiwnhM3/cky+G53Q2SbUGUbTlWRDGaYKZOyWjB3dXVhaGgI27Zt\ng8mBipQhskwoYBXMskhGLAYcO+bDhg32gjkxJ9rWFsPhw/KCubJSNvuWkQwSOTOgpimPZFjxodTn\nMFdXyyMZstPVCgvts28pf8kK5khEjGnIOsyyuyHx9Slbj7INp/JIBjvMlFlZLZg/+9nP4m//9m+z\n+RYoD8lucQPqDnNfn9XNKy9X/8wNG2I4ckRdME9OsmCm5Jwd5vjxw87CZGJCQ0VFehnmyUkNhqPW\nlp2u5jwsgvKXYcA2Ug6QF9GyDvP4uLrDXF1tYHzc/oMLCsR1J5+SwQwzZVbWCuYnnngC69atw/Ll\ny9ldpoyanRWPbzVNdcHc1eXDunViKyMxJ7pmjYETJ+QFc0WFWDCXlXFKBomcGWbVyWmquyFxqgyz\nz2d1q6en7c8XFYmRjMJCE+Ew1ygBsZj9WOz4c85IRjgsdphl19X4+pTdfZOtu0DAFIpoZpgp0+Rb\n+zNg7969eOyxx/D4449jeHgYuq5j6dKl+MAHPmB73T333IOWlhYAQGVlJTZt2nThly1+W4eP+TiV\nxyBMOPIAACAASURBVMHgO1BSYtq+HgwCPl8Me/d2CK8/cWI31q6Nuf78NWtiOHgwhI4O8fsrKt6J\nyUnN9vqSEqC/fxIdHb/N+v8ffJw7j2dnb7wwZaCjowOjo4UoKrpeeP3EhIaurj0YGgql/O9VVNyE\nyUkN+/f/5sLXS0pMdHYeRWHh+Quvj0Zn8PLLnVi16sqc+f+Hj7Pz2DCASGTOdn07efI0olEdQP2F\n1w8Pt6OgwGf7/omJd6Gy0pT+/J6eakxOXmN7fXHx2xAO2//9QAA4ebIPHR1dF74/EpnFnj2vYeXK\nLVn//4ePL93H8f/e29sLALjrrrugopk50N79whe+gPLycvzFX/yF7flnn30WW7duzdK7ojerH/6w\nAB0dftx//3xL7exZDb/3exU4dGhCeP299xZj3ToDf/zH9p0oiX88wmGgtbUKp0+PC8fFfvWrRZib\nA/76r+dHDnR2+vCXf1mCZ5+dWsT/ZXSpq6+vQn//+IXpAz09Ot73vjK8/vqk7XVLl1bhxIlx6eZV\nwL42nXburMC3vz2Ntrb5XMbdd5fihhvCuO22+aDo295Wjm9+cwZXXME2Xr47cULHH/xBGV59dX4d\n/v3fF8HnAz796fnrmmzNtLeX48EHZ7Bx4/xz8fV55IiOj3ykDHv2zP/cgwd9+NjHSvCb38xfG7/2\ntSJMTQF/8zfz/9Y111Tgu9+dxoYNqR/gQ6TS2dmJ3bt3S7+W9SkZRJlmTR2wPzc5qc6Ednf7sHKl\ne9FQUAA0Nho4c0b8lZJFMoqKGMkgu1jMyoom5kJlB0GEw1Z+1LmGvaqsNDExYV+nsvXIDDPFxWJi\njj4WE8fKyY7LnprSUF4uv7ZWVIgnnlrxCzGvLI6VM2EYvIZS5viTv+Ti+/znP5/tt0B5xBor5/2i\n3t2tY+VKsYvh7OCtWGGgp0fHqlX218oKZufJakSRiPXBK3HCpuyo4akp68Od2yROVXcZUH+Ac86+\nZYaZ4mIx2aY/DX6/4XgOwh22qSkNZWXyDHN5uYmpqeRj5WQTMThWjjKNHWbKO7K5tqqCORYD+vt1\nLF+e/LZfS4uB3l7xV8rq6Nn/KMimElB+k5+cJnaY3T7ceWEVKfbnZIeUyKYVUH4yDHHTn+r0v8SC\n2TSB6WmxYI4rLbU2myZObfH7xfnfqoLZOe2F6GJiwUx5R3bS3+SkvAg5f15DTY0pnL4G2DcNAEBr\nq4GeHnFShqyjx4KZnCIR8Xb27Kw4JUO1VhM512ai8nLxNrisw2wVzFyjJI9kyArmSMR+ImAoZHWm\nnV3n+Pr0+eJHYc9/LRAQZy7LJmLoOgtmyiwWzJR3ZGPlVF27vj4dzc3ersrNzQbOnhV/pWS3HYuL\nrfeR/S23lCuc3TlAHclQdey8KCsTP8DJboNbkYy0/xl6E1FlmGWHmSSu4WBQPvM+kXXq6fx6LCiQ\nHVIiZpg1jQUzZRYLZso7MzPqXKjTmTPqOIYzJ7psmYGzZ8WOXFmZbGOLdcF3/mGg/CU7anhuDsLd\njelpzfUQnYEBDY2N1yq/LluPstPVuOmP4mQZZtlsZmvT3/zj2VnxlErAfu10HuLk94vFsSySwQ4z\nZRoLZso7sq6d6jZ3f7+OpUu9XZWtglneYXYWKEC8y8xb3mSRdZjDYe3CXOa46WkIkaK4yUnguusq\ncP31Fdi/X36QTlmZ/I6Hs8NcUMBNf2SRnfQnL6Jhi2QEg2L8zcm5AVoWyVAVzLxDR5nEgpnyTiob\nqfr7dSxZIi+YnTnRpUsNnD+vCxd2WYECWJ0V5+lqlL9km/5CIbGIdttE9cgjhbj66ihuv/0wvvUt\nSfAe8g9wsg6z3887IGSxNv3Zn1PlmhNjGrIN1oD92llSYt/P4T3DzKOxKbNYMFPekW2kUuVCU+kw\nFxZaG/xGRpwbqqw/JM7b28XFYpFC+Uu26S8UEteqW9fuZz8L4Pbbw7j22rN4+umAtOB1ZkYBa406\nO8yBAMd2kcUqju1rzjCSF8yy5oSTcwZ4/GcmFsiyDDOnZFCmsWCmvDM3J276CwblBfO5c+oOs2zW\nbVOT1WVOpGny3Gh84x8RIG6YAuQdZtVaDYWA117zY9euCG6++So0Nxv43e/EWEZ8lFeiggJxaksg\nII73ovxkmmL8wlkwG4bViU58narDnHjtlH1Yc3aUZXllZpgp01gwU96ZmxO7dtZtbvG1g4Mampq8\nB+WamkwMDIhFhuw2eHGx2Omj/BWJyCIZYoZZ1WE+eNA6kTK+IfCqq2Lo7BTPprKiQOKUDNltcEYy\nCLAKU+dBObGYBl2fX4fRqPUhK/F1sjskTrKRhs5DSWTFMadkUKaxYKa8I+t6BIMQunamCQwO6qiv\n95ZhBqzjsfv7xV+rsjJrs1Yi2R8Kyl/hsIaCAmckQ+wwz8zIJw8cOODD5s1WW66jowNXXBHFgQOy\nDrP4QU22wY+RDIqTdZidm/5kc5llU14A+7VTdmgOO8yUi1gwU96R5epkXbvJSQ0FBfLiRKWx0cDQ\nkKxglh1HLP6hoPwVDotzbWVTMlSzbY8e9WHDhvkq47LLYjhyRCyYnWO8APmpfoxkUJysMHVu+nPm\nlwH5+nUqLJR1mO0b+mQTMVgwU6axYKa8MzcnG9Ul5kKtebbqK7Isw1xfb2JoSCwySkvF2+DsMFOi\nWMzbHOaZGXnBfOKED+vWWQVze3s71q41cOKELhQaZWWQdpida5GRDIozTTGSIWaY7RENwFq/zjsk\ngP3aKfuw5iWSwbFylGksmCnvyE76k3WY3eIYKvX18g6zrGCW3Yqk/CXr0EUimlBwqA6D6O7WsWrV\n/HqtrbXypM6pLdax7PbvlZ2uxkgGxckiGc7nZGPmZOvXSRYH8hbJMGEYbDhQ5rBgpryj3vRnf254\nWENtrbqFIcswu3WYxVFe7DDTvEhELDisw0zsa1DWYY5GgbNn50+ljK/NFSsMnD5tv8zLxhnKbov7\n/YxkkEXVYU58TjZmztrI6j6HWXaipNhhFotjbvqjTGPBTHnFNOM7t+efMwz5RqqRER319and86uv\nNzA4KOswWxsLExUWiuOUKH/FYuKUDNmoOdnR7ufPWx/unPGN5csN9PU5C2arS514O1t2WITsOcpP\nqg5zYsEsO/lPdhiPU0GBeCdD0+zrU1YcO19DdLGxYKa8YmXqTMesUGsDnrM7MjSkobY2tQxzba2J\nsTH5qX7ODrPsdDXKX9GoZjtWGLA2TTmfs6a82L/3zBkdy5bNr9X42pQd167rVpGSGAeSfXhjJIPi\nDEODpjmnCNkzy7JIhiyXD9ivnX6/GMlwdpR1XTzpjwUzZRoLZsor1q5t+3OqTVQjIxrq6lK7IldX\nWwWzsxuiOl2NBTPFycZyRaNih9ma8mJfl6oDdpYuNXDunHiZt3LM82uvoECMXzCSQXGqg0vskQxN\neI0sZuQku5PhzCzLNvg5IyJEFxsLZsoroZBYbKgLZt21wyzLMAcC1gi5iQn71Vy+6c/kpj+6QHZw\niZVhtj8ny+CfP6+jqWl+rcbXZlOTKZw8CVixjMTT/gIBdphJTXZwiTOSYRXVYrZeFslIvHb6/WLc\nwnnstaqbzA4zZRILZsoroZC4a3tmRpyaAQBjYxqqq1O/ItfWmsJkAivDzE1/pBaLySMZzlvasikZ\ng4M6GhvFtdrYaEhPnnSuvUDARDTqPqmA8pfXTX/iaYDJO8x+v7j2nB1mWTeZkQzKNBbMlFdCIQgz\nmGdm5EcNj41pqKlRX5FlGWYAqKkRC2brOGL763hwCSWSj5UTO3SyDvPgoIaGBjHDXFcnH3NYVGTv\nKMtmLvt8YiFD+UlWMMs7zPbXxGIafD73DLOui3cyZJllFseUbSyYKa+Ew+JBELKjsgFgdNS9YFap\nrhYjGcXFPLiE3KlOSkvsMFtTXsSTKoeHdWnevqFBPuawqMi+4VSWI/X52GGmebIub2KBrOowO4to\nJ2f8QvVvM5JB2caCmfKK7JQ/2Ug5ABgb01FdnVqGGQCqqgyMjdl/tUpKxIkYzkkFlN9kBbNz018o\nZBW3ziJkZMQ+0SW+NqurrSPZnYWvc+6ylSO1b1ZlwUxuvHWY5ZGMxGun6sQ+51g52aY/FsyUSSyY\nKa/INlEFg+Kmv3DYKk7KylL/N6qrTYyPixMxnKerFRaK45Qof8nHytmLaNn6Bay7IbJDdnw+oKJC\nXI/OUyY1LT4VY/45ZpgpFaqNgck6zLJT/GRzmJ1YMFOmsWCmvBIKiR3m2VmxYJ6Y0FBZabqOLlJl\nmCsrxVnMskhGQYF4whXlL/lYOXsRLVu/gBgfSlybsky9LA7kjGVYp63xAx15lyznHJe4PlWHkjhx\nrBxlGwtmyivWpj/7c7KDICYmNFRVpde+qKoSO3olJWL8wiqYedUniyyS4Tz9LxQSO8yxmHWXpKJC\nvl4rK8X1KIsD+f32AtnnM9lhpgVRFcwdHfMLXVX4JotkOF9DdLGxYKa8Iu8wi7OZx8etDrMbdYbZ\nyo0mKiqyHxQBWIdFsMNMcbLb15GIvYi2jnW3r8vJSQ3l5fbTKxPXpiwiVFgoHkoSCJhCh5kFMy2U\nrCD+4Q/7L/x304TtVL/4c06xmPgaFsyUSf7kLyF681AdBOGckjExoe7YJVNRIZ+SIRbMjGTQPGsE\nl/3edDRqn5Ih6zDH40MqlZXiBzjVQSXMMNPF0tHhv9BZ/tGP1qOlZRbt7VF8//sFOHTIj3/6p/m5\nm8eO+XD4sA9tbdbvw969fuzday9XfvrTAkSjwK23Osa7EF0kLJgpr8imZMzNiTENLx1mVYa5okIs\nUEpKxE1/BQUcK0fzZCO4nFMGIhENBQXJP9wlrk3ZeiwsFD+sWQWyBsC88Jgn/dFiaW+Por19fkF9\n5jNWJugjHwnj8GH7J7PLLouhrW3+uauvjuKaa+yF8fveF8ZNN7HjQJnDSAbllUhE7NDNzoq3uaem\nFrfDLItkFBaKs28pf8lGcDkjGbI7JJOT7mtV1mG24kDOk/3smWVd58EltHBeYhOaZn+R83u8HJxC\ndLGxYKa8Eg6LHbpQSDwae2rKyoW6UWWYZQVKvDhO3A0eCLDDTPNkBXM0at/0J+swT01pKCuzP5e4\nNsvKTExPJ48DWVMx5h9bs5lT/99BFKfarFdZue/Cf1cV1M4Zz6rRckSZwoKZ8orsqGFZhzlZ186N\n7Ba4pllFc2JulB1mSmQYyY8WlnWYp6fdP9yVlZmYmkreYXZu8nMW0ETJeD1cZNOmEdv3ONe97Htk\nHWaiTGLBTHlFFsmQbfrz0mFWZZjjHT3nBb2w0H7aHzPMlMgqju3POSMZzscAEAwCpaX25xLXpqzD\n7PeLH9acm/xUJ7ARycjWi6bJN44mrk/ZB0XZKYJOjGRQprFgprwSDtunDgDWpr+iIvvrvBTMKgUF\nVnfOOee2qMj+XEEBO8w0zzBg6yYDVrFhzzCLkYzpaQ2lpeq1WlpqIhgUp2Q4u8c+nz2zLDuBjShO\ndhqfc734fMnXkOyEwPjPi2OGmXIBC2bKK7JIhmxyhiwX6qTKMAPyrl5hob2jXFAgjvai/KWakpH4\nnLzDLBbMiWtTXjCLc5jFDrMpzMel/CXr8iYWw7IOs6pgTlyfsnUv+7eZYaZsY8FMeUW2aSoUEjvM\nwaC6YO7r03HTTWX4+c9blf+OLDfq7DAHAmKOlPKXs3CwDmbQbM9Fo/IpL86j3ROVlFixjUR+f/JN\nf6rb6ZR/ZFlkbx1mUzhwxMl5FwWI320R/z2ibGLBTHklHBY7zKGQ/Da3qmD+4heLsXatgf/4j8sx\nMCC/ipeXix3moiJ7h1l2W5zyl7NIsApo01YoRKMa/H77upyZEae8JGZEi4vt2XnAKrrFSIa46Y8Z\nZgLk3WPnc6oOs+wal7g+YzFxTTtzzcwwUy7gwSWUV2Sb/uQdZqCsTPz+6Wng6acD2L9/ApEI8P/+\nXwE++lExVyGPZDg7zFYBxAs/AeJYOdVcZudzMzPiptVEslMm/X5ZJIMZZpKTdY/lHebkm0udotHk\nUSRGMigXsMNMeSUSETf9pdJhfuklP668MoqqKhMtLQfw/PPyz5yy2+BFRfZOn6bFN1ql+T+G3lQM\nQ4Ouz685WcEci8kz+M6COTEjWlwsnjIpO8XPmTdlwUxxskiG2GE2hfUSCMhjPYnrU7avxHm3RTVJ\ngyiTWDBTXpFdnMNh8Whs2UYqANizx49rrrEqjba2EezZ45deuEtKTMzMJD8sIhDgpAyyOLtoshyn\nLJIhu0OSqKhIdqqfWAw7C2QWzBSn6yZM0zlb3r4p1BnpsZ5Lvk9Dtq/EMDTb6X9eRs8RXWwsmCmv\nyE/6E6dkBIPyjVSvv+7H1q3WX4Wbb96B0lKgp0f8NSorEycTOKdkAPJOH+Un53gt2bitaFQsomVT\nXhIzokVFYofZypaKh+uwYCYZL5EM2UQMWVYesK9P2eQX2RxmFsyUbSyYKa/ILs6hkL3DbBjy47IB\n4PBhH9ra5tsol18exeHDPuF1sg5zICDrMItZUspPzgLAecofIC+Yk3WYZQfk6LopdAPFDjPHypHF\nWyRD7DAXFCS/vsn2lTgnZzjjSoC8iCa6mLjcKK/IxnI5u87xqQPOi/HkpDWfubnZqio6Ojqwbp2B\n48fFX6PSUjHDXFgo3p5kJIPinAWALMNsGLIPfOJdk8SMaPxI9sTiRnZnw1kAybqKlJ9UHebE52SR\nDL9fPms+cX3K7vo5PxjKIhmyIproYmLBTHklEhEzoHNz9g7zzIw8jnHypA+rVsVsF+41a2I4cULe\nYZadrubsMDOSQXGyDLM4PUATnpN16BL5fOJMZdntc2dHmUdjU5yqw5zsoJuiouQNgfD/b+9sg6Qq\nz/R/ndMv0z3T0zMDM8ObjICKGEWXwJaKI/F9rVS2LNa1iiWR8gOJAYtUrIopK1pbsbQSTUqNywYU\n/bBimViKYXdjrKUSNkZHKy5IaYJv8PeFgYF5A+Z9erp7+vw/PNvd5zzPfbob3JlGzvX7RJ/p6T5Q\nD09ffT3Xfd/pShxmSTAzkkGmFwpmEigk52Jy0vIUAo6Py226Pv/cxoIFRZXR3t6OBQtyOHTI/G8k\n9b6VHWZGMohCd5iljKZynb1rM50unWEGii5zHj+HWc8wc3AJAeTog+4oS2sqGjX3QcC7PqUaEj2O\nJIljSUQTMpVwuZFAobflUu6cdzjE6KhqC6dz+LCNtjavLXfuuX6C2Sy0YpcMUgpdFEgZTWmMsNT5\nRUeJGbd7bLqBukCWXEUSTOQpft5r+mh1QDnM+p6nI2XwK4tkUDCT6YXLjQQKvehPOg70c5iPHLEL\n+WVA5fDmzMmhp8c2Piji8crayjGSQfJIkQzZYfZekwpZ3RlRIH+SUXwsdcDQr/G4m+SRp/h5x16r\nISWW53l67/k87vUpdXmpJJLBoj8y3XC5kUCRzXqP+qRBJqmUhVjMFMxHj9qYN8+rMqJRYMYMB93d\n3g8FeRwxIxnEn0oiGVJvZmkN6+gnGX6CWf/iR4eZAJU5zLZtDi+Jxcwpkzqp1Ok5zNJpCyFTCZcb\nCRS6c5FOS5PT5DZdx47ZmDPHm2EGgNmzlcvspraWkQxyaqhhDcXHfoJZEg66w6xnmPW8qeQY6u/F\nSAbJo9aP2bdb6orhPjGLxdR+quNen/qJXv41vWPizWJtRjLIdMPlRgKFnvdU7pz3OdKoYQDo6bEx\na5bZZ2vWrBy6u73/lSRnRXKTpVZMJJgoh9lxPbYMEStdk3oz64TD3uNzv4I+va0cIYDct9uvjZxb\nMMfj5R1mXTBLmXxpjedyZp9yQqYSCmYSKPTRwvmiPzeSw5zLAX19Flpais/N5/BaWx309prtlPT+\noyrjp19zDOeGBBP9yFsX0H7XJIdZzzDrQkZqKyc5ynSYCSCvF79r7uLSujqzvSbgXZ9jY94ia0kw\n+/Uk55c6Mp1QMJNAoTsVUiRjfNzMMA8OKhfE3a85T0tLDn193v9KNTVmhjkSMQv81AfMKf81yFlK\nuaI/uRDQ7M2so68zv0EU+mMKZgLImXe96A/IT/YrPo7HlSAuhd73XuqV7yeYGckg0wmXGwkU2azX\njZMKpiYmLMNh7u+30Nws97ptbnZw/LjuMEuCmZEMUjlShlkSsEo4lO7DrItf9bi0PUfBTPJUEr8A\nzOFMiYTZLQjwrs/RUQt1dfqpn/f5UoaZXTLIdFPV5dbV1YX29nZccsklWL58Of7whz9U83ZIAMhm\nvY6yX9Gf3ubo+HELM2bI6mHmTAcnTuhDSiqLZNBhJqWQBPPp9KM1W8aZa1kS1YQAsmCWrummQDSq\n1p1fL+bJSbVPxuPFa1KbRDnDTMFMppeqLrdIJIKtW7di//792LlzJ+64445q3g4JAHpbOd1xBvKT\n07zXTpywMXOm90wyn8ObMSOH48dPL5IRDpsDJAjxw8911oWDnmGWBpWU65IhPYcEE1X0510g0qCS\naNRrCliWcplHRry/m1+f+SFR7vWbzVpGXYkUyWBbOTLdhMs/ZepobW1Fa2srAKCtrQ3pdBqZTAaR\ncmOrCDlN9I1XjcU2i/50h/nkSQtNTbJ6aGpyMDhYvujPL5JBh5l8ESopfqq0oM8d06gktkGCgVTg\nFw6b+5keyQCA+nolmKUTuqEhC/X1eiROimTIglk3OwiZSs6Y72e7du3C8uXLKZbJlKJvvJU6zAMD\nFhob5ZxoY6ODkyfLDynxi2Qww0y+KKZgvqbkzyUBbVmOFsmgvUwUfpEMM8Nsiuj6egfDw95r+b1z\neNhCIuFdZ5mM6TDr3Y3y1/TsPiFTyRkhmLu7u/GDH/wAW7ZsqfatkLMcXTBnMqZzMTFhOsyDgxYa\nGuTNubHRwcBA+THY7JJBpouODu+3wP37w+jqKm73hw/b2LvX+5wdO2qwb1/xP0MqZRlChwQTyWGW\n9rNIxDxZSybNE7g8Q0PmvkqHmZypVH25pVIp3HbbbXj00UexcOFC4+cbN25EW1sbAKChoQFLly4t\nfDvN56D4mI8rfZxO31zYeDs6OvCXv7QiEvmq5/kTEzchGvX+/tCQhVzu/6Gj47PC623duhVLly7F\n5Ze3Y3jYwhtvdMCy1POVYFbXrr5aPf/AgffR27sQQKTwfidPLkMu13jG/PvwcfUenzx5Eu+//ylu\nuOFCAMDevXuRSl2BPB0dHTh69CuYO3eO5/eBbxQe//WvMzE4uAw/+1kcnZ2dWLr0ODZsuAgAcPDg\nfiQSfWhvby+coHR0dBTeP5mcwMBAJ4AWAMCePW8D+DvP+59J/158PH2PbRsYG5vwrJcjRz7H2FgY\nQHPh+RMTK5FORz2/39DwdxgctDyvl//z3r2tSCa9+284/DVj/81kgGPHDqGj42Dh/UdHU3j33b24\n4ALv758J/158/OV5nP9zZ2cnAGD9+vXww3Kc6pV1OI6DtWvXYtWqVdiwYYPx8927d+OrX/1qFe6M\nnK3Mn9+IDz4YQH29evzqqxE8/3wUzz8/WnjOpk21+Nu/zWLduqJFvHFjLdrbs1i7tnjN/eExb14j\nPv54AIlE8b1mzWrE4cMDBbfkv/87jM2bY9i5c6TwnA0bavG1r2WxZo1PGTkJDP/4jwl897sp3HBD\nFgDw2Wc2br01gX37hgrPue++OObOzeGuu4o23uzZjTh0aMATI9q4sRdbtrQWHl99dT22bBnD0qXq\nXP3tt0P453+uxa5dw4XnfOc7tbjxxixuu02txaEhYOlS9dok2Bw5YuHmm5PYv3+wcO1f/qUGfX02\nHnxwvHDtH/4hgbvuSuH667OFa9/9rtrj/umfzL1zx44Idu2K4umni/vvn/4UxmOPxfAf/1HcJx94\nII5k0sHddxfnbF96aRKvvDKCtjZz+iohp8u+fftw/fXXiz+raiTjzTffxMsvv4xt27Zh2bJlWLZs\nGbq7u6t5S+QsR8ow60d92ax5JDg8bBan5MUyIOf0olHv8WQ4bB5r2jYjGeSLIeWR166dKz4vT7mC\nP+kxCS7S4BIpkhGLmbUbUo1H8VTFRmOj94XTaXP/Va3m9M4ZHI1NppdwNd+8vb0dab8GjYRMAbmc\nmWGWiv70zhmSYHZTbJ1UfE6x8E9dk/LKzDCTL4o0ta+93buoJIEstadzF/rpj0lwkYaU+LWVS6W8\n15qazBqPPCdPmsXUanCUXuBX2bhsQqaSM6Loj5DpQu/dKbWVy2TMzXlkxKzmdmeg8q2T3EQi3q4Y\n0ihZbvjkVJDcZMn9c69NAMjlzI4CUis63YXm8BIC+PeQ1zti1NSYDvOMGabDnF+fJ05YmDnTLPrT\nuxSpiazeayz6I9MNBTMJFOoYr/hYimRIo1n18a06dXUORkfNNnLuDxnJTbZtGCKakDzScBEp1lOu\nEkWfiibFLSpxoUkwCYUcZLOlDQFACV3dYZ45M4f+fllqHD9uG/2ZUynTYZYiGdLeTchUQsFMAkNe\naHgdZnNalBTJGB2Fp6AP8GaYa2uBsTHvz/WepFKGORRyjGuEAP5usvQ8fYqfe20CkmA24xaVDDYh\nwaSSqX6AyjBPTHjXYnOzg/5+eX3291toafFugBMTqDCSwQwzmV4omElgUJm38iNXpWEmY2MWamtL\nO8zlIxmyw0zBTPK4RarkHEsOs4r6lH5d/fhaF9Du13ffCx1mAshDlyIRM34Ri5kOc0tLDr29stTo\n7bXR0mI6zJVGMugwk+mEgpkEBmmD1YsAAdnNGB+3EI/7Z5hrax2Mj5eOZITDZoZZEkAkmOgCVnKO\npXHV0hQ2PcOsH19L656RDOKH2ssszxqRIhmxmLkPtrY66OuTM8x9fabDrARzZZEMZpjJdELBTAKD\nPC3KPNbLZLzXcjnlmsTj/q8djztIpXSH2RvJkFrI0WEmeXSBrI+qBgDbNiM8UkGWjr7OJYdZd5QZ\nySB5bNtce9GoWfQXj8PYB5uaVH2H7jyn08DAgIXmZu9CGx8391qproQZZjLdUDCTwCCJBOnaoFw4\n+wAAG2VJREFU5KTXYU6lVDGL7ra5c6KxmMo5u9GPMaXxshTMJI9tewVypR0xJIdZzzDrblwuJ7eV\nM3POp/iXIGct+olZJKJErxvlMHuv2TYwe3YO3d3FxdXe3o7eXgstLY4heqXTvHTaQjRavJZf73SY\nyXRCwUwCgyQApKI/XVxIVds68bhZ7KILGQpmUgo9niMJZinCI7X30tFbJUrrnoKZlEI3AKSiP+mk\nDQDmznXQ1eVdcEeO2Jg3z9z8pNM8fZiJ1PqTkKmGgpkEBsexRMFstpWzPHm5VEo5yDrunGgs5p3q\nBygh4z5il91Bx8ipkmCiF/lJRX+SmywJFz3DrIqmvENJpDy/lKMmBDBrMMJhs+gvHofhMAPA/PmT\nOHy4uLg6Ojp8BbOfw+zONVMwk2pAwUwCg3LQvBuxVPyki2h9s5aoqSlf9CcJZjrMJI/kMEvxCzPD\nXN5h1k9NJifNQSZS6zlC8uj7mZ/DrO+DANDWlkNnp1duHDoUwoIF5uY3NmYKZl0gZ7OWUQRIyFRD\nwUwCg5TblIr+dMGczzDruHOiNTVmnk9vI6eKZtglg8joEQz/vLJ3DUWj5umGe206jjk9za9jjFsw\nS18mSXCRIhl6DK221hzgBAALF+bw+efeDPNnn9k491yzH6JU9KevXzrMpBpQMJPAoGc0Af+iP7dQ\nyGS8BScS0ah5PKkfn/tNaaNgJoAsmM1IhtlzWSq+cjM5qV673IRLfXy29AWTBBfdAFBjsL3PqatT\nDrHOeedN4uBB74L75JMQzj9fdpj1nvf6MCkKZlINKJhJYPDrDFAu16wXnORx50SjUdlhdruBfpGM\nckMnSDDQv1BJa0O6poSL3OcWMN05wN9h1tvKScNNSDCJRr3jsWtqTIdZRTLM3128OIcDB0KFL4Bv\nvNGBjz6ysXixufmNjVmoq9MFs3cNM5JBqgG3QxIYJHHsF9NwO22VuBmyw+x1A/0EM7OiBDDXglo/\nZi7eFMxmJMPNxITZ5SWXMwWHGckwc84kuOgnGWrP8z6nttYRHeYZMxwkEk4hx9zfH0M0qoaa6IyM\n0GEmZyYUzCQwSI6ZX0xDL/pzb9Z53DlRaXSsXqAlFWwRkqeSDLNeeAWo3rd6Ky/32pS6vMiRDDPD\nTIeZ5NEH5Kgx2N51V1cnZ5gBYNmyLN55Ry26UOhK/M3fyEdrY2NAIuG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- "text": [ - "" - ] - } - ], - "prompt_number": 12 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that there is a lot of error associated with each value of $x$. We could write a 1D Kalman filter as we did in the last chapter, but suppose this is the output of that filter, and not just raw sensor measurements. Are we out of luck?\n", - "\n", - "Let us think about how we predicted that $x$=4 at $t$=4. In one sense we just drew a straight line between the points and saw where it lay at $t$=4. My constant refrain: what is the physical interpretation of that? What is the difference in $x$ over time? In other words, what is $\\frac{\\partial x}{\\partial t}$? The derivative, or difference in distance over time is *velocity*. \n", - "\n", - "This is the **key point** in Kalman filters, so read carefully! Our sensor is only detecting the position of the aircraft (how doesn't matter). It does not have any kind of sensor that provides velocity to us. But based on the position estimates we can compute velocity. In Kalman filters we would call the velocity an *unobserved variable*. Unobserved means what it sounds like - there is no sensor that is measuring velocity directly. Since the velocity is based on the position, and the position has error, the velocity will have error as well. What happens if we draw the velocity errors over the positions errors?" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "mkf_internal.show_x_with_unobserved()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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ZUZCZacBtMT755EkNF13kPF6DCbNVLBYUGEyYKSqrlgwxAvHcVpizsoCJibj8\nE0TzxoSZ6C0dHeq8EmYnPcw5OWLjCC5UofkaGrKuLgNAU5OKdevkLRmy2CwuFsl3d7c8FgsL2ZJB\n0VklzIODzivMTnqYBwbMi1DZkkGJxLMj0VvmmzA7tXy5js5O/qrR/AwPK8jPlyfMExMiQZlPCxFg\nv107WzLIifFxBVlZ4XE5PQ34fPLpGbFISwPcbnM1mS0ZlEj8FCd6S2enispK54uonPQwAyJhPnuW\nv2o0P3b9y6dOaaipCUCzaEm2is3Vq3WcOSOPRbZkkBOTk+aEOTj+0OmEISfnzoIC3XTHIyuLFWZK\nHH6KEwHw+4H+/vjNEQ0lEmae5Gl+hoZU5OfLK8jNzSpqauZ/N6SmJoBTp6wqzGzJoOhkFWYx/jB6\nPA4MKPjP/0xBT0/082FhofkCLjubFWZKHJ4diSC2dS0qsl5gJeOkhxlghZliY9eS0dwsKsxWrGJz\nzRodZ85YbV7ClgyKbnJSLEYNNTho328PiHi++upsPPVUCt71rhQMD9vHmuyOByvMlEj8FCcC0NWl\nYtmy+PcvA2JSBhNmmi+7RX9iC/f5x+vq1QHLloy8PO6iRtFNTIhpFaHs2oeC7r03De98px+PPTaB\nbdt6cffdabbvLyxkSwYlF36KEwHo7lZRXj6/BGQ+Pcxc9EfzFa3CvGqVdbxaxWZVlbh4C0iK0/n5\nBkZGmIyQNcOQV5jt2ocAsSjwV79KwV13TQMAvv/9fDz6aArGx63/LVmFOTMTmJkRLXREi42f4kQQ\nCfO5qjAvW6ajp4e/ajQ/Q0MqCgrkMdnSomLlSucLVINSU0Ui0tVljse8PCPqbXJa2jwewOWCqXXN\n7uIOAP74Rze2bAmgokK8Z/lyA9u2BfD889Y9cKKnPjweFUVs484t3CkR+ClOhGBLxvwW/DntYS4t\n1dHby81LaH6GhxXk5ZmDZmICmJhQUFZmHVB2sbliRQBtbeZTPyvMFM3EhLm6DDhLmG+4wTv7uKGh\nAddf78Vzz6VYfk9+voHhYXOcZmayLYMSgwkzEcRmDvNtyXAqOJvU7vYjUSSrvtCODhUVFbrjEV6R\nqqt1acKcnW1gclKRtmsQAfKRcoB9v71hAC++6MauXeF9FLt2+fDyyy7oFqfdvDxdescjM9PA1BQT\nZlp8TJiJENuiP6c9zIoClJWxLYPmZ3RUXmHu7BQJsx272Kyq0tHaao5FVQVycrjwj6yJCrP5+eFh\n6/ah06dsoQBAAAAgAElEQVRVpKYaqK6ee72+vh4VFQaysw2cOGG9CFV2xyMjgwkzJQY/wYkA9Paq\nKC11ljDrOtDYqM1rBFewLYPIqZERBbm58grzfHf4C1VVpaOjwzpJYR8zWZmYgGVLhuziDgD273dh\n+3b5bYu3vc2P/ftd0tesWoQyM0Wlm2ix8ROcCCJhtusJDZqaAm64IQu3356JuroMvPii/GQfqazM\nYIWZHNN1sUFETo4sYdaibuFu18NsNxecfcxkRzYhAxAXd1YJ8xtvuLBtW3g7RjA+t2wJ4MAB+TnU\nrsI8OTnfIydauIR/gu/btw+bNm1CbW0tPvjBDyb6cGgJmpwUY4pkyUmkr341A1VVOl57bQxf/epr\n+NSnMjEwED3BKCvT0d3NRIScmZgQ0wBcklyivV2NmjDbsZsLnpvLCjNZs+phtmofAoBDhzRccol8\nDtyWLX4cPCjfSMe6wmywwkwJ4aw8do7ouo5bb70VP//5z7Fjxw4MDg4m8nBoiervV1FSEn0R1cmT\nKn7/ezdef30Uqgp8+tO1aGvz4u6703D33dO231tayh5mcm5kREVenjwpPnt2YT3M5eU6urtV6Lro\nWw7FCjPZmZ5WkJ4enhgbhkiYZe1Dfj9w4oSG2trwloxgfNbWBnD6tAa/33xxmJMjtsGOjFMu+qNE\nSegn+BtvvIHi4mLs2LEDAFBYWJjIw6ElqrdXQUlJ9Oryj3+chttv9yAnZ+65z39+Bo8/noKxMfvv\n5Sxmmg+rBARY+ESX9HSxY5rszkhuroGxMSYjJDc1Je58hJqcBFJSxJ9Ira0qiov1sHNmqIwMcW6U\n7T6paSJOIxehssJMiZLQT/D29nbk5ubi2muvRV1dHR588MFEHg4tUX190Rf8TU8DTz/txkc/6pl9\nrqGhAcuWGXjXu/x44gnreaIAUFwsT1CIZKwSZl0P9tvH3sMMiLYM2e6TYkoGL+xIbnLSXGG2u7hr\natKwbp15wV9ofNbWBnD8uLwtQ3YBx41LKFESemacmZnB3r178fDDD+Oll17Cvffei5aWlkQeEi1B\nfX1q1ArzH/4gdqqSbW5y001ePP10tIRZR18fExFyxmoR1cCAguxsA6mpC/v55eW6dLe/nBxWmMna\n9LR50Z9dwnzypIZ16+wv7lavDuDUKXnCLBtzKBb9MUZp8SW0h7msrAy1tbWoqKgAAGzduhUnTpzA\nypUrZ99zxx13oKqqCgCQm5uLjRs3zvY/Ba9S+ZiPF/K4p+cqlJTotu//4x/dqK09joaGtrD+0IaG\nBlx5ZT0++9lM/P73+5Cd7ZN+f3Gxga6uABoaGhL+38vHyf94dFTBzEwvGhoOhr1++nQuyst3RP3+\n+vp629fLygz89a/NyMtrC3u9v78aHs+6hP/383FyPm5qugjr11eEva5p70JuriF9f0PDZrz//QWm\nnxcan6tXX4mXX3ZJv98wdmBsLCXs+zMzd6G3V02K/z34+Px/HPy6vb0dAPDJT34SVhTDSNyGvaOj\no9iwYQMOHz6MzMxMbN26FU8++STWrl0LANizZw/q6uoSdXi0RHzhCxnYssWPj3/cK31d14GLLsrF\n7t3jlvNvP/KRTPzt33px000+6euBALBsWR7Onh2B2x23Q6cL1AMPpKKzU8W//Ev4YtLf/96NX/4y\nBb/5zcLmat19dxp8PuBrX5sJe/6JJ9z4wx9S8NOfcm4XmX3lK+mortbxmc/Mtab94Q9u/OIX8ph8\nz3uy8Y1vTGPHDr/lz9y3T8PXvpaB3bvNW6F+5COZ+MhHvLjuurnz6r//eyqOHdPwr//KvgyKv8bG\nRuzatUv6WkLvEefm5uLee+/FlVdeibq6Onz4wx+eTZaJFkt/v4LiYuvrxsOHNeTnG6ZkOfQK9fLL\n/WhosM6ENQ0oKDDmtdkJLV2jo/IZzN3dirQtKFJobMpY7TzJlgyyMzWlICMjPP7GxqxbMlpbVaxc\nad/DvGqVjpYWeSoia8lITzcwbT+UiOiccDl9o9frxYkTJzAyMoJ3vOMdmJychKIoyIhcMjtPN998\nM26++eYF/QyihRgYUFFYaN1n99e/ulBfb10hAYD6ej9+9jP7xtKiIh39/SrKyuS7XhEFjY0p0lnL\n89mR0o7V1BYmzGRneloxTckYHxd99ZHGx8Vr0TaEKioy4PUqGBuDaZqGbNFfejrHylFiOKownzx5\nEp/73OfwyCOP4OGHHwYAHDlyhFMt6IIwOKigqMj6pL5vnwuXXWZOmEN7mWtrAxgcVNDTY30iLy42\n0NfHEz1FZ7XLn5OJLkB4bMqUlhrSWJVV9IiCxFg5c4U5O9v83o4OscGObL59aHwqitiuva3NvPAv\nO1s+JWNmxvRWonPOUcL87//+7/j0pz+NH/zgB3C9NV38kksuwfHjx8/pwREthv5+1bIlwzCA115z\nYft2+wqzqgJ1dQE0NlrftCkp0TEwwEkZFJ1V1a6vz9nM8GjYkkGxkLVkjI/Ld0l1soV7UHV1AG1t\nVmMOZS0ZjFFafI4+vfv6+rB58+aw59xuN3R94bcGiRLJ4xF/rLbFPntWgd8PVFebYz2yT3TLFj8O\nHJCPRwLErUdWmMkJUbUzx2Rvr9iVMppoPcyFhSIR8UdcBwZ3VyOSmZoyz2G2urgLVphlIuPTbi54\nZDympbElgxLDUcJcXV2NF154Iey51157LWz8G9H5aGBAQWGhYbkt9qFDLmzaFIi6bTYgKsxvvGFd\nYS4u1jE0xAozRWfdkqGgtHThFWZNE9tgR26mk5UlbrtHJtJEgNjAKbKHeWxMHqudndYJc6SKCh1n\nz5rPjVlZBiYmzC0ZXPRHieDo0/u2227Do48+ii9/+cvweDz47ne/i1/84hf4H//jf5zr4yM6pwYH\n7Rf8HTqkYdMmefYQ2Se6ZYsfBw9qsBrUmJ/PKRnkjKxqZxiih7m4eOE9zIC4gItsEVJVeZJCBAAz\nMwrS0pxVmLu6rLdwj4zP5cvlCXN2trnCzJYMShRHUzKqqqpw33334Y033sDg4CCKiopQV1eH9PT0\nc318ROdUf7/9gr8jRzTcfLN8PnOk0lIDLpcY/VVebv6ZhYUGhoZ4oqfoZEnI6KiC1FQgXqddq0Wo\nWVnAxASQlxeff4cuHNPTiin+rBNm1TJhjrR8ubwlIxiLodLTDczM8DxKi8/x/WG3243a2lrs3LkT\n69atw+TkJAYGBs7lsRGdc4ODqm3CfPiwho0b5WPgZH2itbUBHDsm72MuLDQwOMiWDIpO1pIxMKCg\nqMhZAhKthxkQFeb+fvltcC78I5mZGUgrzFlZspnhKpYtc9bDvGyZfGpLdra8JWOKe5ZQAjiqMP/b\nv/0b/vKXvyArKwuqGn6Cvf/++8/JgREtBtHDLD+pj4+LGc0rVjhf3Lp+vUiYr7rK3MZRUKCzwkxR\n+f2iRzMzM/x5kTDHb2PW4mID/f3OkhQiQN6SMTFhTpgNwz5hjlRaqqOvT4VhIGy9SFaWfNEfWzIo\nERwlzK+//joeeuihBW9SQpRshocVFBTIk5BTpzSsXh2AZjH4QtYnWlsbwF//Kv+1EhVmnujJXjAB\niVxoOjCgOq4wO+lhLimxrjAzYaZIhiEu5NLSwp+fnDQnzKOjCtxu80VfUGR8pqWJ+c5DQ2IRdpAs\nFlNTAZ9PXFi6HG+9RrRwjsLtyiuvxHe/+10sW7bMVGG+4447zsmBES2GoSEV69fLWy5OntSwdu38\nRieuWxfAL34h3/EvL0/c6g4EYJmEE4meUPPzwYku8VJYaODUKXmFmaPlKJLPJ6q/bnf485OTCjIz\nw+Oyt1eZ946UZWUGenpUFBbOnY9lCbOiiD7+6WlIf0+IzhVHDZUvvPACNmzYgNraWtMfovPZ0JCC\n/Hz5ib2pScO6ddbbWMv6RNes0XH6tHxShsslkhHupEZ2xsch7QmdT4XZSQ9zQYF8ESorzCQzM2Ne\ncKrrop84spLc12c/L1wWn6ItI7L9AggEAG/Euuu0NLGdNtFiclRhXrlyJS6++GKUlpaGVZgVJ8Np\niZLYyIiC/HyrlgwVf/d3ziZkBOXnG3C7Dct5ucG2DKs2ECJZxQ4QFeaqqvhtFlVQoEsXobKHmWSm\np839y1NTIomOuPEc07xw2SJURRGtGlNTClJS5n5eaiq3x6bF5yhh7uzsxEMPPSR9jYv+6Hw2NGSd\nvDY3a6ipsU5QrPpEV6/WceaMhtJS88K/4CzmNWtiO1668E1MyMd0DQ6q2LLF+o4HAPT0KPjwh7PQ\n3389fvrTCVx2mfX7rcYcssJMMh6PfMGf7OKut9d+Xrjs3Gk15jAz0zzmMC3NgMejAGDhgRaPo4SZ\nSTFdqKwW/ek60NamYsUK+wRFpqYmgFOnVOzYYX6toEDHyIgKYP4/l5YGqwqzuLizrzB//vOZ2LXL\nh82bA/jkJ7Pw6qujlguvrBahZmWBbUNkMj1tbsmwitXBwflPdLEbcyi2wmaFmRJrQUNhH3jggXgd\nB1FCDA+r0h7m7m4xBzcry/p7rfpEV63S0dIiX9WXl2dgeJjJCFmTTR0A7NuHAODAAQ0nTqj40pdm\nkJv7IjZv9uPRR1Ms35+bKxb3RW6DLVoyYj58ukDJRspZxWp/v32/vezcaTXmMCPDwOSkebScqDAT\nLZ4FJcyvvvpqvI6DaNF5PGIxiSwpbmnRsHJlbFXgFSsCaGuT/2oxYaZoxG1u8/N27UMA8F//lYJb\nb/Ui5a0c+X/+Tw8eeUQ+sQUQk1pyc83xKJt9SzQ9LSq7oSYnFcimzcZSYS4qkm/slJkZrDDPSU0V\n52+ixWTZkvHkk0/ipptuAgD85je/gaIoMN5a+h/82h9ZmiA6jwwPi4qdbO1qc7OKlSvtb39b9TBX\nVelob7dOmEdGmIyQtclJSG9z280M13Xgd79LwXPPjQMQsRkI+HH2rIqODhWVlfJYDrZlFBfP/VxZ\ngkLk9coqzKICHCnaRBfZudNqY6eMDPHvhEpL4/bYtPgsK8xDQ0OzXz/11FMYHBzE0NAQhoaGMDg4\niMHBwdkEmuh8JEbKyWO4o0ONeSLBihU6Wlvlv1r5+UyYyZ5s5zS/X1TzIrfLDjpyRENOjoFVq+Zi\nVtOAq6/24U9/sl6qkptrjkfZLXAij8dcYZ6eVqQJc7S7ITJWi1AzM83xmJrKlgxafJZn0ttvv332\na7fbLd2gZN++fefmqIgWweioirw864T5He+wv4PS0NAgrZQUFYkZoWNjQE5O+GusMFM0k5MKysvD\nL9aGhxXk5hqm8V1BL73kwrve5Zt9HIzN+no//vQnN267TT4eMT/fwOho+CJUWYJC5PEoSE2NHCsX\nW8IsO3daLUINjpULxUV/lAiOepi/8IUvSJ9fu3ZtXA+GaDGNjCjIy5NXkdvbrW9jR6MoQGWljrY2\n88K//Hwdw8MLWjpAFzhZhdmuHQMAXnvNhcsuM1/g7dzpxyuvuKQb6QBAXp5u6mHOzBTzdYlCeTyY\n7Y8Pkk3O0HVgbEyxLEZYyckRibHPF/58Vpb5Ai49nRVmWny2n9x33nknAGDr1q3S17/+9a/H/4iI\nFsnoqKjayXR0aFFbMqx6mAGgokLH2bPmXy/ZLXCiULLJA8PD1gmIYYiEefv2uSpxMDYrK3XoOnD2\nrDzmZC1CrDCTjLyH2VxhHh0Vo+ZcNkNrZedORZGfH9PTzdVkVpgpEWwT5v7+/sU6DqJFJyrM5iTE\n5xM7VUXeFp8Pq4SZPcwUjVj0F/6cXcWup0eMhquoMMerogBbtvhx4IA8e5FNyZDdAieSVZinpsxz\nmKPdDbGTn2+Ox7Q0czxy0R8lgm3CbBgGent7bf8Qna9GR+WLqHp6VBQVGXC77b/fag4zACxfrkur\nehwrR9HI+kJHRlTLuyFHjmjYuDEQNu0lNDY3bgzg0CH5XHDRw8wKM0Un62GWtWQEpw/ZsTp3Wi1C\nnZ4Of87tFiNBiRaT7U5/Xq8Xn//8521/wKOPPhrXAyJaLKOjCpYvN1flzp6VPz8fy5freOEF869X\nXp5IUAwD0nF2RJOTCtLTzbe5c3PlMXn4sAsXX2w9M7y2NoAnn5RvYCK7gMvIED3Mug7LRYa09Mim\nZExNKSgpCY/LkRHrVrdoZBdw6ekiMQ+VmioWVhMtJtuEOTU1Fb/61a8W61iIFtXoqIING8wn9q4u\nFcuWRU+Y7XqYRYXZnG243eK25uSkfMMUoulp821uq/YhAGhqUvHOd4Yv+AuNzdraAL7zHeudJ8VW\n7XM0DUhLE0mK1bbatPRYTcmI7Gu2WxsSZHXuFItQw+NR1pLBCjMlAusHtGRZndi7u9UF9S8D1gkz\nIG47jo2xOkJyU1Pm29xW7UMAcPKkhnXrrCvMNTU6urtV6SKpnBx5LLKPmSJ5veYe5pkZmHb6Gx2d\n/4SMoOAduFAZGeZ+5dRUJsy0+GwT5osuumixjoNo0VndOuzudlZhtuthLivT0durSsd5ZWebPxSI\nguQ9zPJY1XXg1CkNa9aEJ8yhselyid0nm5vlU1tkCTP7mCmSrMI8M2N+zkmF2ercmZPjrCXD7WZL\nBi0+24T5n/7pnxbrOIgWnVUlpKtLXXAPc0aGuJUom4jBCjPZke2eZhWrPT1iBF3kBjmRVq8O4MwZ\nc1uGLEEB5vqYiYKsdvqL7Le3W6AajeyOh6wlgxVmSgS2ZNCSZXWbW1SYo5/w7XqYAaC01EB3tzkZ\nsboNTmQYIlGNvM09NiaP1ZYWDStXmi/uImOzpkbHmTPm0z1bMsgpr1dBSkp4DHo8ot891Pi4dftQ\nkNW5U3YBJ5+SwQozLT4mzLRkjY3Jbx329iooK1tYhRkAli3T0dPjPEkhmpkRC5q0iGKwVRLS3Kxi\n1Srr/uWg6mr5zpPZ2QampoBAxI9IT+ecWwon62GenjYv+rO6uHNCXmEWiXkoVpgpEZgw05Kk6/It\niA0D6OtTTaOSZOx6mAHRxyxLmNmSQVZkt7gBkTBnZ5ufb2tTUV1tjtXI2KyuDqC11RyLqiomYYyP\nm5OUyL5RWtp8Pphm08/MmBeojo9DGquh5tPDnJpqvnhLSWGFmRYfE2ZakiYnxYk+cvvWiQkxHzke\nI99KSw309rLCTM7J2jEA66pdR4eKysroF3eiwmw1tUU3xWN6OlsyKJzPp8DtNi/6i6wwO2nJsJKV\nZWBiInqFOSWFFWZafEyYaUmyqtg5rS4DTnqYdfT2ynuYOSWDZCYnzTOYAet4bW/XUFUVvYe5slJH\nV5cKXRLaVn2jbMmgUD6fvCUj8o6IVayGsuthjkyYU1MNeDyRFWaYniM615gw05JkdVLv7VVRWrrw\n/mUAKCnR0d/PlgxyTtYT6vOJapqs8uy0wpyWJpKRvj5z3GVlQVrVY0sGhbJa9Bc5OWN83Nzq5pSI\nxfDn0tJgmiEuWjJi+ieIYsaEmZaksTGrhFlBSYmzk320HuaiIgP9/ZySQc7Je0JFrEZupe73A/39\ninRmuCw2Kyrkm+lkZxumJIUtGRTJ5zO3sMlmM8vWhgQdO6bittsy8cgjh6Svi1g0j5WTVZiZMNNi\nY8JMS1I8WjKiKS7W0ddn/hWT9ekRAfO7xd3bq6CgwDAtxLJitftkVpZhWvTHlgyKJJuSIaswT05a\nJ8yf/3wmAgHgvvs2Szd1CsZi6GupqeJCMvQ5l8uAz8f4pMXFhJmWJKskZGBAQVGRswpztB7mkhID\nAwOyW+DmBIUIsF5EJVuEareFuyw2y8tFH3MkWTzKdlejpS1y0Z+ui+dCk2i/XyTWkbOZAVFd7u1V\n8bOfTULXs3DkiHnModstqtihLRiaJv74/eHvC31MtBiYMNOSZDV1YGBARVFRfCrM+fmi9cLnC39e\ndtuRCBBJamTFbmJCPqbL6RbuQWVl8qktVrfB2ZJBoSIX/YnqcnirkFi0ClP7EADs3u3Ge97jhaYB\n11zjw+7d8lsjsk1zglXmILcbpvMq0bnGhJmWJKsK8+Cg8wpztB5mTQMKCgwMDoaf/NmSQVY8HnNL\nxsSEfHJGT49qucGOLDbFXHD5HY/IeJTtrkZLW+SiP6/X3L88Pg7Ldox9+1zYsUOUhfPzD+OVV1zS\n92VmyhLm8BYhtmRQIjBhpiUpHi0ZThQVmSdlsCWDrMzMmG9nWy2i6utTUFrqPFZLS+Ub6cjikVMy\nKFLkor+ZGfPdkKkp+cUdABw86MLWrWJLybVrR3DwoLklAxDTYCIXoUYu8mNLBiUCE2ZakqxGHw0M\nqCgsjM8cZgAoLDQwNBSejLAlg6zIxspZLaLq7bVeoCqLTaudJ2Wzb2WTCWhpi1z0JyrO4e+ZmlKQ\nkWGO1aEhBZOTmB2B+L73bYPfD4sxhwYmJ807+4VWlNmSQYnAhJmWpMnJxakwy1oyMjNFdSYQiNs/\nQxeImRlzS4ZdwlxW5jxWi4vlYw4zM+VzmCN3V6OlzedT4HLNxZvHA9PFnVXC3NSkYe1afba3WVGA\niy4K4ORJc5VZ1sMsNiqZe+xyGfD7eUFHi4sJMy1Jk5MiUQjl94vKc36+fRLS36/gBz9Iw/33n4j6\n7xQW6hgaCv81U5TgbUee8Cnc9LS8JUN2m7uvT0FxsfMe5oICsQg18la26BkNfy41lT3MFC4QQNgI\nQ49HVmE2zxEHgDNnVKxZM1chaGhoQE2NjtOnzSlIRgYrzJScmDDTkiTrCx0aUpCXZ0C1+a3weoG/\n+7ssNDeruOeeLXj5ZfnClaCCAnNLBiDaMsbHYzp0uoDJxspNTkI6Vq6/X0VxsfMKc3ARauSoQ1lF\njxVmiuT3h/cwe70wLfqz6mFuaVGxYkX4xd3KlTpaW80VZtlIw9TU8HhkwkyJwISZliQx/ij8xD48\nLDaCsPPrX6egoMDAAw9M4Uc/8uOf/ildOoA/SNbDDHBSBsnJFv3JYtUwxEQXq357q/562SLUzExz\nLKamsoeZwskS5sid/2Qb7wBAe7uGqqq5WK2vr0d1dQBtbfIKc+SmOZEJMlsyKBGYMNOSJKswDw9H\nb8d4+OE0/K//NQNFAa691ge/X7EcjwSIlozBQfnsW07KoEiyCrOsJWN8XCQrGRnz+/myPmbZGK/0\n9PC5t0R+vwJNM8Ieh46ZA6xbMjo61NkFf0HLl+vo7DSfG9PTze1AkRdwrDBTIjBhpiVJloQMDanI\nz7eekHHsmIrxcWDnTtEEundvAz70IQ9++1vrvYnz8613+4vs0yMSC6nCn5MtpBoctJ/mYjUjvKDA\nwPBwZEuGaPsIxQozRfL7w3uYvV6YtmWXXfABQFeXErYrZUNDAyoq5DtPyloy3G7zWDmvV7G9u0cU\nb0yYaUmSTR6I1pLx/PNuXHedL6zH+brrfHjuuRTLE3dBgYHRUXPiIesbJRILqcxVu8gFqrFOc5Hd\n8ZBVmNPSzLfFaWmLbMmI3PkPCE55CX9O14G+PvMmO6Wl4m5H5LSgtDRzhTklJfwCTtMAVTU4aYgW\nFRNmWpImJswLqYKL/qy8+KIbV145dx+wvr4ea9boUFVIV3sDQF6euaIHiKpe5GQCIlmFWdYXGu3i\nzqqHWTbm0GorYi76oyBdB3RdCSsWeL0K3O7wGBRTXsyxmplphMV1fX093G5xfoxsEcrIkFeYI1sw\nXC6O5qTFxYSZlhzDmP+iP58PaGx04W1vC5/JpSjA5Zf7sHevvI85P1+eMMsWWhHJKsyTk+aWjGjt\nQ1Zki1CD/cqhyUfkVsS0tInqsjE7RxmwbsmIvLjr7VUsp7mUlOgYGAhPQ2TtQGJnv/DnNI27/dHi\nYsJMS87MjKhORJ7sh4etk5BDhzRUVweQkzP3XLBPdPt2P157TZ4w5+SIXuXISghbMkhGjOoKf256\n2pwwR1ugat3DbG7JUFVzVY9j5ShUZP+yeM58cSeb8jIwYN6RMhifxcWGabc/sctk+M9wuw1ThVnT\nWGGmxcWEmZYcWcUOAEZGrFsyGhtd2LpVfnbesiWAAwfkCbOqiokYkX3MXPRHMh6PIplta56GMTQU\nfaKLTG6ugZERc9xF9o2Kih4TEhL8fpGghrKqMEfGb3+/gsJCeawWFRmSCjNMPcwul7klQ9MMBAI8\nh9LiSXjCPD4+jvLycvzrv/5rog+Flgir4fqjowpyc+Un9sOHNVxySfj9v2CfaG1tAO3tqmnSQFBe\nnjlJET3MPNlTOI/HXGGWTcmIVmG26mHOz5cvQo0c5aUo4jg4Wo4AUU0O3RYbEAlsZMIs68EfGlJR\nVBReYQ7Gp9gJVVZhjt6SwR5mWmz225Qtgu9+97vYtm0bFIXJAy2OyUn5/Fq7hPnIEQ0f/aj8HrXb\nDdTUBNDUpKGuznwGl/UxZ2Ya6O5mzFM4r9d8m1u26G90VEV+/vyzBatFqOnp5kWoc9sRc3bXUhc5\nIUM8Z170J+vBt7sbIluEKltwatWSwR7mC5vPJ0bATkwoGB8XX3s8Crxeca70esV7gl8HAmJWuMsl\n4kP8LRac5ucbyM/X3/rbME14cSKhCXNTUxP6+/uxdetWGByoSItE1hMKiIRZ1pIRCAAnT2pYvz48\nQWloaAirMh87Jk+Yc3Nls2/ZkkFmkT2ghiFvyRDtQ/ZzmGVV5vx8eUuGbHe11NTw2be0dMkSZp/P\n3KYhqzAPDyuorjb3MNfX1yM/38DJk+ZFf5GxKG/JYIX5fDAzI8ZgDg+rGB5WZv+MjMw9J74WfwcT\n5IkJBX6/aF/MyhJ/Z2YayMgw4HaLC3q3W5yngl8HYyLYTub3i/VD09MI+/dHRhSUlBiorQ1gw4YA\ntm/34+qrfaYYj5TQhPmrX/0q7rvvPvzsZz9L5GHQEiO7xQ1YV5g7OlTk5xvIzrb+mevXB3D8uCZ9\nLTfXwNgYE2aKLrLCHNx+ODIxGR1VkJMTWw/z2JgCXUfYiDDZ7mqRm0XQ0hUZL4A8iZZVmEdGFGze\nLI/V/HwdIyPhPyQlxRx38ikZwR5mFtsWk2GI/097ehT096vo71cwMKBiYEA8jvzb4xG96gUForqb\nl4eMZosAACAASURBVGfMVnkLCnSsWjX3OC/PQHa2MZscp6UB56L5QNeB9nYVx45pOHpUw333peF/\n/+8MfOUr01i/3vr7EpYwP/3001i7di0qKytZXaZFNT1t3r7VMKwT5qYmDWvXmksZoRW81at17N0r\n3/EvJ8ecMGdlcUoGmUX2MFvtnGZ1NyTIqodZ00S1emICYRNf0tLMLRmpqQa8XiYkNHerO/K5yJYM\nr9dcYZadV4PxKbv7Nhd3c9xuw5REs4c5vnRdVIJ7e1X09AT/VtHbG/51X5+K1FQDpaUGSkt1FBUZ\nKCoSf19yiR9FRQaKi/W3/jaQk2Ock6R3IVQVWLFCx4oVOq67zocvfWkGBw9quPLKHOzebf19CUuY\n9+/fjyeffBJPPfUUBgYGoKoqysvLccstt4S974477kBVVRUAIDc3Fxs3bpz9ZQuOpuFjPp7P48nJ\nK5CRYYS9PjkJaFoA+/c3mN5/+vQurFkTsP35q1cHcOSIJ+xWePD1nJyrMTamhL0/IwPo6hpDQ8Nf\nE/6/Bx8nz+Pp6Wtnpww0NDRgaCgVaWlXmd4/OqqgqWkf+vs98/73cnKux9iYgkOH/jL7ekaGgcbG\nE0hN7Zl9v98/hVdfbcSqVZuT5n8fPk7MY10HfL6ZsPPbmTNt8PtVAMWz7x8YqEdKihb2/aOj70Fu\nriH9+a2t+Rgbe1vY+9PT3wmvN/zfd7uBM2c60NDQNPv9Pt809u17AytXbkn4/z7J/tjnA5555g0M\nDKShqGgzurpUvP56NwYG0uHzlaCrS1SMMzJ8qKjQUFpqQFF6kJ8/g23blqO+3o/e3oMoKPDguuvq\nkJHh7N/v6UmO/367x8GvW1s7Afwn7ChGEpR3v/WtbyE7Oxv/+I//GPb8nj17UFdXl6CjogvVr3+d\ngoYGFx54YK6kdvasgne/OwdHj46a3n/XXelYu1bH3/99+EqU0A8Prxeors5DW9uIaTHBD3+YhpkZ\n4Otfnxs50Nio4UtfysCePeNx/C+j811xcR66ukZmpw+0tqp4//uzcPDgWNj7ysvzcPr0iHTxKhAe\nm5F27MjBT386gdraub7S22/PxDXXeHHzzXONou98ZzZ+9KMpXHIJy3hL3enTKj70oSy8/vpcHP7L\nv6RB04Avf3nuvCaLmfr6bDz00BQ2bJh7Lhifx4+r+PjHs7Bv39zPPXJEw2c+k4G//GXu3HjPPWkY\nHwe+8Y25f+ttb8vBz38+gfXr57+Bz4UkEBCbw3R2qrN/zp5V0dU192doSGwes2yZjvLyyD8Gyst1\nlJXppgk9S8XEBPC5z2ViclLBV77yMnbt2iV9n2uRj4so4cTUgfDnxsase0JbWjRcc41P+lpQSgpQ\nWqqjs1PFqlXhJ/CcHAN9feENgGlpbMmgcIGAuC0a2hcq2wjC6xX9o5Ex7FRuroHRURXAXJzK4pE9\nzBQUCJj76AMBWWya2zTGxxVkZ8vPrTk55h1PRfuFuV9ZVLPnuFwGdP3CP4dOTMCUDIc+7u4Wa2yW\nL9dRUSH+rFihY+dO/2xSXFJiRF3QtlQ1Nmr47Gczcemlfjz44CSOHbN+b1L8T/jNb34z0YdAS4gY\nK+f8pN7SomLlSnMVI7KCt2KFjtZWecJsXvQXvrMakc8nLrxC+/1kWw2PjytR+wKtqsuAPB5ls29l\nvaS0NAUCskV/ClwuPeI5mO6wjY8ryMqS9zBnZxsYH48+Vk42EeNCGSs3NgZ0dGhob1dn/3R0zH3t\n8SioqNDDEuLLL/fPfr18+dKtDC9Ec7OK73wnHfv3u/DP/zyND30oenUgKRJmosUkm2trlTAHAkBX\nl4rKyui3/aqqdLS3m/cCEhW98A8F2VQCWtrkO6eZq3h2F3dOiCQl/DnZJiWyaQW0NOm6edGf1e5/\noQmzYYjZuZEJc1BmplhsGjqFw+UKzv+eY5Uw6+dBN8b4ON5KgOVJscejoLJSR1WVjqqqACordVx6\nqR9VVToqK8XiuWRbNHc+O3FCxU9+koZnnnHjjjs8uP/+ScvWtkhMmGnJmZpSUFAQfqYdG5MnIT09\nCgoKDOkVfGSfaHW1jtZW82g5WUWPCTNF8vnMt7Onp81TMqxiNZRdD3N2tvk2uKzCLBJmxijJWzJk\nCbPPF74joMcjEuHIqnMwPjUtuBW2SJ4BcdEYOXNZNhFDVZMjYfb7gbNnVbS2qmhrE39aW7W3/lYx\nM2NOiLdt87/1WEdhIRPic03XgT17XPjJT9Jw9KiGT3zCg337xiy3bLfChJmWHNlYOauqXUeHiooK\nZ2fligodu3ebR8vJbjump4vjMIxzM2eSzj+R1TnAuiXDqmLnRFaW+QJOdhtctGTE/M/QBcSqh1m2\nmUloDE9Oymfeh8rMFDPpMzPF+1JSZJuUGKY5zIqyOAlzcO5wa2toUqzNft3VpaKkREd1tfizYoWO\na6/1oqpKfF1czIQ4Ufr7FTz2WAp++ctUpKcb+PSnPfj1r70xt7AwYaYlZ2rKui80UmendTtGZAVv\n+XIdZ8+az4xZWbKFLeKEH/kBQ0uXbKvhmRmYTu4TE4rtJjq9vQpKS9+B0EV9oWTxmJpqYGwsvJ2I\ni/4oSNbDLJvNLBb9zT2enjbvUgmEnzvnNnESP8vlMifHspaMeFaYdR3o6lLQ0qKhpUUkxs3N2myS\nDIg1KsGEeNMmP264QXxdUcEe4mTi8wHPP+/Gf/2XmIZ13XU+3HvvFN7+dv+CL1yYMNOSI6vaWd3m\n7upSUV7u7KwsEmZzD7PsFjgQrDKbd8aipUlWYfZ6ldm5zEETE5itxkUaGwOuvDIHExMKnn56HJs2\nmUfCZWUZ6OwMj9P0dHOFOSWFi/5IkO30J0+iEdaSEVo5thK5AFrWkmGVMM9nKK7XK3Z3a2lRTYlx\ncDfXFSsCWLFCx6pVOt77Xi9WrtSxcqXYoY6Sl2EAhw5peOyxFDzxRApqagK45RYvHnhgMmyDpoVi\nwkxLjtVCquXLzYlxV5eKqip5whzZJ1perqOnRzXdvszKMrdkAKKyMjUF5ObG9t9BFxbZoj+Px5xE\n2y2ievTRVFx2mR+Fhadw//2r8dBDU6b3yC7gUlMNzMyEP+dymRMXWprEor/w56z6mkPbNGQLrIHw\nc2dGRvh6Duc9zMGtsefMzIipRs3NGs6cEYmxSIpV9PaK4odIiEViXF/vx8qV4munC78oORgGcOyY\nht/+1o3/9/9SoOvATTd58dxz46ipOTe9OkyYacmRLaSy6gvt6lLxtrc5m12UmioW+A0OKigpmftZ\naWnigySygpieHkxSWL0g+aI/j8ccq3ZVu2eeceMzn/HA5zuLz31uPXw+cxIe7BkNlZZmrjC73RfG\n2C5aOJEch8ecrkdPmGXFiUiRM8CDPzM0IQ/tYZ6ZERv6jIwoePJJN558MgXNzSrOnNEwMCAW2K1a\nFcCqVTo2bAjghhtEpbiyUjf9LtD55+RJFb/9bQp++9sUTE0B73+/Dw8/PInNmwPnvFecCTMtOTMz\n5kV/k5PyhLm7W8WyZc56mAGgrExUmUtK5sohijLXN1pQMPdvBBf+EQHyfnZZhdkqVj0e4I03XNi5\ncwLZ2Zfi+9/X8eabGrZtCy/NBUd5hUpJMU9tcbvN471oaTIMc/tFZMKs66ISHfo+qwpz6LlTdrGm\nacDx4yo6OjQ0N6vYvduNY8c0bNqUg74+sa5keFjBwICKyy/34/rrvaipEf3E3KDjwhJst3jmGTee\ney4FIyMK/uZvvPjRjyZx6aXnPkkOxdCiJWdmxly1E7e5ze/t61NQVua8AlxWZqC31/wbHLwNHp4w\nmyt9tHTJqsEej7mHeXJSQV6e+SLuyBENK1cGZhcEXnppAI2NLlPCLFqBzFMyZLfB2ZJBgEiGIxOT\nQECBqs7Fpt8vLrJC3ye7QxL6M8+eVTE+DjzzTAp273bj9GnRSuH3A7femoWaGh01NQEsX67D4wHu\nv38KlZUiKb7xxix84hMeXHFF8t4GMQxRFJmYUCL+iMXnPp8Cr1f8nnm94mtFEb97mib6wdPSgPx8\nA/n5OgoKDFRV6Rf8QnGfD3jlFReee86NZ59NQVqageuv9+GeeyaxbVvAdPG2WJgw05Ijq3pMTsJU\ntTMMoK9PRXGxsx5mQGyP3dVl/m3OyhKLtULJZt/S0uX1mheAyirMU1NAebn5+w8f1mYX+TU0NOCS\nS67EG2+YT/GylgzZAj+2ZFCQrMIcuehPNpc5eAft1Vc1nDkjkuHTpzUcPjyD3t5s5Ocb8PnE3bar\nr/bhiiv8qKkJ4IorcvDSS2OzF3+/+50bTzyRErbj6mLPYTYMsQlJf7+K/n5R3R4YUNDfr2J4WMHI\niILhYQXDw+GPNU18tsz9EY8zMgykphpwu8XveEqK2L7aMMR/l98vLkqmp4HhYRVDQwoGBxX09KhY\ntSqAyy4L4BOf8ODii80Le89H4+PAn//sxnPPufH8826sXKnjuut8ePzxcaxbpyfFaD4mzLTkyPrq\nZH2hY2MKUlLkY5GslJbq6O+XJcyy7YjNu6vR0uX1mufaer3marLVbNsTJzSsXz/34XnRRQH8x3+Y\n513NjfGaI9vVjy0ZFCRLTEN7jKemgDff1GAYwA9+kDabGJ84ocHnE0nmmjUB1NTouPFGL6666gD+\n9m83ISsLuOOODNTX+/HhD88FoMsVXNAn4lw2ESNeCbPXK+4k9vSIhYE9PSp6ehT09orHfX0iKR4Y\nEJ8HxcVi973g30VFYnvqiy82ZivBeXni67w8+aZXCzE9DTQ1afjTn9y49tps3HnnNL74RU/0b0xC\nzc0qnn9eJMivv+7C1q1+XH+9D1/72jQqKpJvbQ8TZlpyZmZko7rMfaFinq31GVnWw1xcbKClxZww\nZ2aab4OzwkyhAgFnc5inpuQJ8+nTGq68UvRQ1NfXY3BQx+nTqmlznKwsSCvMkbHIlgwKCiarnZ0K\nTp3S3qoSa2htTcPXv56BwUEFVVU6AgGR0F1+uR8f/7gHjY0ajh1z4cc/jpzWsmn2K9nFmqaF392Q\nJcdOxsqNjYmF293dYoOR0D/d3Qq6ulSMjysoKjJQVqajrExHaamB0lIddXV+LFtmoKREn02Ooy1g\nXAzp6cDmzQFs3hzAa6+58O1vZ5w3CbPXK1otnn/ejT/9yY3xcQVXX+3Dbbd58MtfTtjOl08GTJhp\nyZHt9CerMNu1Y1gpLtaxf7/8Nrisb5QVZgqKnDAAiMkZkS0ZVptBtLSoWLVqLl6DW+4ODoqEIEhs\nyx7+vbLd1dxu82IsuvBNTABnzmg4fVqdTY4bG8Ws4quvzsHq1QGsWaMjPd3Ae97jwy23iF3tRkYU\nbN+eg298Y+6kdvSoK2qFVdYOFDlGTl5NNtDdraKhwYXOTtX0p6tLha6LcZ+hfzZu9OOaawwsW6Zj\n2TKRCCeqJzZWhgH87GepePNNDa++Oprow7HV1aXghRdEgvzSSy6sXq3j3e/24d/+bRKbNiWuHzkW\nTJhpybFe9Bf+3MCAYrvXvKyHubjYQH+/uWr8/9n78vio6rvrc5eZ7AsQQiAQsu97AgEMIGDFBRUF\nREFRETdcqiJufbS21S6P1ap91bY+fVr7trb1rdW6VW21ImELhJAdEsgeQjayz3a3948vd2buzA1L\nEhbJPZ/PfJKZTHIzyS83557f+Z6jH+VlKMwGXBAEbw8oRRFq16CewiyKNECltlKqazM6WkZTE4uw\nMBf7cMUZuuDjo6cwKxCEb9F/MwOnDUUB2tpILSZSzDrfP36cQWyshPh4GQkJEr7zHQF5eQK++MKM\n9993DWLcfHMAcnIkp69YL2aOBllPnsOs1yjJcaQOHz/OoqWFxb/+xePgQQ4bNwY4CXFHB4PSUh4J\nCRJmzqSEjKwsEVdfTRaJyEgFwcEXXy31gQMcfvADP/T0MPjss0GNr/tCgM1GKvJXX5nw1VcmHDvG\nYPFiEVdcIeDnP7dg6tQLz2pxujAIs4EJBUVRJ7ddj8kyefA8VbueHvaM/7inTpXR2alnyaDBQnf4\n+BgKngEXJMk7JUMvak6v2v3YMbq481TzZs2S0dLCIi/PnTCTSu1u1dAri9B7zMC3Cw4H+URra4kM\n19a6VOOAAAUJCaQWJyRIuPxyAQkJRDw9ie8XX/Be69DT6qPX/KdXxqPCYqHmvbY2atzr6mLR3EwE\nuaODwaJFIYiKkhEVJYNlaSDu6qsdTnK8das/1q934OqrJ8YiLSnh8Prrvti9m8fWrVbccovjgsiV\nVhTKRlYJ8p49PFJSJCxbJuDVV4eRkyN5radvKwzCbGBCwWYjxU6bFUoDeJ5/1F1dDKZMOTMP85Qp\nCnp79Vv9PBVmvXY1AxMXoshoaoUBGvrzfIxSXrSf29rKapoq1bWpV9fOskTC3fPI9S7ejJSMbw/6\n+xnU1rqIcV0dvd/aSrsOCQkSEhNlLF4sYtMmOxITZYSEnL4YIMsMGMYzRUgbK+fZ/CfLQG8vg44O\nBu+8Y0ZTE4umJhaNjRyamq5GXx+VjIgiEBqqYM4cEZmZIqKiZNx+ewA+/XQIs2fTmv7ySx6vv+6L\nVatc5PhMq7G/jRAEKiP69a990d7OYNMmO157bVg3AvVc4vhxBtu3u1RkAFi6VMAtt9jx1lvDCA29\nOH8xBmE2MKHgcDCnPUTV08MgIeHMtrsmTSLCLMtatWWkdjWDMBtQoRfLJYreCjOlvGjX60gFOzNm\nyGhv997xIB+zS6k2m70TMciSYazPCwWqjcKlFruIscXCnFCLSTG+6SYHEhKo7W48MntHKi4RBCoY\naWwkn/PAAIM1awLR3ExqMc8DISEyWBaYPZsI+4YNDsyeLWH6dBIu/vu/fSEI0AyumUxaMqxHji82\nq4U7qqtZvPOOD/72NzPi4iTcf78NV14pnLdSFqsV2LOHx7Zt5EM+fJhDQYGIJUsEbN5sQ2LihRH7\ndrZhEGYDEwp2uzfZGJkwn7wWW8/DbDJRhFx/P4NJk1xfMyBAQXe39j8OKcyjeRUGLkboFZd41qkD\n+h78Y8dYRES4CLO6NiMiFJSV6RFm2hKfPJnu6w34GQrz+YEkAU1NLA4d4pyqMb1PNorERAmJiRKS\nkiRccw0R4xkzzp5Xt7+fwZEjFLf28su+aGhg0dDAoqSExzffBCI6WkZ0tIywMBkmk4JNm+yYPVtC\nVJSMV17xBc8Djz+uPdEVFRUhMpLOnTyv3/TnPuTHMPpq8sWkMHd2MvjHP8z4y1/MOHaMxc032/Hx\nx4OIjz/3HmVJona9bduIJJeU8EhNlbBokYDnn7ciP1+86MtT9GAQZgMTCna7d+qAxeKdmgHQdqI7\n6T1dTJmioKfHkzADTU3eQ399fcZQlQGCJOlbMjyHpvRSMjo7WUyb5r1Wp02TdZsnXQOn9DkmkwJR\nPHlSgYHxhd0OHDlChNhFilnU13MIC5ORlCQjMVFCQYGIDRvsSEqSz9pWd18fkeL6eqqirq9nceQI\nh8ZGFg4Hg8mTZTgcDAYGGOTni1izRsZPf+qLhx+24fLL6aqqsZHFjh08li932SYkyTsW0RM8r0AU\nvW1DnoTZEyOR6G8TenoYfPSRCR98YEZZGYfLLxfw9NNWXHqpeE59v4pCXvdvvuHx9dcmFBXxCA9X\nsHixgHvusWPBgiEEB5+77+dChUGYDUwo2O3wymC2WLwj5QAizO5V1p7Q8zADwOTJRJjj412PUR2x\n9nlGcYkBd+jHynmrznoKc2cng7g4F7tV12ZYmH6Rjq+vVtXTy1zmOG8SbeDMMTwMp4Xi0CGXYtzS\nQv7ipCRSjJcvF/Dgg2SrCAgY/+9DVYpVMtzQQG/r61kIAq2f2FgZsbESLr1UxMaNdsTGUuzaxx+b\n8O67Zjz3nCuP8Be/0K5X/TZABhznrZC6nztZ1nsng2W9L9a+7eRYRWcng88+M+HDD83Yu5fHsmUC\n7rzTjssuE3SFm7OF5mYW27fzJ250klm8WMBVVwn46U8tmD79IvmBjyMMwmxgQsHh8FY89KqyARps\nOBlhHgmTJpElwx1+fkZxiYGTQ48weyrMlPLi3VTZ3c1qspZVhIfrxxz6+moHTvUSMTjOUJjPBEND\nQG0ttdsdOqS+ZdHZySIujobukpIkrFrlQFISkdOz0QLX0EAte2qWslpJbbcziImh48bFSVi0iMpF\nYmNlTJ16akuH3sfdCbIsez9HLznDE572i5GO/W22ZNTXs/jkExM+/dSMmhoWy5aJWLeOyjrOxsWR\nHo4eZVBUZHKSZKuVQWGhiIULBWzdakNs7MTwIY8FBmE2MKGg1/KnFykHAL29LCZNGvlMrudhBoDQ\nUBm9vdr/Ev7+3okYalKBAQOAPmH2HPqz24ncepKQnh5toou6NidNokp2zwQDz9xlnqckBPdhVYMw\n62NwUJ8Yd3eziI8nb3FysowNG+xITpYwe7Y8rsNaogi0tLAaMkwEmaLZZs+WER9PNdQFBSLWr3cg\nNlbCtGnj63P2jJXTV5i9B1kB7blzpLQL98f0CPOFbMmQJGDfPqqv/vRTM44fZ3DllQK2bLFi4UJx\n3C+U9NDZyaCoiHeS5OPHGVxyiYiFC0Xcf78NSUkGQT5TGITZwISC3hDV8LD30J/DQeRkNPE9kyYp\n6OvzTsTwbFfz8fFuuDIwcaEfK6cl0XrrF6DdEL2SHY4DgoNpPbp/3LNlkmHUVAzXDsxE9zAPDOgT\n454eFgkJLmJ8++0uYjyevtPjxxnU1REZdi8XaW5mER4uIy6OiHF8vIzlywXEx1M+8flKUtBTmPVI\ntCf0Wvw8yfC3wcPc18fgyy+p9vnLL02IiKBGu1deGUZ+/tlvtOvoYLBjB49du4gkt7czWLBARGGh\niDvusCMt7dvVqnchwiDMBiYU7HZvhdlq9SbM/f0MQkJOrsiM5GEOCfHOYtazZJjN3g1XBiYu9GPl\ntCRab/0C3vYh97WpeurdCbOeHUi1ZaiEmeMwITzMQ0NwEuKaGhdB7u1l3IixhI0bRSQnU/rDeBFj\nQaBhOSLFrlKRw4dp2E6NiouPl7FmDSVixMTI59TreibQI8x651D39ckw+oTZExdarJyiAFVVnJMk\nV1TwuOQSAZdfLuCZZ6yYOfPssvm2NgY7d5qwYwePnTt5dHUxmDdPxIIFIl5/nWqnz9fF08UK48dp\nYEKBhv60j+kVQfT3M6OeSA8NVdDU5GnJ8LZfEGG++AmJgdODXuayZ/uf3a7/nOFhBsHB+us1JMR7\nx0PPDsTzKkGmr8NxykWlMFutNHynkuKaGhYHD3Lo6iIrRUoKEeNNm4gYz5o1fsRYLRZxz08+fJjU\n4unTZSQkkFqclydi7VoixuHhF06tsyy7WgP7+hgMDTHo7mbw9dc86uvJH330KIPjxxm8+KIvOI52\nLCoqOPT1MfjqKx6JiRIiI+k1FRXxKCykSb+RXuOpLBmezzkXUF/zV1+Z8J//mBAQoGDpUgEPP2xD\nYaF4Vi9kmpsphUQlyAMDpCAvWCDizjvtSE29eBr1LlQYhNnAhIK+wuydzdzXx5yyCWtkD7OCigrv\nAT+r1ZO0KIbCbMAJve1rQdBaMqjWXbsuBwYYBAVp2yvd16aeRcjHx7uUxGRSNIN/31YPs8MBHD7M\nOomxemtrYxETIyM5mcjxLbc4kJxMiu14EI2RikXq6jgMDzOIj6c0jIQE2UmKz8bg3+licBDo6GDR\n0cHi2DEGXV0suru1b3t6GPT2UpwcxwGrV3MICVEQFKTg2DEWpaU8+vpk+PgoGBpinMRakgBJYtHf\nz8Bi4fDaa744eJCDj4+CLVts2L27FYWF4c6fmyxr16IeEZYk7+ecbcIsCMC+fTy++opI8uHDHAoL\nBSxdKmLrVhtiYs5ORrIa87ZzJ5HjHTt42O1EkC+5RMTmzTYkJ8uGxeIcwyDMBiYURiqC8EzJ6O8f\nWbE7FYKD9VMyvAmzYckw4IJeBJcoalMy9BRm1T40EkJCaPDPHSMVlbgT5gvdwyyKlAjhUozpbVMT\nxbUlJ5NifMMNDqSk0BCcZ0TfaKAqrYcOqcTYZaUIClJO1FCTlWPFCgcSE89usYgnZBno6mJw9Cjr\ncaPHjh0jkizLlNNNNwXTplGEXE6OiKlTFYSF0f3JkxV88w2P994z4w9/GHYeZ+XKQDzyiA2LF5NS\nXFvLYu9eHt/7nmvrwmbzQ1ycjLvvtqOoiMdvf2vGww8HAEhCVJQVhYUi3n7bjKoqHi+/7MrdrK3l\nUF3NITWV/h6Ki3kUF2vpyocfmiGK0NRljxWKQs2FaqPdrl0mxMZKWLpUwA9/aMWcOWensEOSyN6x\naxd5kHfv5sHzwPz5Ii65RMAjj9iQkGAM6Z1vGITZwISCXkqGzeZt0zgdhXkkD3NwsDdB8ff3Hvoz\nm41YOQMu6EVweaYMCAIDs/nUF3fua1NvPfr4eF+sEUF2WTLIojG61zKeUJXbmhrOeauuJoI6bZrs\ntFJcfbUDW7aQtcEzdm80sFiAw4ddhSKHDtH7zc0sZs6UnaR4yRIRd99tR2KidE7KHaxWoLWVRUsL\n3VpbWbS10dvWViLHwcEKIiNlzJih3hQkJ0uYPl3G9OlEkoOCTt8HPB5KpiwDpaU8nnjCCkUBnnyS\niPXttztQXa29MktOlpCa6nqsoEDEvHlaYrxypQNXXz12xaG1lcG2bSZ88w212vn7K1i8WMRNNznw\n+usW3WHascJmo5+FSpD37uUwbZqC+fNFXHklkfNZswyCfKHBIMwGJhQEwVuhs1oZhIRolb3BwfFV\nmPUsGT4+3tm3BiYu9CK4PC0ZejskAwMnX6t6CjPZgTyb/bSeZZY998Ulvb0MqqtdpJgIMgs/PyAl\nhawUl1wi4q677EhKGp+Cj4EBOMmwe+NeRweL2FhXfvL111N+clzc2bVROBwUG9fUxKK5Wb0RUW9p\nIQ9xZKSMWbMoFWPmTBmXXCI6358x48IZClQUssesXh2I+noWP/6x9UQxhvaKhmEUr8/zvH+6o594\nVwAAIABJREFUA4WnQleXNm6tt5fBokUiFi0S8NRTNkRHj7/NYmAA2LOHlONdu3hUVJCne948Ebfd\nZsebb4q6OeoGLiwYhNnAhILD4a3Q2e3e1diDg+QLPRlG8jDrERSVHLvn3JpMhsJswAU9wiyK2qE/\nPYV5cJBBYKD2Mfe1GRhI/lJ36NmBKBXDdZ+ymUf3Wk4Fi4VIqpYYk9dXJcapqS47xXiofH19DA4e\nZDUxcbW1HAYHGY2N4rbbRCQljX9+sgpFodzshgYixY2NVEHd3MyisZGKTqZPlzF7toyoKHq7fLmA\nWbMooSMiQrngvat9fQz+/ncT/v53MzgOeOopK9atczgv9kJCSgGkABjZh+yZ8TxStNyp0NtLcWtF\nRdRo19bGYP58ilu77TY70tPHP26trY3Bnj28kyTX13PIyRExbx55n/PzRQQFje8xDZx9GITZwISC\nXtWw1ao/SDUWhdmTMDMMkWZ3cm4ozAbc4X4xpYJ8za51qKcwDw2d/OIuMFBBZ6f2C+spzJ5Dfp4E\nejQQRfL7VldriXF7O7XfqcR40SIBqamuFIWxoLeXcWYmu5Pj4WEGSUmumLilSwUkJ8uIjBz/4SlZ\npma1hgaqoFbfNjayqK/nwPMKYmKIDEdHS5gzR8SaNTKio+n7+TbGgQkCRfTdeWcAvvySx9KlojPm\n7PbbtVdnGRk9zvf1hl31SLSewqyHgQFg924e33xjQlERkdW5c6nR7rXXhpGVNb5xa5IE1NRwTnK8\nZw8Hq5VBQYGIuXNFvPiiBdnZ0lnxPhs4t/gW/lkaMDB66Fky9Ib+BgcZzJ59cnltJA+zquh5qiI+\nPormWIaH2YA7iBxrH/O0ZHjeB4DhYXhZE9zXpp7CzPPeF2ueQ34jNbCNhM5OBlVVnJMcV1fTUFx4\nuIy0NNcAXmoqpUOMdQBPJcaeqrHVyjhJcVKShO98R0BS0viQcXeopLi+nkN9Pet8e+QIDR6GhiqI\niZEQHS0jNlbGNdc4EBMjIyZGxqRJF8f2uyAAX37J4/33zfj4YxMcDgYLFtjx859bMGmSgqef9tPd\npXBfn3oXinotgp5Qn9Pby2DXLlfc2pEjHHJzSUH+6U8tyM0dX7I6PAyUlPBOBXnvXh4RETLmzhWx\neLGAxx+3Ij7e8B9fjDAIs4EJBYdDmzoA0ACG55DQ6VgyRoLZTOqczaa1evj6arNvzWZDYTbggixD\noyYDRGC1HmZvS8bQEIOAgJHXakCAguFh75QMi0X7PI7Tepb1GtgA+ryDB7XEuLqagyQBqakS0tIk\nzJ1L7WLJydKo2jLdoXqM3dMwDh3iMDRE9g2VHC9fTsR4PBMpVPuEdw012ShCQhTExlI0XVychPx8\nEXFxpBqPh7/6QsTgIPDllyYcPMhh48ZAJCdLWLnSgXXr7HjwwQDceacrfoXjTm3r0WsIBEa2ZHR3\nM9i5k0dFBYcDB/zQ28tizhyKW/vJT8afILe3u+wVxcU8Dh3ikJZG/uONG+341a+GDf/xBIFBmA1M\nKIykMHsmZ+j5Qj0xkocZcKl67sq1j4+qKKsKs3e0l4GJi5FSMtwf01eYvQmz+9rUJ8wKBEF7MO8Y\nOdoR+egjE6qrOVRVEVk9epTsFGlpZKlYulRAWpqEiIixEdXhYVcVtTs57u11KcbJyRKWLBGQkjK+\nirHFAtTX07CflhizYBggPp7SN+LiZNxwgwNxcTJiYsZ+MfBtQWcng/p6Gt4rLuZRUCAiJETBCy9Y\nnLFuDQ2slxI8EmF2X596694dbW0MvvmGx5EjLObPD8bRoywKCkT4+ipYs8aBBx6wj0tcIEAWoqoq\nDnv38igu5lBcTAUhBQUiCgpEPP+8FdnZZ7egxMCFC4MwG5hQEAQGgYHaM7jd7q0wDw+PTJhbWljc\ne68/srNnYwS+jMBABYODDKZOda8j1irMJpO3j9TAxIUncaBiBkbzmF4boNXKYPLkkWU8f38io+7g\nee3QX28vA4sFeO89E373Ox+n31gQgHfeMSMtTcJ11znw1FNU0zwWgiIIQF0dq4mJq6nh0NFBRJyK\nRWTccYcdKSk06DYeHmNFIQuFe/10XR2Vi3R3s4iOJlIcHy9h0SJSyBMSZE3l+ESBzUY+4C+/NOGL\nL0zo7KTz4datNvzv/w4hOBi44YZATRuqXsU1tUWe/BznvouiKLQ2+voY/PCHfqiqcpW++PkBb7wx\njIwM8iBv2BAw5mzt3l4Ge/eqBJlHaSmPyEgZc+aIWLRIxGOP2RAfbxSEGCAYhNnAhILD4T30Z7fr\nb3OPRJh/+EM/JCTI+POf0/HQQwOYNs37eUFB3r5RX1+tZ9lkujBybg1cGCBLhus+EWitiiqKDHhe\nu94sFiAyUvu13Hc+/PxIKabPp5iv6moOlZUcbropAJWVvHNItbFRxtKlAtats8NsVnDffYH48589\n2PYZvJ6WFlYz7FddTcNvarFIaqqEG28kX3NMzPgMuzkcwJEjKhnWFosEBCgnSLGMhAQJy5YJiI+n\nNIqJXCusEtWvvjLhq69M2L2bdw5GvvnmMI4eZfHnP5tx3XUuD5mnx13P8z7S4Ki6PkWR1siRIyw2\nbAjA7t08/PwUWCxATo6IZ5+1IjFRxrZtPF55xRc5Oa4tkDONlZNlKlcpLuadBLm9nUVenoj8fBEP\nPGDDnDmS5iLAgAF3GITZwISCniVDX2GG7nbr0BDw+ecmlJf3QxCADz4w4557vH0VeoNWPj6eCjMR\noNHmiRq4uOAZKzdSLrPnYxYLAz8/GUxrK7jaWjAWCyDL6BZCUG5LxL9rY1Bfz2LJkiDU1nKYPl1G\nQABFk916qwNpaVZERcm49tpA3HuvHYWFxHAOH2ZPO1auq8s7P/nQIQ7BwQpSU8m6sWyZgAcftCEh\nQRqXLe2BAbJwqLe6Oha1tRxaW1lERREhTkiQcemllNuckCCfsoxoIqGzk8H27Ty+/ppa7WSZcV4s\n/frXw5rBRLUZ0B0MoyXIpDCffLh0cJCqplVPcEkJD19fKllZv96GH//YgpkzFWRnB2P1asGZiTya\nWLnBQWD/ft5JkPfu5TBpkoK5c0XMmSPh7rtpB+PbmEhi4PzAWCoGJhQEwXvo70wU5l27eGRniwgN\nVRAVVYH//CdTlzDrbYP7+rqUPoBO9jRo5a16G5h4kGUGLOtac3qEWZLc1srgIHzefReOrwsw/NE2\nfDJUg3JkohyZKEMWLPBHJsox21QOk3IFXs16B3FvLUFAfAT++Eczdu3icfXVLjbj6TfVG/pT85PV\nNIyaGnpfFOEkxjk5ItatsyMlZXwIanc3c6JQhNUUjAwMuPKTExNl3HyzAwkJpFQbEV7eGBwkm4Va\n+9zSwqKwUMTixeKJC5mRkx08yTGgpzArXuvFaqWLmiee8MOePZRgkZkpYsaMRtx773TMmTOMN9/0\ngckErF7tWoueuy0jJWm4f7yujsW+fTz27SNy3NjIIT1dQkGBiA0b7PjlL0Xd3UADBk4XBmE2MKGg\nl8PscHhXY+sNUgHU1jRvHilwqak9+NWveF31w99fgcVy6rIIk0n/ezIw8eC5jjxJA0A12J3Ndvzm\n2q9RtceKcuEyVCADuxGDBdiJLJRhM95AJsoRhWYwANqFCHyJRVj8h/uhvMPDsWoVzHE/gCxHa762\nO0GWZapfHhoCfvYzXyc5bm1lER9PVorUVNqyT02VMH362AbwFIUUTzUajt4SQZZlONv2EhMlXHaZ\ngKSks5OffDHBYqHz1fbtVNhx8CCVZyxcKOLlly3IyTl9dZVlFSiKZ7a84qUo22zAb3/r48wjPn6c\nxbRpMubNE7FqlQVZWRJ8fICiooMoLAwDQCJGQICWacsyo2n/8yTMfX0MOjsZvP++Cb//vQ9KSjiE\nhirIz6dM61tvpUIS48LJwHjCIMwGJhT0m/68UzKGhxn4+3sT5gMHeGzaRIryNdfMxVNPAY2NLGJi\ntCf8wEDvZAJXSoYLPG/4mA0Q3OO1JIkUM1EEnn/eFxUVPCorOfR2SZgk9iEWbViEA3gQv8DTeAGP\n4he4Ep/pfl1f2GAFeSAYUYTPX/+KQHBQYh4ALFE4bgtAVRWHtjYWr7/ug+ef93PaKQYHGTgcwLXX\nOvDkk2Mf+FMUoKOD0eQmq8oxw8CZhJGcTEOGSUkSwsPHNz/5YsXwMFBcTFnEO3ZQ/XJ6uoTCQgHP\nPmtFfv7o0x30BvpEEThwgENpKaVJlJTwsFrpsUsvFfDEE1b8618mNDWxePBB7S6cu8deL/nF/eJR\nkoCmJhZHjzJ44AF/7NvH4+hRFmazgunTFWzcaMcbb4gIDzfUYwNnFwZhNjChoHdyttu1CrMs69dl\nA0B1NYfUVNfgSXq6iOpqzosw6ynMJpOewqxAEFxRcwYmHgYHaV3V17P4v//XB2++6YuDBzlMniw7\nEwQ2XNeFudYn8WH7VBxFJF7GFufnCzDDFzYovr6QMjIgh4UBLAtmYABcRQV8+uywwRcCeBxCEsqR\niXdxI4obopEezWLIJxAp6bR9HhMjY8sWG1JTJQwMMLjyymA884ztJN/9yOjuVotF1Jg4SsbgOBcx\nTkuTsGoVEWP3RJmzBUUhlb6ri0FPD4PeXha9vQyGhuhmsxERZBiaawgPJ3U0KeksdYSPAQMDpCDv\n3GnCjh08qqs5ZGRIWLBAwJYtNhQUiOMaezc0BPzpT2bnwFxdHYtjx1hccYWA++6zITZWxvLlQfjl\nL10B3998o57fRobnXEl3NyW2/J//Q38H+/fzCAxUwDAK8vJE3HMPeY9vvTUAN9/swBVXGGH2Bs4N\nDMJsYEJBL5bLU3W2WIgse273DgxQPvPMmfTPs6ioCImJ30Fdnfe+cECAt4fZx8c7Rk61ZBi4+KEo\nZHOorKSEiooK8v92dLBISpLQ388gM1PCunUOpKWJcDgYzJsXjKevL0PQDTeAbW+HjK3g4dqSkKdM\ngdUcA/Hpl9B340ynt+fjj4sRELAAVVUcqnZZYPunL0KYAcxSmpGFMvjDgmg04M/iOkRxHbA89Ftc\n/7s1+M53BMybRxeEg4OnLp0A6O9CTcFwJ8gOB5CcLCMlRXKWWyQnnz1iLAhAezuL1la6HT3K4OhR\nFkePErHr6GDR1cXA11dBWJiCyZMVTJ5MrXtBQQoCAxX4+hJRVhS6gCgu5vHIIwF46ikrtm4d3YXD\neKG7mxrtdu2iCua6Og7Z2VQ9/V//RQqyv//4HGtoiHbTaGCOw86dPASBwaxZMubMkXDHHXb87Ge+\nWLfOgRUr6ATW18d45HjTxZ5e1ryaw2yzAW1t9HspLg5ASQl3IuKQAc8DmzfbkJcnYfduHn/6kxl3\n3OFSHDw9/wYMnG0YhNnAhIIgeMdy2Wxahdli0bdjHDnCITZW0hDp+HgJxcXef0b+/vrtap4Ks2HJ\nuDghCDQcV1FBN5Ug+/kBaWkS0tPJcvC971EZhpor+53vCJg/nxZEZyfAKhKCVq4E29EBAJDAgYUM\nOTgY1mefxdCqm9G3PBxftYXjwx8xqKykYbzh4SXIzmaQliZh/nI/vPsZUHPEgim7SuD37LP48EgG\n3sZtiEYTYAcCbr8dXMalkOVg52vwHOqy2YC6Om0SRnU1h74+V7FISoqEyy8XkJw8dl+zJ0QROHqU\nRVMTi8ZGFs3N6o1DczOL7m4G4eEKZs2iUpMZM2TExckoLBQRESEjIkJBeLjslYijB0kCvvjChJde\nMmHKFFnTXncuoCgUt6YS5F27eHR0MJg7V8L8+SJeeMGKnBzxtF7L6Rzr8GHtwFx9PedsbFy3zoHV\nqx344x998JvfuNRj7yp1b0+zr69LEFAUivsrKeHx0Ufp+P73g3DwIAdfXwVpaRJuusmBrVutSEiQ\nERcXgi1bbM6IN72hv5EaAg0YOFswCLOBCQVR9J6+liRG48u0WrUNfSoaG1lnzBFAPjxZlvH//p+3\nwuznp6C7W/u4vsJsWDK+7RgYACoreQ05rqvjMGuWjIwMCRkZIi67TEB6unRSn6WieJCC3j6wAyJY\nichyDyajDgloCkrH+sV3o/L3wTjyDFVS790rY8ECCffeS3YKzxa8p57yB2diIVx5JYTCQii3vgvx\nG9fpnxEEmMtKgfrZkOVINDay2LmTx8AAcPvtAaip4dDSQus/JYUG/m67zY7U1PErFgGIlDc2smho\nIIuK+n5DA4u2NhZhYQqioyXMni1j9mwZS5aIiIpyYNYsGdOnjy3HWVGAmhoW771nxl//6oOICBmb\nN9uwcqVw1ocLRRGorOSwZw+px8XFPGQZKCggBfnOO+lnPR5Z0QMDQEkJRa3t28ejpIRDUBANzOXn\nU8JJerqkERG+/tr7B8txWsLsefHf08Pg4EEWZWU8Vq8OxP79HAIDFeTlSZg3bxry8y3IzJTw4IMB\nuOoqh7MxEKBzsntNvB451iPRBgycTRiE2cCEgiaWC6p/Tksuhoehu7XZ0kL5ru6YPVtGU5MeYaYt\nXXecLCXDwIUPtSmuokJLjru6WKSkSMjIIMJx++1Ebs50e1wlBZIE1Nez2LvpQwxLG3E1PkYZsjCI\nIIQECJgSG4JrlzmwKd2C5GQJl14ajB//mJS5kUBk5sSFWVAQhM33QOjsRvfBKahABiqQgTIpDTXf\nV7DphRBMmqwgLk6GojC45hoHHn+cBv7GI3VALas4fFhbQ11fz6Kzk8XMmTJiYmTExtIxL7+c8nij\nomSvNJuxQpaB/fs5fPqpCZ98YobVCqxcKeDddweRmnr2fMsDA648YnVgbuZMGQUFIq64QsD3v2/F\n7Nkjx7ydLiQJOHTIXT3m0drKIitLRH4+XfS89pqIiIiTX7Drt/i5HrPZgPJysuHcfbc/Skp4dHWx\nmD1bgiAAGzfa8frr+rFuejn4esKGnsJsEGYD5xIGYTYwoeA59Odw6FcN6ynMra0s4uJc/zWKioow\nd24hOjpYr8xcaqs6daycYcm4MCGKlFKhkmPVUsFxOKEak6XimWckxMaOviVuaAioquKcCvVzz/nh\n3nsDMNV/CEldCWCgYBP+B9k4gOlr5uKZqN+B40Vs2OBaSHqDrKpHVAXPKygvZ3H0KPmmt2/nUVMf\njTifNmTai5GBCkxGL1aJf8OddynwfWEruroYXHJJsEb5OxP09zNuLXuu5r2mJhbh4WSXUMtFli8X\nEBcnY9as8Wn7OxmOH2fwn/9Q7fO//23ClCkKrrrKgTfeGEZurjTu2/yKQhdAxcUuT3BTE4esLBEF\nBSI2bx6/hrn2dgYlJfyJG4cDB3hMmyYjL4/KOlSl+kyTTtztOWqCS0sLgz/+0QdvvOGLQ4c4xMdL\nkGUGixaJeOQRGxITZWzfzuPVV31x1VXaNeS+Pm0275Qi97psYOQcZoMwGziXMAizgQkFUdRu9ekV\nmdhsNBjkiaNHWSxapGW3ZjMwebKCY8cYREa6Pse9jtj13JNZMgycL1gslFJRUcGhvJyI68GDHGbM\nkJGeTuT4/vttyMiQxlR8cOwYc4J8u0h4WxvrTIsIDFRw440ObFzbi5nL56EdCuZhN67HBxDz8jD4\n2quQX2Rg9mr/c61hNbZt//6p2L/fh4b+qmiQ6pFHApCVRf7pK690wM/PhE8+sSDgsd/B53e/wxq8\ni0TUIeJ/PsDAhmuBycleZRWeUBQiaWqZiHvjnsXCID6espPj42WsWuVAYiIVi4xH09/pwmYD9u7l\nsW0btdrV1XG45BIBy5aJePJJm9eu0VgxPEwDc3v3cs5ECV9fYO5cEXPnUolGevqZk1a945SV8di3\nj3OSZKsVyMuTkJcn4qGHaGDOvbHvTKEoQFsbDRs2NLC47rpAlJbymDqVElzmzBHx5JN2ZGbSjkpY\nWChuvtnhvID09VVgtZ78/GazjU5hpur4Ub80AwbOGAZhNjCh4KlcOBzepSF6J3CAJvCnT9d6mAEg\nIkJGRweLyEiXoc/fX9+SYfMYtDcsGecWfX1EWsvKXAS5uZlFQgIR48xMCWvXEqEZbSSXaqkoL9eS\nY0EAMjOJsF5xheAccFLX49q1gUhJkRH+x9fBtbZCxiwwUKBwHCyvvAL4+GiIg8NBQ3hDQ8BLL/mi\nsZHIsSQB6el5SEuTsGiRiPvus2P9+kB88MEgZs4k8vT11zx27TKBYQDLc8/B9NlnYNrpY4wgwP/Z\nZ8G8/q6TMKtFJocOsc4kjEOHSDH29VWcpSJJSRKuuYYa92bMOD/5yYJANouiIhOKiohIJidLWLyY\nrA4FBeK4FVqo6jH5gTns28fj8GEOKSlkz1mzxoH//m+L5mJ6NFCtFfv3u9Tj+noOycl0nBUr6LXF\nxIzNxtHXx6C0lKLc9u+nt5IEZ2zmgw/akJsrYfJkBfff74/580Vnqgrg2jFzEWbvcx6gzWH23NFT\nfdHamnjvYW3DkmHgXMMgzAYmFDxb9Uid0z7HZtO3ZHR0UGuVJ6ZNk3HsGAvA9Y9DT1kxmRQMDmrP\n8J6DMwbGB4pCim55OY/ycpUcU/NYWpqEzEwRixaJeOABO5KSRt8IZrW61OnKSiLgNTUcpk51qdOb\nNtmRkSGekkAqCqVi+Lz9Nt0HAwYK7Pfei67p6aj8hpTE/n4GH3xgwuHDNFhoszEIC1Nw1VU2pKdL\niIjwPo7JpECSXMOlLOu27oKCYHnhBWAjIINBC2ai8l8cvvmFHUNDDJYtC0JtLRWZJCURKVbb1BIT\nZUyefH4HVm02oLSUyjp27CBCGRMjYeFCEffea8f8+UMIDj711zkd9Pcz2L+ffg8qQfb3dw3MrV1r\nQUaGNKb0ClXVLSnhncS1rIxHeLiMnBw6zvr1dmRkSGPydFutQEUFh9JSOkZpKY9jx1hkZorIzZWw\nZo0DP/2pFTNnyigu5vDMM/647DLXDpveuUslzOr35ed3aoXZkzDrNZ96Ks4Axcq57xYaMHC2YRBm\nAxMKoqhVKtShP3foKcyyDHR1MZoMWdWHFx6uoLPTO07JM3+U573VZJ5XiYyB0UKWgYYG1o0Yk6or\ny+Q3zsqiDOBnnyW/8WhVqf5+xkm81eM0NlJVdHo6qdM33GBFero4KoImywBTUYGGo744gBvwNRaj\nG2GI+/srGHibQ3q6CIuFQVSUhIcftiM5mbbBY2NDsHmzXbP17u1h1nrlOY7Wp1p4UVNzM77irPhY\nWoFgDCANVZi9qwk8PxUvvGBBSoqMkJALg5z09THYu5fD7t0Ut1ZRwSMxUcKCBVRqMW/e8Lj4gQWB\nLoZKSlSCTA1zmZki8vIkrF/vwC9+YcH06WM7Vm8v41Rz1bcMA+TmEnF95BEbcnLGZq0QBMrKVonx\ngQMcDh/mkJAgISdHQmGhiO9+l3zHeh5y9wG/Uz3mHC4FEBDgHa8JaNenxaIdstYjzJ4zIoARK2fg\n3MMgzAYmFDyVCj1LhtXq7WHu7ycVRE/RmTpVRleXd4Scp4fZZPIe8KN/MGf8MiYs1GG88nIeZWUq\neeURGiojM/PMFN2TQfUbl5W5FOqeHhapqaROL1hA6mVy8uhVPrsdOHjQpU6XlHC4bdschOE/yMYB\nRKMRgT4CPv5EcEa3PfusH8LCZOTmuqQ9SWJOehFgt9PP7cMPTejtZVFdzeHAAQ79/Qx+9CM/Z0xc\nffQg1h95HHfjfwAAnT3ZeN9cotlyP9dQFIqZKy6mRIk9e8hCk5tLA3Nbt9owZ87YG+3U3GOVHJeU\nUBX5zJnqwBz9vlNSpDENJVoslCZRWso7rQ+dnSyys4kc33yzAy++aPGKBTwTqEN5Bw64jlFTQ7sR\nubkisrMl3Hor2Y5OVwlnWT1y7H2xbzYrGlHAz49e88ngmXuvl5U/EmE2LBkGziUMwmxgQkEUtR5m\nvaE/u53x+kfS3U3b3u5QFZKwMMUrWs7XV48wew/4GZaMkeFwUPlHWZl6o3/8ERFEjrOyRCxfLiAj\nQxq1LUBRgKYm1km+VXVaFOH0NI9HGsbAAFBRobWHNDRwiIkhkp+eLiEmRsKTB+/CWun/AgCOIBYf\nhd6J6GhXIobeEB4RB8U5gFdVxaG6ehnefptHVRWHxkYWsgzs2sVj3jwJd91lh80GvPmmLz77bND5\ndXZvn4zAJjvUIkG+pQlK0LmthLZYaGBu3z7XwBzP03BZQYGIW24hK8JYB+Z6etxVXSKWHAfk5YnI\nyZHw5JNUDDIWK4fDQQq1SlpLS+l3npwsISdHxKWXinj0URsSEka/rtT1u38/5yTIZWU8wsLIvpGT\nI+K666zIyBARFDT613Iy+4U7PMuZAgO904IArYd5eJhBQIDnrp/2+XoeZiMlw8C5xnklzG1tbVi7\ndi36+vrg4+ODn/3sZ7jsssvO57dk4CKHKGoV5ZGG/jxjjnp6mBFJ2ZQpCkpLtf8UfHxOz5JhKMwE\n1QtcXu5SdQ8d4jB7toysLBGZmRKuv370dgeA/uEfOcJqjlFeziEwEMjMpGNs3Ejq9FgUvo4Oxql8\nqwS5s9OlTs+bJ+Kuu0itdL8w+/pTESG2Tud9OTAI8PMFoCXMLEvrqLaWvrbdDqxfH4jqag4MA6Sn\nU+rG0qUCHnrIhsRECUuWBOO556zObOHduzmv18eYzRATEoGaE/ehgDmLV3PuzW8lJUSQ6+pcg2wr\nVzrwk5+MTW0FKE2ivNzl092/n3YLcnJE5OaSF/vll8e2I6EO5al2h9JSHgcPcoiOlpCdLSE3V8TG\njRTpNtodCfWCyF05PnCAg68vkJNDRH887Bt60CPMeo95FjGZzXRBpxffCdDn2+3QpKboxSTqe5gN\nwmzg3OK8EmaTyYQ333wTGRkZaG5uxoIFC9Da2no+vyUDFzk8Y+U8FWcAcDgYr39qx4+zmDJFq7ap\nPrzJk2X09IzOksHz3nWyFzuGhuAckFOV3fp6ynEl5VjCunV2pKVJCAgY3TFEEaitpZYx9RiVlaS8\nqcd46CEbMjMljS/9TKAolByhKuDqgKHDAac9ZMUKB556imLVTqUiMkODmvtSYiJwnNRPKM1DAAAg\nAElEQVTpqipSvr/6ikd/P4uf/MQPM2fSYCEA3HMPpReoA3+eHma92mJPtZphAHnWLCdhBgBFHD+F\nuauLEhhUy4Pa/JafTzFoq1ZZkJU1toE5u51yrVVifOAAj6Ymiu7LzaXGxccftyI+fvRedlmmZIyy\nMpdyXFlJecc5OWR5WLWKhv9Gu34BsgUdOECkmG6UWJGdTcrxpk125OScunRkPMCy3vYLz2psgEix\nuyjAMKQyDw1pBQd1faolUe6/C1FkvOZK9CwZRqycgXON80qYw8PDER4eDgCIioqCw+GAIAgwjXW/\nzYCBEeB54qVabO+hP0+FubeXGVG1mTRJQX//qYf+RrJkXMwK89AQWRFoq5j+6be2UjNeZialLahq\n62iVN4eDBpoOHHCR8IMHOURGuqwbV19N1o3RDoOpg4UqMVZJuI+PS53esMGOrKwxqNNDQ+hDCD7D\ncpQiB9u61qG1nUVaWiiSk+nnFR6uYNEiB555xuokYxERoVi2TDwp0WQYb4Ksa++YOcv1OVCgSKMj\nzIODlBFcWuoir319DHJyXGrrSM1vpwtBIMtOaanLjnDoEIfYWBpkmzNHxN1309oabQqK6qFWj6Fe\nHIWEKMjKotfyxBMCsrLGVjzS2ck4SbH61m53keP16x34+c/HrraPFnoDfjzvfT7ztGQAQFCQN2FW\nMTDAICjI0xKnZ8nQJ8xnu+TGgAF3XDDL7fPPP0deXp5Blg2cVXieeEdSmIODtSfxvj7G6x+iquCF\nhiro7T11SclIloyLxcM8OEjk+MABFzluayNynJ0tYuFCEQ8+SDFuo/0zV32hdAwiMIcOcYiOJutG\nVpaENWtInR6tZ1OWySqgkhfV2xwaKiMri4jr5s1UZDJadU/dXi8vdx1je1MsduN/MAd7kYv9uDSz\nCxWSjLKyAeea/d73/DBjhuylXHqSqIqKZSgstI/4cT0CzTAKpPBprvtQAOXUhNlioR0D9ee1fz9d\nFKWmEqFcvlzAU09ZERc3elVX3TEoLaXfeWkp+dkjI2VkZ5Md4cYbyd98ppXkKhQFaG5mnT5gVdkN\nDASys0k5fughG7KzJUyZMnpy3NXFONevat+wWFzkeO1ainObNWvs1djjhZEsGd4eZm8SHRSkYHBQ\n+5h67hwcZBAYqP1ZCoK3wuyZbqQ+xrIXRnKLgYmBC4IwHzt2DI899hg+/PDD8/2tGLjI4UmYBcFb\nubDbvRXm/n5mxFit0FAFfX2nrsG+mFIyVHKsKsdlZeNPju12l3LsSY6zs4kc33QTTfuPdutbloHD\nh1kneVEV5ClTiBxnZ4t49FFSEMcyWKh6T91fiyTBeYybbnJgaF8Lnu5+BFficwBARe7v8esq7/V5\nOvjkExPuu89FmCsr6feTkUGsp6WFxb592tP/3/7mgylrJuGuE/dt8MWAHASg1/kcq9Vlp1F/9/X1\nHJKSyOZSUEBFKcnJo/+9e9ppSksp+m7GDLooys6WsHLl2AbZ1FQM9XeuEnFfXxc53rzZhqwsUvVH\ni54eb3I8OEjkOCtLwqpVDjz/vBWzZ1845FgPegqznppsMnnvrAUHe+/AqRgY8D6vGgqzgQsV5325\n2Ww2rFmzBi+99BJiYmK8Pr5582ZERUUBAEJCQpCRkeG8Oi0qKgIA475x/7TvOxxXOE+8RUVFKC8P\nh8mUq3m+3X45zGbt5w8MMJDlwygqanB+PdV/X1BQiMFBBtu3F4Fh6PlEmOmxhQvp+bW1VejsjAFg\nch6vtzcHshx6wfx89O5nZhaiooLH++8348iREBw9Oh1tbSxmzuxDfHw3rrwyHN/9rh1dXd+A5xXN\n5+/Zc3rHcziAd96pwOHDIRgaSkZZGYeaGgbTpw9jwQIeWVkS0tL2IiZmAMuWzXd+viAAAQGn93q2\nby9Ce3sAOG4uSkt5bNs2jPr6YISHsye21I9g+fI+/OEPKZg8WRn1zysxcSEOHODwj38cxeHDIWhu\nDocoArNndyM+vg+33joDL70koqFhu3O9AMDvHh2CO61obGyEza0mraioCEePpmLGjOma4wErnPf/\n8Y8Y1NQkYedOExYulDBv3jG8+GIkAKCurhKBgV0oLCx02l/cvc7BwXZYBtqcx7OAJrF+/WsflJdz\n2LnTjvb2ACQnkx0hJOQQNm7sw7p1GfDxcX0/GRmn//MSRQZhYYtw4ACHzz7rxJEjIWhtnYTp02VM\nn34M8fF9eOaZKGRmiigvH93v45JLCtHYyOKvf63DkSMh6O6ORnk5B4ZxIDa2H0uXBuOee+yw23di\n8mS75vNra4Hw8NM73kcfFePIkVCIYibKyjjs2SPBYuGRk0MEOTm5AitW9GP16hywrOvzo6MvrL93\nvfssC1gsds16aW1thMXCAwhzPt9uXwCHw6z5/JCQ5ejvZzRfT31/375wBAdrz788v9jr/CsIQHt7\nE4qK6pzHHx624cCBfUhI0H7+hfDzMu5/e+6r7zc3NwMANm3ahJHAKIqek+3cQFEUrFu3DosWLcJ9\n993n9fEvv/wSubm55+E7M3CxYtasUFRX9zmVqU8/NeFPfzLjT38adj7nwQf9MWeOiA0bXPLJ5s3+\nKCwUsW6d6zH3fx6RkaE4dKhPkwc7bVooWlr6nGrJV1/x+OUvffH++0PO59x3nz8WLyaF8ULA8LCr\n/Uv1Ura10fa6qupmZ1Pb22jVHVF0957SMWpqXMoxqW8i0tPHtr3e0uLynqrb60FBijO1ICtLGpNy\nDNBAnntqwf79PIaGXNvrarTX6XhP12a04tG2rbjihMJc+ejruObv92D//gHnc1RLxv33u2S8iIhQ\nNDX1aTzgCxdK2L6dc7sfhDfesDgV5j17ODz7rD8+/5wGDbu7GWzc6I9wrgembdtQihy0YiZs8MWG\n20Wn3WUsXnN1x8DdB15TQ1nH6trKypKQkTH6JBT3gTz3gc+AAFKOMzMl59uxDMu522nUY1kscL4G\n9ecVEzN6G8qFhNZWBldcEYzKyn7nY6+95oOuLhY/+pHV+dgNNwTi/vttWLbMtW127710jrv5Zu9z\n59/+ZsLnn5vx1luu8++2bTxeftkX//iH6zz5gx/4IThYwSOPuC4gMzOD8fHHQ4iKOrfRhwYubuzf\nvx/Lli3T/dh5VZh37NiB9957DwcPHsRvfvMbAMA///lPREREnM9vy8BFDD0Ps+dWnyh6bwkODnoP\np7inEKg+PXc/ntms3V7kee9tTZY9f5YMm83lPVWHs5qatLaKhx6yj4kcu5coqFvS6va6miiwevXY\nEwU6OhhNxe+BAxzMZu32enb26BMxALLvVFVxzmrkkhK6mEhLIwJ+zTUOPPusFbGxo9teVzym9tiG\nhtP6PD0/8rp1/gDsXs+TZcrt3b6dfMY33RSAigoew8O0Tn3DHLgVn+K/8Dwi0I44vhkvv3zmF3Pu\nSShqfN+RI3RRlJnp8pqnp0ujLh2RJLLTuBNXtcRGvbB78EGyVYwlCaWtTes1LyvjIQhwEuO1ax34\n8Y8vfFvFWKBXXKJnMfP19Z7d0JvxUM+dvb0sQkO1X1gvgo6i5jyTM4xqbAPnFueVMNNW7IWhrBmY\nGJBlbw+z3tCfZ3KGHmF2hxqdpOaPAu6Df/SYnl/5XHmY1SQJlRi7V+NmZ49fokBDA6s5Rnk5j6lT\nZWRnEwlfscKKzMyxFUL09zPOY6jKrtUKp5q7caMd2dnimCuL29oY7NvHn6hFJjLm3vx2zz308xqv\nOWXFgzlydbWn9XkqEXbHfffZYbHQ77yqisPRoyweeMAf9fWU8DBzpgSGAdavdyAjg8jeXXcF4Jq+\nf2HDQSpO6UUoGO7U8ujx4wzc68LLyzm0trLOZI+8PBF33EEZxO55u2cCh4NaEd0LZqqrOYSHy07V\neMuWsXvNW1pcMYGqQs0wLnJ8yy0OvPiiFTNnXrzkWA96JSUjxcq5uYgAUIqQ54yHit5e72FqKo7y\nHPA7vbpsAwbOJs67h9mAgXMJz+xOvVg5QfA+OQ8NeU9zu1sy1Ogkd5hM2lQMvSrZs3HCP5XlISeH\nqnHT0kZPYADallZJa0kJEeSAAFeJwpYtpOqOpUTBbieLCNkd6G17O4uMDKoSvu46B37wAyuio8dG\nYBwOoKyMyjPUm90O5OeLyM+X8MQTtjE3v50SnoT58BFgmvYp+ukWlO7Q2EjkuLKSw759Dhw/HoD4\neGoRNJmATZtsuPpqEZMmKdi9m8MPfuCPa67RxrZwLS3O9xUwGsKs5k6rirGqIPf3M8jIEJGRIWHJ\nEgEPP0xlKaO9kBgeJiXfXZ2uq9OW2KhDf2OxbriX2FANOgc/PyAjgxTwjRvtyMwcW6HJxYKRMuQ9\nEzF8fLwV5smTFdTWai+81HPn8eMMoqO1V3s0dK09FjWyah8zhv4MnGsYy83AhAJt47nu61ky9KpZ\nPetbPREQoGB42DtGzv2fjJ6azLLwItFnAkUhz6ZKKEtLqQ55xgyXH3j1asuYtr4BitXzVHXtdpeq\ne/fdVKIwllxd1X+qllqUlBDRj4uTkJsrYcEC8URznTzmf5Q9PQyKi3ns2cOjuJjU49hYUtqvuELA\nM89YERNzblVExccHcnAIcMKyzNisgMWqeY7DQSr+//6vGdXVHKqrOVitwPXXByE9XUJqqoSrrhKw\nbNk+rF2b5SQZ8+YFIz/fdfGiKN4vTLFYwR0+DAAQwKMaKXDIPP7rv/xQUUEKsq8vVYZnZIi48UZX\nwsNofbp9fYyTrNJbHi0tLJKSJKdyvGEDqdOj9bOrec3u6nRVFYewMBkZGeQ5vv9+mzPn2oA3OE6B\nKJ5cEACI6HoqzFOmyOju1v+D7elhkZurlaltNm+FWc+SoXfuNmDgbMIgzAYmDNRta63C7N0WpWfJ\nGB72EgA1HmZ/f8qjdYdnTayeh5njFK/HToajR11eXbUaNyhIQU4ObX1fdZUVWVljU0KtVq2qW1rK\no6ODRWYmKcfXX+/Aj340ds/m8eOMsxJZJcnBwQpyc8kTvHIl2TfG4m0GXFvtO3fy2L2bbu3tLPLz\nRRQUiHjiCRtyc0cfUTZ+YCDNnQv8+11Y4YtqpGCwT8Rzz/mhpobIcWcng/BwGYLAIDVVwnXXCVi3\nLhC7dw94xHNlab6yZ42wolDuMkCDi5WVPOpLB/EbZRNewqOoQQoizV0QJBZhYTIeeoiKX0ZLKFUv\nsFoXTuo0h95e8oBnZYlYtGjsUYRWq6pOuxTqQ4c4REXJzoKZFSusYyqxmYg4nVY/gDzMdrv2pBAW\npqC7W9/D3N3NYOpUb4X59CwZhofZwLmFQZgNTBiQ5+3Ulat6ZSYWCwN//5MrzKe2ZOgrzCMR5t5e\nl6qrEldBcKm6991HloexqGKyTJm3+/e7SGtdHXmbc3MlLF4s4uGHbUhKOnW188kgCDQEpvqB9+3j\n0dnJIjdXRH4+1fzm5o5NoVahKLTdXlTEY+dOHjt3miBJwLx5IubPJ49zWpp0QahTDgcNrtXUcKiv\nZ/G8/514DCvQjCjMRhOsAo8gDOC22/yRmirhd7/zwaRJMh5+2DXQZzIppyy/Udd5YyOLqioOn35q\nQm0th+zsYPT0sEhNFTHYcxxzcBib8D9IRyV6tr6A7Dce0hzrdCCK9JoqKninMl1RwYHnXer0DTc4\n8NxzY0uRGBigLHB3dbqpiUVCwvhVrBsg6JUumUze9gtfX2+FeepUGZ2d+r/kzk7WayDTZmNO25Jx\nIfwNG5g4MAizgQkDvROs5xAgoK9mWK0M/PxG9jD7+yuwWk9uyeB5bw+zOrBlsQDl5aTqqraHzk4W\nWVmk6q5e7cBPfjL29q/2duYEOXYdKyxMRm4uKdRr19qRmSmdtGb5dI+jeoH37SPiNHu2jPx80Wmt\nGCsJV6EolPywbRuPoiITduzgwbJAYaGAhQtJQR5tcsV4wWYDDh/mcOgQi4MHOdTWkvLZ3Mxi1iwZ\nSUk0hDf/Mh/cMfQoUpr/hXZMx0JsxzPHtsJy1a8A0AWWp51Cr4Xt3//ehZCQQlRVka+5rY3FkiXB\nCApSkJ4uIThYRni4jD/8YRgxMTL8//ct3L43Gpfh3yhAMRSTCceuvRbMmyd/XRYLNS8SKSZF9+BB\nDhERMtLTx7cVUSXgqkLd3U2JLllZIi65xFWYMtqhVQMjg85lzImdCXpMz5Lh6+t9HgwPV9DVpX1M\nPXd2dXkrzESYT8+SYXiYDZxLGMvNwISBfluU97aeIGgfk2UiPCcbkPPzU2CzeSrM2qEYNUJOkmji\nv6SEwzff8Dh+nMXzz/shJYWU4yVLBDz6qBWJiWMjlENDlBFcUuKKQbPb4bQ83H+/Dbm5Y6v5BUgl\nrax0H5jjMDzMID9fxJw5Ep580jruA3PHjzPYto3H11+bsG0bD7udwaJFAhYtEvC9752/iK++Pga1\ntSxqa7kTN3q/vZ1FdDQR46QkGlZMSpIRH+/KNV67NhDz5ktImHkpzI9/ChYyFDDwefddONavh7hw\nIVhWa+FRCcwXX/BobyciWVXFoaVlOZKTFaSl0dBfUJCC998fRHo6ffLXX/N49VVfxMfLYI4dg9/z\nz0PBb6kOG4Dj+ushT5qsOU5HB/mNq6qIHFdWUhpGQoLkJMdr1jiQljb637Uk0e6AOwGvrKQ/gowM\nOsZ11znwzDMSYmPH54LLwKnBsnCuPfVnbjZ7D/35+VF7nzsmTaL5DpsNmgtxh4P+XsLCtOcfq9X7\nXKs3V2J4mA2caxiE2cCEgaePc6THJEmrMNtsNMziScDcPcy+vuRzdgfP0z+F1lYGJSU8vvzShJYW\nFjExoZg+XUZuroipUxUUFjrwwx9aR10Iob6O2lpWE4PW0MAhNZWU42uvHZ80CcA1MEc38opGR0uY\nM0fCsmUCnnzSiri48SWssgyUlnL4179M+Pe/Tair4zB/voAlS8iakpR07giyKFIqxeHDLOrqKJ6v\nro7et1oZJCRISEyUkJgo45ZbHEhMJOvBqXy5LKtAUQD77bfD/PbbYKr6IIMWp/8jj6D171+ivd0f\nvb0MtmzxR1UVFX8MDwN//asPcnMlrFjhwBNPSIiP1x7v1Vd9NcRElk+sZ1mG/5YtYAYHoYABCxlC\nQAhK17+AHR+aYLEwWLUqEJWVHESRSGt6uoTLLxfw6KM2JCSMXtG12Sj2To2kq6igIU91GC8jQ8K9\n97rU6YmeVHG+oe6YqSRVrxrb11dBR4f2F8WyQESEjGPHWGciRmFhIVpbGUydqniRXquVQUiIZzYz\nA7PZtX7VHRVDYTZwLmEsNwMTBu7biSr0hv48t/r0prY94edHwy4DA3A2vjU0sLjppkDwPJCXJyI6\nWkZoqIydOwedA0ff/74fJk+Wz5gs9/S4Bub27SMLx+TJCvLzReTlSbjlFiqFGAsJB1xEXE2U2LuX\nR2cng/x8CXPniti61Ya8vLMzMOdwUOvXxx+b8cUXJoSEKLj8cgHPPmvFvHniWd16l2WyARw5Qt5i\n9e3hw2SjmDZNRnw8KcRpaTQImZAwNmKn2nMEhUfJg79Cyb1/Qj+CcS3+gfL6THTnhiNoKo/waQqW\nLHHg+usdSE2VsGxZEF591YKYmJGnRz2jEmndKxAefx7F/xxAGTZjH/JwANm4zRGFiEfIx86yCu6+\n24709LHFq/X2Ms5BPzWOrrGRRVyc5CTHN9xgRXr6WY7uMzBqqD5m9ZyiN/Snt9MGADNmKGhrYzUR\ncq2tLCIjvdes3m6eZ5mJXvSnAQNnGwZhNjBhoCiMLmH2jpVjNH45z61EFf/5zw6Ehi7C/v08vvjC\nhLY2Fq+95ovMTMoInjpVxtNP23DttQIYhgau/vlPk2Y6n1IyTs5C1IG5fftcA3Pd3a6BuXvvtSMv\nT/Ta2hwN7HZScvfsoTSJPXt4hIYqKCgQMXcu2TjGy3usB0UBdu3i8Ze/mPHxxyYkJspYscKB736X\nfMjjCUGg9IyGBhZNTSwaGjg0NLCor+fQ1MQiKEhBXBxt/cfH0wVCXBypxWPJr1ahJkeoCRjl5Rye\nesoPmzYFIDJyPhIig4A24Hb8HhmoQKxcjx/4/xa2xStx993aRklP4uLurwdIoWtrY7BzJ1kp/vMV\nj6oDCmK+eh7pqEQWyhCCAdwY8TVu3bkeQaGUyLFwYTCWL/f44ieBLJP6rg76kX2DQ18fpWFkZrr8\nxklJY7+gM3Du4JrBUJz3PYf+/PzIUuGJWbMktLS4lImioiIcO7ZElzDrzYs4HFpfs0GYDZwPGITZ\nwISBopCq5g69oT9PEq1uBzY3s07CSoNsVyA2lkFenoiYGAnJyRJef93iVKfLygIxebJLldNLxNB7\nrLOT0fiBy8t5REXRwNzCheKJYojxIa39/QyKizln3Fp5OY+EBAkFBSJuusmBV16xjEtyxakgScBf\n/mLGa6/5gmWBdevs2L7disjI0R9bFIGjR1m0tLhuTU0smpvpbUcHi4gIGdHR6o1ymGNj6f2x5FZ7\n4vhxIsbqrbqaQ00NCz8/ICWF8pPDwhTccIMDmzbZ4e8PdDRHYHG+gBvE951fx9xwGMLbfwR3dQJF\n0MHbKz84CNTUTEJdnRmVlUSQh4eBdeuCkJEhIj2qD4uPfo5AKRL/xnfAgRbgNebPEPPUdQgKpYXl\nWZDiCauVvPjuxLiqikdwsELHSZewdq0Dzz8vjSmr2cCFAc8h5pEUZs+hPwCIipLR3KxdAE1NnFdp\nCUCJRJ6E2ZMgiyLjNQRowMDZhkGYDUwYOH2bbtAb+pMkUpW3bydi/PXXPNraWFx+edCJ5jcRzzxj\nRXa2y4rw1ls+qK1lNVYOzxg5GprRfgOyTCrjW2/5YO9eslj09ZHlYc4cEY89RpaH8dqm7uxksGsX\nj127KHKtsZFDbi7lET/2mA35+ec+j1hRgNWrA2GzMfj5zy0oLBRPuvWvKDQs1NnJoKuLRUcHg6NH\nWbS3062tjW6U8apg1iwZs2ZJmDVLxpw5IlavljF7tozISHncbR2Dg1SS4U6ODx7kYLEwSEmRnLeV\nK8lO4T5wuWFDAKKjZWdBB+vnAykoCFJECriaGgAABwnKwBCCrr4a1jvvQt1134XFEoy33jLj+HGK\njOvoYJGUNA+pqeQ3vv56B669NhBl21vg/8e34fvyy/hH/xIcxm1OsiyHhEBIzQcb7gOAWJD7xWRX\nFw38VVbSraKCR3MzWSrS0+m2YoWA9PSxNTsauHDhGS1nNsMrc9nf37vACQBiYmTs2OE6ORYWFuIv\nf6EsdE/oDf15tv8ZCrOB8wGDMBuYMCCFWfuYSqIPHmSdynFLC4srrwxGRoaE/HwRS5cK6OhgsGvX\n4IhEzmz23p70jPtiGCLQ//ynCcXFvJMgh4QokCQBixaJ2LLFhoSE8VPjWlsZ7Nxpws6dRJI7OhjM\nm0fRbi+9ZEFW1vmP4RJFUiqnTJHx1ls+eOcds1N5t1oZDA8zGBigW28vg+PHKRN72jQFU6fKCA9X\nMH26jOnTZeTliYiMlBEZqSAi4tSDdqPF8DBQW0tk2HVj0dPDIjGRSHFSElVFp6RIiIw8tf/Xs/aa\nZQGFYTD4/vsQV92JmioGezAXDYjFNmkxKn+Tjkm/6cWgyQp7bTuuX2nG954OQVy84rxwY7q7geL9\nYJk1mJKVDmZoiH7m4MGDyIocHo6hP/8Z0k+CAdhQW8uispLDrl08ensZpKaGwGp1DfxdeikVjCQm\nGpaKiQRPAYBqsLXPCQgghdgTcXESfv977WI5coTDTTc5vJ6rl3nvWSZlEGYD5wMGYTYwYaCSY/eB\nuffeM6O9ncUHH5iRlyeeqA+W8cknQ4iLI/WtpITDRx+ZvQiPu0/UbPaeGGdZ8nP+4Q9mFBeTovv/\n2zvz8KjKNO3fZ6lKZQ8JWwJJyL6ArJGwCYJIu2Lj3tqg04Pa6NhX93SrPY5fqyPdo+0gOmorak8r\nTKutiAujgopsAaERhXHYAgkkIftCUkkqtZzl++PlVNVZEgIkqSzP77rqSupYVA7x5dR9nvd+7qeu\njsMbb4Rh+nQJ//zPbhQViRBF4NFHDWn/F0hlJYddu2woKhKxa5cIp5PDzJnMN/qP/8hGDPe3KCab\nDTh4sAX/+78sL7i9nfPf3ISHq4iIUBETwx4JCSqGDVP7TOS7XMDx43pRfPQoy8jOyJCRk6MgN1fG\nsmUsA3jcuAu3ynAcEwJHj/I4fJhFATqdHC5ZmInmMzuQP/IUuLpaJKART+AJXIIfMAzNmO/7Gvfu\neQoL9myFkpAANT4eLrcbkbIMvqoK7YhAGK73i2UAkCFABo+vp/4S317zL/jhL7HYu1fAzp1RSExU\nMH68jNRUGZGRKjZvbsXYsaHNsSZCj92ujcdmwjUszFxhZpYM85/NzlZQXCz4G6937izC0aPXIjvb\nPHHH5eIQGWkUzPoKM1kyiFBAgpkY1EgSG6ywb5+InTtZxWzq1Fh/w9y0aRJGjVKwcmVAsL7wguO8\nG0zsdhUuF4eiokDc2vbtLEf28st9KCyUcOutHixfHoUPPggIl/37RZPQPh9qazns3Clixw6bXyDP\nmiVhzhwJP/+5G7m5A8M7arcDBQUyCgrOMbKul2hthX+YCHvwOHaM2RvS0pgozsuTceedXr8wvphI\nKy3X+PBhzc/M1sumTTaMHasgP19GZqYMUQQ+/rjtrAd4GF77ZTNOrz+Eua6d/vdywA03WFcq39gI\nNDYi2FXjQjjs8GA9bsJBTMJBTMI3mIVmfhhO89djwmkFkybJOHRIwK9+1YHrrmNlxPJyHhs32pGc\n3LPNlsTAxBgjx3bV9K+JiFAtK8zx8SqiolgfSGqqgoYGB+x2WE4pbWujCjPRPyHBTAwqGhs5f7Pc\nvn0iDhwQkZTEvKsFBRKKikQUF7f4ReTTT5vjL4yNgMaLtUZW1mX4+GPWLLd5M8tYrqriUVgo4Y47\nvJBl4K67vLjuOmb8a2jgztlIdS6amzns2sXE//btNtTUcJgzhzUDDiSBHCqamkLWNKAAACAASURB\nVNhgkWPHBJ1Abmpi+clssIiCpUu9yMm5eGEMaE14AWGsfQWA/HzW8Dd9uoSqKh633OLBHXew9dLW\nBrz2mkMXF8dPyIWLy0H7VBccL78MobhYJ5idiMYPuMQvjA9iEn7AJfAgDOuwFBMjTuD2H7VhWiaH\nw2USXn3V5X/vjRttukZHq4xyYuhis+ktGWwMtl4cR0Zae5gBYMoUCfv3s4mfgjATkydb3xy7XDA1\n3BorzGzy3wX9NQjigqElRwxYJIkJEU0c79snoqGBw9SprGHuF79wo6BA9se4VVdzePllh04EdCcl\nQ7s4Hz/O+9Mk9uwR0dTEobBQQmEhyz3eu1fE3/4WmF6yfr1dl4BhlYhxrm1urxf49lsRW7eK2LrV\nhuJiAQUFEubN8+FPf2rHxIn9z2IRarS4Nk0UaxP3jh8X4PFwZ4eKMHE8d66EnBzWEHixv0efDzhx\ngtcJ48OH2Qhnzdecny/jqqt8yM+XMXKk3tdcVGSDzRY4YDXyWhRVSKoI951LcWzWUhx9/xiK30jB\n7z25+I1rFWrVkRiPQ365fIfwLiIyRuG26v/EX5+rh+/aOwCHA43/bYdwWv/eRoFslVFODF2MfRpW\nFeaoKFYhtqKwUMLu3SJuvNGH3btFzJxpHVfY3m6uMHs8+lg5SbIuYhBEb0KCmRgwNDZy+PbbQPX4\n++9FJCay6vGMGRIefLDrjGCrpj+rYSaKwsT4t9+yuLWNG204cEDEzTdHYcYM9rMeeMCNhoYdmDuX\neZi//FLEN9/o9wiNAlkbTGF1XsGUlPDYssWGrVtF7N5tQ0YGax57/PEOTJ8uUaPVWdxuoLSUjZ4+\nfpxN2ztxgk3ei4pS/RP38vNZKkV2ds9MjFMUlt8cSMJg35eUCBg7VvEnYdx+O0vCSEvrnhjXJv1p\naIK5uZlZNw4dEvDxxzYcOyZgw4Y4xMUpyM+fCi6RxyUF8VhxrwtZYccheCR8v9+NKbP+AUpiIg6f\njETY8ij4brrJ/96SZN7SNt4oWt1MEkMXoyVD8zAHX0NZhdn6unrllT7cfHM0nn66Ax99pGD9enNC\nBmDd9Ofx6LPwyZJBhAISzES/RFFYRXfvXtE/hrm2lg3ruPRSCf/0T+6zDXrdrzJYiVWACVuXi1Vy\nd+9mPuDCwlikpcmYMUPC9Oms+WnDBv3s66Ii/XsYq4FWgtkojjmOfQh98YWIr76yYcsWG9xuDgsW\n+HDLLV689JJLFz021NC8vkwI82eFMfu+upr5IbOzmd93/nwJ997rQVaWgtjYi/+dqSpQU8OZIuKK\niwXExKh+YTx/voT772eDOC52oElFBYcPPrDh0CEmkCUJmDgxFnl5bKJgcrKCsDAV//VfLv/f8ZFH\nwpGeriA7jwOQCgVAa2MjlPR0ACxpxChAfD5zhc4YsWgVw0gMXYx53zzPjgXbJUSR9SNYTevLzWVJ\nNnffHYmYGBfGjzf/G/V62XU0WBzLMnsEWzDIkkGEAlpyRL/A5QK+/148K5ADcWvTp7OM4BUrWArB\nxVa8tAqz0wns3Sti+3YR1dU8nn/egfHjZcyaJcFuV1FU1IrUVKZ2P/nEhrIycyxD8CQ1UbQeShIs\nkJlgZh84lZUcvvjChnfftaO8nMd330lYuNCHt95qx/jx8pATKm1tLGbqxAlWJS4pCVSLw8JUZGYq\nyMhgFeM5cyRkZbFhGD1VZWpq4nD0qL5ifOSIAEFgPuO8PBYxuHSpB7m5FyfIVZXlYR86FLBtaN8n\nJLCpiuPHy1i61Isvv7Tj5Mkz/nX/wQc2fP65XffzraarBa/Njg7zaHcrwWEcCU8eZiIYqyQgVmXW\n+4ujolS0tZmHjwDAiy+2Y/VqB958kwPHWTf8RUXpd4G0SavBx8iSQYQCEsxESKiq4vD3vzOBvG+f\niKNHBeTlyf6Gueefd2H06J67IDY2cvjySxtaWoDLL49GSQkb2CEIwPz5Ep5+2uUfGPGXv+gFSXe8\nnMaMUoBtsbNRskwkFRdz8HiA+fOjUVbGY+FCHyZOlHHllV784Q89EyvXn/F4gJMneZSUMEFcUiKg\ntJR9bW7mkJbGRlBnZbGq7T33eJCZqfToIIzmZs4fDRf8MA4WWbyY5SePGHFxP9vlYhnTwWkYhw4J\nkGVg/HhmF5k5U8Ly5R688UYYCgsl3HVXQJUYRYUgmEVLZ8MiNNrbOURG6o91x5JhZWEihi7Gpj+A\nZTGzaLnAOo2MZILZ6t9Obq6CNWtcpuMammAOxuhfZscQ8vx4YuhBgpnodWQZOHRI8Nsr9u5lAkWr\nHj/1FJuad7Hb2cE0NnLYvVtEUZGIoiIbTp/mMXGiBJ4HnnnGhcmT2dCFJ54IR1xcYLoawKrAwSO0\nO/NyBucwC4K15aOigsfKlQ588okdLhfz+61c2YHCQgmiCKxe7YDTOXjKyW43UFbG4+RJJoY1gVxa\nykZRJycrSE+XkZGhYNIkCUuWMJGclKT2qDhzOuG3UGiPY8cEtLWxpr/cXPZYuNCH3NzuDRbpCllm\nfupgYXz4sICqKh6ZmbI/DWPBAh/Gj7f2UoeFmdeQ5mPW1p/dbhYtEREqqqr0v7zgtdneDlOurbUl\nw1hh1v87IIY2VsOZtApzMNHRaqeNfxrB6zOYtjZzQoZWYQ7G5zOLaILobUgwEz2OywV8910gTWLf\nPhGjRysoLGTpDg8/3IHMzJ4dhKAJ5F27mEiuqBBQWChhzhwf/vM/2zFpkozqah7XXhuFwkK92dh4\nHkafsdX4bCMcFxh7XVHBY/16O774woavvwZ++lMvXnmlHZmZMi65JA6zZ0u6PwcMLMHsdAJlZQJO\nnuRx6hQTxydP8igt5VFfz0RxWpqCtDQZWVkKFi3yISNDQUrKxUe0GWlp4fyZydpI6qNHBTidAWGc\nk8OEam6uctEDODRfs1EYHz8uYNQoxZ+E8eMfe/Hoo+zGoLu2EUEwj04XRb033ugjBdgWuFX2rYZV\n6oC1JcPsYaYKM6FhbPoDtEEl+gpzdLR6wUUAp5NDdDRVmIn+CQlm4qJpbOSwdy8bvbxnj4gjRwTk\n57OGuZ/9zINXX23H8OE9Ww1oaWF5xDt2mAXyCy+wkc8XKs6MHd5WHd+A3ieqKOz3cNNNUTh4UMAN\nN/hQUCDhppt8+MlP2KdMa6tZjGvv35+QZaCqikdZGXucOsXj1Cnh7FceHR0cUlMVjBvHcoonTJBx\n/fVepKczQdobzThNTZx/oIhWLS4uFtDayvkj4rKzZVx2mYTcXNYcd7Fiz+kM2CmCY+J4PuBr1tZ4\nbq5sqoydL9Yxckzcarsvdjt7HozmGQ0meG1abXMHv6eG0ZJBsXJEMFY3aw6HaspijolR0dratWC2\nqi4DQGurea263TAlA/l8JJiJvocEM3FeqCpw6pQ+j7imhvdHuz3+eAemTpV0FoeewOViTXraVDst\nj3ju3IsXyFYYBXNnwqGjA1i3LgyrVjngcnF4+GE3/vpXLxwOYMUK/S/BSnRbJWf0NorCqqQVFTxO\nn+ZRViagrIxHeTkTyFVVPBISVKSkKEhNZaJ44UIfUlNZRJoxP7in0JrhAkNFApVjjwfIyVHODhYJ\nWCl6wsrhdgMnTugb/g4fFtDYyCMn59z5yT2FVdKKKGo+eLZIrBqvWPZt5+/b1mau2nm9HGJj9f4P\noyXDKKCJoY1VhdnhMDecXmyFOSbGXGE2Nq16vRzs9n5WaSAGPSSYiS5RFDZaevduVkHeu1cEx8Gf\nR7x8uQf5+T0/PMPnA/bvF7Bzpw07drCJfRMmyJg714cnn+xAQUHf5RF3VmH+xS9qsHVrDiZPlvC7\n37nwl784cMstwQ1b1u8VTG8Ir/Z2oLKS1z0qKgKPqioecXEqxo5VkJysIDVVweTJEhYvZt+z6LKe\nPy8NWWa2leCJeyxLmQfH4ayNgonja6/1ISenZ/KTJYn5jI0xcRUVPMaNY3aK3Fw2/jovj90o9KVg\ntBbMes8yi/E6d0Uv2CPa1sYhIUEvjo2T0wD2c4wVZhLMhIbV7kZ4uLnCHB2Nc1aYO/Mwt7ZypgQa\nqwozWTKIUECCmdDh8wEHDwYE8p49IkaMUDFzpoQf/ciHJ57oQEpKz/qPASYkS0p4bN1qw7ZtzIuc\nmqpg3jw2sW/mTOmit7x7knfesWPDhky88047LrtMwjffiKis1Jc66+t50weH0Wva0sKhsbF7v0xF\nAc6c4VBdzaO6WvvKo6aGPdfEsdvNYcwYxf9ISlIwfbqEm25iYnjsWMXURNMbeDzs/2lg2h6buFdS\nIiA+XkV2NkvEmDpVwu23s8Eiw4f3zGCR06d5U0TciRMCEhOVoCQMLx55REZmptIvPnytGkc1S4aG\nVYX5XBW9lhYOaWnnrtAxr77xOVXxCIZV0x8bj61/XUxMz1aYXS5zRJ3PRxVmou8hwTzE6ehgDXq7\nd7PH/v0iUlNZHvFtt7F4t1GjeufC1NTEYft20S+SZZnD/Pk+3HijFy+84Opx33NPwFI3RHz4oR0u\nl4BNmyRwHItLq63VC+YtW2wQBBX33svayI0fNrIMfPihHadOCfjZzzxobOTQ1MSjsZGJ6Lo6HvX1\n7GttLY+GBg6RkSoSE1UkJioYPZoNArjkEgmLFql+gRwf3zuWgc44c4bD8eOBoSLFxUwkV1bySElR\n/KOor7zShwceYENGoqMv/udqDXhaCoYmjI8dExAdHRgsMm+ehJ//3IPsbNkUr9afsBbMekuGVYU5\nNtYsUIKrd1aNVF6vuULHcphVw/ML/MsQgw6rRAyHQ2v6CxAbq6C5uWufVGceZqfTqsJsPfmvP9zk\nEkMLuhwOMVpbgb//nVWPd+8W8cMPInJymEC+7z4PCgvbezT3NhhZZjYLbaLd8eMCZs3yYf58Nmo6\nO7vnK9c9zZw5EubMkXDnnV7cdlsk3n03DO++G4b4eBVRUQp+8pNIf/V27FgZtbU8br01Cm432xrn\nOBWXXBILp5NDezvzBUZHq/jNbyIQH68iIUE5+1VFQYGEUaNUjBypYORIBSNGqCEbiy3LQHk5jxMn\n9KOojx8X4HZz/mpxVpbirxanp/dM5VbzNhuzk48e5SGKzMKRlydjyhQJd9zBBovExfW/m61zIQiB\n3G4NoyXD4TCLlthYtUuB0txsFiGswqx/nXHcMFkyiGACmcsBrJr+YmNVlJVd2IW8pYXDqFH6u8aO\nDnODKlWYiVBAgnmQ09oK7NkjYtcuG4qKRBw7JmDSJAkzZ0p46CE3Lr20d60OdXUcvv7ahq++smHr\nVhGJiQoWLpTw5JMdmD5dGjBVAqP3ODlZweTJJXj55ZFoaODw5Zcinn8+HMuWedHRwbzJ9fVhyM9n\nDWrsgq/ittui8emnrYiNVREVpeKll8LQ1MTjySc7rH5sn+N0wj9hL7hqfPIkj+HDFWRlKcjKkjFx\nIrN4ZGb2jL9Yo77eKIxZKgaAs9nJCvLzZdx4oxe5uRc/WKQ/0ZmHWW/JMFeYo6NVuFx6gRvsEW1p\n4Uw3waxCZ5XNHHhu9DQTQxsrO5BV0x+7gbswD3NLi/nmzsqSQRVmIhSQYB5ktLayNAlNIB89KmDK\nFAmzZzOROm2a1Kv+VUUBDhwQsGkTE8mlpTzmzmVjn5980oUxYwaewOksyeKSSxrBcSMxYoSKrCwF\nMTEqrr46oG6++MKGqVNlLFrESoStrUyApKToKyh9XVX3+dhwEU0UB4+ibmvjkJHBfL1ZWSwuLjub\nDRvpSTtDQwPnj4cLnrwnSWwamDZYZPFiJox7K5miP2GsJlsds9oW53nmG21u5pCQYF6oZ85wpoq7\n12seBmG0ZCgKp3tODG3CwlQ4nfqdjIgI6wpzS8uF/WNtbuYxbJj++uh2W3mY0a/tVcTghATzAMdK\nIE+ezATy44+zNInebvDq6AB27LBh0yYbNm+2ITpaxVVX+fDUU6yK3N3BDf2JYIHcmWBesSJP93pj\nvJnVnznXkJSeQlWB2loOpaVmUVxRwWP0aAWZmaxCPGmShBtvDEzc6ylhqqpMGGvRcFqG8tGjArze\ngDDOyZFxzTUsJq4nq9UDDVE0V/BEUYUkBX4hVhVmAIiPV9HUFBDMwdW7piYO8fHnHgZhtGRQhZkI\nJizMeiy7sZE5Lu7cgrkzD/OZM+YKs5Ulw+PhTMKaIHobEswDDJeLeZDZwA4bjhzRC+Rp03p2xHRn\nnDnD4fPPbfjsMxt27LBh0iSWovHJJ25kZg7sC5lRIPM8TBPYjFhNRbMagNLZz7tQmpo4lJSw5Ant\na2kpj9JSAWFhKtLTmRDOzFQwfboXmZksS7knb6K05ruAMA5kKCuKPibuqquYME5MHLrCuDOYONYv\nImOUV1iYOZUAAIYNU9HYyCErS3/c52Nb2laNVMHZtqrKJv2Rh5noDCsPc0QE+0wKZtgwFWfOXGiF\n2WwfsrJkWDWtEkRvQ4K5n+PzAd99J2DHDht27hTx/fcixo9necT/7/+xCnJfCGSA+ZE/+8yGTz6x\nY/9+EfPm+bB4sQ8vvujqtUbB3saqesyOBZIJeF41pRcAeh+eqnLgefPv4FyikP2crnE6gZMn9YL4\nxAn2VZI4ZGayBrv0dBlXX+1DejobydzTjW+qClRWBqwUweI4LAz+oSL5+TKWLPEiJ4d5jEkYdw9B\nMOfcWsXKybJZzA4frqCxkQfATNDa2mxqYnYM482cx6PPtmXVZP3/K6owE8FY2YHCw1U0NekXl7bb\n0RWdeZibm832oY4Oc4648YaPIPoCEsz9DEUBDh0SsH27iJ07bdizR0RaGhv5++CDbsyYIfVIJFd3\naWri8NFHNnz4oR0//CBg4UIJd9/twbp1bQPSQ2YUqBynmqrHHKeaKszGZiwjVmOEje+rqpylJYPj\nWEWmtJTHyZOsOhz8taODQ1oaqwxnZsqYM0fCXXd5kJGh9EhusRFJYh5nLTeZTd1jOcrR0ap/FPXU\nqRJ+8hOWiGHlnSXOD1E0rzO7XT+OmOMCjVbBzbrDh6uorzcvhPp63rIxsqNDb8kw2jEAdi2iWDlC\nw243V5gjI82WjJgYFe3t3HnHEioKa/ozC2az397jMR8jiN6GLochRlVZhu+2bWzkc1GRiIQEFZdd\n5sOdd3rwyivtJv9hb+NyAZs22bB+vR27d4tYuFDC/fd7MH++b0BfpIxCGGAi91zHrIQMoPfhSZK1\n4NAGP2jRaLKs4t137WfFsYBvvhHR2Mjhz392ICODieL0dHaDdNddnl4dRd3RwRIxNFGsPU6e5DFy\npILsbJahPHMmu0nKyZERE9Pz50EwbDa9OGbHrJIJWKNVVFRgkY4apaC+PnDHpq3NujoOI0aYt0eM\ngsNK3EgSDS4hAlh5mMPDzSkZPB9IyugsS9+quux0srxl43W0vZ3lzwfjdps9+ATR25BgDgFnznDY\nsUPEtm0sas3n4zBvng9XX+3D738fuiSJAwcEvPlmGD7+mKU73HKLF2vWtPdpRbs36cx+YbRbGI91\nJpiD0ZIMiot5lJUxMXzwoIDSUgdWrQpHeTmP8HBWodm6VcS4cQquvNKH6GgFDoeK3//e3WvWheZm\nDseOmSfu1daykdDaYJHrrmOJGBkZMiIieudciM4RBHNKhrHCDDCRYvQxjxyp4tgxcxZzbS1r8DRi\nFBwsUk7/j4MsGUQw1h5mc4UZYD7mpqbOBbMVjY3WKS/t7daDSwZy8YYYmJBg7gO8XuDbb0Vs3cqm\n2hUXC5gxQ8L8+T7ce68bubmhG9jhcgHvv2/HW2+FobGRw7JlXuze7URi4uC7e+8s7cKqwqwXzCyp\nQFGA6moOFRU8ysoE7NxZAVVNQ3k5G7985gyHO+6IQmqqgrQ0GQ4H2ym47TYfUlJkSBKHqVNjsGZN\noEumvNwBt/vio+U0fzGrGOurxi4X5xfF2dky7rpLQna2jHHjFNpy70dYxcpZVZjDw7XpaoGFO3q0\ngu3bA/8zNY9obS1nOamzo0Mfy+X1wjQUhyb9EcF05mFub7dObWGeeusGcCsPc2OjOc0FYE1/wbsp\ngHXKC0H0NnQ57AVUFThxgsfXX7ORz7t325CRIWP+fB8ef5xFrYVqYpuG0wm88YYDa9aEYepUCf/y\nLx1YsEAa1BWlc9kvtBg0WQY+/dSGlhYOZWUCvv+eCc8xY+IQG6siJUVBaqoCnhcwe7aEW29VUFrK\n48svbXj77Xb/ey9bFolLL5UxfjwrTzc2Wlfszkcsd3QApaVMEAcPFzlxQkBUlIrMTNlvpbj2Wh+y\ns3s2Ko7oPWw2a8FsbAS0mq6WmKigqspcYa6s5C1Tazo69MkDXq9Vhdl8jBi62O3mdRcZCcsK84gR\nChoazu+i09TEm5r7AKC9HaYdL2r6I0IBCeYeor0d2LnThq++EvHVVzb4fByuuMKHW27x4qWXXP2m\nKUqSgDVrwrB6tQMLF/rw0UetyMsb2DFw3UVRmPjYs0dAeTnLI9682Qank8OGDXacPs3D4VDR1sbh\n009tyMlRkJcnIzlZxv/8jw0bN7YZLtzDAbDyX1OTzRRzZIya6yx6zoiqsol32vjp4uLA5L2aGh6p\nqYp/FPWCBWykeVYW+YsHOoKgz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- "text": [ - "" - ] - } - ], - "prompt_number": 13 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Think about what this plot means. We have a lot of error in our position estimates. We therefore have a lot of error in our velocity estimates. But look at the intersections between the velocity and the positions. Take the intersection at $t$=2. The intersection between the velocity and the position is where our aircraft is most likely to be, which I have roughly depicted with a red ellipse ('roughly' because I set the size via eyeball, not via math). The size of the error is much smaller than the error of the positions, despite the fact that velocity was derived from position. \n", - "\n", - "What makes this possible? Imagine for a moment that we superimposed the velocity from a *different* airplane over the position graph. Cleary the two are not related, and there is no way that combining the two could possibly yield any additional information. In contrast, the velocity of the this airplane tells us something very important - the direction and speed of travel. So long as the aircraft does not alter its velocity the velocity allows us to predict where the next position is. After a relatively small amount of error in velocity the probability that it is a good match with the position is very small. Think about it - if you suddenly change direction your position is also going to change a lot. If the position measurement is not in the direction of the assumed velocity change it is very unlikely to be true. The two are correlated, so if the velocity changes so must the position, and in a predictable way. " - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Kalman Filter Algorithm" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So in general terms we can show how a multidimensional Kalman filter works. In the example above, we compute velocity from the previous position measurements using something called the **measurement function**. Then we predict the next position by using the current estimate and something called the **state transition function**. In our example above,\n", - "\n", - "$$new\\_position = old\\_position + velocity*time$$ \n", - "\n", - "Next, we take the measurement from the sensor, and compare it to the prediction we just made. In a world with perfect sensors and perfect airplanes the prediction will always match the measured value. In the real world they will always be at least slightly different. We call the difference between the two the **residual**. Finally, we use something called the **Kalman gain** to update our estimate to be somewhere between the measured position and the predicted position. I will not describe how the gain is set, but suppose we had perfect confidence in our measurement - no error is possible. Then, clearly, we would set the gain so that 100% of the position came from the measurement, and 0% from the prediction. At the other extreme, if he have no confidence at all in the sensor (maybe it reported a hardware fault), we would set the gain so that 100% of the position came from the prediction, and 0% from the measurement. In normal cases, we will take a ratio of the two: maybe 53% of the measurement, and 47% of the prediction. The gain is updated on every cycle based on the variance of the variables (in a way yet to be explained). It should be clear that if the variance of the measurement is low, and the variance of the prediction is high we will favor the measurement, and vice versa. \n", - "\n", - "The chart shows a prior estimate of $x=1$ and $\\dot{x}=1$ ($\\dot{x}$ is the shorthand for the derivative of x, which is velocity). Therefore we predict $\\hat{x}=2$. However, the new measurement $x^{'}=1.3$, giving a residual $r=0.7$. Finally, Kalman filter gain $k$ gives us a new estimate of $\\hat{x^{'}}=1.8$.\n", - "\n", - "** CHECK SYMBOLOGY!!!!**" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from mkf_internal import *\n", - "show_residual_chart()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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+Wrbs50vO92T16VMoy1qrjRvd94+MlNau/dftzEypefNkPfZYnrp0WXPZ8Tfo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\nVs+Iq1ndutJ11xXr0KENtT0Uv1H6Ywq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- "text": [ - "" - ] - } - ], - "prompt_number": 14 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "The Equations" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The brilliance of the Kalman filter is taking the insights of the chapter up to this point and finding an optimal mathematical solution. The Kalman filter finds what is called a *least squared fit* to the set of measurements to produce an optimal output. We will not trouble ourselves with the derivation of these equations. It runs to several pages, and offers a lot less insight than the words above, in my opinion. Furthermore, to create a Kalman filter for your application you will not be manipulating these equations, but only specifing a number of parameters that are used by them. It would be going too far to say that you will never need to understand these equations; but to start we can pass them by and I will present the code that implements them. So, first, let's see the equations. \n", - "> Kalman Filter Predict Step:\n", - "\n", - "> $$\n", - "\\begin{aligned}\n", - "\\hat{\\mathbf{x}}_{t|t-1} &= \\mathbf{\\Phi_t}\\hat{\\mathbf{x}}_{t-1} + \\mathbf{B u}_t\\;\\;\\;&(1) \\\\\n", - "\\mathbf{P}_{t|t-1} &= \\mathbf{\\Phi_tP}_{t-1}\\mathbf{\\Phi}^T_t + \\mathbf{Q}_t\\;\\;\\;&(2)\n", - "\\end{aligned}\n", - "$$\n", - "\n", - "> Kalman Filter Update Step:\n", - "\n", - ">$$\n", - "\\begin{aligned}\n", - "\\mathbf{\\gamma} &= \\mathbf{z}_t - \\mathbf{H}_t\\hat{\\mathbf{x}}_t\\;\\;\\;&(3) \\\\\n", - "\\mathbf{K}_t &= \\mathbf{P}_t \\mathbf{H}^T_t (\\mathbf{H}_t \\mathbf{P}_t \\mathbf{H}^T_t + \\mathbf{R}_t)^{-1}\\;\\;\\;(4) \\\\\n", - "\\\\\n", - "\\hat{\\mathbf{x}}_t &= \\hat{\\mathbf{x}}_{t|t-1} + \\mathbf{K}_t \\gamma \\;\\;\\;&(5) \\\\\n", - "\\mathbf{P}_{t|t} &= (\\mathbf{I} - \\mathbf{K}_t \\mathbf{H}_t)\\mathbf{P}_{t|t-1} \\;\\;\\;&(6)\n", - "\\end{aligned}\n", - "$$\n", - "\n", - "Dash off, wipe the blood out of your eyes, and we'll disuss what this means. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These are nothing more than linear algebra equations that implement the algorithm we used in the last chapter, but using multidimensional Gaussians instead of univariate Gaussians, and optimized for a least squares fit. Each capital letter denotes a matrix or vector. The subscripts indicate which time step the data comes from; $t$ is now, $t-1$ is the previous step. $A^T$ is the transpose of A, and $A^{-1}$ is the inverse. Finally, the hat denotes an estimate, so $\\hat{x}_t$ is the estimate of $x$ at time $t$." - ] - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Kalman Equations Expressed as an Algorithm" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Different texts use different notation and variable names for the Kalman filter. Later we will expose you to these different forms to prepare you for reading the original literature. However, I find much of the notation very dense, and unnecessary for writing code. The subscripts indicate the time step, but we know the left hand side is for this time step, and the right hand side is for the previous step. For most of this book I'm going to use the following simplified equations, which express an algorithm.\n", - "\n", - "$$\n", - "\\begin{aligned}\n", - "\\text{Predict Step}\\\\\n", - "\\mathbf{x}' &= \\mathbf{F x} + \\mathbf{B u}\\;\\;\\;&(1) \\\\\n", - "\\mathbf{P} &= \\mathbf{FP{F}}^T + \\mathbf{Q}\\;\\;\\;&(2) \\\\\n", - "\\\\\n", - "\\text{Update Step}\\\\\n", - "\\mathbf{\\gamma} &= \\mathbf{z} - \\mathbf{H x} \\;\\;\\;&(3)\\\\\n", - "\\mathbf{K}&= \\mathbf{PH}^T (\\mathbf{HPH}^T + \\mathbf{R})^{-1}\\;\\;\\;&(4) \\\\\n", - "\\mathbf{x}&=\\mathbf{x}' +\\mathbf{K\\gamma} \\;\\;\\;&(5)\\\\\n", - "\\mathbf{P}&= (\\mathbf{I}-\\mathbf{KH})\\mathbf{P}\\;\\;\\;&(6)\n", - "\\end{aligned}\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is an algorithm, so $=$ denotes assignment, not equality. For example, equation (6) has P on both sides of the $=$. This equation updates the value of P by the computation on the right hand side. \n", - "\n", - "Here, a $'$ means estimate, so $\\mathbf{x}'$ is the estimate of the state $\\mathbf{x}$. Many texts use $\\hat{\\mathbf{x}}$ or $\\mathbf{x}^*$ to express this. I find these choices unfortunate because we will often want to express these in matrix form. The notation of a hat over a matrix is clumsy at best, and an asterisk followin a matrix normally means the *complex congugate* of the matrix, which is *not* what is intended. So I use $'$. \n", - "\n", - "What do all of the variables mean? What is $\\mathbf{P}$, for example? Don't worry right now. Instead, I am just going to design a Kalman filter, and introduce the names as we go. Then we will just pass them into Python function that implement the equations above, and we will have our solution. Later sections will then delve into more detail about each step and equation. I think learning by example and practice is far easier than trying to memorize a dozen abstract facts at once. \n", - "\n", - "Look at the code below for the predict step (which we will present a bit later). \n", - "\n", - " def predict():\n", - " x = F*x + B*u # equation (1)\n", - " P = F*P*F.T + Q # equation (2)\n", - " \n", - "Notice how simple it really is. It really isn't much different from the predict step in the previous chapter, and it is a nearly exact transliteration of the equations above. As you become familiar with this notation you will find yourself able to read textbooks and paper and implement the equations without much difficulty. \n", - " \n", - "> Later, if you become interested in the details of numerical computation you may change the implementation to be faster or more numerically stable than this written form, but for most of this book our code will follow the Kalman filter equations almost exactly. " - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Tracking a Dog" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's go back to our tried and true problem of tracking our dog. This time we will include the fundamental insight of this chapter - that of using *unobserved variables* to improve our estimates. In simple terms, our algorithm is:\n", - "\n", - " 1. predict the next value for x with \"x + vel*time\"\n", - " 2. get measurement for x\n", - " 3. compute residual as: \"x - x_prediction\"\n", - " 4. compute kalman gain based on noise levels\n", - " 5. compute new position as \"residual * kalman gain\"\n", - " \n", - "That is the entire Kalman filter algorithm. It is both what we described above in words, and it is what the rather obscure Kalman Filter equations do. The Kalman filter equations just express this algorithm by using linear algebra. \n", - "\n", - "As I mentioned above, there is actually very little programming involved in creating a Kalman filter. We will just be defining several matrices and parameters that get passed into the Kalman filter algorithm code. Rather than try to explain each of the steps ahead of time, which can be a bit abstract and hard to follow, let's just do it for our by now well known dog tracking problem. Naturally this one example will not cover every use case of the Kalman filter, but we will learn by starting with a simple problem and then slowly start addressing more complicated situations.\n", - "\n", - "\n", - "##### **Step 1:** Choose the State Variables\n", - "\n", - "*State variables* are the variables that the Kalman filter estimates. They include the *observed variables* - the data that is directly measured by a sensor, and the *unobserved variables*, which we can infer from the observed variables.\n", - "\n", - "For our dog tracking problem, our observed state variable is position, and the unobserved variable is velocity. \n", - "\n", - "The Kalman filter is implemented using linear algebra. We use an $n\\times 1$ matrix to store $n$ state variables. For the dog tracking problem, we use $x$ to denote position, and the first derivative of $x$, $\\dot{x}$, for velocity. The Kalman filter equations use $\\mathbf{x}$ for the state, so we define $\\mathbf{x}$ as:\n", - "\n", - "$$\\mathbf{x} =\\begin{bmatrix}x \\\\ \\dot{x}\\end{bmatrix}$$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### **Step 2:** Design State Transition Function\n", - "\n", - "\n", - "The next step in designing a Kalman filter is designing what is called the *State Transition Function.* This is a set of equations that mathematically describe the behavior of the system we are filtering. So, for the dog tracking problem we are tracking a moving object, so we just need the Newtonian equations for motion. In other words, these are the equations that we use to predict the next state from the current state. \n", - "\n", - "We know from elementary physics how to compute a new position given a previous position, velocity, and time, like so:\n", - "\n", - "$$ x' = {velocity}*{time} + x_{previous}$$\n", - "\n", - "In more formal mathematics we would write:\n", - "\n", - "$$x' = \\dot{x}(\\Delta t) + x$$\n", - "\n", - "where $\\dot{x}$ is velocity, and $\\Delta t$ is the amount of time between $t-1$ and $t$. In our problems we will be running the Kalman filter at fixed time intervals, so $\\Delta t$ is a constant for us. We will just set it to $1$ and worry about the units later.\n", - "\n", - "As in step one we must express this in the form of matrices so that our linear algebra software and solve the equations for us. The Kalman filter equations require that we write it in the form:\n", - "\n", - "$$ \\mathbf{x}' = \\mathbf{Fx}$$\n", - "\n", - "where as in step 1 $\\mathbf{x}$ is the matrix containing the state variables, and $\\mathbf{F}$ is the matrix that when multiplied by $\\mathbf{x}$ yields our equations. Note that this is just part of the Kalman filter equation (1) above. We will deal with the second half of equation (1) in the next step.\n", - "\n", - "Since $\\mathbf{x}$ is a $2{\\times}1$ matrix $\\mathbf{F}$ must be a $2{\\times}2$ matrix to yield another $2{\\times}1$ matrix as a result. The first row of the F is easy to derive:\n", - "\n", - "\n", - "$$\n", - "\\begin{aligned}\n", - "{\\begin{bmatrix}x\\\\\\dot{x}\\end{bmatrix}}' &=\\begin{bmatrix}1&\\Delta t \\\\ ?&?\\end{bmatrix} \\times \\begin{bmatrix}x \\\\ \\dot{x}\\end{bmatrix}\n", - "\\end{aligned}\n", - "$$\n", - "\n", - "When we multiply the first row of $\\mathbf{F}$ that out we get:\n", - "$$ \n", - "\\begin{aligned}\n", - "x' &= 1 \\times x + \\Delta t * \\dot{x} \\mbox{, or} \\\\\n", - "x' &= \\dot{x}(\\Delta t) + x\n", - "\\end{aligned}\n", - "$$\n", - "\n", - "Now we have to account for the second row. I've let it somewhat unstated up to now, but we are assuming constant velocity for this problem. Naturally this assumption is not true; if our dog moves it must accelerate and deaccelerate. If you cast your mind back to the $g-h Filter$ chapter we explored the effect of assuming constant velocity. So long as the acceleration is small compared to $\\Delta t$ the filter will still perform well. \n", - "\n", - "Therefore we will assume that\n", - "\n", - "$$\\dot{x}' = \\dot{x}$$\n", - "\n", - "which gives us the second row of $\\mathbf{F}$ as follows, once we set $\\Delta t = 1$:\n", - "\n", - "\n", - "$$\n", - "{\\begin{bmatrix}x\\\\\\dot{x}\\end{bmatrix}}' =\\begin{bmatrix}1&1 \\\\ 0&1\\end{bmatrix} \\times \\begin{bmatrix}x \\\\ \\dot{x}\\end{bmatrix}\n", - "$$\n", - "\n", - "Which, when multiplied out, yields our desired equations:\n", - "\n", - "$$\n", - "\\begin{aligned}\n", - "x' &= x + \\dot{x} \\\\\n", - "\\dot{x}' &= \\dot{x}\n", - "\\end{aligned}\n", - "$$\n", - "\n", - "In the vocabulary of Kalman filters we call this *transforming the state matrix*. We take our state matrix, which for us is $(\\begin{smallmatrix}x \\\\ \\dot{x}\\end{smallmatrix})$,and multipy it by a matrix we will call $F$ to compute the new state. In this case, $F=(\\begin{smallmatrix}1&1\\\\0&1\\end{smallmatrix})$. \n", - "\n", - "\n", - "You will do this for every Kalman filter you ever design. Your state matrix will change depending on how many state random variables you have, and then you will create $F$ so that it updates your state based on whatever the physics of your problem dictates. $F$ is always a matrix of constants. If this is not fully clear, don't worry, we will do this many times in this book.\n", - "\n", - "Refer back to the first Kalman filter equation $\\hat{\\mathbf{x}}_{t|t-1} = \\mathbf{F_t}\\hat{\\mathbf{x}}_{t-1} + \\mathbf{B u}_t$. There is an unexplained $\\mathbf{B u}_t$ term in there, but shorn of all the diacritics it should be clear that we just designed $F$ for this equation!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### **Step 3**: Design the Motion Function\n", - "\n", - "The Kalman filter does not just filter data, it allows us to incorporate control inputs for systems like robots and airplanes. Consider the state transition function we wrote for the dog:\n", - "\n", - "$$x_t = \\dot{x}(\\Delta t) + x_{t-1}$$\n", - "\n", - "Suppose that instead of passively tracking our dog we were actively controlling a robot. At each time step we would send control signals to the robot based on our current position vs desired position. Kalman filter equations incorporate that knowledge into the filter equations, creating a predicted position based both on current velocity *and* control inputs to the drive motors. \n", - "\n", - "We will cover this use case later, but for now passive tracking applications we set those terms to 0. In step 2 there was the unexplained term $\\mathbf{Bu}$ in equation (1):\n", - "\n", - "$$\\mathbf{x}' = \\mathbf{Fx} + \\mathbf{Bu}$$.\n", - "\n", - "Here $\\mathbf{u}$ is the control input, and $\\mathbf{B}$ is its transfer function. For example, $\\mathbf{u}$ might be a voltage controlling how fast the wheel's motor turns, and multiplying by $\\mathbf{B}$ yields $\\begin{smallmatrix}x\\\\\\dot{x}\\end{smallmatrix}$. Since we do not need these terms we will set them both to zero and not concern ourselves with them for now.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### **Step 4**: Design the Measurement Function\n", - "\n", - "Now we need a way to compute the state variables to our measurements. In our problem we have one sensor for the position, and it outputs position directly. We do not have a sensor for velocity. If we put this in linear algebra terms we get:\n", - "\n", - "$$\n", - "z = \\begin{bmatrix}1&0\\end{bmatrix} \\times \\begin{bmatrix}x \\\\ \\dot{x}\\end{bmatrix}\n", - "$$\n", - "\n", - "In other words, the measurement sensor provides one times the sensor's measurement of $x$, and zero times the nonexistent velocity measurement. This is simple, because the problem is simple! A slightly more complicated problem might use a temperature sensor might, for example, output a voltage, and we would need to provide an equation to convert from voltage to temperature. \n", - "\n", - "In the nomenclature of Kalman filters the $[1\\space\\space0]$ matrix is called $H$. If you scroll up to the Kalman filter equations you will see an $H$ term in the update step.\n", - "\n", - "$$\\mathbf{\\gamma} = \\mathbf{z} - \\mathbf{H x}\\tag{3}$$\n", - "\n", - "Believe it or not, we have designed the majority of our Kalman filter!! All that is left is to model the noise in our sensors.\n", - "\n", - "\n", - "##### **Step 5**: Design the Measurement Noise Matrix\n", - "\n", - "The *measurement noise* is a matrix that models the noise in our sensors as a covariance matrix. This can be admittedly a very difficult thing to do in practice. A complicated system may have many sensors, the correlation between them might not be clear, and usually their noise is not a pure Gaussian. For example, a sensor might be biased to already read high if the temperature is high, and so the noise is not distributed equally on both sides of the mean. Later we will address this topic in detail. For now I just want you to get used to the idea of the measurement noise matrix so we will keep it deliberately simple.\n", - "\n", - "In the last chapter we used a variance of 5 for our position sensor. Let's use the same value here. The Kalman filter equations uses the symbol $R$ for this matrix.\n", - "\n", - "$$R = 5$$\n", - "\n", - "In general the matrix will have dimension $m{\\times}1$, where $m$ is the number of sensors. We have only 1 sensor here, so we can get by using a scalar. However, we could equally have written\n", - "\n", - "$$R = \\begin{bmatrix}5\\end{bmatrix}$$\n", - "\n", - "but I endevour to keep the nomenclature as simple as possible." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### **Step 6**: Design the Process Noise Matrix\n", - "\n", - "What is *process noise*? Consider the motion of a thrown ball. In a vacuum and with constant gravitational force it moves in a parabola. However, if you throw the ball on the surface of the earth you will also need to model factors like rotation and air drag. However, even when you have done all of that there is usually things you cannot account for. For example, consider wind. On a windy day the ball's trajectory will differ from the computed trajectory, perhaps by a significant amount. Without wind sensors, we may have no way to model the wind. Wind can come from any direction, so it is likely to have a near Gaussian distribution. The Kalman filter models this as *process noise*, and calls it $\\small\\mathbf{Q}$.\n", - "\n", - "Astute readers will realize that we can inspect the ball's path and extract wind as an unobserved state variable, but the point to grasp here is there will always be some unmodelled noise in our process, and the Kalman filter gives us a way to model it.\n", - "\n", - "Designing the process noise matrix can be quite demanding. For our first example, we will set it to 0, like so: $\\small\\mathbf{Q}=0$. It is unlikely that you would do that for a real filter.\n", - "\n", - "> Some books and papers use $\\small\\mathbf{R}$ for measurement noise and $\\small\\mathbf{Q}$ for the process noise. Others do the opposite, using $\\small\\mathbf{Q}$ for measurement noise and $\\small\\mathbf{R}$ for the process noise! Read carefully, and make sure you don't get confused. I use the following mnemonic. Radars are used to measure positions, and they have measurement error. So, for me, $\\small\\mathbf{R}$ is the **R**adar's measurement noise. I've read a lot of Kalman filter literature in the context of radar tracking, so it makes sense to me. I don't have a good one for $\\small\\mathbf{Q}$, other than to note that it alphabetically follows the p in **P**rocess." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### **Step 7**: Design Initial Conditions\n", - "\n", - "Finally, we need to specify the initial conditions for the state variables and their associated covariance matrix. If you have a rough idea of the values you can use that as your initial settings, or, you could always read the sensors for the first time, and calculate a initial value. The Kalman filter will converge and find the solution even if your initial conditions are far off, but the more accurate they are the faster and better the output will be. \n", - "\n", - "The covariance matrix ($\\small{\\mathbf{P}}$) is a $n{\\times}n$ matrix that specifies the variances and covariances of each state variable. This is a complicated topic, and I'd rather demostrate the matrix rather than talk about it abstractly here. For now, recognize that the Kalman filter will be calculating the covariance matrix at each step, just like the 1-D filter computed the variance of the mean at each step. So as with those examples we can make an initial guess and trust that $\\small{\\mathbf{P}}$ will converge to a smaller value as the filter progresses. I find this description somewhat unsatisfactory for several reasons, but until you've seen some examples it is hard to talk about in an understandable way." - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Implementing the Kalman Filter" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As promised, the Kalman filter equations are already programmed for you. In many circumstances you will never have to write your own Kalman filter equations. We will look at the code later, but for now we will just import the code and use it. I have placed it in *KalmanFilter.py*, so let's start by importing it and creating a filter." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "from KalmanFilter import KalmanFilter\n", - "f = KalmanFilter (dim=2)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 15 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That's it. We import the filter, and create a filter that uses 2 state variables. We specify the number of state variables with the 'dim=2' expression (dim means dimensions).\n", - "\n", - "The Kalman filter class contains a number of variables that you need to set. x is the state, F is the state transition function, and so on. Rather than talk about it, let's just do it!" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "f.x = np.matrix([[0], [0]]) # initial state (location and velocity)\n", - "f.F = np.matrix([[1,1], [0,1]]) # state transition matrix\n", - "f.H = np.matrix([[1,0]]) # Measurement function\n", - "f.R = 5 # measurement noise\n", - "f.Q = 0 # process noise\n", - "f.P *= 500. # covariance matrix \n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 16 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's look at this line by line. \n", - "\n", - "**1**: We just assign the initial value for our state. Here we just initialize both the position and velocity to zero. \n", - "\n", - "**2**: We set $\\small\\mathbf{F}=(\\begin{smallmatrix}1&1\\\\0&1\\end{smallmatrix})$, as in design step 2 above. \n", - "\n", - "**3**: We set $H=(\\begin{smallmatrix}1&0\\end{smallmatrix})$, as in design step 3 above.\n", - "\n", - "**4**: We set $\\small\\mathbf{R} = 5$ and $\\small\\mathbf{Q}=0$ as in steps 5 and 6.\n", - "\n", - "**5**: Recall in the last chapter we set our initial belief to $\\mathcal{N}(\\mu,\\sigma^2)=\\mathcal{N}(0,500)$ to signify our lack of knowledge about the initial conditions. We implemented this in Python with a list that contained both $\\mu$ and $\\sigma^2$ in the variable $pos$:\n", - "\n", - " pos = (0,500)\n", - " \n", - "Multidimensional Kalman filters stores the state variables in $\\mathbf{x}$ and their *covariance* in $\\small\\mathbf{P}$. These are $f.x$ and $f.P$ in the code above. Notionally, this is similar as the one dimension case, but instead of having a mean and variance we have a mean and covariance. For the multidimensional case, we have\n", - "\n", - "$$\\mathcal{N}(\\mu,\\sigma^2)=\\mathcal{N}(\\mathbf{x},\\mathbf{P})$$\n", - "\n", - "$\\small\\mathbf{P}$ is initialized to the identity matrix of size $n{\\times}n$, so multiplying by 500 assigns a variance of 500 to $x$ and $\\dot{x}$. So $f.P$ contains\n", - "\n", - "$$\\begin{bmatrix} 500&0\\\\0&500\\end{bmatrix}$$\n", - "\n", - "This will become much clearer once we look at the covariance matrix in detail in later sessions. For now recognize that each diagonal element $e_{ii}$ is the variance for the $ith$ state variable. \n", - "\n", - "> Summary: For our dog tracking problem, in the 1-D case $\\mu$ was the position, and $\\sigma^2$ was the variance. In the 2-D case $\\small\\mathbf{x}$ is our position and velocity, and $\\small\\mathbf{P}$ is the *covariance* of the position and velocity. It is the same thing, just in higher dimensions!\n", - "\n", - "\n", - "All that is left is to run the code! The *DogSensor* class from the previous chapter has been placed in *DogSensor.py*." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from DogSensor import DogSensor\n", - "\n", - "def dog_tracking_filter(R,Q=0,cov=1.):\n", - " f = KalmanFilter (dim=2)\n", - " f.x = np.matrix([[0], [0]]) # initial state (location and velocity)\n", - " f.F = np.matrix([[1,1],[0,1]]) # state transition matrix\n", - " f.H = np.matrix([[1,0]]) # Measurement function\n", - " f.R = R # measurement uncertainty\n", - " f.P *= cov # covariance matrix \n", - " f.Q = np.eye(2)*Q\n", - " return f\n", - "\n", - "\n", - "def filter_dog(noise, count, R, Q=0):\n", - " dog = DogSensor(velocity=1, noise=noise)\n", - " dog_filter = dog_tracking_filter(R=R, Q=Q, cov=500.)\n", - "\n", - " pos = [None] * count\n", - " zs = [None] * count\n", - " cov = [None] * count\n", - " \n", - " for t in range (count):\n", - " z = dog.sense() # get the next measurement\n", - " \n", - " pos[t] = dog_filter.x[0,0]\n", - " cov[t] = dog_filter.P\n", - " zs[t] = z\n", - " \n", - " # perform the kalman filter steps\n", - " dog_filter.update (z)\n", - " dog_filter.predict()\n", - " \n", - " return (pos, zs, cov)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 17 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is the complete code for the filter, and most of it is just boilerplate. The first function *dog_tracking_filter()* is a helper function that creates a *KalmamFilter* object with specified $\\small\\mathbf{R}$, $\\small\\mathbf{Q}$ and $\\small\\mathbf{P}$ matrices. We've shown this code already, so I will not discuss it more here. \n", - "\n", - "The function *filter_dog()* implements the filter itself. Lets work through it line by line. The first line creates the simulation of the DogSensor, as we have seen in the previous chapter.\n", - "\n", - " dog = DogSensor(velocity=1, noise=noise)\n", - "\n", - "The next line uses our helper function to create a Kalman filter.\n", - "\n", - " dog_filter = dog_tracking_filter(R=R, Q=Q, cov=500.)\n", - " \n", - "We will want to plot the filtered position, the measurements, and the covariance, so we will need to store them in lists. The next three lines initialize empty lists of length *count* in a pythonic way.\n", - "\n", - " pos = [None] * count\n", - " zs = [None] * count\n", - " cov = [None] * count\n", - " \n", - "Finally we get to the filter. All we need to do is perform the update and predict steps of the Kalman filter for each measurement. The *KalmanFilter* class provides the two functions *update()* and *predict()* for this purpose. *update()* performs the measurement update step of the Kalman filter, and so it takes a variable containing the sensor measurement. \n", - "\n", - "Absent the bookkeeping work of storing the filter's data, the for loop reads:\n", - "\n", - " for t in range (count):\n", - " z = dog.sense()\n", - " dog_filter.update (z)\n", - " dog_filter.predict()\n", - " \n", - "It really cannot get much simpler than that. As we tackle more complicated problems this code will remain largely the same; all of the work goes into setting up the $KalmanFilter$ variables; executing the filter is trivial.\n", - "\n", - "Now let's look at the result. Here is some code that calls *filter_track()* and then plots the result. It is fairly uninteresting code, so I will not walk through it." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def plot_track(noise, count, R, Q=0, plot_P=True, title='Kalman Filter'):\n", - " \n", - " ps, zs, cov = filter_dog(noise, count, R, Q)\n", - " \n", - " p0, = plt.plot([0,count],[0,count],'g')\n", - " p1, = plt.plot(range(1,count+1),zs,c='r', linestyle='dashed')\n", - " p2, = plt.plot(range(1,count+1),ps, c='b')\n", - " plt.legend([p0,p1,p2], ['actual','measurement', 'filter'], 2)\n", - " plt.ylim((0-10,count+10))\n", - " plt.title(title)\n", - "\n", - " plt.show()\n", - " if plot_P:\n", - " plt.subplot(121)\n", - " plot_covariance(cov, (0,0))\n", - " plt.subplot(122)\n", - " plot_covariance(cov, (1,1))\n", - " plt.show()\n", - " \n", - "def plot_covariance(P, index=(0,0)):\n", - " ps = []\n", - " for p in P:\n", - " ps.append(p[index[0],index[1]])\n", - " plt.plot(ps)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 18 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, call it. We will start by filtering 100 measurements with a noise factor of 30, $\\small\\mathbf{R}=5$ and $\\small\\mathbf{Q}=0$." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "plot_track (noise=30, R=5, Q=0, count=100)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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MT7GXLYutRo0c9D73SYlFJv38888EBATg5+fHtm3bUvYPHjyYr7/+Os25Q4cO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VeYHscFAIAMQiKP6jpgEg8wLZ4aAQx54fMntwzOwuSVizY0RmD46Z0yqBAjmo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\nyAAwXSSKuy0OAEqmQI55sXX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- "text": [ - "" - ] - } - ], - "prompt_number": 19 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There is still a lot to learn, but we have implemented our first, full Kalman filter using the same theory and equations as published by Nobert Kalman! Code very much like this runs inside of your GPS and phone, inside every airliner, inside of robots, and so on. \n", - "\n", - "The first plot plots the output of the Kalman filter against the measurements and the actual position of our dog (drawn in green). After the initial settling in period the filter should track the dog's position very closely.\n", - "\n", - "The next two plots show the variance of $x$ and of $\\dot{x}$. If you look at the code, you will see that I have plotted the diagonals of $\\small\\mathbf{P}$ over time. Recall that the diagonal of a covariance matrix contains the variance of each state variable. So $\\small\\mathbf{P}[0,0]$ is the variance of $x$, and $\\small\\mathbf{P}[1,1]$ is the variance of $\\dot{x}$. You can see that despite initializing $\\small\\mathbf{P}=(\\begin{smallmatrix}500&0\\\\0&500\\end{smallmatrix})$ we quickly converge to small variances for both the position and velocity. We will spend a lot of time on the covariance matrix later, so for now I will leave it at that.\n", - "\n", - "In the previous chapter we filtered very noisy signals with much simpler code than the code above. However, realize that right now we are working with a very simple example - an object moving through 1-D space and one sensor. That is about the limit of what we can compute with the code in the last chapter. In contrast, we can implement very complicated, multidimensional filter with this code merely by altering are assignments to the filter's variables. Perhaps we want to track 100 dimensions in financial models. Or we have an aircraft with a GPS, INS, TACAN, radar altimeter, baro altimeter, and airspeed indicator, and we want to integrate all those sensors into a model that predicts position, velocity, and accelerations in 3D (which requires 9 state variables). We can do that with the code in this chapter." - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Walking Through the KalmanFilter Code (Optional)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The code in the $KalmanFilter$ is a nearly verbatim transcription of the linear algebra equations. I take advantage of numpy matrices to implement the linear algebra. It is worth looking at this code if for no other reason than to realize how easy it is to implement linear algebra with Python and numpy. For most of this book you will only really need to know how to call this class, not how to implement it from scratch.\n", - "\n", - "> **sidebar**: numpy provides two data structures which can be used to perform linear algebra: *numpy.array* and *numpy.matrix*. The usual advice is to use *numpy.array*, not *numpy.matrix*. Ever the contrarian, I have chosen to use *numpy.matrix*, but for what I think are good pedalogical reasons. *numpy.array* is usually recommended because it can be sized to any arbitrary number of dimensions, and *numpy.matrix* is constrained to two dimensions. However, for Kalman filters we only need 2 dimensions. More importantly, *numpy.matrix* allows you to use very natural sytax. Multipying a by b is written *a*b* if using *numpy.matrix*, but *a.dot(b)* if they are *numpy.array*. It is also more natural to mix scalars and matrices using *numpy.matrix*. Finally, the resulting code is extremely close to the equivelent Matlab code; if you are more familiar with Matlab than Python this code should feel very familiar to you.\n", - "\n", - "\n", - "The constructor of the class creates variables for each of the Kalman filter variables, and assigns them a reasonable default value. This is the code in its entirety:\n", - "\n", - " def __init__(self, dim):\n", - " \"\"\" Create a Kalman filter of dimension 'dim'\"\"\"\n", - " \n", - " self.x = 0\n", - " self.P = np.matrix(np.eye(dim))\n", - " self.Q = np.matrix(np.eye(dim))\n", - " self.u = np.matrix(np.zeros((dim,1)))\n", - " self.B = 0\n", - " self.F = 0\n", - " self.H = 0\n", - " self.R = np.matrix(np.eye(1))\n", - " self.I = np.matrix(np.eye(dim))\n", - "\n", - "The function *predict()* implements the Kalman filter prediction equations.\n", - "\n", - " def predict(self):\n", - " self.x = (self.F*self.x) + (self.B * self.u)\n", - " self.P = (self.F * self.P * self.F.T) + self.Q\n", - "\n", - "This is nothing more than a transliteration of these equations.\n", - "\n", - "$$\n", - "\\begin{aligned}\n", - "\\mathbf{x}' &= \\mathbf{F x} + \\mathbf{B u}\\\\\n", - "\\mathbf{P} &= \\mathbf{FP{F}}^T + \\mathbf{Q}\n", - "\\end{aligned}\n", - "$$\n", - "\n", - "Finally, the *update()* function implements the Kalman filter update equations in an equally straightforward way:\n", - "\n", - " def update(self, Z):\n", - " \"\"\"\n", - " Add a new measurement to the kalman filter.\n", - " \"\"\"\n", - " y = Z - (self.H * self.x)\n", - " S = (self.H * self.P * self.H.T) + self.R\n", - "\n", - "\n", - " K = self.P * self.H.T * linalg.inv(S)\n", - " self.x = self.x + (K*y)\n", - " self.P = (self.I - (K*self.H))*self.P\n", - " \n", - "Finally, for those reading this online or in a printed form, here is the code in KalmanFilter.py absent the unit testing code that is included in that file." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "import scipy.linalg as linalg\n", - "\n", - "class KalmanFilter:\n", - "\n", - " def __init__(self, dim):\n", - " \"\"\" Create a Kalman filter of dimension 'dim'\"\"\"\n", - " \n", - " self.x = 0 # state\n", - " self.P = np.matrix(np.eye(dim)) # uncertainty covariance\n", - " self.Q = np.matrix(np.eye(dim)) # process uncertainty\n", - " self.u = np.matrix(np.zeros((dim,1))) # motion vector\n", - " self.B = 0\n", - " self.F = 0 # state transition matrix\n", - " self.H = 0 # Measurement function (maps state to measurements)\n", - " self.R = np.matrix(np.eye(1)) # state uncertainty\n", - " self.I = np.matrix(np.eye(dim))\n", - "\n", - "\n", - " def update(self, Z):\n", - " \"\"\"\n", - " Add a new measurement to the kalman filter.\n", - " \"\"\"\n", - "\n", - " # measurement update\n", - " y = Z - (self.H * self.x) # error (residual) between measurement \n", - " # and prediction\n", - " S = (self.H * self.P * self.H.T) + self.R # project system uncertainty into \n", - " # measurment space + measurement noise(R)\n", - "\n", - "\n", - " K = self.P * self.H.T * linalg.inv(S) # map system uncertainty into kalman gain\n", - "\n", - " self.x = self.x + (K*y) # predict new x with residual scaled \n", - " #by the kalman gain\n", - " self.P = (self.I - (K*self.H))*self.P # and compute the new covariance\n", - "\n", - " def predict(self):\n", - " # prediction\n", - " self.x = (self.F*self.x) + self.u\n", - " self.P = self.F * self.P * self.F.T + self.Q" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 20 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Adjusting the Filter" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Your results will vary slightly depending on what numbers your random generator creates for the noise componenet of the noise, but the filter in the last section should track the actual position quite well. Typically as the filter starts up the first several predictions are quite bad, and varies a lot. But as the filter builds its state the estimates become much better. \n", - "\n", - "Let's start varying our parameters to see the effect of various changes. This is a *very normal* thing to be doing with Kalman filters. It is difficult, and often impossible to exactly model our sensors. An imperfect model means imperfect output from our filter. Engineers spend a lot of time tuning Kalman filters so that they perform well with real world sensors. We will spend time now to learn the effect of these changes. As you learn the effect of each change you will develop an intuition for how to design a Kalman filter. As I wrote earlier, designing a Kalman filter is as much art as science. The science is, roughly, designing the $\\small{\\mathbf{H}}$ and $\\small{\\mathbf{F}}$ matrices - they develop in an obvious manner based on the physics of the system we are modelling. The art comes in modelling the sensors and selecting appropriate values for the rest of our variables.\n", - "\n", - "Let's look at the effects of the noise parameters $\\small{\\mathbf{R}}$ and $\\small{\\mathbf{Q}}$. I will only run the filter for twenty steps to ensure we can see see the difference between the measurements and filter output. I will start by holding $\\small{\\mathbf{R}}$ to 5 and vary $\\small{\\mathbf{Q}}$. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "plot_track (noise=30, R=5, Q=10,count=30, plot_P=False, title='R = 5, Q = 10*I')\n", - "plot_track (noise=30, R=5, Q=0.1,count=30, plot_P=False, title='R = 5, Q = 0.1*I')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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G4fBhDImJWHr0wNqhQ4ljzP36YR48GGubNuDpWfzF336r8NxvpuAGvA4dHLjh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KqcWn2XXxMt9qXuZvTVHqV7iAX5OfKN58BX8q/6VccAs6lgqlWdBE/A3+WV5O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\nddSQ4Rp8svheoY02PuvwEc6MSceiHosQXT/aqUXxsWNyDBwYhFmzsm0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- "text": [ - "" - ] - } - ], - "prompt_number": 21 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The filter in the first plot should follow the noisy measurement almost exactly. In the second plot the filter should vary from the measurement quite a bit, and be much closer to a straight line than in the first graph. \n", - "\n", - "In the Kalman filter $\\small{\\mathbf{R}}$ is the *measurement noise* and $\\small{\\mathbf{Q}}$ is the *process uncertainty*. $\\small{\\mathbf{R}}$ is the same in both plots, so ignore it for the moment. Why does $\\small{\\mathbf{Q}}$ affect the plots this way?\n", - "\n", - "Let's remind ourselves of what the term *process uncertainty* means. Consider the problem of tracking a ball. We can accurately model its behavior in statid air with math, but if there is any wind our model will diverge from reality. \n", - "\n", - "In the first case we set $\\small{\\mathbf{Q}}=100$, which is quite large. In physical terms this is telling the filter \"I don't trust my motion prediction step\". Strictly speaking, we are telling the filter there is a lot of external noise that we are not modeling with $\\small{\\mathbf{F}}$, but the upshot of that is to not trust the motion prediction step. So the filter will be computing velocity ($\\dot{x}$), but then mostly ignoring it because we are telling the filter that the computation is extremely suspect. Therefore the filter has nothing to use but the measurements, and thus it follows the measurements closely. \n", - "\n", - "In the second case we set $\\small{\\mathbf{Q}}=0.1$, which is quite small. In physical terms we are telling the filter \"trust the motion computation, it is really good!\". Again, more strictly this actually says there is very small amounts of process noise, so the motion computation will be accurate. So the filter ends up ignoring some of the measurement as it jumps up and down, because the variation in the measurement does not match our trustworthy velocity prediction. \n", - "\n", - "Now let's leave $\\small{\\mathbf{Q}}=0.1$, but bump $\\small{\\mathbf{R}}$ up to $1000$. This is telling the filter that the measurement noise is very large. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "plot_track (noise=30, R=1000, Q=0.1,count=50, plot_P=False, title='R = 1000, Q = 0.1')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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bJ9FFntyOzGNh0B8dBVcnO4weJaB/fw1sbUvWtiw2FvZDh+LhsWP5x2QLAuQH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- "text": [ - "" - ] - } - ], - "prompt_number": 22 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The filter output should be much closer to the green line, especially after 10-20 cycles. If you are running this in Ipython Notebook, I strongly urge you to run this many times in a row (click inside the code box, and press CTRL-Enter). Most times the filter tracks almost exactly with the actual position, randomly going slightly above and below the green line, but sometimes it stays well over or under the green line for a long time. What is happening in the latter case?\n", - "\n", - "The filter is strongly preferring the motion update to the measurement, so if the prediction is off it takes a lot of measurements to correct it. It will eventually correct because the velocity is a hidden variable - it is computed from the measurements, but it will take awhile.\n", - "\n", - "To some extent you can get similar looking output by varying either $\\small{\\mathbf{R}}$ or $\\small{\\mathbf{Q}}$, but I urge you to not 'magically' alter these until you get output that you like. Always think about the physical implications of these assignments, and vary $\\small{\\mathbf{R}}$ and/or $\\small{\\mathbf{Q}}$ based on your knowledge of the system you are filtering." - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "A Detailed Examination of the Covariance Matrix" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So far I have not given a lot of coverage of the covariance matrix. $\\small\\mathbf{P}$, the covariance matrix is nothing more than the variance of our state - such as the position of our dog. It has many elements in it, but don't be daunted; we will learn how to interpret a very large $9{\\times}9$ covariance matrix, or even larger.\n", - "\n", - "Recall the beginning of the chapter, where we provided the equation for the covariance matrix. It read:\n", - "\n", - "$$\n", - "\\mathbf{P} = \\begin{pmatrix}\n", - " {{\\sigma}_{1}}^2 & p{\\sigma}_{1}{\\sigma}_{2} & \\cdots & p{\\sigma}_{1}{\\sigma}_{n} \\\\\n", - " p{\\sigma}_{2}{\\sigma}_{1} &{{\\sigma}_{2}}^2 & \\cdots & p{\\sigma}_{2}{\\sigma}_{n} \\\\\n", - " \\vdots & \\vdots & \\ddots & \\vdots \\\\\n", - " p{\\sigma}_{n}{\\sigma}_{1} & p{\\sigma}_{n}{\\sigma}_{2} & \\cdots & {{\\sigma}_{n}}^2\n", - " \\end{pmatrix}\n", - "$$\n", - "\n", - "(I have subtituted $\\small\\mathbf{P}$ for $\\Sigma$ because of the nomenclature used by the Kalman filter literature).\n", - "\n", - "The diagonal contains the variance of each of our state variables. So, if our state variables are\n", - "\n", - "$$\\begin{pmatrix}x\\\\\\dot{x}\\end{pmatrix}$$\n", - "\n", - "and the covariance matrix happens to be\n", - "$$\\begin{pmatrix}2&0\\\\0&6\\end{pmatrix}$$\n", - "\n", - "we know that the variance of $x$ is 2, and the variance of $\\dot{x}$ is 6. The off diagonal elements are all 0, so we also know that $x$ and $\\dot{x}$ are not correlated. Recall the ellipses that we drew of the covariance matrices. Let's look at the ellipse for the matrix." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "P = np.array([[2,0],[0,6]])\n", - "e = stats.sigma_ellipse (P, 0, 0)\n", - "stats.plot_sigma_ellipse(e, '|2 0|\\n|0 6|')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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cZc8eD/O8iIvMTCkYbHgCIAAcCU0vwmK2yV4mZMdNbJEzIT+beDxSTk5Q333n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\nCW/LFq+2bk3SqFF2PJACgD1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- "text": [ - "" - ] - } - ], - "prompt_number": 23 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Of course it is unlikely that the position and velocity of an object remain uncorrelated for long. Let's look at a more typical covariance matrix" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "P = np.array([[2,2.4],[2.4,6]])\n", - "e = stats.sigma_ellipse (P, 0, 0)\n", - "stats.plot_sigma_ellipse(e, '|2.0 2.4|\\n|2.4 6.0|')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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CiFn79klPPpmoxx4rYU9eAyxZ4lPXrvZtegHYG00vQmK2yVxkV32TJsXrjDMq\nbPWQA/ILraJi/84Ndm56yc5s5Od8bFkGICbt2ePShAkJ+uCDQqtLQTWsWuVRgwZVSktjnhdAeFjp\nRUjMNpmL7Kpn3LgE9e0bUMuW9noQBfmF9tlnXmVl2WPu+nDIzmzk53ys9AKIOVu2uPTGG3H67DNr\nH0SB6ps/36ebb7Z+SzkA5mKlFyEx22Qusju6p59O1IAB5WrQwH6/Kie/3youlnJy7L/SS3ZmIz/n\nY6UXQEz5/nu3PvjAp+XLWeU1xeefe3XqqRVKSbG6EgAmY6UXITHbZC6yO7LHH0/UrbeWqm5d+63y\nSuQXyrx5Pp17rn13bTiA7MxGfs5H0wsgZnzzjUdffeXVoEHMhprk00996tnT3qMNAOyPphchMdtk\nLrI7vDFjEjR8eKn8fqsrOTzyO9SmTW7t2uVShw722Uv5cMjObOTnfDS9AGLCihUerVnj1XXXscpr\nkvnzvTrnnAq5+W4F4BjxNoKQmG0yF9mFNmZMokaMKFF8vNWVHBn5HWrePHNGG8jObOTnfDS9ABxv\n2TKPfvjBrWuuKbe6FNRAaam0cKFPvXub0fQCsDeaXoTEbJO5yO63xoxJ1J13liouzupKjo78/mfx\nYq/atq005tHDZGc28nM+ml4AjvbFF15t3OjWVVexymuajz7y6YILyA1AZLiCwWBUfoSeN2+eOnXq\nFI1TA0C1XXxxsi6/vJzRBsMEg1JmZqreeadQrVtXWV0OAJvLyclRdnb2EV/DSi8Ax1q82KvNm926\n4goaXtOsWuVRQkJQrVrR8AKIDJpehMRsk7nI7n/Gjt2/L6/XoAeuk99+H37o0/nnB+RyWV1J9ZGd\n2cjP+Wh6ATjSV1959OOPbl1+Oau8Jto/z8uuDQAih5leAI507bVJ6tGjQn/8Iw+jMM2WLS51715H\n69fvNWqVHoB1mOkFEJPWrnXrq6+8uvZaGl4TzZ4dp/POC9DwAogoml6ExGyTuchOGjcuQX/6U6kS\nE62upObIT/r3v326+GLzRhvIzmzk53w0vQAc5aef3Jo/36cbbmCV10R5eS6tXevROeeY1/QCsDea\nXoTEM8jNFevZjR+foBtvLFOdOlZXEp5Yz2/mzP2jDfHxVldSc7GenenIz/mYmALgGFu2uPT++z4t\nX15gdSkI07//7dOwYazSA4g8VnoRErNN5orl7F58MUFXXVWuevWisilNrYjl/PLyXFqzxqNzzzVz\ntCGWs3O2IYdJAAAgAElEQVQC8nM+VnoBOEJBgTR1apwWLmSV11SzZsWpT5+AEhKsrgSAE7HSi5CY\nbTJXrGY3eXK8evasUKNG5q7ySrGbn2Turg0HxHJ2TkB+zsdKLwDjBQLSxIkJevPNIqtLQZjy8lxa\nvdqjnj3NbXoB2BsrvQiJ2SZzxWJ2M2bE6eSTK3XaaZVWl3LMYjE/SXr33Tj9/vdmjzbEanZOQX7O\nR9MLwGjBoDRhQryGDSu1uhQcg+nT43T55eVWlwHAwWh6ERKzTeaKtew++8yrsjKXevWqsLqUiIi1\n/CRp3Tq3tm93KyvL7AxjMTsnIT/no+kFYLQJExI0dGip3LybGetf/4rTJZeUy+OxuhIATsa3CYTE\nbJO5Yim7775z69tvPY76tXgs5SdJVVX7m14nZBhr2TkN+TkfTS8AY02alKAbbigz+uanWPfllx75\n/VL79ubfhAjA3lzBYDAqm1rOmzdPnTp1isapAUB79rjUsWMdLV1aoBNPNHtv3lg2YoRfGRmVuuMO\nHj0MIHw5OTnKzs4+4mvYpxeAkaZMiVPv3gEaXoOVlUnvv+/TggUlVpcCIAYw3oCQmG0yVyxkV1Ul\n/eMf8Ro0yHmrg7GQ3wFz5vjUpk2l8U/ROyCWsnMi8nM+ml4Axpk716u6dYM64wzmQE325pvxuvZa\n829gA2AGZnoBGOeyy5J16aXluuoqGiZTbdni0tln19Hq1Xvl91tdDQDTVWeml5VeAEbZsMGtVas8\n+r//o+E12dtvx6tfvwANL4BaQ9OLkJhtMpfTs3v55Xhdd51ztylzen7S/pnsKVPidM01zprJjoXs\nnIz8nC/spvef//ynWrVqpdatW2vWrFmRrAkAQioqkqZPj9PAgc5qlmLN5597lZgoderETDaA2hPW\nTG95eblOOeUULVu2TKWlpTr33HO1YcOGQ17DTC+ASHvjjTjNmePTm28WW10KjsGQIX5lZlZq6FB+\neAEQGVGb6V22bJnatWunE044QY0bN1bjxo21cuXKsIoEgOqaPDle11/v7EZp8WJnb59eUCB9+KHP\nEY8dBmCWsJrebdu26aSTTtLEiRM1ffp01a9fX//9738jXRssxGyTuZya3apVHm3b5lbPnhVWlxJV\nU6dutbqEqHrnnTj16FGhtDRn7M37S0699mIF+TnfMS0p3HzzzZKkd999Vy6X6zd/P3ToUGVkZEiS\nUlNTlZmZqaysLEn/+5+LY3ser1q1ylb1cMzxiy+217XX+uTx2KOeSB+vWlVPe/d21Ntvt5a0XpmZ\n+RoypI1t6ovEcbduWXr11Xj1779CixfvsLyeSB8fYJd6OCY/Jx8f+Ofc3FxJ0qBBg3Q0Yc30Llmy\nRGPGjNHMmTMlSeeee67++te/qkOHDgdfw0wvgEgpLpYyM1O1aFGBY57edThjxiTo3ntLrS4jKr78\n0qMhQ5K0fHmB3OwdBCCCqjPT6w3nxGeccYbWrFmjHTt2qLS0VJs3bz6k4QWASJoxI05nnVXh+IbX\n6V59NV433FBGwwvAEmG99cTFxWnMmDHq1q2bsrOzNW7cuEjXBYv9+tc9MIcTs9t/A1ts3PiUmvq1\n1SVERX6+Sx9+6NPVVzs3Rydee7GE/JwvrJVeSbr88st1+eWXR7IWAPiNNWs82rLFrV69AlaXUisy\nM/OtLiEqpkyJU9++AR1/PKv1AKwR1kxvdTDTCyAS7rsvUcnJQT3wgDPnXGNBVZV0+ul19NJLxTr9\ndB5IASDyojbTCwC1obx8/xZXc+YUWl0KjsH8+V7VqRNU5840vACsw+0ECInZJnM5Kbu5c31q0aJS\nzZpVWV1KrXFSfge88kq8bryxTCF2tnQUJ2YXS8jP+Wh6AdjW22/H6cornXvjUyzIzXVr2TKvLr2U\nHAFYi5leALaUn+9S58519O23e1WnjtXVIFwPPZQoSXrssRKLKwHgZMz0AjDWO+/EqXfvChpegxUW\nSlOnxunTT5nJBmA9xhsQErNN5nJKdm+/Haerriqzuoxa55T8JOntt+OVlVWhjIzYmMl2UnaxiPyc\nj6YXgO18951b27a51aNHhdWlIExVVdLEifEaMoSt5gDYA00vQsrKyrK6BITJCdm9/Xa8Lr+8XB6P\n1ZXUPifkJ0mffOJTampQZ50VO9uUOSW7WEV+zkfTC8BWqqr2z/P27x97ow1O8uKL8frTn5y/TRkA\nc9D0IiRmm8xlenZffulRampQbdvGxhzor5menyStXevW9997dPHFsbVNmROyi2Xk53w0vQBs5Z13\n4nTJJbHVLDnNiy8m6MYbyxQXZ3UlAPA/7NMLwDYqKqR27VL10UeFMfUUNifZts2lLl3qaPnyAtWr\nF5VvLwDwG9XZp5eVXgC28dlnXjVuXEXDa7CJE+PVv385DS8A26HpRUjMNpnL5OwYbTA7v4IC6fXX\n4zVsWGzehGhydiC/WEDTC8AWysqkDz7wqV+/2G56Tfbaa/Hq2TMQMw+jAGAWHkOMkNiv0FymZjd/\nvk9t21aqQYPY/rW4qfmVle2/ge2f/yyyuhTLmJod9iM/52OlF4AtvPeeL+ZHG0w2bVqc2rWrVPv2\nsfMwCgBmoelFSMw2mcvE7MrK9j/Bq2/fgNWlWM7E/CorpQkTEnT77bH9yGETs8P/kJ/z0fQCsNyi\nRV6dckqVTjwxtkcbTPXBBz7VqRNUt24VVpcCAIdF04uQmG0yl4nZzZ4dp759GW2QzMsvGJT++tf9\nq7yx/shh07LDocjP+biRDYClKiulDz/0ac6c2P7VuKk+/dSr4mIXoykAbI+VXoTEbJO5TMvuyy+9\nOvHEKjVtyjZXkln5BYPS008nauTIErn5bmJUdvgt8nM+3qYAWGrmTG5gM9Vnn3m1a5dL/fqRHwD7\ncwWDwajcOTJv3jx16tQpGqcG4BDBoHTaaXX01ltFatuWlV7TXHRRsq69tlxXXME8NgBr5eTkKDs7\n+4ivYaUXgGW+/dYjr1dq04aG1zRLlnj13/+6demlNLwAzEDTi5CYbTKXSdnNmePTBRcEYv6u/18y\nJb9nn03QiBGl8nI79EGmZIfQyM/5aHoBWObjj33q04d5UNMsXerRxo1u9e/PKi8AczDTC8ASO3a4\ndPrpqfrhhz2Ki7O6GtTEZZcl6w9/KNeAATS9AOyBmV4AtjV/vk89egRoeA2zdKlHP/zg1pVX0vAC\nMAtNL0JitslcpmT3ySc+9e7NaMOv2Tm/YFAaPTpRd99dyg8rIdg5Oxwd+TkfTS+AWldRsf9JXr16\n0fSaZMECr7Zvd7NFGQAj0fQiJJ5Bbi4TsluxwqPGjat00klRuaXAaHbN78Aq7z33lLBjw2HYNTtU\nD/k5H00vgFrHaIN5PvjAp/Jy8fQ1AMai6UVIzDaZy4Ts5s71KTub5ikUO+ZXWSk98USiHnigVG6+\naxyWHbND9ZGf8/H2BaBW5ee7tHGjR507V1pdCqrpvfd8SkoKsqcyAKMxmYWQmG0yl92z++wzr7p0\nCcjns7oSe7JbfoGA9OSTiXruuX08Oe8o7JYdaob8nI+VXgC1atEin7p3r7C6DFTTlClxaty4iswA\nGI+mFyEx22Quu2e3aJFXPXr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- "text": [ - "" - ] - } - ], - "prompt_number": 24 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here the ellipse is slanted, signifying that $x$ and $\\dot{x}$ are correlated (and, of course, dependent - all correlated variables are dependent). You may or may not have noticed that the off diagonal elements were set to the same value, 2.4. This was not an accident. Let's look at the equation for the covariance for the case where the number of dimensions is two.\n", - "\n", - "$$\n", - "\\mathbf{P} = \\begin{pmatrix}\n", - " \\sigma_1^2 & p\\sigma_1\\sigma_2 \\\\\n", - " p\\sigma_2\\sigma_1 &\\sigma_2^2 \n", - " \\end{pmatrix}\n", - "$$\n", - "\n", - "Look at the computation for the off diagonal elements. \n", - "\n", - "$$\\begin{aligned}\n", - "\\mathbf{P}_{0,1}&=p\\sigma_1\\sigma_2 \\\\\n", - "\\mathbf{P}_{1,0}&=p\\sigma_2\\sigma_1.\n", - "\\end{aligned}$$\n", - "\n", - "If we re-arrange terms we get\n", - "$$\\begin{aligned}\n", - "\\mathbf{P}_{0,1}&=p\\sigma_1\\sigma_2 \\\\\n", - "\\mathbf{P}_{1,0}&=p\\sigma_1\\sigma_1 \\mbox{, yielding} \\\\\n", - "\\mathbf{P}_{0,1}&=P_{1,0}\n", - "\\end{aligned}$$\n", - "\n", - "In general, we can state that $\\small\\mathbf{P}_{i,j}=\\small\\mathbf{P}_{j,i}$.\n", - "\n", - "So for my example I multiplied the diagonals, 2 and 6, to get 12, and then scaled that with the arbitrarily chosen $p=.2$ to get 2.4.\n", - "\n", - "Let's get back to concrete terms. Lets do another Kalman filter for our dog, and this time plot the covariance ellipses on the same plot as the position." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def plot_track(noise, count, R, Q=0, plot_P=True, title='Kalman Filter'):\n", - " dog = DogSensor(velocity=1, noise=noise)\n", - " f = dog_tracking_filter(R=R, Q=Q, cov=20.)\n", - "\n", - " ps = []\n", - " zs = []\n", - " cov = []\n", - " for t in range (count):\n", - " z = dog.sense()\n", - " f.update (z)\n", - " ps.append (f.x[0,0])\n", - " cov.append(f.P)\n", - " zs.append(z)\n", - " f.predict()\n", - "\n", - " p0, = plt.plot([0,count],[0,count],'g')\n", - " p1, = plt.plot(range(1,count+1),zs,c='r', linestyle='dashed')\n", - " p2, = plt.plot(range(1,count+1),ps, c='b')\n", - " plt.legend([p0,p1,p2], ['actual','measurement', 'filter'], 2)\n", - " plt.title(title)\n", - "\n", - " for i,p in enumerate(cov):\n", - " e = stats.sigma_ellipse (cov=p, x=i+1, y=ps[i])\n", - " stats.plot_sigma_ellipse(ellipse=e)\n", - " if i == len(cov)-1:\n", - " s = ('$\\sigma^2_{pos} = %.2f$' % p[0,0])\n", - " plt.text (30,1,s,fontsize=18)\n", - " s = ('$\\sigma^2_{vel} = %.2f$' % p[1,1])\n", - " plt.text (30,-4,s,fontsize=18)\n", - " plt.xlim((0,40))\n", - " plt.ylim((0,40))\n", - " plt.axis('equal')\n", - " \n", - " plt.show()\n", - "\n", - "\n", - "plot_track (noise=5, R=5, Q=5, count=20, title='R = 5')\n", - "plot_track (noise=5, R=.5, Q=5, count=20, title='R = 0.5')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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hb18r77yTztCh3iQlgVKtGgcOGPDwuFrice7caSAszCYrLtwGd3rvuyJ3zV8K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\n/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VlZVcc801HDlyxOm169atIykpqVEHL4QQwndYrTBiRCjPPFPOhAnnp3APHNBx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- "text": [ - "" - ] - } - ], - "prompt_number": 25 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The output on these is a bit messy, but you should be able to see what is happening. In both plots we are drawing the covariance matrix for each point. We start with the covariance $\\small\\mathbf{P}=(\\begin{smallmatrix}50&0\\\\0&50\\end{smallmatrix})$, which signifies a lot of uncertainty about our initial belief. After we receive the first measurement the Kalman filter updates this belief, and so the variance is no longer as large. In the top plot the first ellipse (the one on the far left) should be a slighly squashed ellipse. As the filter continues processing the measurements the covariance ellipse quickly shifts shape until it settles down to being a long, narrow ellipse tilted in the direction of movement.\n", - "\n", - "Think about what this means physically. The x-axis of the ellipse denotes our uncertainty in position, and the y-axis our uncertainty in velocity. So, an ellipse that is taller than it is wide signifies that we are more uncertain about the velocity than the position. Conversely, a wide, narrow ellipse shows high uncertainty in position and low uncertainty in velocity. Finally, the amount of tilt shows the amount of correlation between the two variables. \n", - "\n", - "The first plot, with $\\small\\mathbf{R}=5$, finishes up with an ellipse that is wider than it is tall. If that is not clear I have printed out the variances for the last ellipse in the lower right hand corner. The variance for position is 3.85, and the variance for velocity is 3.0. \n", - "\n", - "In contrast, the second plot, with $\\small\\mathbf{R}=0.5$, has a final ellipse that is taller than wide. The ellipses in the second plot are all much smaller than the ellipses in the first plot. This stands to reason because a small $\\small\\mathbf{R}$ implies a small amount of noise in our measurements. Small noise means accurate predictions, and thus a strong belief in our position. " - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Question: Explain Ellipse Differences" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Why are the ellipses for $R=5$ shorter, and more tilted than the ellipses for $\\small\\mathbf{R}=0.5$. Hint: think about this in the context of what these ellipses mean physically, not in terms of the math. If you aren't sure about the answer,change $\\small\\mathbf{R}$ to truly large and small numbers such as 100 and 0.1, observe the changes, and think about what this means. " - ] - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Solution" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The $x$ axis is for position, and $y$ is velocity. An ellipse that is vertical, or nearly so, says there is no correlation between position and velocity, and an ellipse that is diagnal says that there is a lot of correlation. Phrased that way, it sounds unlikely - either they are correlated or not. But this is a measure of the *output of the filter*, not a description of the actual, physical world. When $\\small\\mathbf{R}$ is very large we are telling the filter that there is a lot of noise in the measurements. In that case the Kalman gain $\\small\\mathbf{K}$ is set to favor the prediction over the measurement, and the prediction comes from the velocity state variable. So, there is a large correlation between $x$ and $\\dot{x}$. Conversely, if $\\small\\mathbf{R}$ is small, we are telling the filter that the measurement is very trustworthy, and $\\small\\mathbf{K}$ is set to favor the measurement over the prediction. Why would the filter want to use the prediction if the measurement is nearly perfect? If the filter is not using much from the prediction there will be very little correlation reported. \n", - "\n", - "**This is a critical point to understand!**. The Kalman filter is just a mathematical model for a real world system. A report of little correlation *does not mean* there is no correlation in the physical system, just that there was no correlation in the mathematical model. It's just a report of how much measurement vs prediction was incorporated into the model. \n", - "\n", - "Let's bring that point home with a truly large measurement error. We will set $\\small\\mathbf{R}=500$. Think about what the plot will look like before scrolling down. To emphasize the issue, I will set the amount of noise injected into the measurements to 0, so the measurement will exactly equal the actual position. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "plot_track (noise=0, R=500, Q=5, count=7, title='R = 500')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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qeh0e7lsJ4JXGX1EgIsJg3z6VhARZlE8IUXYk4RNCVEimCbt3q6Sm2lm0yEFa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- "text": [ - "" - ] - } - ], - "prompt_number": 26 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "I hope the result was what you were expecting. The ellipse quickly became very wide and not very tall. It did this because the Kalman filter mostly used the prediction vs the measurement to produce the filtered result. We can also see how the filter output is slow to acquire the track. The Kalman filter assumes that the measurements are extremely noisy, and so it is very slow to update its estimate for $\\dot{x}$. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Keep looking at these plots until you grasp how to interpret the covariance matrix $\\small\\mathbf{P}$. When you start dealing with a, say, $9{\\times}9$ matrix it may seem overwhelming - there are 81 numbers to interpret. Just break it down - the diagonal contains the variance for each state variable, and all off diagonal elements are the product of two variances and a scaling factor $p$. You will not be able to plot a $9{\\times}9$ matrix on the screen because it would require living in 10-D space, so you have to develop your intution and understanding in this simple, 2-D case. \n", - "\n", - "> **sidebar**: when plotting covariance ellipses, make sure to always use *plt.axis('equal')* in your code. If the axis use different scales the ellipses will be drawn distorted. For example, the ellipse may be drawn as being taller than it is wide, but it may actually be wider than tall." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 26 - }, - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "Kalman Filter Math" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This chapter is optional, especially the first time you go through this material. If you are a hobbiest you will be able to get quite far by just skipping this chapter and going on to learn the examples. However, to have any hope of reading the original Kalman filter or optimal estimation literature you will have to acquire some of the more formal mathematics. I will not endevour to teach linear algebra, statistics, or calculus here as each topic deserves a book on its own. \n", - "\n", - "blah blah blah" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Walking Through the Kalman Filter Equations" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "I promised that you would not have to understand how to derive Kalman filter equations, and that is true. However, I do think it is worth walking through the equations one by one and becoming familiar with the variables. If this is your first time through the material feel free to skip ahead to the next section. However, you will eventually want to work through this material, so why not now? You will need to have passing familarity with these equations to read material written about the Kalman filter, as they all presuppose that you are familiar with the equations. I will reiterate them here for easy reference.\n", - "\n", - "\n", - "$$\n", - "\\begin{aligned}\n", - "\\text{Predict Step}\\\\\n", - "\\mathbf{x}' &= \\mathbf{F x} + \\mathbf{B u}\\;\\;\\;\\;&(1) \\\\\n", - "\\mathbf{P} &= \\mathbf{FP{F}}^T + \\mathbf{Q}\\;\\;\\;\\;&(2) \\\\\n", - "\\\\\n", - "\\text{Update Step}\\\\\n", - "\\mathbf{\\gamma} &= \\mathbf{z} - \\mathbf{H x}\\;\\;\\;\\;&(3) \\\\\n", - "\\mathbf{K}&= \\mathbf{PH}^T (\\mathbf{HPH}^T + \\mathbf{R})^{-1}\\;\\;\\;\\;&(4) \\\\\n", - "\\mathbf{x}&=\\mathbf{x}' +\\mathbf{K\\gamma}\\;\\;\\;\\;&(5) \\\\\n", - "\\mathbf{P}&= (\\mathbf{I}-\\mathbf{KH})\\mathbf{P}\\;\\;\\;\\;&(6)\n", - "\\end{aligned}\n", - "$$\n", - "\n", - "I will start with the measurement step, as that is what we started with in the one dimensional Kalman filter case. Our first equation is\n", - "\n", - "$$\n", - "\\mathbf{\\gamma} = \\mathbf{z} - \\mathbf{H x}\\tag{3}\n", - "$$\n", - "\n", - "On the right we have $\\mathbf{Hx}$. That should be recognizable as the measurement function. Multiplying $\\mathbf{H}$ with $\\mathbf{x}$ puts $\\mathbf{x}$ into *measurement space*; in other words, the same basis and units as the sensor's measurements. The variable $\\mathbf{z}$ is just the measurement; it is typical, but not universal to use $\\mathbf{z}$ to denote measurements in the literature ($\\mathbf{y}$ is also sometimes used). Do you remember this chart?" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import mkf_internal\n", - "mkf_internal.show_residual_chart()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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+Wrbs50vO92T16VMoy1qrjRvd94+MlNau/dftzEypefNkPfZYnrp0WXPZ8Tfo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\nVs+Iq1ndutJ11xXr0KENtT0Uv1H6Ywq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- "text": [ - "" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The blue prediction line is the output of $\\mathbf{Hx}$, and the dot labelled \"measurement\" is $\\mathbf{z}$. Therefore, $\\gamma = \\mathbf{z} - \\mathbf{Hx}$ is how we compute the residual, drawn in red. So $\\gamma$ is the residual.\n", - "\n", - "The next line is the formidable:\n", - "\n", - "$$\\mathbf{K}= \\mathbf{PH}^T (\\mathbf{HPH}^T + \\mathbf{R})^{-1}\\tag{4}$$\n", - "\n", - "Unfortunately it is a fair amount of linear algebra to derive this. The derivation can be quite elegant, and I urge you to look it up if you have the mathematical education to follow it. But $\\mathbf{K}$ is just the *Kalman gain* - the ratio of how much measurement vs prediction we should use to create the new estimate. $\\mathbf{R}$ is the *measurement noise*, and $\\mathbf{P}$ is our *uncertainty covariance matrix*.\n", - "\n", - "So let's work through this expression by expression. Start with $\\mathbf{HPH}^T$. The linear equation $\\mathbf{ABA}^T$ can be thought of as changing the basis of $\\mathbf{B}$ to $\\mathbf{A}$. So $\\mathbf{HPH}^T$ is taking the covariance $\\mathbf{P}$ and putting it in measurement ($\\mathbf{H}$) space. Then, once in measurement space, we can add the measurement noise $\\mathbf{R}$ to it. Hence, the expression for the uncertainty in the measurement is:\n", - "\n", - "$$(\\mathbf{HPH}^T + \\mathbf{R})^{-1}$$\n", - "\n", - "Taking the inverse is linear algebra's way of doing $\\frac{1}{x}$. So if you accept my admittedly hand wavely explanation it can be seen to be computing:\n", - "\n", - "$$ \n", - "gain_{measurement\\,space} = \\frac{uncertainty_{prediction}}{uncertainty_{measurement}}\n", - "$$\n", - "\n", - "\n", - "In other words, the *Kalman gain* equation is doing nothing more than computing a ratio based on how much we trust the prediction vs the measurement. If we are confident in our measurements and unconfident in our predictions $\\mathbf{K}$ will favor the measurement, and vice versa. The equation is complicated because we are doing this in multiple dimensions via matrices, but the concept is simple - scale by a ratio.\n", - "\n", - "Without going into the derivation of $\\mathbf{K}$, I'll say that this equation is the result of finding a value of $\\mathbf{K}$ that optimizes the *mean-square estimation error*. It does this by finding the minimal values for $\\mathbf{P}$ along it's diagonal. Recall that the diagonal of $P$ is just the variance for each state variable. So, this equation for $\\mathbf{K}$ ensures that the Kalman filter output is optimal. To put this in concrete terms, for our dog tracking problem this means that the estimates for both position and velocity will be optimal - a value of $\\mathbf{K}$ that made the position extremely accurate but the velocity very inaccurate would be rejected in favor of a $\\mathbf{K}$ that made both position and velocity just somewhat accurate." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our next line is:\n", - " $$\\mathbf{x}=\\mathbf{x}' +\\mathbf{K\\gamma}\\tag{5}$$\n", - "\n", - "This just multiplies the residual by the Kalman gain, and adds it to the state variable. In other words, this is the computation of our new estimate.\n", - "\n", - "Finally, we have:\n", - "\n", - "$$\\mathbf{P}=(\\mathbf{I}-\\mathbf{KH})\\mathbf{P}\\tag{6}$$\n", - "\n", - "$I$ is the identity matrix, and is the way we represent $1$ in multiple dimensions. $H$ is our measurement function, and is a constant. So, simplified, this is simply $P = (1-cK)P$. $K$ is our ratio of how much prediction vs measurement we use. So, if $K$ is large then $(1-cK)$ is small, and P will be made smaller than it was. If $K$ is small, then $(1-cK)$ is large, and P will be made larger than it was. So we adjust the size of our uncertainty by some factor of the *Kalman gain*. I would like to draw your attention back to the g-h filter, which included this Python code:\n", - "\n", - " # update filter \n", - " w = w * (1-scale_factor) + z * scale_factor\n", - "\n", - "This multidimensional Kalman filter equation is partially implementing this calculation for the variance instead of the state variable." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we have the measurement steps. The first equation is\n", - "\n", - "$$\\mathbf{x}' = \\mathbf{Fx} + \\mathbf{Bu}\\tag{1}$$\n", - "\n", - "This is just our state transition equation which we have already discussed. $\\mathbf{Fx}$ multiplies $\\mathbf{x}$ with the state transition matrix to compute the next state. $B$ and $u$ add in the contribution of the control input $\\mathbf{u}$, if any.\n", - "\n", - "The final equation is:\n", - "$$\\mathbf{P} = \\mathbf{FPF}^T + \\mathbf{Q}\\tag{2}$$\n", - "\n", - "$\\mathbf{FPF}^T$ is the way we put $\\mathbf{P}$ into the process space using linear algebra so that we can add in the process noise $\\mathbf{Q}$ to it." - ] - }, - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "The Extended Kalman Filter" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Kalman filter that we have developed to this point is extremely good, but it is also limited. Its derivation is in the linear space, and hence it only works for linear problems. Let's be a bit more rigorous here. You can, and we have in this book, apply the Kalman filter to nonlinear problems. For example, in the g-h filter chapter we explored using a g-h filter in a problem with constant acceleration. It 'worked', in that it remained numerically stable and the filtered output did track the input, but there was always a lag. It is easy to prove that there will always be a lag when $\\mathbf{\\ddot{x}}>0$. The filter no longer produces an optimal result. If we make our time step arbitrarily small we can still handle many problems, but typically we are using Kalman filters with physical sensors and solving real-time problems. Either fast enough sensors do not exist, are prohibitively expensive, or the computation time required is excessive. It is not a workable solution.\n", - "\n", - "The early adopters of Kalman filters were the radar people, and this fact was not lost on them. Radar is inherently nonlinear. Radars measure the slant range to an object, and we are typically interested in the aircraft's position over the ground. We invoke Pythagoras and get the nonlinear equation:\n", - "$$x=\\sqrt{slant^2 - altitude^2}$$\n", - "\n", - "So shortly after the Kalman filter was enthusiastically taken up by the radar industry people began working on how to extend the Kalman filter into nonlinear problems. It is still an area of ongoing research, and in the Unscented Kalman filter chapter we will implement a powerful, recent result of that research. But in this chapter we will cover the most common form, the Extended Kalman filter, or EKF. Today, most real world \"Kalman filters\" are actually EKFs. The Kalman filter in your car's and phone's GPS is an EKF, for example. " - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "The Problem with Nonlinearity" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You may not realize it, but the only math you really know how to do is linear math. Equations of the form \n", - "$$ A\\mathbf{x}=\\mathbf{b}$$.\n", - "\n", - "That may strike you as hyperbole. After all, in this book we have integrated a polynomial to get distance from velocity and time:\n", - " We know how to integrate a polynomial, for example, and so we are able to find the closed form equation for distance given velocity and time:\n", - "$$\\int{(vt+v_0)}\\,dt = \\frac{a}{2}t^2+v_0t+d_0$$\n", - "\n", - "That's nonlinear. But it is also a very special form. You spent a lot of time, probably at least a year, learning how to integrate various terms, and you still can not integrate some arbitrary equation - no one can. We don't know how. If you took freshman Physics you perhaps remember homework involving sliding frictionless blocks on a plane and other toy problems. At the end of the course you were almost entirely unequipped to solve real world problems because the real world is nonlinear, and you were taught linear, closed forms of equations. It made the math tractable, but mostly useless. \n", - "\n", - "The mathematics of the Kalman filter is beautiful in part due to the Gaussian equation being so special. It is nonlinear, but when we add and multipy it using linear algebra we get another Gaussian equation as a result. That is very rare. $\\sin{x}*\\sin{y}$ does not yield a $\\sin(\\cdot)$ as an output." - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "The Effect of Nonlinear Transfer Functions on Gaussians" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Unfortunately Gaussians are not closed under an arbitrary nonlinear function. Recall the equations of the Kalman filter - at each step of its evolution we do things like pass the covariances through our process function to get the new covariance at time $k$. Our process function was always linear, so the output was always another Gaussian. Let's look at that on a graph. I will take an arbitrary Gaussian and pass it through the function $f(x) = 2x + 1$ and plot the result. We know how to do this analytically, but lets do this with sampling. I will generate 500,000 points on the Gaussian curve, pass it through the function, and then plot the results. I will do it this way because the next example will be nonlinear, and we will have no way to compute this analytically." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "from numpy.random import normal\n", - "\n", - "data = normal(loc=0.0, scale=1, size=500000)\n", - "ys = 2*data + 1\n", - "\n", - "plt.hist(ys,1000)\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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5PlETALDBms3yn/3Zn+ndd9/turdlyxZ9+OGH+vDDD3X+/HlJkud5euONN/TBBx/ovffe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\n/ScXzBcIoyZgAs0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- "text": [ - "" - ] - } - ], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is an unsuprising result. The result of passing the Gaussian through $f(x)=2x+1$ is another Gaussian centered around 1. Let's look at the input, transfer function, and output at once." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from nonlinear_plots import plot_transfer_func\n", - "\n", - "def g(x):\n", - " return 2*x+1\n", - "\n", - "plot_transfer_func (data, g, lims=(-10,10), num_bins=300)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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Pzk/Hg9OGsSAWGY/b5C2tZhsee78EAoBNd2TKHYeoF1F7ii0WC7Kzs3HkyBGY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\nrsar02qHsa1bqmiqxTkmHRbFREREJAm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- "text": [ - "" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The plot labelled 'input' is the histogram of the original data. This is passed through the transfer function $f(x)=2x+1$ which is displayed in the chart to the upper right. The red lines shows how one value, $x=0$ is passed through the function. Each value from input is passed through in the same way to the output function on the left. The output looks like a Gaussian, and is in fact a Gaussian. We can see that it is altered -the variance in the output is larger than the variance in the input, and the mean has been shifted from 0 to 1, which is what we would expect given the transfer function $f(x)=2x+1$ The $2x$ affects the variance, and the $+1$ shifts the mean.\n", - "\n", - "Now let's look at a nonlinear function and see how it affects the probability distribution." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from nonlinear_plots import plot_transfer_func\n", - "\n", - "def g(x):\n", - " return (np.cos(4*(x/2+0.7)))*np.sin(0.3*x)-1.6*x\n", - "\n", - "plot_transfer_func (data, g, lims=(-4,4), num_bins=300)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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wu6b3zTffRH5+PhoaGpCbm6v4zvPqj9JEpqSsgLLyKikr4Ni8DU1mnCmtbfG1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\nAQDTKp5IqGcoqt7hmCKxuELdw3LZbXLYbWrti6h3OKrRk5f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- "text": [ - "" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This result may be somewhat suprising to you. The transfer function looks \"fairly\" linear - it is pretty close to a straight line, but the probability distribution of the output is completely different from a Gaussian. Recall the equations for multiplying two univariate Gaussians:\n", - "$$\\begin{aligned}\n", - "\\mu =\\frac{\\sigma_1^2 \\mu_2 + \\sigma_2^2 \\mu_1} {\\sigma_1^2 + \\sigma_2^2}\\mbox{, } \n", - "\\sigma = \\frac{1}{\\frac{1}{\\sigma_1^2} + \\frac{1}{\\sigma_2^2}}\n", - "\\end{aligned}$$\n", - "\n", - "These equations do not hold for non-Gaussians, and certainly do not hold for the probability distribution shown in the 'output' chart above. \n", - "\n", - "Think of what this implies for the Kalman filter algorithm of the previous chapter. All of the equations assume that a Gaussian passed through the process function results in another Gaussian. If this is not true then all of the assumptions and guarantees of the Kalman filter do not hold. Let's look at what happens when we pass the output back through the transfer function again, simulating the next step time step of the Kalman filter.\n", - "\n", - "**process function? what is the correct name**\n", - "\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "y=g(data)\n", - "plot_transfer_func (y, g, lims=(-4,4), num_bins=300)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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ROYBJA/wwZYAf/rj7Mpp4LQmR7MmuprcreoMBF0rqsCez3FjuoFAAzk6yH9cTkQRY00vA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\nrKzApbIC15TPA4DZ4s5wdjBzegEAxmpvb7c6woRMzSWZm41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- "text": [ - "" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As you can see the probability function is futher distorted from the original Gaussian. However, the graph is still somewhat symmetric around $0$, let's see what the mean is." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print ('input mean, variance: %.4f, %.4f'% (np.average(data), np.std(data)**2))\n", - "print ('output mean, variance: %.4f, %.4f'% (np.average(y), np.std(y)**2))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "input mean, variance: 0.0013, 0.9985\n", - "output mean, variance: -0.0293, 2.2394\n" - ] - } - ], - "prompt_number": 6 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's compare that to the linear function that passes through (-2,3) and (2,-3), which is very close to the nonlinear function we have plotted. Using the equation of a line we have\n", - "$$m=\\frac{-3-3}{2-(-2)}=-1.5$$" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def h(x): return -1.5*x\n", - "plot_transfer_func (data, h, lims=(-4,4), num_bins=300)\n", - "out = h(data)\n", - "print ('output mean, variance: %.4f, %.4f'% (np.average(out), np.std(out)**2))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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hMx29PEySt+eV22lpaULlaS1fWVkZAECv1yMpKQndjYh99mUiHCNXbsv9b8ap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\nrkJggg==\n", - "text": [ - "" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "output mean, variance: -0.0019, 2.2466\n" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Although the shapes are very different, the mean and variance of each are almost the same. This may lead us to reasoning that perhaps we can ignore this problem if the nonlinear equation is 'close to' linear. To test that, we can iterate several times and then compare the results." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "out = h(data)\n", - "out2 = g(data)\n", - "\n", - "for i in range(10):\n", - " out = h(out)\n", - " out2 = g(out2)\n", - "print ('linear output mean, variance: %.4f, %.4f'% (np.average(out), np.std(out)**2))\n", - "print ('nonlinear output mean, variance: %.4f, %.4f'% (np.average(out2), np.std(out2)**2))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "linear output mean, variance: -0.1088, 7470.5861\n", - "nonlinear output mean, variance: -2.0348, 26163.5214\n" - ] - } - ], - "prompt_number": 8 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Unfortunately we can see that the nonlinear version is not stable. We have drifted significantly from the mean of 0, and the variance is half an order of magnitude larger. " - ] - }, - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "Unscented Kalman Filters" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the previous chapter we developed the Extended Kalman Filter to allow us to use the Kalman filter with nonlinear problems. It is by far the most commonly used Kalman filter. However, it requires that you be able to analytically derive the Jacobian blah blah limp prose.\n", - "\n", - "\n", - "However, for many problems finding the Jacobian is either very difficult or impossible. Futhermore, being an approximation, the EKF can diverge. For all these reasons there is a need for a different way to approximate the Gaussian being passed through a nonlinear transfer function. Recall the result of this transform:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from nonlinear_plots import plot_transfer_func\n", - "from numpy.random import normal\n", - "import numpy as np\n", - "\n", - "data = normal(loc=0.0, scale=1, size=5000000)\n", - "\n", - "def g(x):\n", - " return (np.cos(4*(x/2+0.7)))*np.sin(0.3*x)-1.6*x\n", - "\n", - "plot_transfer_func (data, g, lims=(-4,4), num_bins=300)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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x0Ro8PCYCGnebhwpEQrG5iO2tt95CQUEBGhsbcenSJdl3ntd/lCYyOWUF5JVX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\nFoZBhB8AAAAASUVORK5CYII=\n", - "text": [ - "" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For the Kalman filter predict-update cycle to work, we need an approximation of the mean and variance of the Gaussian being passed through the transfer function. The EKF solves this by performing a Taylor Series expansion...\n", - "\n" - ] - } - ], - "metadata": {} - } - ] -} diff --git a/build_book.bat b/build_book.bat index 314c68c..dd90c7e 100644 --- a/build_book.bat +++ b/build_book.bat @@ -1,5 +1,6 @@ -python merge_book.py Preface.ipynb Signals_and_Noise.ipynb g-h_filter.ipynb discrete_bayes.ipynb Gaussians.ipynb Kalman_Filters.ipynb Multidimensional_Kalman_Filters.ipynb Kalman_Filter_Math.ipynb Designing_Kalman_Filters.ipynb Extended_Kalman_Filters.ipynb Unscented_Kalman_Filter.ipynb > book.ipynb +python merge_book.py Preface.ipynb Signals_and_Noise.ipynb g-h_filter.ipynb discrete_bayes.ipynb Gaussians.ipynb Kalman_Filters.ipynb Multidimensional_Kalman_Filters.ipynb Kalman_Filter_Math.ipynb Designing_Kalman_Filters.ipynb Extended_Kalman_Filters.ipynb Unscented_Kalman_Filter.ipynb >Kalman_and_Bayesian_Filters_in_Python.ipynb + ipython nbconvert --to latex --template book --post PDF Kalman_and_Bayesian_Filters_in_Python.ipynb diff --git a/discrete_bayes.ipynb b/discrete_bayes.ipynb index 28bfc07..1ee9d16 100644 --- a/discrete_bayes.ipynb +++ b/discrete_bayes.ipynb @@ -1,7 +1,7 @@ { "metadata": { "name": "", - "signature": "sha256:1556bb7a2a15e47b34d1f079dab4a85d78afe50dc20aeb56325b4990232aeebd" + "signature": "sha256:266729b27e2d51ad90c675d939766430799e16551541a85150394b3f7eead92e" }, "nbformat": 3, "nbformat_minor": 0, @@ -53,7 +53,7 @@ "\n", " .text_cell_render h1 {\n", " font-weight: 200;\n", - " font-size: 36pt;\n", + " font-size: 30pt;\n", " line-height: 100%;\n", " color:#c76c0c;\n", " margin-bottom: 0.5em;\n", @@ -63,7 +63,6 @@ " } \n", " h2 {\n", " font-family: 'Open sans',verdana,arial,sans-serif;\n", - " text-indent:1em;\n", " }\n", " .text_cell_render h2 {\n", " font-weight: 200;\n", @@ -71,8 +70,8 @@ " font-style: italic;\n", " line-height: 100%;\n", " color:#c76c0c;\n", - " margin-bottom: 1.5em;\n", - " margin-top: 0.5em;\n", + " margin-bottom: 0.5em;\n", + " margin-top: 1.5em;\n", " display: block;\n", " white-space: nowrap;\n", " } \n", @@ -243,13 +242,13 @@ ], "metadata": {}, "output_type": "pyout", - "prompt_number": 3, + "prompt_number": 1, "text": [ - "" + "" ] } ], - "prompt_number": 3 + "prompt_number": 1 }, { "cell_type": "markdown", @@ -300,7 +299,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now let's create a map of the hallway in another list. Suppose rehere are first two doors close together, and then another door quite a bit further down the hallway. We will use 1 to denote a door, and 0 to denote a wall:" + "Now let's create a map of the hallway in another list. Suppose there are first two doors close together, and then another door quite a bit further down the hallway. We will use 1 to denote a door, and 0 to denote a wall:" ] }, { @@ -1069,7 +1068,7 @@ "level": 2, "metadata": {}, "source": [ - "Drawbacks and Limitations to the Discrete Bayesian Filter" + "Drawbacks and Limitations" ] }, { diff --git a/g-h_filter.ipynb b/g-h_filter.ipynb index 94f6ff3..43015e4 100644 --- a/g-h_filter.ipynb +++ b/g-h_filter.ipynb @@ -1,7 +1,7 @@ { "metadata": { "name": "", - "signature": "sha256:bfe3bacdc2d7cc23284f14dd3891819fd6c39a8b8ad0a092f81df050553181c4" + "signature": "sha256:d6a065bada82f52a1594d059481391c11dc03d589aca8e9e3b225011088ca167" }, "nbformat": 3, "nbformat_minor": 0, @@ -53,7 +53,7 @@ "\n", " .text_cell_render h1 {\n", " font-weight: 200;\n", - " font-size: 36pt;\n", + " font-size: 30pt;\n", " line-height: 100%;\n", " color:#c76c0c;\n", " margin-bottom: 0.5em;\n", @@ -70,8 +70,8 @@ " font-style: italic;\n", " line-height: 100%;\n", " color:#c76c0c;\n", - " margin-bottom: 1.5em;\n", - " margin-top: 0.5em;\n", + " margin-bottom: 0.5em;\n", + " margin-top: 1.5em;\n", " display: block;\n", " white-space: nowrap;\n", " } \n", @@ -244,7 +244,7 @@ "output_type": "pyout", "prompt_number": 1, "text": [ - "" + "" ] } ], @@ -345,7 +345,7 @@ "output_type": "display_data", "png": 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eDle1yV0SORGDyYyXd5bgronhimmCRbERViilzv8oDXMSx6zEMCcxzpZTZ48Ja/aU4r7M\nCKgHuG2ZrbLycnfFA1lRWJQegRe+KcI7P1XAaEc30jnbe2oolJbVe/urEOrngWmjAuQuZcDYCBMR\nEZ3Hu/urcMGIYUgJ95O7lPNKj9Zg3TWJKKzrwEObC1De3CV3SeTAjta0Y1tBPR5UwNz8YHBGmIiI\nqB/H6zvwxBfH8dZ1iQjwcpe7HGGSJGHTkTq8u78St08Iw6yEQLtsVEi5uo1m3PNpHv6QGorLRipv\nNZgzwkRERENgMktYlV2K29JC7aoJBgCVSoW5ScF45arR2HSkDs9tL0Jzl1HussiBvPNTBeKGeymy\nCRbFRlihlDb/o1TMSRyzEsOcxDhLTp/n1cHVRYWZQ9gOSu6sYgK88NrceIT5eeDuDXn4qaxF1nrO\nRe6c7IkSstJVtWHHiUbcmxkpdylDwkaYiIioD/UdPfjX/ircnxlpFydk9Uft6oI7J4bjscnRePV7\nPdbuKYPBaJa7LLJTnT0mvLKzBA9kRtn8dEVL44wwERFRH/78bRG0vh7444QwuUuxqJYuI17LKYW+\nqQtPTI7ByEAvuUsiO7M6pxSdRjMeuyxa7lL6xRlhIiKiQfiprAX5tR24ebxW7lIszs/TDU9NjcH1\nySF4/ItjWK+rgdl2a2Jk5w6Ut2KPvhmLLgmXuxSLYCOsUEqY/7EHzEkcsxLDnMQ4ck5dRjNW55Ti\n3owIeA5wz+C+KDErlUqFy+MDsWpOPL4vasKTXxxHfXuPrDUpMSelkiurdoMJK74vwUNZURjmYd8j\nEb3YCBMREZ3m/QNViA/yxsWRGrlLsbowPw+suGo0LtD64J5P85Bd1CR3SaRgb+4tR2q4HyZEKn8/\nbVGcESYiIvpVcWMnHt16DG9cm4hAb/vaLm2ojta048UdxUjWDsOi9Ah4ubvKXRIpSK6+Ga/vLsOb\n1ybCW20f7w3OCBMREQkySxJeyy7FLSlap2uCAWBMiA/WXp0IALjn0zwcrWmXuSJSipYuI1Zll+KR\nSVF20wSLYiOsUJyVEsOcxDErMcxJjCPmtK2gAT1mCVcmBln0ee0pK2+1Kx6ZFI0/TgjH0q9O4L39\nlTCZbfOLY3vKSW62zmrtnjJkxvjjojBfm76uLfTbCC9ZsgRarRbJycmnHsvNzcW4ceMwduxY3HDD\nDacef+6555CUlISkpCQ8//zz1quYiIjIwho7e/D3HyvwYFYkXF3se89gS7g01h9rr0mArqoNj2wp\nRGVLt9wlkUyyi5uQV9uB2yeEyl2KVfQ7I7xnzx6o1WosWLAAOp0OZrMZY8aMwTvvvIOMjAzU19cj\nMDAQRUVFmDFjBgoKCmAymZCYmIhvv/0W0dFn7i/HGWEiIlKil3YUQ+PphrsuiZC7FEUxSxI2HK7F\nR4eqccfFYZgxejhUdn64CIlr6uzB3Rvy8My0WCRph8ldzoANeUY4PT0dgYH/OVZy3759CA4ORkZG\nBgCc+pifnx/c3d3R2dmJzs5OqNVqaDSOf7ctERHZvwMVrfi5qg23pjrmitdQuKhUuD45BMtnxeET\nXQ3+/G0xWrqMcpdFNiBJElbvLsPUUcPtsgkWNaAZYb1eD41Gg1mzZiElJQXr1q0DcLIhfuCBBxAZ\nGYmoqCgsWbIE/v7+VinYWXBWSgxzEsesxDAnMY6Sk8FoxmvZpVicHmm1XRIcIau4QG+8PjcBw73d\ncc+neThQ0Wrx13CEnGzFFlntONGEksYuLHDwHxAHtBtyV1cXcnJycPjwYWg0GqSlpWHmzJlQqVR4\n4403UFJSAoPBgMzMTFx55ZXQah3vRB4iInIcHx6qRnSAJ9Kj+VvM8/Fwc8Gi9AhcHOmHl3aUYEpc\nABakhULtyvvuHU19Rw/W7SnDC/81EmoLHCqjZANqhLVaLcaOHYuIiJMzVKmpqcjLy0NraysmTJgA\nX9+TdxOOHz8eBw4cwKxZs856jkWLFiEqKgoAoNFokJycjKysLAD/+QmH11nIyspSVD1Kvu6llHqU\net37mFLq4bV9X/c+ppR6BnNd163CpgpfrL0mkd/PB3CdFuGHBWFN2FLUiQcqWvHE5GiU/rLPIs/f\nS0n/vUq87n3MGs8vSRKe3XgQyT5mJAT7KOK/dyDvn+zsbOj1egDAwoULcT7nPVCjuLgYs2fPhk6n\nQ3NzM5KSkqDT6eDj44PU1FSsX78eLS0tWLhwIX744QeYTCZcdNFF2LRpExISEs54Lt4sR0RESiBJ\nEh77/BjSozW49oIQucuxS5Ik4Yv8evz9xwrckhKKOWODeCOdA/iqoB4bDtdi9dx4uNv5av+Qb5Zb\nvHgxMjIykJ+fj8jISOzatQsrV67E1KlTkZKSgvnz5yM+Ph5paWm45pprMH78eKSlpeGOO+44qwmm\ngfntT8fUN+YkjlmJYU5i7D2n7cca0G4wYe7YYKu/lr1ndS4qlQpXJAZh5Zx4fF3YgKe3nUBDR8+g\nn89Rc7IGa2VV02bA2z9U4NHLouy+CRbl1t8H16xZgzVr1pz1+PXXX3/WY0uXLsXSpUstVxkREZEV\ntHQZ8b8/VOD5y0dyz2ALiNB4YuWceLy7vxKLPs3DA1lRnLm2Q5Ik4a/f63F1UjDiAr3lLsdmzjsa\nYUkcjSAiIrm9uksPDzcXLM6IkLsUh6OrasNLO0qQFuGLOyeGW20nDrK8LUfr8GV+PVbNiXeYHxCH\nPBpBRETkSHRVbfiprAUL0hx7Syi5JGuH4Y1rE9FlNGPxZ/koqOuQuyQSUNnajX/uq8Sjl0U5TBMs\nio2wQnFWSgxzEsesxDAnMfaYU4/JjFXZpbg7PRw+atutVNpjVkPho3bF45NjcEtKKJ768jg+PFQF\nk/n8v3x2tpyGwpJZmSUJK3bqMW9cCKIDvCz2vPaCjTARETmFf+tqoPVV49IYHvhkC1PiArDm6gT8\nVNqKxz4/hpo2g9wlUR82/lILo1nCdU66ewpnhImIyOFVtHTj/o35eP3qBGh9PeQux6mYzBL+ravB\nv3U1WJQejilxw+UuiX5V1tyFBzcVYNWceIRrPOUux+JEZoT73TWCiIjI3kmShNU5pfjduBFsgmXg\n6qLCDReOQEq4L/7yXTFy9S24LzPSpuMpdDaTWcIrO/X4fUqoQzbBojgaoVCclRLDnMQxKzHMSYw9\n5bTjRBMaOnpwbbI8v/q1p6ysaXSQN9ZekwhvtSvu3pAHXVXbGR9nTuIskdV6XQ3cXVWYMzbIAhXZ\nLzbCRETksNq6jXgztwwPZEXBzcnuhlciTzcX3J8ZicUZEfjzN0V458cKGAVupCPLKm7sxCe6Gjwy\nKQouTn4aIGeEiYjIYb2WXQoJEh7IipK7FPqNxo4erPhej6ZOI56YEo0IJ/71vC0ZzRIe2JSPKxKD\ncGWiY68Gcx9hIiJyWkdr2rFb34TbJ4TJXQr1IcDbHS9cPhKXxw/HQ5sL8fGhatS2c2cJa/vwUDU0\nnm64IiFQ7lIUgY2wQnFWSgxzEsesxDAnMUrPyWiWsCpbj7smhsPXQ977wpWelZxUKhXmjA3GiitH\n48f8Ety9IQ/3b8zHx4eqUd7cLXd5ijXY99Sxug5s/KUWD10aBZWTj0T04q4RRETkcDYcroG/lzsm\njwyQuxQSEBXgiblhBlySMQE/V7Yiu6gZj2wpgL+XGzJj/JEV44+YAE82b0NgMJnx8s4S3DkxDME+\narnLUQzOCBMRkUOpbjVg8Wd5WDUnAeEabpdmr0xmCXk17cgubkJ2cTPcXFTIitEgM8YfCcHebIoH\n6J0fK1Dc2IVlM2KdJjvuI0xERE5FkiS8vrsU11wQwibYzrm6qJCkHYYk7TDcOTEcx+o7kV3chJd3\nlqDTaEZWjD+yYjRIGjEMrtwRpF95Ne34Ir8eb1yb6DRNsCjOCCsUZ8rEMCdxzEoMcxKj1JxyiptR\n2WrAvHHKOS5WqVkpTX85qVQqjA7yxm1pYfjbvLFYPnMU/D3d8Mbectz0/mH89Xs9fixtQY/JbMOK\n5TOQ91S38eRIxKL0CAz3drdiVfaJK8JEROQQ2g0mrN1ThiemxEDtynUeRxYV4In5AVrMH69FZWs3\ncoqb8f7BKizf0YUJEX7IivVHWoQfPN34PvjnvkrEDvfC5DjOy/eFM8JEROQQ1u4pQ2ePCY9Mipa7\nFJJJfUcPdv86U5xf246UcF9kxvjjkiiNUx7pfLiqDf/9bRHevHYMNJ7Ot/bJGWEiInIKBbUd2Hmi\nEW9fN0buUkhGgd7umD02GLPHBqOly4i9+mbsPNGI1TmlSBoxDFkxGqRHa+Dv5fgjAp09JryyqwT3\nZ0Y6ZRMsir8zUCjOlIlhTuKYlRjmJEZJOZnMElZm6/HHCWHwU+Bf+ErKSsksnZOfpxsujw/E85fH\n4f2bLsCM0cOxv7wVt31yFI9uLcRnv9Ta7QEeIln97ccKjA3xQUa0vw0qsl/K+45BREQ0AJuO1MJH\n7YoZo4fLXQoplLfaFZPjAjA5LgDdRjP2l7ciu7gJ7+6vRLifB7Ji/JEZ4+8wO40cKG/F7pJmvHlt\notylKB5nhImIyG7Vthtwz4Y8vDo7HlH+nnKXQ3bGaJZOHeCxu6TJIQ7waDeYTp7QlxmJCZF+cpcj\nK84IExGRQ1u3pwxzxgazCaZBcXNRISXcDynhflicEXHqAI9nvzoBVxcVLrXDAzzeyi1HSriv0zfB\nojgjrFCcKRPDnMQxKzHMSYwSctpT0oyihi7ceOEIuUvplxKysgdy59R7gMddl0TgXzeMxVNTY+Di\nosLLO0tw84e/YO2eMvxc2QqT2Wa/SD+nc2X1Q2kz9pe34s6J4TauyH5xRZiIiOxOZ48Ja/aU4uFL\no6DmXrFkYb0HePQe4qFv7EJ2cRPe2FuO2vYeZERrkBXjj4vChsFdIXtWt3YbsTK7FI9eFu2UW8UN\nFmeEiYjI7ryVW47Gzh48PjlG7lLIyfQe4JFT3AR9068HeMT4Iy1S3gM8XtpRDB+1KxZnRMpWg9Jw\nRpiIiBzO8foOfF3YgLeu4x3xZHuhvh64PjkE1yeHnDrAY/PROryyq0S2AzxyiptwpKYD665JsNlr\nOgplrOfTWeSelbIXzEkcsxLDnMTIlZPJLGFVdiluSwtFgJ0cisD3lBh7zKn3AI8XrxiFf92QhEui\nNNh5ohE3f3AYT315HF/k1aGps8fir3t6Vs1dRqzeXYpHJ0XBy50jEQPFFWEiIrIbn+fVwdVFhZkJ\ngXKXQnSG3gM8Lo8PRIfBhB9KW5BT3IS3fqjAqEAvZMb4IzNGg2AftUVfd3VOKabGDUeSdphFn9dZ\ncEaYiIjsQn1HD+7ekIeXrhiF2OFecpdDJOT0Azz26psR5ueBSy10gMeO4414d38l1l6TCA/eNHoW\nzggTEZHDeGNvGWYmBLIJJrvi4eaC9GgN0qM1Zxzg8ciWAmg83ZAVO7gDPBo6erB2Txmev3wkm+Ah\nYHIKZY+zUnJgTuKYlRjmJMbWOf1U1oL82g7cPF5r09e1BL6nxDhDTr0HeNyfFYn/u+kC3J8ZiQ6D\nCc9+dQK3fXIU//tDOfJq2nG+X9Z//302VmWXYlZiIBJDfGxUvWPiijARESlal9GM1TmluDcjQtbt\nqYgsqfcAjyTtMNw5MRzH6juRXdyEl3eWoNNoRma0P7JiNLhAOwyuLmeuFP/c4obq7m48PS1GnuId\nCGeEiYhI0f7+YwUqW7rx1LRYuUshsoneAzyyi5tOHeCRGaPB+DBfNHUZsejTfCyfFYe4QG+5S1U0\nzggTEZFdK27sxBf59XjjWu4ZTM4jKsAT8wO0mD9ee+oAjw8OVuPFHSXw9XDF3KRgNsEWwt8xKZQz\nzEpZAnMSx6zEMCcxtsjJLEl4LbsUt6RoEehtH3sG94XvKTHMqW+9B3j8dXY83rpuDP6QGobItmNy\nl+Uw2AgTEZEibStoQI9ZwpWJQXKXQqQIgd7umBIXAFfxzSXoPDgjTEREitPY2YM71+dxDpKIBk1k\nRpgrwkREpDhv55Zj+qgANsFEZFVshBWKs1JimJM4ZiWGOYmxZk4HKlrxc1Ubbk0Ntdpr2BLfU2KY\nkzhmZTn9NsJLliyBVqtFcnLyqcdyc3Mxbtw4jB07FjfccMN5HyciIhJlMJrxWnYpFqdHwsvdVe5y\niMjB9TszI3NhAAAgAElEQVQjvGfPHqjVaixYsAA6nQ5msxljxozBO++8g4yMDNTV1SEoKOisx+vr\n6xEYGHjW83FGmIiI+vOvfZU40dCJZTNGyl0KEdm5Ic8Ip6enn9HQ7tu3D8HBwcjIyAAABAUF9fl4\nX00wERFRf0qburDpSC0WpUfIXQoROYkBzQjr9XpoNBrMmjULKSkpWLduXb+P0+Bx/kcMcxLHrMQw\nJzGWzkmSJLyWU4r547UIGaa26HPLje8pMcxJHLOynAGdLNfV1YWcnBwcPnwYGo0GaWlpmDlz5jkf\nj409+zjMRYsWISoqCgCg0WiQnJyMrKwsAP/5H8trXote63Q6RdWj5GudTqeoepR63Usp9Sj12tLv\np7Wf56K6wQ1zZ41SxH8fr/n9XMnX/H5+7u/f2dnZ0Ov1AICFCxfifM67j3BxcTFmz54NnU6Hb775\nBs888wx2794NAJg/fz5uueUWqNXqPh+fNWvWGc/FGWEiIvqtli4j7lh/FM9fPhIJwT5yl0NEDkJk\nRthtIE+YlpYGvV6PxsZG+Pj4QKfTIS4uDiNGjOjzcSIiovP53x8qMCk2gE0wEdlcvzPCixcvRkZG\nBvLz8xEZGYldu3Zh5cqVmDp1KlJSUjB//nzEx8dDo9H0+TgN3m9/TUt9Y07imJUY5iTGUjnpqtrw\nU1kLFqQ5xp7BfeF7SgxzEsesLKffFeE1a9ZgzZo1Zz1+/fXX9/lYX48TERH1pcdkxqrsUtydHg4f\nNfcMJiLbO++MsCVxRpiIiHp9cLAKR6rb8fzlI6FSqeQuh4gczJD3ESYiIrKGipZurNfVYHFGBJtg\nIpING2GF4vyPGOYkjlmJYU5ihpKTJElYnVOK340bAa2vhwWrUia+p8QwJ3HMynLYCBMRkU3tONGE\nho4eXJscIncpROTkOCNMREQ209ZtxML1R/HstJEYO4LbpRGR9XBGmIiIFOXvP1YiPUrDJpiIFIGN\nsEJx/kcMcxLHrMQwJzGDyeloTTt265tw+4QwK1SkXHxPiWFO4piV5bARJiIiqzOaJazK1uOuieHw\n9RjQoaZERFbDGWEiIrK6j3+uxv7yVvxlZhy3SyMim+CMMBERya661YCPD1XjvoxINsFEpChshBWK\n8z9imJM4ZiWGOYkRzUmSJLy+uxTXXhCCcI3j7xncF76nxDAncczKctgIExGR1eQUN6Oy1YB547hn\nMBEpD2eEiYjIKtoNJtzx76N4YkoMxoUOk7scInIynBEmIiLZ/HNfJVIjfNkEE5FisRFWKM7/iGFO\n4piVGOYk5nw5FdR2YOeJRtxxcbiNKlIuvqfEMCdxzMpy2AgTEZFFmcwSVmbr8ccJYfDz5J7BRKRc\nnBEmIiKL+vRwDXaXNOOlK0ZxuzQikg1nhImIyKZq2w34vwNVuC+TewYTkfKxEVYozv+IYU7imJUY\n5iTmXDmt21OGOWODEeXvaeOKlIvvKTHMSRyzshw2wkREZBF7SppR1NCFGy8cIXcpRERCOCNMRERD\n1tljwh3rj+LhS6OQEu4ndzlERJwRJiIi23h3fxWStcPYBBORXWEjrFCc/xHDnMQxKzHMSczpOR2v\n78DXhQ24cyL3DO4L31NimJM4ZmU5bISJiGjQTGYJq7JLcVtaKAK83OUuh4hoQDgjTEREg7b5SC2+\nPd6IFVeNhgu3SyMiBeGMMBERWU19Rw/+tb8KD2RFsgkmIrvERlihOP8jhjmJY1ZimJOY7OxsvLG3\nDDMTAhET4CV3OYrG95QY5iSOWVkOG2EiIhqwY22uyK/twM3jtXKXQkQ0aJwRJiKiAWnq7MEDmwqw\nOCMCF0dq5C6HiKhPnBEmIiKLOlbXgfs2FmDqqOFsgonI7rERVijO/4hhTuKYlRjmdG7fHW/Ak18e\nx8KLwxDXeVzucuwG31NimJM4ZmU5bnIXQEREymYyS/jbjxXILm7Ci7NGYWSgF7Ir5K6KiGjoOCNM\nRETn1NJlxP98VwwA+NOUGPh5cv2EiOyDyIwwv6MREVGfTtR34rntJ5AZ448/TgiDqwv3CiYix8IZ\nYYXi/I8Y5iSOWYlhTiftOtGIx784hj+khuLOieFnNcHMSRyzEsOcxDEry+GKMBERnWIyS/jHvkrs\nON6Iv8yMw6ggb7lLIiKyGs4IExERAKC124i/fFeMHpOEp6bGwN/LXe6SiIgGjTPCREQkpLixE8u+\nLsLESD/cMTEcbpwHJiInwBlhBXp++wms3LxX7jLsAuekxDErMc6YU3ZxEx7degw3jx+Be9IjhJpg\nZ8xpsJiVGOYkjllZTr+N8JIlS6DVapGcnHzqsdzcXIwbNw5jx47FDTfccMbnt7a2IiwsDCtWrLBO\ntU7g58pWFNZ1YmedGrqqNrnLISIHZpYk/HNfJdbtKcOf/ysOM0YHyl0SEZFN9TsjvGfPHqjVaixY\nsAA6nQ5msxljxozBO++8g4yMDNTX1yMw8D/fOJ944gkcOXIEkydPxsMPP3zW83FG+Pwe3VqIGaOH\nI8DLHSu+L8HquQkI9lHLXRYROZh2gwnLvytGe48Jz0yNRYA354GJyLGIzAj3uyKcnp5+RqO7b98+\nBAcHIyMjAwDO+Fh+fj5qa2uRmpoKG95/51AOVbSitr0H00YNx4RIP8wdG4zntxfBYDTLXRoRORB9\nUxfu25iPEb5qvDhrFJtgInJaA5oR1uv10Gg0mDVrFlJSUrBu3bpTH3vyySexbNkyS9fnVN7dX4Wb\nx4+Aq4sK2dnZuPHCEQgZpsbq3aX84eIcOCcljlmJcfSc9pQ045EthZg3bgTuzYiEu+vgbhVx9Jws\niVmJYU7imJXlDGjXiK6uLuTk5ODw4cPQaDRIS0vDzJkzcfjwYcTHxyMyMvK8DduiRYsQFRUFANBo\nNEhOTkZWVhaA//yPdcbrgxWtKKtvgUdVNTD65MdzcnKQ6Q58UBuIrXn18K/PU0y9SrnW6XSKqkfJ\n1zqdTlH1KPW6l1LqsdT1999n4/t6dxzu9MHzl49EfcEBZNfy/cRr5Vzz+zn//Fni+3d2djb0ej0A\nYOHChTif8+4jXFxcjNmzZ0On0+Gbb77BM888g927dwMA5s+fj1tuuQW7d+/Ghx9+CDc3N9TV1cHF\nxQUrV67ETTfddMZzcUa4b5IkYcnWY5iVEIjpo4ef9fHy5i48uLkQy6bHIkk7TIYKiciedRhMeGln\nCZo6jXhmeiwCOQpBRE7A4vsIp6WlQa/Xo7GxET4+PtDpdIiLi8OsWbPwwgsvAACee+45+Pr6ntUE\n07kdrGxDQ0cPpsQF9PnxcI0nlkyKwn9/W4zX5yYg0Id/iRGRmPLmLiz7ughJWh/8aWoM1IMchSAi\nckT9fkdcvHgxMjIykJ+fj8jISOzatQsrV67E1KlTkZKSgvnz5yM+Pt5WtTokSZLw7v5K3DxeC9fT\n9u787a9pJ0ZpcNWYIDz/zQkYTLx5rtdvc6JzY1ZiHCmnH0qb8eDmQsxNCsaDWVEWbYIdKSdrY1Zi\nmJM4ZmU5/a4Ir1mzBmvWrDnr8euvv/6cX7N06dKhV+VEDla2oanTeM7V4NPddNEIHKvrwNo9ZXgw\nK8oG1RGRPZIkCR8eqsamI3UcqSIi6sd5Z4QtiTPCZ5IkCY9sKcQViUF9zgb3pcNgwv2bCnDNBcG4\nMjHIyhUSkb3p7DHhlV161LQZsHR6LIK4DzkROakh7yNM1nWwog1NXWKrwb281a5YOj0W//ipEkeq\n261YHRHZm8qWbjy4qQDe7i5YceVoNsFEROfBRlgmkiThX33MBvfqb/4n0t8TD18ahf/+pgj1HT3W\nLFPxOCcljlmJsdec9pW14IFNBbhyTBAevjQKajfrfnu315zkwKzEMCdxzMpy2AjL5EBFK5q7jJg8\nUnw1+HTp0RpckRiIF7YXoYc3zxE5LUmS8MnP1Xh5ZwmenhaDOWODoVKd/cM1ERGdjTPCMpAkCQ9t\nLsScsUGYOkpsNrgvZknCc18XIdDHHfdnRlqwQiKyB11GM/76vR5lzV1YOn0kQoZxFIKIqBdnhBVq\nf3krWruNuGyQq8G9XFQqPDY5GgcrWvFFfr2FqiMie1DV2o2HNhfARQW8elU8m2AiokFgI2xjJ/cN\nrsLvU/qeDe4lOv/jo3bFshkj8fcfK5BX43w3z3FOShyzEmMPOR2oaMUDmwowY/RwPHZZNDysPA/c\nF3vISSmYlRjmJI5ZWQ4bYRvbV96KNoMJk2KHthp8uih/Tzx0aSSe/6YIDU5+8xyRI5MkCRsO12D5\nd8V4YnIMrr0ghPPARERDwBlhG5IkCQ9uLsDVSSED2jJN1L/2VeJgRStevGIU3HmMKpFD6TaasSpb\njxMNXVg2IxZaXw+5SyIiUjTOCCvMvvJWtBvMmBTrb5Xn/32KFj5qV7yVW26V5yciedS0GfDwlgIY\nzRJWzolnE0xEZCFshG3k5GxwJX5/jn2Df2sw8z8uKhUenxyNn8pa8VWBc9w8xzkpccxKjNJy+rmy\nDfdvysfkkQF4ckoMPGWYB+6L0nJSMmYlhjmJY1aW4yZ3Ac7ip7JWdBjMuNRKq8G9hnm4YdmMWCzZ\negwxAV6ID/a26usRkXVIkoTNR+vw3v4qPDY5GmkRfnKXRETkcDgjbAOSJOGBTQW4LjlkyFumicou\nasIbuWVYPTcBAV7uNnlNIrIMg9GM1btLkV/bgWUzRiLMj6MQREQDxRlhhfixrAWdRuuvBp8uK9Yf\n00YNx5+/KYbRbLOfdYhoiOraDXhkayE6esxYNSeeTTARkRWxEbay3n2DbxmvhcsAtjmyxPzPrSmh\n8HBzwdsOfPMc56TEMSsxcub0S1Ub7ttYgIxoDZ6eGgMvd1fZajkfvp/EMSsxzEkcs7IcNsJW9mNZ\nC7qNZmTZcDW4l6uLCk9MiUZuaTO2FzbY/PWJSNyWo3VYtr0ID10aiZsu0nJ/YCIiG+CMsBVJkoT7\nNxVg3rgQix6gMVBFDZ147PNj+J+ZcRgdxJvniJSkx2TGmj1lOFzVjudmxCJc4yl3SUREDoEzwjL7\nobQFBqMZWTG2Xw0+XexwL9yXGYHntxehqZMnzxEpRX1HDx7degxNnUasmhPPJpiIyMbYCFtJ72zw\n71NCBzQb3MvS8z+TYgMwOS4Af/62GCYHunmOc1LimJUYW+V0tKYd923MR1qEL56dHgsftXLngfvC\n95M4ZiWGOYljVpbDRthKcktb0GMyIzNGI3cppyxIDYWbiwr/+4Pj3jxHZA++zK/Hs1+dwH0ZkYP+\nYZmIiIaOM8JWIEkS7t2Yjxsv1Np0yzQRLV1G3LcxH39IDcXUUcPlLofIqRjNEt7YW4b95a1YNmMk\novw5CkFEZC2cEZbJXn0LTGZJUavBvfw83bB0+kis21uO4/UdcpdD5DQaO3vw2OeFqG41YPXcBDbB\nREQKwEbYwk7OBlfi9+OH9utOa87/jAz0wuL0CCz7ugjNXUarvY4tcE5KHLMSY42cCmo7cO9n+Rin\nHYbnLh9pd/PAfeH7SRyzEsOcxDEry2EjbGF79S0wS0CGAleDTzc5LgCTYv3xP98WOdTNc0RK83Vh\nPZ7adhz3XBKBBWlhnAcmIlIQzghbkCRJWPxZPuaP18q+ZZoIk1nCn748jlGBXrhjYrjc5RA5FKNZ\nwtu55cgtbcHS6bGIHe4ld0lERE6FM8I2tlffAglAZrSyV4N7ubqo8NTUGHxf3IQdxxvlLofIYTR3\nGfHkF8dQ2tyF1XPj2QQTESkUG2EL+c9ssGWORrXV/M/Jm+disWZPmV3ePMc5KXHMSsxQczpWd3Ie\nODHEBy9cHgdfDzcLVaYsfD+JY1ZimJM4ZmU5bIQtZI++GRKADDtZDT5dXKA37rkkHM9vL0KLnd88\nRySn74434Mkvj2PhxWH444QwuLpwHpiISMk4I2wBkiRh0Wf5uCVFi4xo5c8Gn8ube8tQ3NiF//6v\nOP4FTjQAJrOEv/1YgeziJiybPhIjAzkKQUQkN84I28jukmaoAKRH2d9q8OkWXhwOkyThH/sq5S6F\nyG60dBnx1LbjOF7fidfnJrAJJiKyI2yEh8gsSXh3fxVuSQm1yGxwLznmf1xdVPjTlBjsON6IXSfs\n4+Y5zkmJY1ZiBpLTifpO3LcxHyOHe+F/ZsbBz9Mx54H7wveTOGYlhjmJY1aWw0Z4iHaXNMNFBVwS\n5Sd3KRbh7+WOZ6fHYvXuMhQ1dMpdDpFi7TrRiMe/OIY/pIbizonhHCciIrJDnBEeArMkYdGnefhD\nahjS7fAmuf5sL2zAewcqsXpugsPe9U40GCazhH/uq8R3xxvx7PRYjA7ylrskIiLqA2eErWx3cTNc\nXVQOsxp8uumjh2NipAbLvyvhyXNEv2rtNuLZr07gaE07Vs+NZxNMRGTn2AgPkvnXfYMtPRvcSwnz\nP3dMDEe30Yx/7VfuzXNKyMleMCsx58qpuLET920sQITGA3+ZNQr+Xu42rkxZ+H4Sx6zEMCdxzMpy\n2AgPUk5xM9xdXTAx0vFWg3u5uajw1LQYfHOsAdlFTXKXQySb7OImPLr1GOZfNAL3pEfAjfPAREQO\ngTPCg2CWJNyzIQ+3TwjDRDvfMk1EQW0Hntp2HC9fOQoxAdwaipxH764wXxXU49npsUgI9pG7JCIi\nEsQZYSvJLm6C2s0FFzvwavDp4oO9ccfFYXju6yK0dfPkOXIO7QYTln51AocqWvH63AQ2wUREDqjf\nRnjJkiXQarVITk4+9Vhubi7GjRuHsWPH4oYbbgAAlJeXIysrCxdccAFSU1Oxfft261YtI7Mk4b39\nVbglRWuV2eBeSpv/uTw+EGkRvnhxRwnMtvslwnkpLSclY1ZisrOzoW/qwn0b8xEyTI0XrxiFAG/n\nngfuC99P4piVGOYkjllZTr+N8HXXXYetW7eeujabzbj11lvxxhtv4MiRI1i7di0AwN3dHevWrcPh\nw4fx6aefYsGCBVYtWk7ZRU3wcHPBhAjnWA0+3V2XRKC9x4T39lfJXQqR1eS3uuKRLYWYlxyC+zIj\n4e7KX5wRETmq884IFxcXY/bs2dDpdPjxxx/x0EMPnfcnkZCQEJSXl8Pd/cxVFHufETZLEu7akIc7\nLg7DxZGOPxvcl8aOHty7MR+L0iOQGeMvdzlEFmM0S3hvfyW+KmjAM9NjMSaEoxBERPbM4jPCer0e\nGo0Gs2bNQkpKCtatW3fW52zbtg2pqalnNcGO4PuiJng66WpwrwBvdzwzLRYrs0uhb+ySuxwiiyhv\n7sZDmwtQUNeB169OYBNMROQkBtQId3V1IScnB2+//TZ27tyJlStXoqio6NTHq6qqsGTJklMjE47E\nVrPBvZQ8/5MY4oM/TgjDsu0n0G4wyVqLknNSGmZ1NkmS8EV+PR7cXICpcQH47/+Kw5H9uXKXZRf4\nfhLHrMQwJ3HMynIGdHauVqvF2LFjERERAQBITU1FXl4eYmNj0dXVhXnz5mHFihWIjY0953MsWrQI\nUVFRAACNRoPk5GRkZWUB+M//WCVe7zrRBGNXO7qLdUCk/PXIfT0zIRC7dMfx+Ib9eO2GNLioVLLU\no9PpFJGHPVzrdDpF1SP39Vc7s7Gl0gMG9TC8dMUolB/Zh905Begld31Kv+b7ideWvub3c/75G+p1\n77/r9XoAwMKFC3E+A5oRbm5uRlJSEnQ6HXx8fJCamor169dj9OjRmD9/PiZNmoR77rnnnM9lrzPC\nJrOEuzfk4c6J4ZjgJFumiegxmfHY58eQGu6L36eEyl0OkbB9ZS1YsUuPyXEBWJAWCjVviCMicjhD\nnhFevHgxMjIykJ+fj8jISOzatQsrV67E1KlTkZKSgvnz5yM+Ph45OTlYv3493nrrLYwfPx7jx49H\nVZXj7Cywq6gJ3moXpEX4yl2Kori7uuCZabH4PK8ee0qa5S6H6LwMRjPW7S3Diu/1ePSyaNw5MZxN\nMBGRE+PJcudhMp/cKeLuS8KRZsOb5LKzs08t+Svd0Zp2PPvVCbx61WhE+nva9LXtKSe5OXtWRQ2d\nWP5dMSL8PfFAZiT8PN36/Dxnz0kUcxLHrMQwJ3HMSgxPlrOAXUVNGKZ2RWo4V4PPZUyID25LC8Wy\nr+W/eY7ot8yShA2Ha/DY58dwXXIInp4ac84mmIiInAtXhPthMku4c/1R3JMeYdPVYHu1MluPpk4j\nnp0eCxcb7KxBdD717T14eVcJOntMeHxyDML8POQuiYiIbIQrwkO0q6gRvh5uXA0WtCg9Ak2dRnxw\nsFruUoiQXdyERZ/lIWmED169Kp5NMBERnYWN8DmYzLbdN/i3Tt8KxF6of715bsvROuTqbXPznD3m\nJBdnyaqzx4RXd+nxdm45lk4fiVtSQuHqIv5n2FlyGirmJI5ZiWFO4piV5bARPoedJxrh5+mGFK4G\nD0igjzuenhqDV3bpUd7Mk+fItvJq2nHPp/mQIGHdNYkYO4InxBER0blxRrgPJrOEO9Yfxb0ZEUgJ\n52zwYGw5WoeNv9Ri1Zx4eKtd5S6HHJz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RUu3/kRrmJI5ZiWFOYkItJ5MJGDNGi7ffNiMsrGpf66us9Hpg1iwzpk61IDk5HP/8Zxjs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\nr4kxG3wZ8z9myMkcWZkhJzPkZI6szJCTObKKHcc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SBKSk6PHCCw4WwQGgUgEvvuhEixYePPusAdu2afDGGyVoT/H5oN69GzqTCeqd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\nExEREZFUrtoIT58+HVFRUUhISKj6sYCAAHTo0AEdOnTA1KlTq3785Zd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WAACgDUpPd2rVqpOKiam7SB4woEKLFhUpKYmbhQSDBgvkCRMm6J133vFsu1wuTZs2TU8/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\ntGCBKoYOVfnEiS16/Yg335R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lZXjwwQexbt26OgtkUTf92LlzJ6KiorB3717rGNLT0x3e0a0xY3Ulr8bq2LEj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\ncalUivHjx+PAgQMsjomIiIjqwAL5LtatWzesWrU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D94ceIv377zE3bKh2OOVScXuK7zpSHBQURPXq1S2+Fh0dTWhoKAD79u2jefPm3HfffXh5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\nl99N8/II6NqVwmnTsERFld99SujCBQ2bNnmyZ4+OvXt1/PSTB6GhVp5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aNTMgIkJbqrH19i678Rw7VoNx40zXeD6uRQsDWrSwngX+RTu9bbm4Bbsu76rS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\nsRs26PDoo84S7ezqaae3Nd3XyLrJhVK4/vlP2MeMQejAgcj94guUx2b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Ro0c3+JjVpk2bhi1btuDo0aNmpa8KCgowb948AMDHH3+MoKAgrFixAqWlpfD3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\nuXOxYMECi1gmT56M33//HU8++STKy8sxa9Ysea7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Y+RcIUQ6HQ3l5eT2ub2xs1LFjxxQf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\n2t38ut1JSEhQQUGBCgoKOFcViHCEVgARKyoqSiNGjNCIESPU1tamQ4c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FO3Ze3ImErQl4JvkZ5P6ei6XdlmLvkL1IbJnosQWxMz8v/ObNg61VKx7rXQWU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45pkkQqHY1KbszE35JRY1wCIiUvLl5pLUrx8pDz1E7iuv4B06FFJTC3RodjY8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\nYZGTOP/ExTkoloqKCmzfvh3Lli3DhQsXAHD+ieTm/ADOP1F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WLB8xMVVYrIhICbQPsJRI+yGal7Izt0jPz750aVHzO3PmSZtfAI8HDhywMG9e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\ndQpXrlxBbm4uAKCjowOLFy/GhQsXcOHCBTQ1NSE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zz0I7382dW+TWzp7hw0m69FIcixfjz5esxLz5ZmhL6HIkx4Dl7ndl7qtOHfwd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\n5dIIefddbF9/TfasWXlPjp9+GntyMpmffopRp85lXVtEREREqr6UlBT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PeXRPoopigUy6wC/SSQvzgrT4PC8yMyGePQvHoEEw7N7t22dVlqrCuHp1hVak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PwY9/bIPBoPA1GqLAmehauteyEnnpvi6YzQZRZgAKoN9bvfGg1bpgZrNUHpCd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\n", 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uGi+/7MbKlQXo7GT43e96UF7eicsvD0TP5aNGofyNLhw6lHroJ1RVQZo8GdKM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\nxxk2G0huru6NgdSbR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kHJWUEPPwwzgXLqTwlVfwdulidkSWs3u3jeHD4znlFINVq1zUq2eYHZJY3HFf\nOt1///08/PDDZeO8vDySk5PJyMhg7ty5NGzYkNzcXPbu3VvuvEiwUJ+cWJnyUw4XsWULCb17Y9+z\nh/zVq00tiq2am0uXRtKrVyJXXOFh9uwCFcVSJZWuGC9YsIBmzZrRuHFjDOPIhBoxYgQA8+bNq3De\nVsE2z5EjR9KkSRMAkpKSaNWqVdlbMYd+wTTWWGONNdZY4yPHNq+XnuvXE/XSS2y64QZ2p6bS7dRT\nTY3vECv8fA4fv/rqPu65ZzO33nq+JeLRuO7yMSsri127dgGQlpZGddiMoyvewzz00EP85z//ITIy\nkl9//RW73c6oUaNYv349CxYsAKBHjx4899xzuFwuJk+efMx869atj3jM5cuX0759+2oFKSIiEu7s\n//d/xI0ahXHqqRQ+9xzGWWeZHZKI5eXk5NCzZ88qXx9Z2QcnTZrEpEmTAHjkkUdISEhgzJgxNG/e\nnF9++QW3283u3btp3bo1Ho+Hr7766ph5EREROQk+H1H/+hfRzz9P8fjxeIYNQwfvitSOSgvj8jgc\nDiZPnkzXrl0BSE9PB8DpdJY7LxIssrKyyt6SEbEa5Wd4su/YQdzIkRjR0biWL8cfaEO0ErNz0+OB\nAwds1K+vHmI5eVUujCdOnFj258GDBzN48OBjrqloXkRERKrB7ycqI4PoqVNxjx1LSVoa2HXU2NG2\nbbNz221xpKaW8tBDbrPDkRBQ7RVjkVCl1TixMuVn+LD/8AOxo0dj83pxLVmC/7zzzA6pUmbkpt8P\nL78cxTPPRPPgg8UMG6Y7d0jNUGEsIiJiBX4/ztdeI+bJJ3HfeSclt98OERFmR2U5e/bYGD06joIC\nG0uWuDjvPL/ZIUkI0fsyIgFHHz0kYiXKz9Bm272b+GuuIWrWLFwLF1IyenTQFMV1nZsffeSkSxcv\nixerKJaapxVjERERsxgGzrfeIubRRym5/Xbcd9wBkfqnuTLDh5eYHYKEMP32iQSoh1OsTPkZemy5\nucTddRe2vXtxvf8+/vPPNzukE6LclFCiVgoREZG6ZBg4MzNJ7N4db7t2uJYuDdqiuDYVF8N//xsc\n7SQSOlQYiwSoh1OsTPkZGmw//0zc0KFEP/ccBXPn4h43DhwOs8M6KbWRm5s3R5Camsgbb0TV+GOL\nVEaFsYiISB1wzJ9P4sUX42venPwVK/C1aWN2SJbj88G0adFcc00899zjZurUIrNDkjCjHmORAPXJ\niZUpP4PEYYVDAAAgAElEQVSXbd8+Yv/+dyK++oqCt97C17Gj2SHVqJrKzR9+OHizjqgog5Ur8znr\nLN3JTuqeVoxFRERqiWPRIhIvugj/mWeSv2pVyBXFNWnfPhv9+nmYP79ARbGYRoWxSIB6OMXKlJ/B\nxbZ/P7G33UbMhAkU/PvfFE+aBDExZodVK2oqNzt08DFqVInufC2mUvqJiIjUoMilS0ns2hUjKYn8\n1avxde5sdkgiUkXqMRYJUA+nWJnyMwjk5xP74INErl5N4YwZeC++2OyI6kR1c9PlgiVLHAwcWFpL\nEYmcOK0Yi4iInKTIVatI7NYNIiLI//TTsCmKq2vdugi6d09k9WoHft3NWSxIhbFIgHo4xcqUnxZV\nUEDMffcRN3o0Rc8+S9Gzz0JCgtlR1amq5KbHA5MmRXPzzfE89lgx06cXqZdYLEmtFCIiIicgcu1a\nYkePxnvhheSvWYORlGR2SJa0c6edG2+M48wz/XzyST4NGujECbEuFcYiAerhFCtTflpIYSExjz+O\n8/33KZoyhdLLLzc7IlMdLzdPOcXg9ttLuPZaDzZbHQUlcoL0RoaIiEgVHTpxwrZvH/mffhr2RXFV\nJCUZXHedimIJDiqMRQLUwylWpvw0ly0vj7ibbyZ23LiDvcQZGRj16pkdliUoNyWUqDAWERGpiN+P\n89//JvGii/Cddx75WVl4e/QwOypL+v13G488EkNJidmRiJw49RiLBKiHU6xM+Vn3IrZsIfbuuyEy\nEtcHH+Bv2dLskCypW7durFgRyR13xNGvnwdDe+skiKkwFhEROVxhITFPP41z9myKx4/HM3QoOlus\nfIWF8OijMXz4oZN//rOQSy7xmh2SyEnRb7pIgPrkxMqUn3WjbHNdXh75WVl4hg1TUVyBvXttdOuW\nyDff/Mqnn+arKJaQoBVjEREJe7bcXGIfeICIzZspevZZ9RFXQYMGBi+/XIjbvZFTTlGrj4QGvQwW\nCVAPp1iZ8rOW+HxEvfIKiRdfrM111WSzQceOPuWmhBStGIuISFjS5rqqKyqC2FizoxCpfVoxFglQ\nD6dYmfKzBhUWEjNxIvEDBlByww24Fi1SUVwBrxeefz6Kzp0TKSoq/xrlpoQSFcYiIhI2tLmu6jZt\niqB37wRWrHDw/vsFWjGWsKBWCpEA9cmJlSk/T44211VdURFMnhzDnDlOJk4s5vrrK7+ds3JTQole\nJouISOjS5rpq27PHzi+/2MjKymfIkMqLYpFQo8JYJEB9cmJlys/qi9iyhYTLLsP5zju4PvgA94MP\nQkyM2WFZXtOmfmbMKKJ+/ardwk65KaFEhbGIiIQWba4TkRNUaWG8b98+OnXqRNu2bWnTpg2ZmZkA\nZGZm0qxZM5o3b87ChQvLrq9oXiQYqE9OrEz5WTVlm+tyc7W57jh27rQzbVr0ST+OclNCic0wjArf\nK/F6vXg8HmJjY9m3bx8tW7Zkz549NG/enOzsbNxuNz169GDHjh14PB5atGhxzPzRli9fTvv27Wv1\nmxIRkfBStrlu0yaKpkzBm5pqdkiW5fXCjBlRPPdcNGPGuBkzpkSvHSRk5eTk0LNnzypfX+mvQmRk\nJLGB81n2799PVFQU2dnZpKSkUL9+fRo3bkzjxo3ZtGlThfMiwUJ9cmJlys8KHL25bs0aFcWVOPwI\ntqVLXdx558kXxcpNCSXHPa6toKCACy+8kG+//Za3336bvLw8kpOTycjIoF69ejRs2JDc3FwKCgrK\nnW/Tpk1dfB8iIhJmdOe66lm6NJLRo+N4+OFirrtOp02IlOe4rxPj4+P58ssvycnJ4e9//ztutxuA\nESNGMGjQoGOuP3zept86CSLqkxMrU34eRpvrTki3bl6ysvKPey5x9R9XuSmho8o3+GjRogVnn302\nZ599Nrm5uWXzeXl5NGrUCJfLdcx8cnJyuY81cuRImjRpAkBSUhKtWrUq+8U69JaMxhprrLHGGh8x\n/vRTGnzxBR1ffx3vBRewdOpUPKeeSrdAL4Dp8Vl8vGHDwXH9+taIR2ONa2N86M+7du0CIC0tjeqo\ndPPdTz/9RFRUFKeddhp5eXl07NiRnJwcOnfuXLbJLjU1le3btx+z+e7Q/NG0+U6sKisrq+wXTMRq\nwj0/7V9/TeyDD2LftYuiyZPVR1wJw4Cff7ZxxhlVO4f4ZIV7boq1VXfzXWRlH9y1axe33norAIZh\nMHXqVBo0aMDkyZPp2rUrAOnp6QA4nc5y50VERE6U7fffiX7qKZzvvov77rspSUsDp9PssCxr5047\n994bS3S0wVtvFZodjkjQqXTFuDZoxVhERI7L6yXqtdeIfvppSvv1o/j++zFOP93sqCzL64UXX4wi\nPT2a0aPdjBpVgsNhdlQi5qvRFWMREZG6FrlyJbHjx+Nv0ICC+fPxpaSYHZKlffllBHfcEUtSksHH\nH7v4wx/8ZockErR0pLdIwOGN+yJWEw75af/2W+KGDCH23nspHj9eRXEV7d1rIy2thPnzC0wpisMh\nNyV8aMVYRETMlZ9PzDPP4Jw9G/eYMRS++ipERZkdVdDo1ctrdggiIUMrxiIB2lUtVhaS+enz4Xz9\ndZL+/Gds+/eTv2YNJXfeqaK4EnW7K6hqQjI3JWxpxVhEROpc5Jo1xDzwAMTGUjB7Nr62bc0OydJ+\n/dXG44/HcPbZPu66q8TscERCllaMRQLUJydWFir5ad+5k7ibbiL29ttx33knrg8/VFFcCY8HXngh\nigsvTCQmxuCmmzxmh3SMUMlNEdCKsYiI1IWCAqLT04l69VVKbruNwhkzICbG7KgsbdmySMaPj6Vx\nYz8LF7po3lynTYjUNhXGIgHqkxMrC9r89PtxzplDzGOPUdqtG/mrV2OceabZUQWFZcscTJpURO/e\nXmw2s6OpWNDmpkg5VBiLiEitiPj8c2IfeACAgtdew9epk8kRBZfJk4vNDkEk7KjHWCRAfXJiZcGU\nn7Y9e4i99Vbib76ZkrQ0XB9/rKI4hAVTboocjwpjERGpGUVFRD/1FIkXX4z/7LM5kJ2N57rrwK5/\naiqydm0kvXsnsHu3hXslRMKIWilEAtQnJ1Zm6fw0DBzz5hH78MN4O3bEtXIl/iZNzI7K0n780c7E\niTF88UUEDz9czJlnWvCA4iqydG6KVJMKYxEROWERGzce7CMuKqIwIwNvly5mh2RphYUwfXo0r7wS\nxa23lvDPfxYSG2t2VCJyiN7fEglQn5xYmdXy07Z3L7GjRxM/ZAgl11+Pa8UKFcVVsG+fnV277Kxa\nlc/Yse6QKIqtlpsiJ0MrxiIiUnVuN1Evvkj0P/+JZ8gQDmRnQ2Ki2VEFjSZN/MyYUWR2GCJSARXG\nIgHqkxMrMz0/DQPHwoXETJyIr2VLXEuW4D/vPHNjEkswPTdFapAKYxERqVRkVhYxkyZhKyigaOpU\nvD16mB2SpZWUQEZGFFu2RPDSS1odFgkm6jEWCVCfnFiZGfkZsXEj8QMGEDtmDCXDh5O/erWK4koY\nBixe7KBLl0TWrYvkH/9wmx1SndBzp4QSrRiLiMgR7F9/TcwTTxC5YQPF996L54YbwOk0OyxL+7//\nszN+fCw//WTnmWeKSE31mh2SiJwAFcYiAeqTEyuri/y079pF9FNP4fj4Y9xjxlD44ouExLEJdeCT\nTxxcdlkpN99cgsNhdjR1S8+dEkpUGIuIhDnb3r1ET5uG8513KBk+nAMbNuikiWq67bYSs0MQkRqg\nHmORAPXJiZXVRn7a9u8netIkErt0gYgI8tetw/3AAyqKK2EYB/+T/9Fzp4QSFcYiIuGmsJDoZ58l\nsVMn7L/8Qv4nn1D8xBMY9eubHZmlrV4dyaWXJrBsmd5sFQlV+u0WCVCfnFhZjeRnSQlRr79O9LPP\n4r3wQlwffoi/adOTf9wQt359BI8/HsPu3XbGjSvWxrqj6LlTQokKYxGRUOfz4Zwzh+innsLfvDkF\nc+bga93a7Kgs75dfbNxxRyxbtkTy978Xc/31nrDbWCcSbtRKIRKgPjmxshPKT8PA8cEHJHbtivOt\ntyh68UUKMjNVFFdRUpLBZZeVsn79AW68UUVxRfTcKaFEK8YiIqHGMIhcuZKYxx4Dv5+iSZPw9uoF\nNpvZkQUVpxOGDfOYHYaI1CEVxiIB6pMTK6tqfkZ8/jkxkyZh37uX4gceoPSqq8CuNwcrk5trY+dO\nO507+8wOJSjpuVNCiZ4tRURCQMRXXxE3ZAhxaWl4rr2W/LVrKf3LX1QUV2LfPhsPPRRD166JZGdr\nnUhEVBiLlFGfnFhZRflp/+474m65hfhrrsF78cXkf/75wVs4R6rQq0h+PjzxRDQXXJBIcTGsWZPP\nnXfqBh0nSs+dEkr0zCkiEoRsP/1EzDPP4FiwgJLbbqPw2WchPt7ssILCzTfH07ChnxUrXJx9tt/s\ncETEQlQYiwSoT06s7FB+2vbtIzo9HeesWXiGDiV//XqMU081ObrgMnt2AU6n2VGEDj13SihRYSwi\nEgzy84meMYOol1/Gc/XV5GdlYSQnmx1VUFJRLCIVOW6P8Z49e+jWrRt/+tOf6NChA8uWLQMgMzOT\nZs2a0bx5cxYuXFh2fUXzIlanPjmxJJeLqOnTiW3TBvsPP+BatoziZ55RUVwJvx/eecdBr14JFBSY\nHU3o03OnhJLjrhg7HA5mzJhBq1at2LVrF126dOH7779n3LhxZGdn43a76dGjB1deeSUej6fceRER\nqR7bb78RlZFB1L//jbd7d9Y+9hht/vpXs8OyNMOADz908MQTMcTFGUyYUKy2axGpluMWxg0aNKBB\ngwYANGnSBI/Hw2effUZKSgr169cHoHHjxmzatIn8/Pxy59u0aVOL34JIzVCfnFiB7aefiP7Xv3DO\nnk3pVVfhWrIE/x/+gJ5FK7dhQwT/+EcsHg9MmFBMnz6lup9JHdFzp4SSavUYL1myhA4dOvDzzz+T\nnJxMRkYG9erVo2HDhuTm5lJQUFDuvApjEZHK2b/9lujp03EsWIBnyJCDPcSNGpkdVtCIiIBRo9z0\n71+qo5tF5IRV+ekjLy+P++67jxdeeKFsbsSIEQwaNOiYaw+ft+kluwQJ9cmJGSK2bCFu+HASLrsM\nf3Iy+V98QfFjjx1TFCs/K9e2rY+rr1ZRbAblpoSSKq0Yu91uBg0axNSpUzn33HP56aefyM3NLft4\nXl4ejRo1wuVyHTOfXM4GkZEjR9KkSRMAkpKSaNWqVdlbMYd+wTTWWGONQ3kcsW4dxRMmkPjdd3jv\nvJPC9HSyNm2CrVstEZ9Vxzt3JtCrV1vOOMOwRDwa/49V4tE4vMeH/rxr1y4A0tLSqA6bYRhGZRcY\nhsGQIUO4+OKLuf322wHweDy0aNGibJNdamoq27dvr3D+cMuXL6d9+/bVClJEJCQYBpErVhD97LPY\n9+zBfeedeK67DqKjzY7M0gwD1q2LZPr0KDZujOTFFwu55BKv2WGJSBDIycmhZ8+eVb4+8ngXrFmz\nhnfffZevv/6al156CZvNxqJFi5g8eTJdu3YFID09HQCn01nuvIhIWPP5cCxcSPSzz2IrLaX47rsp\n/ctfdNvm4/D7D54yMX16NL/9ZmP0aDf//nchMTFmRyYioeq4K8Y1TSvGYlVZWVllb8mI1AiPB+fc\nuUQ/9xxGUhLue+6h9NJLOZFG2HDMz++/tzNiRByjR7vp27eUiAizI5LyhGNuSvCo8RVjERGppqIi\not58k+h//hNf06YUTZ2Kt1s3dH5Y9Zx7rp+PP3aZHYaIhBEVxiIBWvGQk2U7cIComTOJeuklvH/+\nMwWvv46vht4hC+X83LPHhs9no0kTv9mhyAkI5dyU8KODbURETpLt55+JeeQREtu3x/7tt7jef5/C\nN96osaI4VG3damfUqFguuiiR7Gyt04iI+VQYiwQcffSQyPHYd+0iZuxYEjt3hsJCXCtXUvTCC/hb\ntKjxrxUq+WkYsHZtJNddF8eAAQmcd56fnJx8Bg3ymB2anKBQyU0RUCuFiEi12b/++uBd6pYsoWTY\nMPLXrcNo0MDssILC/v02/vGPGIYPL+G11wp1Up2IWIpOpRARqaKInByi09OJzM6mZMQISoYPx0hK\nMjusoGMY2ocoInVDp1KIiNQkwyAyK4voadOI2LED95gxFL74IsTGmh2ZpR04YOO332yce+6xG+pU\nFIuIVanHWCRAfXJyhNJSHPPmkdCnD7H33Ydn4EAObNhAya23mlIUB0t+7tlj46GHYmjfPpGFCx1m\nhyN1IFhyU6QqtGIsInIY2969RL3+OlGvv47vvPNw33EHpVdcge4uUbmtW+3861/RLF7s4PrrPXzy\nST5nnVWnnXoiIidNhbFIgM7iDG8RX3xB1Msv4/j4Y0r/8hdcc+fiP/98s8MqY+X89Hjg1lvjGDCg\nlJycfE45RQVxOLFybopUlwpjEQlfJSU4588nauZMbL/9Rsnw4RQ/9RTGKaeYHVlQcTrh009d6h0W\nkaCnHmORAPXJhQ/bnj1EP/44Sa1b45w7F/ff/07++vWUjBpl2aLYCvnpdsO335b/z4aK4vBlhdwU\nqSkqjEUkPBgGkWvXEnfTTSRedBG2/HxcCxdS8O67lF56qXqIK7F7t40nnoimXbskZs6MMjscEZFa\no1YKkQD1yYWooiKc77xzsF2ipISStDQKp0+HxESzI6uWus5Pvx+WLnXw6qtO1q+PZOBAD/PmuWjZ\n8tjj1yS86blTQokKYxEJSfadO4l65RWcs2bh7dSJ4ocfxnvJJWDXG2VV9Z//OLnyylJeeaWQuDiz\noxERqX36F0IkQH1yIcAwiFy1iri//pWEnj3BMHAtW0bh7Nl4U1ODuiiu6/y02+HVVwu54QaPimKp\nlJ47JZRoxVhEgp/LRdScOUS9/DJERuK+5RYKX3oJVXSV++UXG7NmOTnlFINhwzxmhyMiYjoVxiIB\n6pMLPvYdO4iaORPn3Ll4u3WjaNo0vF26hOQRCTWVn4YBWVmRvPZaFMuXR3LllaXccktJjTy2hCc9\nd0ooUWEsIsHF7ydy2TKiX3qJiM2bKRk6lPxPPsE46yyzI7O8Awds9OmTQEQE3HRTCdOmFZGUpJtx\niIgcosJYJCArK0srHxZmO3AA59tvE/XKKxhJSZTccguet96C6GizQ6sTNZGfSUkGGRmFtGnjC8VF\ndTGJnjsllKgwFhFLs2/dSvTMmTjmz8fbqxeFM2bg69QpJNslakp+Png8Nk4//djV4LZtfSZEJCIS\nHIJ3i7ZIDdOKh4WUlOB47z3i+/cn4Zpr8DdoQP5nn1H48sv4LrggLIvi4+WnYUBOTgRjxsTSunUS\nS5c66igyCXd67pRQohVjEbEGwyDiv//FOXs2znnz8KWkUHLjjZT26wdOp9nRWVZhIcyd6+S116LY\nv9/GsGEesrPzOeMM9Q6LiFSXCmORAPXJmcO2dy/OzEyiZs+G4mI811+Pa8UK/E2amB2apVSUn7/9\nZmf5cgcPPlhMaqo3mI9qliCl504JJSqMRaTulZTg+OgjnLNnE5mdTWnfvhRNmYK3c+egvgmHGRo3\n9vPmm4VmhyEiEhJUGIsEaMWjlpXTKuG5/noKZ86E+Hizo7O0rVvtLFzYm+hoDx07avOcWIueOyWU\nqDAWkVqlVokTs3OnnXnznLz7roMDB+xcd10JZ57pNzssEZGQpvcsRQKysrLMDiF0lJTgeP994q67\njsTOnYnYto2iKVPI37AB99ixKoqPY/58B717J7Bnj41nnilm06YDdO++jORkbagT69Fzp4QSrRiL\nSM1Qq0SNufzyUq688gAOnbgmIlKnVBiLBKhP7sTY8vJwzp2rVolqKCqCjz5y8PHHDv75zyIij3om\nLu9mfspPsSrlpoQSFcYiUn06VaLaSkth5cpI3n3XyZIlDtq39zFwoAe/2oZFRCxDhbFIgM7iPA61\nSpyU4cPj+PlnO9dc42HSpGIaNKhev7DyU6xKuSmhRIWxiFRKrRI14+WXC4mKMjsKkfC1b98+SkpK\nzA5DasHpp5+Os4bukHrcwvi+++7jrbfeon79+nz55ZcAZGZm8uCDD2Kz2Zg6dSpXXnllpfMiwUAr\nHodRq0S1bd9u5913Dz4xjxvnPubjJ1sUKz/FqoIhNwsKCgBo1KiRyZFITfP7/ezZs4czzjijRopj\nm2EYlb6f99lnn+F0Ornpppv48ssv8Xg8tGjRguzsbNxuNz169GDHjh0Vzh9t+fLltG/f/qQDF5Ea\nVlKCY9UqHB98gGPJkrJWCc+VV6pVogK7d9uYP9/Ju+862bvXzl/+4mHwYA/t2ukmHCJWsmfPHho1\naoTNZjM7FKkFfr+fvLy8cl/45OTk0LNnzyo/1nFXjC+88EJ++OGHsnF2djYpKSnUr18fgMaNG7Np\n0yby8/PLnW/Tpk2VgxExU1j2yRUV4Vi+HMeCBTiWLsXXsiWl/fpRfP/9GGedZXZ0luZyQe/eifTu\nXcojjxTTrZuXiIja+3phmZ8SFIIhN202m4riEGavwXcyq91jnJeXR3JyMhkZGdSrV4+GDRuSm5tL\nQUFBufMqjEUsxuXC8fHHOBcswLFyJd727fH060fxo49iNGxodnRBIyEBtmw5UKvFsIiI1K0TLrFH\njBjBoEGDKp3XqzMJJlZf8TgZtv37cc6eTdyQIZySkkLUnDmU9uzJgZwcCubPx/O3v6koPspvv9mY\nO9fJzTfHsWxZ+WsIdVkUh3J+SnBTbp68mTNn0rRpU5o0acLq1avL5u+9916mTJlyxLVjx46lSZMm\nnH766XzyySd1HWrIq/aKcaNGjcjNzS0bH+rpcLlcx8wnJyeX+xgjR46kSWBHe1JSEq1atSr7xTp0\na0mNNdb45Ma2X39l53PPkbx2LfW3b6f04ovZ0rIle2+8kc6XXWZ6fFYcv/POBrKyzmTbtqZs3RpB\ny5Z76dRpLx06nG2J+DTWWOMTH1tVaWkpEydOZOnSpZx//vlHfGzq1KnHXP/000/z9NNP07Zt2woX\nIPv168fgwYMZOnRorcRsRQcOHOC7774DDv7d79q1C4C0tLRqPc5xN98B/PDDD/Tr16/czXepqals\n3769wvmjafOdWFVWlvX75I7HlpuLc9EiHB98QMTmzXhTU/H060dp797aQFcFixc7WLUqkj59Suna\n1VvuHejMEgr5KaEpGHLzp59+suyJFLt376ZNmzb8/PPPRFTjbai2bdsyffp0Lr744mM+dtVVVzFo\n0KCwKowr+juu8c13o0aNYv78+fz66680btyYF154gcmTJ9O1a1cA0tPTAXA6neXOi0jtsv/4I44P\nPsC5YAH2b76htE8fSkaMoDQ1FWJizA7PcnbvtvH11xH06uU95mOXX17K5ZeXmhCViISjCy+8kN27\ndwNw7rnnAvDWW2/hdrtJS0ujpKSEO+64g/Hjx1fp8aZNm0Z6ejrFxcV88cUXjB8/nqZNm7J8+XIA\nfv/9d8aNG8cnn3xCTEwMd999NzfeeGPZ548aNYrExET27NnDqlWrOPXUU1mzZg3xYbSwUqUV45qk\nFWORk2f/9lscCxYcLIZ37aL08svx9OuHt3t3qKFDzkOFzwfr10ewdKmDJUsc5OXZ6d+/lKlTi8wO\nTUTqiJVXjH/88Ufatm3LL7/8cszpCqNGjeLMM8/kgQceOObzjrdiPHjwYG644YYj5q+99loaNGjA\nlClTyM3NpW/fvrz99tu0bdu27Ot99NFHzJgxg969e/PVV1/xxz/+kWgrvX1WgTpbMRYRCzAM7F9/\nffAkiQ8+wL5vH56+fSmeMAFv164QqV/l8vj90K5dIklJBn36HCyGO3b06SQJEbGM461Pnuj65dGf\nl5eXx/Lly/n222+JiorinHPOoV+/fixatKisMAa46KKL6NOnDwB/+tOfTuhrBzP9ayoSYLk+OcMg\nYvPmspVhW1ERnn79KJoyBV+nTnV7JILFGcbBIvjoH4ndDp984uLUU+v0jbFaYbn8FAkIhdycPDma\np58+tvVs7Njicu9kWd71FV1rlqM35u3ZswfgiCLY5/MxYMCAI64777zzaj84C1NhLGIlfj8RGzYc\nXBlesADsdkr79aPwhRfwtW8POgKxjNsNWVmRZS0SjzxSTP/+x/YHh0JRLCK1a9w4d7WK2upefzIq\nOnnC6XTi85V/l83ybnhx5plnEh0dzXfffVfpcbo1ebOMYKTCWCTArBUP288/41i1isiVK3GsXIlx\nyil4+vWj8M038aWkqBg+ytq1kfzrX1FkZTlISfHSp08ps2YV0LKl3+zQalWwr8hJ6FJu1q6KWin+\n+Mc/snbtWnr06HHMxxo0aMDWrVuPmGvYsCFdunTh4YcfZuzYsTidTnJycoiPjyclJaVWYg9G4f2y\nQMQMJSVErl5NzMMPk9C9O4l//jOOhQvx/vnPuJYsIX/dOtzjx+P7059UFJcjKsrg6qs9bNx4gA8/\nLOCuu0o4/3y/flQiErSOXsEdMGAATZo04Z133uH555+nSZMmjB49+ohrxo8fz4IFC2jcuDETJkw4\n4mOjRo1i1apVpKSk0L9//7L5jIwMfv31Vzp16kSzZs2YNGnSMavO4X5zNp1KIRJQa31yhoH9m29w\nrFyJY8UKItetw9eiBaU9elCamoqvQwdtngswDNi+3c7atZH89pude+6xTr+e2UKhj1NCUzDkppVP\npZCaoVMpRCzM9vvvRK5adbAYXrkSgNLUVEr++lcKMzIwTj3V5Aitw+2GN96IYs2aSD77LJKYGIMu\nXbz06HHsOcMiIiK1SYWxSMBJrXiUlhKxYQOOFStwrFhBxDffUNqlC94ePXCPGYP/j39UW0QFHA74\n5hs7ffuW8thjxTRuHNq9wifK6ityEr6UmxJKVBiLnCD799+XbZiL/PRT/OecQ2lqKsUTJ+K94AKI\nijI7RNO53ZCTE8natZGsWRPJ888XctZZR3ZvRUTAlCnFJkUoIiLyPyqMRQKO2yeXn48jK4vIFStw\nrOB4/kkAABMESURBVFyJrajoYJ/wVVdRNG0aRv36dResxb36qpN33nGyeXMkzZv7uPBCLyNGlFCv\nno5OO1HB0Mcp4Um5KaFEhbFIRXw+Iv7734MrwitWELllC94OHShNTaXwjTfwnX9+2LdHGEb5P4Lk\nZIN773XTqZOXhIS6j0tEROREqDAWCejWrRu23bvLNsxFfvIJRoMGlKam4r7nHrxdukBsrNlhmuqX\nX2x89tnB1ojPPoukX79S7rvv2JMjLrvs2BttyMnRipxYlXJTQokKYwlfhoF9xw4iP//84H/r1mHb\ntw9v9+6U9uxJ0aRJGGeeaXaUlrB6dSRjx8aSl2ejc2cvXbp4mTKliDZtyr/rkoiISDBSYSzho6CA\nyI0biVy/nojPPydy/XqM+Hh8F1yAt1Mnstu1o9XQoQd3g4WhggLIzbXTtOmxp0I0b+7jpZcKSUnx\nheuPx3Tq4xSrUm5KKFFhLKHJMLDv2kXE+vVlK8IRO3bgS0nB26kTniFDKHr2WYzk5LJPOZCVFTZF\nsccD69ZFsmlTBJs3R7J5cwS7d9vp2bOUN94oPOb6M84wOOMMrQ6LiEhoU2EsocHtJmLTpoNF8Pr1\nRK5fD4C3Uye8F1xA0TXX4GvTBqKjK3yIcFrxcLth8uRoWrf20aNHKXfd5aZZMx8Oh9mRSUXCKT8l\nuCg3pbaddtppbNiwgXPOOafWv5YKYwlKttzc/xXBn39OxNat+Jo2xXvBBXiuuorixx7D37hxWJ0a\nYRjw4492Nm+OKFsJ/uqrCLKzDxAXd+S1iYnw4YcF5gQqIiJSRYZhHPH/2mavk68icjJKS4nYuJGo\nl14iLi2NxDZtSOzWDed//oNRrx7FEyawf9s2XCtXUvzUU5QOHIi/SZNqF8VZWVm19A3UjYsuSuCy\nyxJ46y0nNhsMHVrC4sWucD9II2QEe35K6FJunrhZs2aRmppKSkoKf/vb37j++utp2bIlW7duxe/3\n89RTT9G2bVtatGjBuHHj8Hq9AOzcuZP+/fvzhz/8gbPPPpubb/7/9u49Nqpqb+P4d8+VlmmnMy2U\nAh0KLbUXbWmFoogainoAxXiJh1QC6EGFWIn0RC7GHg/4alEjCkTQvjFRwTQRvBwxGojBywsvWj0Q\n8ByBA5SrtAV6v3fPzN7vH0MHSqdC+5Z2Wn6fZDKzZ3b3XtOurj6zuvZaj1NXV+c/7vbt28nKysLl\ncjFhwgS+/fZb/2vp6en88MMP/u3IyEhOnDjh387NzeX5559n7ty5uFwu0tPTaWjwdaR8+eWXTJo0\niTFjxjBr1izOnj3r/5qZM2eSmJjIiy++yMSJE8nOzqa52bd4U3V1NQsWLCApKYmMjAw2btzY7nyL\nFi1ixowZuFwuFi1a5H/tkUceYdSoUQDccccduFwuXnjhhZ769gckPcYi6CiVle0ukDPt24fmcuGZ\nMMG3stzy5Wjx8ddNb7DbDf/5j5Fff/Xd/vKXVhITO14g9/XX9YSH90EBhRBCdJvVauXHH38kKSmJ\njRs3UlxczOeff05YWBjbt29n27Zt2Gw25syZQ2FhIbm5uaiqyrx585g2bRper5c5c+bw2muv8cor\nrwCwePFiVq1axQMPPMDp06f9wRZAURSUK/z93Lx5M++88w4ffvghv/32GyaTiT179vDss8/y6aef\nkp6ezqpVq8jLy6OoqAiAiRMnkpeXx+zZszl06BA5OTn8/PPP3HnnnSxcuJChQ4eyf/9+ysrKuPfe\ne0lLS2PcuHEAfP/992zbtg1d15k0aRKPP/44mZmZbNmyBfCF9507d8pQCnEdaG7GePgwxguzRZh+\n/hnDuXN4br4ZT1YWLXl5eMaPpzcSX7CNkysstLJ5s4VDh4zExmqkpXlIS/NiswX+d5KE4oEt2Oqn\nEG2kbv7/jB49mvDwcJxOJwkJCZSVlbFnzx62bt3KihUrGDZsGADz589n/fr15ObmMnbsWMaOHes/\nxv3338/WrVv92waDgePHj1NXV0dsbGyXy3T77bdzzz33AHDjjTcC8NFHH5GTk0NGRgbg6+lNSEhA\nVVX/+4iLiyMqKgq73Y7L5aKiooLy8nJ27NhBSUkJVquVuLg4Zs6cyVdffeUPxtOnT2fEhelRU1JS\nKCkpITMzs8vl7gkSjEXv8HoxHD+O8cABjAcP+u8Nv/+Od8wYvGlpviCcm4t2ww0DenYIjwdOnTJQ\nUmLg6FEjmZkeJk7sOONDRoaHceM8pKZ6sdn6oKBCCHGdcDidPXKc6qqqLn9NW++tyWTCaDRiMpnw\neDycOXOGhQsXYjD4Rr1qmuYPyefPn2f58uX89NNPNDU14Xa7/SET4P3332fNmjWsW7eOsWPHsnbt\nWpKTk6+6TPHx8R2eO3PmDLt37/b3EIOvt7ttOEV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wahUQGwsYDLBdeqlfu86mf/8bqtmMpmXL2iTNLS3A6tVGLF0aaX99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9Yf32W7hGjQLi4jzXHFdf7bdcw7pyJeQhQ6D26KFdSEiAKy8PhR9txt69EjIz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\nDx46VG4RNGua7rsPtatWBbU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7Ods++cQzhlpPe/eFd6mGogAvvRSLOXMS8Npr9XjkETsk\nqe37ZAOdNSzbtsE1cmS71gcAiI1F3T/+ASUrC7LBoSvenNOna23uvDU0AKII4wXYvqnp6QAQFaUa\n/HpBZgppcqDFYoHFor311KlTiImJwaZNmzB06FCkpaUBALKysrBjxw7U1NToXh8e6q+riIjIEGnT\nJsTNmwfx+HHYf/tbOG64ARBFxD37LFBbq3U/MJlw8mTQpRpKTg6kDz/0/6KmJljXrgVcLu0gYUxM\nO1apTywp8QS3NTXAnXcmoq5OwMqVNeje3TvVfIaSlRWwl7Nl3TrIF15ozkJjY1G3YIH2dxECefhw\nCDU1EI8da9X1Itge3P4o6elQrdagemITnQ1CrnGuq6tDbm4ucnNz8eqrr6K0tBSZmZl48803sXDh\nQnTr1g0lJSUoKyvTvU5kBGvTSA/3RQCqioTbbkPCL38Jx7XXombTJjhuugmwWABRhNKrV8BBGKES\nTc442+3AggU2/P6S7TjZY7A2xMMrY/6nP8Xi7bdj8K9/bQ953UBzxrl5amB8PDBjhgOffVbrN2gG\nDLSka2qC9auv4Ljyynatr/ViRcBmC/m9zqlT22SdhcpKUw4GAs211L16QTdF38H49YLMFFLGGQAS\nExOxa9cu7N+/HzNnzsRTTz0FAJg7dy4AYNGiRa1e3/K64OMwwz333IPs7GwAWjlIbm6u51cs7o3P\nx+fXY7doWQ8fR8fjXbt2RdV6wvZ49GjEvv46jhUV4ci11xp+/7aFCzFuwwbU79kD2Gxtnq9ISUHh\n0qXol5tr+vqF6mpsOHAA8vHjht+/7sABXO5weIanrF+/HqWl8di9ezz+9S8bevaswutJ7yPm0kvh\nOnIIRxctwo+1tZ73V1cfxpYtyfjhh7H4y1+syM8/jIsvLsLs2flBrX9G8+FA9+M77jC2/oNNTei8\nfTvc+Xvv5w+9/jr69ewJsbk/dDTsr27Z2Rjx9ddomjvX8/wldjvULl1MuX+My4Xx99wTFX/e8+br\nBR/7fez+/xPNvx266667EApBVdscdQja1KlT8dRTT+H555/HkiVLAACTJ0/GK6+8gtraWsybN6/N\n9by8vFb3WLlyJfLz89u7FCKis5+qwrp0KeIef1zLDh88iNO7dxvuoGB7911YfvgBDX/5i+7zcU88\nASU1FU2//rWZqwYaGtApJweniooMrVVR4Bk1nTRtGhqefRby2LFYutSKBx6Ix403OnDbbU3o11dG\n8ujRqH//fVjWroV44gQa//hH3ftt2mTBJ59YsWOHBV99VRtU04mUnBzUbNwINcgBIJbVqxH78suo\n+/RT3efjf/UryKNHoynEb9RhUVuLTkOH4tTevUBiIgDAtmABLCtWoOGttyK8OKLw27p1K6ZOnRr0\n+0Iq1SguLkZVVRUAoLS0FAcOHMDAgQOxZ88eVFRU4Mcff0RhYSHy8vIwZswY3etERNEu/r77DE+F\nM4t48CASb7gBcU8/jYYXX0TdokVQrVaIQRzos65ZA9ekST6fl/v2hRSGvsmejgwBotUjR0RMnqz1\nPfasqX9/SM0TBCdPdmLnztN45plG9OunQDx8GILdDnnYMMh5eZCaM4jeRBEYN86F559vxLJl+kFz\nWZmAgwdFT3cMp1M7AFhZ2AShvj6kUgW/Nc52O6zLlsFx1VVB3zeskpLgGjUK1haHLU0ZfkJ0jgsp\ncD5x4gQmT56MvLw8XHrppXjxxReRnp6OefPmYfz48Zg6dSrmz58PALDZbLrXiYzwLtkgAjpuX1hX\nrID0ww8d8lmoqUHcE08g6cor4ZwyBTXr1sE1eTIgCHBNngzrN98Yu48sw7J+PZwTJ/p8idK7N8Qw\n1DiL1dUBW9EtX27BjBlJ+MUvmvDf/54Zraf06+dpSRcb23p4n/Xrr+GcPh0QBMjDhsGyezfaNFSG\nsX2xY4eE2bMTMWBACn7+8wRMn56EjRstkMpKoWRkBN0XGQCUnj216Yey3OY56zffQB42DGpGRtD3\nDTfv7hpmDT+JNvw+QmayhPKmsWPHYufOnW2uz549G7NnzzZ8nYgoaskyhMpKSPv2wXnNNeH9LIcD\nyRdfDNekSajZsMHTysvNOXkyYv7xDzQ114z6I+3aBTUtzW83AyVcGWc/GUtVBV55JQZvvRWLDz6o\nw9ixrYNMOScHto8/1n2vdflyNDWfk1E7d4bSubPWESKEdmzTp7swfXoNiooEbNyofQu89lonrBuL\nQ+8AERMDtUsXbYCKV29l6+LFcM6aFdp9w8w5fTpiX3tN+8cRBIhVVXANGxbpZRFFNU4OpKjmLu4n\naqkj9oVQUQFBUcLa89hNbK4JbnjttTZBMwC4Jk7UJr3Z7QHvZVm7Fk4/ZRoAoPToAaGyEmhsDHnN\nevwNz9iyRcLnn9uwfHlNm6AZAJT+/SEdOtT2jTU1sGzd2iqDLufm6pZrBLMvevRQcd11Tlx3nROC\nAK5GzewAACAASURBVAg+hp8YpWRlQfIu67HbYf36azhmzgz5vuGk9OsHNSEBUnMi7Fwt1eD3ETIT\nA2ciIh1iWRnU+HhIBw6E/7OKirTxyT6onTpBHjTI55jklqyrV/utbwYAWCxaXe7x48Eu1S9/UwNH\nj5axbFktevTQP48u9+2r1Ql7lTtYv/kGrgsuABISzrzWR+DcHt7DT4Kl6AxBsa5aBTk3NyrLNNxa\nlmucq6UaRGZi4ExRjbVppKcj9oVQXg7X6NFaMGQg09seYlERVD+BM6CVawSsc7bbYfnhB7jGjw/4\nmUrv3qb3cg6UsfTb0jcuDkpaWptDdtavv4bzsstaXZPz8mDRKRdsz75ob+As6xwQtH76KRxRWqbh\n1jJwDmVc+tmA30fITAyciYh0iKWlUHr00NrBGRgH3a7PCpBxBrTA2RIgcLZ8/z3kgQOhpqQE/Ey5\nb1+IJtc5hzI1sCXFexCKosC6YoV2MLAFV24upN27Q/4cPaZnnJvLNJxRWqbh5ho3DuKhQ1ppkp9S\nGyLSMHCmqMbaNNLTEftCLCuD0q0b5EGDgmoFF9JnGQic5VGjIB4/DqGiwudrjNQ3uyl9+kA8diyo\ndQYiVlVB6dIFpaUCNm4MfmKcnJPTqs5Z2rZNOwzYq1er16k9egAOB4SyslbX27MvxJISqM1TA0Oh\n9OzZKuNsXbUKcl6ebs16VLHZ4Jo0Cdbly8/ZwJnfR8hMDJyJiHQI5eVQMzIgDxoU9gOCgoHAGVYr\nXBMmwNKi726bl6xeDZefNnQthaOXs1BdjYa4LvjpTxPx3XfWoN+v5OS0yu572tC1+SDBbz/nUAhm\nZJwLCz2PrYsXw3H11WYsLeyc06fD9t//aj0AQx3jTXSeYOBMUY21aaSnI/aFWFoKJT1dC5z37Qvv\nZxkJnBGgzrmmBtKBA9pBOgPC0su5sgpPvNIT+fkyHnww+LpwOScHYvMQFEBrQ6cbOANaP2evwDnk\nfaGqnt8whErp2VMLnBUFaGw8K8o03JzTpsGybt05Wd8M8PsImYuBMxGRDk+pxuDBYc84i0VFUAyU\nCXgGoahtO1NY16+Ha9QobXqIH5WVAhYssGHV0b4QCotQVerSu13QVBWoPXoS9XFd8ac/NYQyR0Sb\nHticcRZKSyEeOwbXhRfqvzYvz9NGrb2EqiqoCQkB/+78io+HmpwMobxcK9MYPjz6yzSaqRkZ2nrP\nwTINIrMxcKaoxto00tMhfZybSzWUvn21qXANDeH5oPp6CI2NUFNTA75U6dMHamwsRJ0MuGXtWjgv\nucTv+7/+2oKLL07GZ59Z8cpfU1CM7ph94UnPIBBv+/eLKCsTDAXWL/wpBvH2Ksx7OxaWkEZrAWr3\n7hBOnwZqa2FdsUKbnGjVL/lwDRvW5oBgqPuivQcD3dyjt22LF0d9Nw1vzksvPWdb0fH7CJkpxC9v\nRETnMPev7tPTAYtFqwc+dAjy8OGmf5RYXKxlmw2maN1Z56YhQ1pdt65ejfo33vD73q5dVfzzn3UY\nPVrrlZx4TS+suXcHXOPSdF//pz/FYc0aCxwOAX36yOjVS0GvXgp+/Ws7UlNbR9Nj82pgixGQkBZv\n6M+hSxQh9+kD6cgRrdThiit8vlTp31/7gaa2FkhKCv0zodU3hzw1sOWasrIgHToEy/LlaPjDH9p9\nv47UdNtthuvjic5nzDhTVGNtGukJ974QamoAi8UzdEMZPDhsdc5G65vd9OqchZISCBUVkPPy/L53\n1CjZEzQDWgbbXy/nv/2tHocPn8bOnacxf34DrrnGgdRUBTZb2xT0pKHlQNf2/6pfycmBtHcvrGvW\nwDltmu8XWixa/fmePZ5Loe4Lsbi4XfXNbkpWFmLeew/yiBFQ0/R/GIlWakYGXOPGRXoZYcHvI2Qm\nBs5ERF4Er4Ni4eysEWzg7JowAZbNm1sNZbGuW6cNPfE7YaQtuU8fQ72cO3VSMXKkjGuuceLXv25C\ncnLb1wgmDc+Qc3IQ849/QB4wIGD5ipyb2+aAYChMK9XIzoZl69azrkyDiIxj4ExRjbVppCfc+8JT\nptEsnL2cgw2c1ZQUyEOGwLJxo+eaZc0aT32zogDvvmvDc88FPuim9O1rWi/nQFMDjVL694dl0yaf\n3TRacnkdEIyGGmdVFOG88sp234vMw+8jZCYGzkREXoSyMqgZGZ7H0ZRxBrzKNVQV1jVr4Jo4EYcO\nibjqqkT8+98xuPZaR8D7yH36QDIrcG7n1EA3OScHANqM2dZ97bBhpvRyFktKDHU1CcQ1YgTsDz10\n1pVpEJFxDJwpqrE2jfSEe1+IZWVQWgTOSu/eECsqgLo68z8rxMDZPX5bPHwYqiDghU+HYsaMJPzk\nJ04sXVqLwYOVgPdRevfWpt3JcsDXBuKeGthe8oABcMycCTk3N/Brhw7VJg06tB8SQt0XZh0OVDMy\nYH/ssXbfh8zF7yNkJgbORERevANnSJLWY/jAAfM/q6hIGyEdBDk/H+KPP0IoK4N17Vps7TQZ335n\nxapVtZg7t8l4qXN8PNTOnSGUlAS/cC9mlWogMRH1H3xgrMtIfLzWyeLgwXZ9pFmlGkR07mPgTFGN\ntWnkLfa553BJuHoqN/Mu1QDCV64RSsYZFgtcEybAumYNLGvWoP+vLsbChXXIzg6cZfYm9+ljyuht\nobo6In2A5dxcT7lGSF8vGhshNDScsz2Mid9HyFwMnInorCGeOIHYV16BbenS8H5OeXnrjDMQntHb\nNTWAokBNSQn40rIyAQsX2jzDSJxTpsCyYgUs69cDUyeGNKkP0FrSmXFA0KxSjWC5cnPbNUFQLC3V\n/q1D/QskovMKA2eKaqxNo5ZiX3oJrosvhn3t2rB+jieYakEJw+htT7bZT9C2dKkV06YlYezYZCxd\nakV9vXbdNXkybIsXQ83IgNqOHsSKSQcEzTocGCw5L8+TcQ7l64VoUn0zRS9+HyEzcXIgEZ0VxB9/\nhHXJEtSsX4+kkSNR09AAxLdjSp0fHVWqIRYV+ezmUFcH/M//xGPtWgv++McGXHKJCzbbmeeVXr2g\nZGfDOWlSu9Yg9+kD2yeftOsegIk1zkGSc3O10dtG5oLrEFjfTERBYOBMUY21aeEhVFQADkfQh9Ii\nKfall9B0221QMzOhDhkCaedOyGPHmv9BTU0Q6uuhdu7c6rKSlQXh9GmtvEJvAkgI/NU3L19uhcsF\nrFlT4/Pj7Pffb6j7hD9m9XIWq6sjUqqhdu0KJCZCPH48pK8XYnExA+dzHL+PkJlYqkF0Hop95RUk\n3XCDp41XtBMKC2H97DM03XMPAMCVnw/Ltm1h+SyxvFzrwyt6fXkURcgDBpha5+wvcL7mGidef73B\nb4zu+MUvIOfnt2sNsnvsdogZWwCAqkYs4wy0r86ZHTWIKBgMnCmqsTYtPKTduyGcPo3YV1+N9FIM\niXv5ZThuvdVTQ7svKQmWrVvD8llCaWmrcdstmV2uEVJHDbMlJ0ONjYVQXh76PerqAKsViIszb11B\ncHfWCLXGmYHzuY3fR8hMDJyJzjeqCmnXLtR98AFi/vpXiIcORXpFfgmFhbB+8gns997ruXaqf39I\nYQqc9TpquJkeOBcXQ+nRA3v/P3v3HR5VmT1w/HvvlPSETgJJhNAikV6kK6AoAlawIq4/cVFBRQVF\ndBELLq4NdS1YsayuoCIKCiKiEkpYRKkSegKBFGrqZNr9/TFJTLlJZpJJZkLO53l49PZ3wjE5vjn3\nvLt9+624tp011NOnfVKmUaz0C4KeUo8fR/PCqoFCiMZBEmfh16Q2zfuUtDQwmXD06YNlxgyCH3gA\nnJ73/60vga+84pptbtGiZF/PG25AzcpCOXXK689TMjLQWrXSPebtxNl5OI17/9WJRx4JrlWlRG05\n4uJq1cvZl2Ua4JpxNu7YUaPvF/Jy4LlPfo4Ib5LEWYhGxrhzJ44LLgCg8M47USwWzJ984uNR6VPS\n0jB/9RWWadPKHjAYsPfsiaEO6pz1WtEVc3ipJZ2mwScfm3CkpNHh4iiWLs31aRthZ7t2tZpx9nXi\n7IyNhbw8lBMnPLzQ6Volshbt/IQQjYskzsKvSW2a9xl27PirE4PBQP6CBQQ98wxKRoZvB6Yj8JVX\nsN5yS5nZZnDFhaN37zqpc1YzMytNpLS2bVHy8lBOny6zf+bMIG6/PYT/+78Q7rgjhJkzg1i0yExu\nbsV7pKcr3HRTCIvfysMcambaoyaMPu5v5IyLq1UvZ/XUKZy+XHlPUXB0787eRYs8u+zkSbTQUAgM\nrJtxCb8gP0eEN0niLEQjY9ixA3vRjDOA44ILKJw4keBHH/XhqCpSjh3D/MUXFWebi9h7966TOufS\npRq7d6u88UYAkyeH0Lt3ONu2G3XLNS65xM64cVbGjLFyxRVW4uKcbNli1C2/yMlR6NnTwdevJaPE\n+EdtraOWNc6+nnEGKJw8mfM/+ggsFrevkRcDhRCeksRZ+DWpTfM+w86dFXr/WmbOxLB9O6ZVq3w0\nqooCX30V680369YbDxkyxNWSbuvW2rVR06FmZOBs3Zp33w3g2mvDOHRIZeRIG599lku3bg4c8fGo\n5VrSXXaZjWuvtXHdda4/d99dyL//nU9YWMX7d+rkZNYsCwFZxypd/KS+OePiUGtT43zqlM8TZ9u4\ncQT06EHgSy+5fY3xp59wduhQh6MS/kB+jghvkgVQhGhMsrNRs7IqJgtBQeS/9BIhU6dydtAgdDO+\neqQcP4558WKyN26s9BytaKlqJS0NLTraa89WMzL439G2vPtuACtX5tCuXdkXJ731gqDiD63oimjN\nmqE4na5ls8st/OIO9dQpHF271sHIPKAo5D/3HOEXXYT16qtxVjMew6ZNBL75JjmrV9fTAIUQ5wKZ\ncRZ+TWrTvMuwezeO+HgwGCocsw8bhu2iiwiaN88HIysr6NlnsU6cWGHZ62KJiYmgKK5Z599+896D\nnU6UEyfoPboZa9dmV0iawXsvCPpFD+diioKjFrPOysmTPm1HV2zdwYMUPPYYIfffDw5HpecpJ04Q\nOnky+a++6nqxUJzT5OeI8CZJnIVoRIw7dpR01NBT8NRTmL/+GnX//nocVVmGbdswrV5NwYwZ1Z7r\n7RcElZMn0cLCwGyudC0Pb804+1XiTO16OSunTpUsTuNr1kmT0AICCHjnHf0THA5C/v53Cm+4Adtl\nl9Xv4IQQDZ4kzsKvSW2adxl27MBerr65NK1ZM+z9+mHYvbseR1V6ABpBjz1GwaxZVLXWdHFcePqC\n4Nq1RpYsMbNtm4H8/IrH1czMSme5S4YYGQk2G0pWltvP1eNvibOjFp01/CVxHjJkCKgq+S+/TOAL\nL6CmplY4J/D558Fmw+JnL8OKuiM/R4Q3SeIsRCNiKNXDuTLO6GjUI0fqaURlmb75BuXsWay33urW\n+Y5evTBu20b2aQfr1xt5440A7r47mE8/Neuef+aMwsqVJqZNC6Zjxyb07BnONdeElqzcp6Sn46xk\n8ZMSiuKVcg1/S5xr08tZPXUKZw1qo+uKs1MnCu+5h+AHHyzz8qhxzRoCPv6YvHffxec9AIUQDZIk\nzsKvSW2aF9lsGJKTq32JyxkT45vE2WIh6IknKHj2Wd0a7NKK4+K7Ta1IsUQy/oJ0nn46iMOHVQYN\nsjNggF33umuusfHee3msW5dDauoZvvwylzvvLCwpy3B3MQxnbcs1nE5XKzQ/6aoBRb2ca1LjrGl+\n0Y4Oyn6/sNx7L0pGBuYvvgBcS7eHTJ1K3ttvV/tbBXFukZ8jwpvkf7mFaCTUfftcM5yhoVWe54yJ\nwbhhQz2N6i8Bb72Fo1s37EOHun1Nr152Qkb04qcxP2O/1bPZW6MROnRw0qHDXy8AKm6UakBRnXO5\nlnSeUE6cQAsJgeDgGt/D22rcyzknBwIC/G8REZOJ/FdeIfTmm7ENGULoHXdguesu7IMH+3pkQogG\nTGachV+T2jTvMbpRpgE1K9Uw7NiBaenSmg4NJT2dwH//m4Inn9Q9brWCs1SDi+K4iIzUCLm4F+Y/\nvPOCoOpOqQauxFmtxYyzv5VpgKt2W8nLcyXCbl+kYfr1V78p0yj//cLRuzfW8eMJv/hinM2aUXjf\nfT4amfAl+TkivEkSZyEaiTJLbVfBGRODevSoR/c2rV5N4Ouv13RoBM2bh/WWW3DGxVU4tnevyqhR\nYSxfbtK91t6rF4bff6/xs0srXvykOo7u3THs2YOaklKz5xw75neJM4qC87zzMBw+XP25eXmYFy0i\nfMgQgp5+moK5c+t6dDVW8Oij2MaMIf+NN0CVH3lCiNqR7yLCr0ltmvcYdu4ss9R2ZbTmzVEKCyE3\n1+17q4cPY9i+HfLyPB9Xcfu5hx4qOw4NFi0yM2ZMGLfdVsi4cbaSY6XjwtG9O4bkZI+WWq6Mkpnp\n6ppRDa1JEwqnTCHoqadq9Bw1Lc2v6puLOeLiMOzc6Zri16EePEjQY48R0b07ph9/JH/ePLI3bcJ2\nzTX1PFJ9ut8vQkLIf+mlGi3sIs4N8nNEeFONEue0tDSGDBnCBRdcQJ8+ffjxxx8BWLx4MZ07d6ZL\nly4sX7685PzK9gsh6omm6S61rUtRcLZt61G5hpqSAqrqeU9lTSNo9uwK7ecyMxVuuy2E998PYPny\nHG6/3YqiVHKPoCAcnTph2LHDs2frUDMy3CrVALBMm4Zx0yYMmzd7/py0NNfKh37GPnAgQY8/TpO2\nbWnSpg0R8fGE9+tH2MiRhF16KWGXXQYmEzlr15L3ySfYL76Yyv9ihBDi3KNoWqlePW7KzMwkIyOD\nbt26kZqayqBBgzh06BBdunQhKSkJi8XC8OHD2b9/P1arlfj4+Ar7y1uzZg29e/f2yocSQpSlHDtG\n+PDhnN2zx61EJ3T8eCxTpmC/9FK37h/eoweOXr1wdO2K5eGH3R6XadkyAl94gZyffy7TSePWW0Po\n0MHJo48WEBBQ/X2CH3wQR5cuFE6Z4vaz9TSJieHMrl1V9pAuzfzppwQsWkTOqlUeJZAhkydjGzUK\n6/XX13SodUvToKAAJTvb9ScnByU/H3ufPn71QqMQQtTU1q1bGTlypMfX1airRqtWrWhVNCsTGxuL\n1Wpl48aNJCQk0LJlSwBiYmLYtm0b2dnZuvt79OhRk0cLIWrAsHMnjoQEt5M7j14QtFpRMzIouO46\nAj74wP1BORwEzZ1L/quvVmg/9+GHeR6Vo9p79cK4fr37F+jJzXUljGFhbl9ivfFGAt5+G9PXX3tU\nruCPLweWoSgQHIwWHOxW6YoQQjQWta5xXrVqFX369CEzM5OoqCgWLlzIkiVLiIyM5Pjx42RkZOju\nF8IdUpvmHUY3Xwws5oyJweBm4qwePYozKgr74MEYt2wBu34P5fIMv/8OQUG67eeqS5rLx4W9T59a\nL71dUqbhSemBqlLw9NMEPfmkRzXWir8nzg2UfL8QeiQuhDfVqo9zeno6M2bM4JtvvuG3334DYErR\nr0q/+uqrMueW3q9U8oPpnnvuITY2FoCIiAi6detW0kamOPBlu3FtF/OX8TTU7dNr15I+cCDt3fx6\n/pmfT6vffyfEjfPVw4c51aQJm3bv5oq2bTHs3MkvRS8WVnb/desSabLga7Ksozn/uMKBA+s8+jw7\niuqZi7d/zcpidFoaypkzaE2a1Ojr1WznTi4s6qjhyfX2oUPJiozk1OzZRL30UvXXOxwo6ekkHjrE\noHbtPH6ebMv3C9n2bLv89wtfj0e2fff9ITExkdTUVAAmT55MTdSoxhnAYrFw6aWX8o9//INRo0ax\nfv165s+fz7fffgvA8OHDeeWVV8jJydHd37179zL3kxpn4YmgRx/Fcu+9aH7YmcAfhfftS+4nn+CM\nj3frfOPGjQQ9+SQ5K1dWe675gw8wbttG/oIFrlrjzp0pvOsu3XOzs+G//w3gvfcC+OLIQPbe/gRD\nnxiEWX+FbI+Ejh2L5aGHsA8fXqPrTUuXYl62jLxFizy+Vt23j7DRo8netAmtRYsqz1WOHSN8xAhX\nvbkQQgifqGmNc41KNTRN4/bbb+fmm29m1KhRAPTr149du3aRlZXFkSNHOHr0KN27d690vxA15nQS\n8PHHmFav9vVIGoacHNfCHh07un2Jw4Nltw2HD+Momjm1DxiAcdMm3fOWLDHTs2cEmzYZeePJVC4w\n7WHkE329kjQDOHr1wlhZP+ecHNSiWYbKuNvDWY+zUyes48cT+K9/VXuu39c3CyGEqFSNEuf169fz\n5Zdf8vbbb9OrVy969+7NyZMnmT9/PoMHD2bkyJEsWLAAALPZrLtfCHeU/xUsgHrkCEp+PiadY6Ii\nw65dOOLjXWtMu0mLjEQ5caLSfr6lqYcPY485j927VX62D8b5a5LrJbtyBg60sX59Nu+/n8fAnNWu\n2uYaZs16cWHv3RtD6TrnnBxMX35JyKRJNElIIGz06LLLD5b/HBkZbi23XRnLww9jXroUNTm5yvMk\nca47enEhhMSF8Cb3f5KWMmTIEKw6P1Cvv/56rtdpr1TZfiFqwrBnD45OnTAmJroSNOkjWyV3l9ou\ne5ERZ+vWrhXuimaTK5O3I5U7/khguzmUmOjODHEYUA8dqrAKYHT0X8m08ccfsV1yiWdjqoajTx+M\ns2dj+vJLzMuWYfr5Z+wDBmC9+mryX32VsMsvx7B1K46+fXWvVzIzcXgwK1+e1qwZlunTCZo7l7zP\nPqv0PPXYMb9c/EQIIUT1ZOVA4deKi/tLU/fswXbppWgmE+q+fT4YVcPi7lLb5TmrKdfQNLh1YjDG\n1ENMnBPF5s3ZfLU0j6BLKy/XcN3Yiemnn2qVOOvFhTMmBq1pUwI+/xzbZZdxdts2chcvxnrzzWhN\nmmAdNw5zFQswqenpbi9+UpnCyZMxJCdj/Pnnyp8jM851Ri8uhJC4EN4kibNocAzJyTji47EPHYpp\n3TpfD8fvubvUdrFDh1QOHFBxtI1BPXq00vMUBe6flE5IKAy/NrRk4r+qOmdwtaHTWrRAi452e0xu\nURSyN2xwJcu33FJhiWXb2LGYli/XLSMBUDIyat+zOCCAgmeeIXjGjEqXH/fX5baFEEJUTxJn4df0\natMMe/bg6NIF+9ChGCVxrprd7vofja5d3To9K0thzJgwxo8P5eWvOvL2Y5mMHx/K888H6p5/YcsD\nrlKOUuUy1SXOJi+UadSkZtHRvTvYbKh//ql7XM3MrPHLgaXZrrgCe79+rt7Oes+RGec6I7WsQo/E\nhfAmSZxFw+J0Yti3D0eXLtiGDHGtFlfFC1+Nnbp/P87ISLdXw2vZUuO3387y++/Z3PNcSyYOPcDf\n/27h/PMd+vc/fBjneeeV2ec4/3yUzEyUrCzda7yRONeIomAbOxZzUWvMMmw2Vw/o5s298qiC+fMx\nf/89xp9+qnBMPXYMp7dn24UQQtQLSZyFXytfm6YeOYIWEQHh4WjR0Wjh4ajSD7dShhq8GBgU5Pqn\n2i6aJtlHGDXKztixNt1z1ZSUii8PGgw4+vfHmJRU4Xzl5EkMe/diHzDAozGVV9OaReu4ca5yjfLj\nysx09V8ut/R3TWkREeS99hoh992HcubMXwdsNpQTJ2QZ6zoitaxCj8SF8CZJnEWDohbVNxezDx2K\n6ddffTgi/1bVUtvVTdRX93IglO3hXJp9wACMGzdW2G/66SdsQ4ZAQEDVD68jjv79UU+cQD14sMx+\nb5VplGa/+GKsY8YQ9Mgjfz0nPd2VoHvQGlAIIYT/kMRZ+LXytWnF9c3FbEOHutrSCV2GHTuw6yTO\nR46oXH55GJs2VT7D6oyORk1Lq7r3cUpKhVINKEqcdWacvdWGrsY1i6qK7YorKsw6qxkZaLXsqKGn\n4IknMP7+O6ZlywBQpL65Tkktq9AjcSG8SRJn0aAYys84Dx6MccMGcOjX4DYk6qFDBLz7rvduqGm6\npRp79qiMHh3G2LFW+vev4usWHIwWFlZprTIU1TjrzTj37o1hz56ynSWK2tDZfVHfXIpVp85ZSU/3\n+owzAMHB5L3xBsGPPIKSkSEvBgohRAMnibPwa+Vr08rPOGuRkWgtW2LYubO+h+Z1QXPnEjR7Nkp6\nulfupxw7BpqGFhVVsm/rVgNXXx3GnDkF3HdfIWo13wGqLNew2VCPH9d/0S0wEMcFF2D87beSXYbf\nf0dr3hxnTExNPk4ZtalZtA8ZgnrwIEpaWsm+uijVKObo25fCW28lePp0SZzrmNSyCj0SF8KbJHEW\nDYfTiWHv3jIzzgC2YcMwNvA6Z8O2bRi3bME6YQIBixZ55Z4BH3+M7fLLS1rFrV9v5MYbQ3n55Xyu\nv776pbQBnG3bVpo4q2lprmSzkmWzy9c5+6ybRnkmE7bLLsP83Xclu2q73HZ1LDNnoh47RsC770ri\nLIQQDZgkzsKvla5NU48eRQsPh/DwMufYhwzB1MBr2ILmzcPywANY7r2XgA8/BJ0l7T2Sm0vAe+9h\nue++kl3h4RrvvZfH6NH6HTL0VDXjXFmZRrHy/Zy9mTjXtmaxZDGUIkpGRp3NOANgNpP35puumW1J\nnOuM1LIKPRIXwpskcRYNRvmOGsXsQ4a4EjS73Qejqj3Dpk2oyckU3norzvh4HPHxmIteJqupgI8+\nwj54MM5OnUr2devmYOhQz75GzpjKVw/U6+Fcmv3CC12lGna7qw1dcjL2gQM9en5dsQ0fjvGPP1BO\nngRcM851mjgDzq5dyfn6a2wjR9bpc4QQQtQdSZyFXytdm2b4888y9c3FtObNccTGYvjjj/ocmndo\nGkHPPotl5sySFm2Fd95JwNtv1/yehYUEvv46lunTaz28qmacDXo9nEvRmjbFGR2NYccOjGvXerUN\nXa1rFoOCsA0fjqmoXEOp41KNYo4BAyAkpM6f01hJLavQI3EhvEkSZ9FglO+oUZp9yBBMDXD5beMv\nv6Cmp2O98caSfbbLLkPJysJQ6sU6j+7538VktozH3qNnrcdX3Yyzo4oZZ/irXMNv6ptLsY4bYNym\nzQAAIABJREFUh3n5ctC0On05UAghxLlDEmfh10rXphmSk3VnnMG1EIqxoSXOmkbQvHkUPPJI2QUx\nDAYK77iDgHfe8eh22dnw5r+NZM58nbmW2Zw8qdR6iM7o6MprnKuZcYa/XhD0dhs6b9Qs2i69FOPG\njaipqWhBQRAY6IWRCV+SWlahR+JCeJMkzqJh0DQMe/firGzGedAgjFu21P6lunpkWrUKCgqwXXNN\nhWPWiRMxrVqFkpFR7X1SUlRmzQqiZ88ITCtW0LxjOP/c2JsWLbRaj1Fr2hTF4XBl5eVU93IggH3g\nQEwrV6I1a4YzNrbW4/Gq8HBsgwZh/uSTOln8RAghxLlHEmfh14pr09SjR9HCwtAiInTP05o0wdGx\nI4atW+tzeDXndBI4bx6W2bPRa6asNW2K7eqrXR02qrFhg5HgYI11v57lAct8jI9PL2lBV2uK4qpT\nLjfrrJw5g2K3ozVrVuXlzuhotFatvF6m4a2aRdvYsQR89BHOyEiv3E/4ltSyCj0SF8KbJHEWDYK6\nZw+Ozp2rPMc+ZAimeuznrB48iPGXX2p0renrryEgANvo0ZWeY7nzTgIWLaLgrJXVq4188YVJ97yb\nbrIyZ46F8/b+hGKxuHo3e5HeC4JqSgqOdu2qT9AVBcudd2KdMMGrY/IW2+jRKCdPSn2zEEIIt0ji\nLPxacW2aYc+eSl8MLGYbOhRjfdWy2e2ETJ5M4Msv1+jaoOeeo2D27EoTz6NHFd5L6smOwi7Mjv+J\nl18O5OzZqv9zDXzlFSz33687g10buomzG2UaxQrvvx9Hjx5eHZO3aha15s2xDx4spRrnCKllFXok\nLoQ3Gas/RQjfMyQnY+/bt8pz7AMGYLzjDrBY6vxFr4A33gCrFcOBAx5fa168GGfLltiHD9c9np0N\nl1wSzkUX2eh24995a9PLFHxX9SyyYcsW1MOHsV53ncfjqY4zOrpCZw01JaXKHs4NieWhh9AqWf1Q\nCCGEKE1mnIVfK65Nc2fGmbAwHPHxGP/3vzodk7pvH4Gvvkrexx+7FtAoKPDo+sAXX8Ty2GOVzjaH\nh8Pu3WdZuDCfvk9eiinreLU9qgMXLKBw2jQw6Zdz1IZDZ8a5uh7Odc2bNYv2YcNc/ZVFgye1rEKP\nxIXwJkmchf+rpqNGabahQzHWZZ2z00nw/fdjmTkTZ/v2OGNjUQ8dcvty5eRJlFOnsA8YgMMBGRn6\nyXNJtYXRWG1rOvXPPzFu2ULhLbd48kncpteSzp0ezkIIIcS5RhJn4dcSExNR0tLQQkPRmjSp9vy6\nXggl4L33UJxOCu+8EwBHhw4elWuo+/bh7NiRX9eZGD48jBdeqL6kxHrrrZi++w4lLQ0lKwv1wAEM\nv/+O8ZdfMH37LcFz51L4979DcHCNP1dV9BZBcaeHc12SmkWhR+JC6JG4EN4kNc7C7xn27Kl04ZPy\n7AMGYPjzT5RTp6ptleYpNTWVwOeeI+e770qmhJ1xcagHD7p1vabBwe8OknW8K/ffH8zcuQVceaWt\n+uuaNcN6001E9O6NFh5e4Y+zbVsskyfX6rNV+fzISJTTp/+qHXc4UNPScMbE1NkzhRBCCH8kibPw\na0OGDMHw73+7nTgTFITtooswrVqF9aabvDcQTXOVaEybhrOoLV52NmSaOxKzz72lsa+9NpSJ2w/R\n+cIObHw/26P3FwvmzaNg3jzv9Wf2hMGAMyrKlSx36IB67BhaixYQEFD/YykiNYtCj8SF0CNxIbxJ\nSjWE3zMkJ1f/YmAptjFjMH33nVfHYP7Pf1DOnHG9gFfkwAED8xYn8Nt/Uxk+PIwHHgjmww/N7Nun\n/5/V66/nMenCXfS+Oc7zph+K4pukuUjpzhpS3yyEEKKxksRZ+LXExET3OmqUYhs1yrUQSn6+V8ag\nHD9O0FNPkf/aa2D865c0vXo5ePX7SAa13Mu//pVP164ONm828t13+p0t2rTRMOzfj6NjR6+Mqz6V\n7uWsHj7s81Z0UrMo9EhcCD0SF8KbpFRD+DdNw5CcjNPdUg1cy1Xbe/XCtHYttjFjav384Bkzybnl\nbzgvuKDi4TZtULPP0u/8s/TrF1r1vWw21KNHcbZvX7sx+UDpzhq+fjFQCCGE8BWZcRZ+bWj79mgh\nIWhNm3p0nTfKNQoK4Id/7eHEjzt5Oegx/ZNUFWe7dhjcaEmnHjqEs00bn9YG11TpzhoGD1YNrCtS\nsyj0SFwIPRIXwpskcRZ+zZOOGqVZR4/GtGoV2O0eX3vokMqcOUF07x6B6bPPOTPuBu6bqVV6vqND\nB1Q3WtIZ9u/H0amTx+PxB+VLNaTGWQghRGMkibPwa6krV3pU31xMi47GGRuLcdMmj647dUphzJgw\nFAV+WHGKqws+o+2sCVW+l+eMi8PgRku64h7ODZG/lWpIzaLQI3Eh9EhcCG+SxFn4tbDU1BrNOAPY\nrrgC04oVFffbYPdulby8itc0a6axfftZnnyygE6H1+A877xqk11HXJx7M8779jXcGefoaNTjx1HO\nnkUpKEBr2dLXQxJCCCHqnSTOou5pGsEPPYRy8qTHl7Y5c8atpbb1WIsS51Mn4ZtvTMycGcTw4WG0\na9eE224LJSVFP/yLG2cE/Pe/FLrRC9rp5uqBhn37cDbQxJnAQLQmTTBs3uzqqOHD1nggNYtCn8SF\n0CNxIbxJumrUknHNGrTmzXH07Onrofgtw/btBHzwAc4mTbD84x/uX1jUUaOmM87O888Hk4kPH9jL\nRktvhg61MWGCq21caDUNMJSzZzGtWUP+iy9W+xyHO6sHahrqvn0NshVdMWd0NKZ163BIRw0hhBCN\nlMw411Lg669j/vJLXw/Dr5m++QbrddcRsGiRa+lmNynHjlFoMNR86WxFwTZmDI+e/yWLF+dy772F\n9O9ffdIMYPr6a2wXX+xWNw8tMhKloMC1lGBlQymabddatHB7+P7GGRODMTHR5z2cQWoWhT6JC6FH\n4kJ4kyTOteFwYNyyBcOuXb4eif/SNMzLlmGZOhXb2LEEvPmm25ca9uwhNybGrXNXrzZy993BaOWa\nX1ivuKJGbenMn3+O9cYb3TtZUXDExVVZrqHu3++qlfZxiUNtOKOjMWzf7vMXA4UQQghfkcS5Fgx/\n/olmNmPYvdvXQ/Fbht27wW7H0aMHlgceIOD991HOnnXv2j//JHTAgCrPOX5c4fbbQ3jkkWDGj7dW\nyEsd/fqhZmaiHj7s9pjVQ4cw7N+PbeRIt69xVlOuYdi7t8G+GFjMGROD4nT6RamG1CwKPRIXQo/E\nhfAmSZxrwZiUhO3yy8FqRcnK8vVw/JJp2TJsV14JioKzXTtsl11GwMKF1V6nnDxJ4JtvYhs7Vve4\nwwHvvhvAsGHhxMU5SEzMZuRInZ7NBgO2yy/3aNbZ/PnnWK+9Fsxmt69xVPOCYEPu4VzMWTT77w+l\nGkIIIYQv1DhxnjFjBpGRkXTr1q1k3+LFi+ncuTNdunRh+fLl1e5v6AxJSdgvvBBH167n1qyzpmH+\nz38wbN5cowVESjN/8w3WK68s2bY8+CAB77xTZT0wmkbwffdhvfZafq6ktGHJEjNffmnmm29y+Mc/\nLAQHV34765gxum3pKnu2+fPPsd5wg3vnF6luxlltyB01ipQkzrGxPh6J1CwKfRIXQo/EhfCmGifO\n1113HStKJSNWq5VZs2axfv16fvzxR6ZPn17l/nOBcfNmV+KckHBO1TkrZ84QPGMGwQ89RETnzoTc\ndhvmDz9EKVpy2V3qnj0oeXk4+vQp2efs0AHbyJEEvvtupdcFvP8+6rFjFFTRgWPCBCsrVuRw/vnO\nasdhHzYMw65dbv1WwJCUBAEBHndJqa7G2bB/f4PuqAHgaN+ewokTISjI10MRQgghfKLGifPAgQNp\n3rx5yXZSUhIJCQm0bNmSmJgYYmJi2LZtW6X7Gzrl+HGU3FycnTq5ZpzPocRZTUnB0akTOevWkb1h\nA7bRozGuX0/4iBGEX3gh5kWL3LqP+ZtvsI4bV+GFOMuDDxLw1luQm1vx2bt3E/jPf5L39ttgNlda\nm2YwgOpu9AYGYh8+3LUEdzUC/vtfCm+80eOX+JwdOlQ+42y1oh49irN9e4/u6XdCQsh/9VVfjwKQ\nmkWhT+JC6JG4EN7ktRrn9PR0oqKiWLhwIUuWLCEyMpLjx4+TkZGhu7+hMyYlYe/f39VRISEBw59/\n+npIXqOmpJTUsWqRkVhvvJH8t9/m7J495L3+OkHPPOPWy3amcmUaxZydO2MfNoyA998ve6CggNA7\n76Rg7lyvlzVYx4ypvs65oMA15gkTPL6/1qIFit2u225PPXQIZ3S0RzXTQgghhPA/Xn85cMqUKUzQ\nSTxK71cacEuuYiWJM+CIj8eQnFzremB/oaak6NexqiqOvn0pvOsugp55pup77NuHeuoUjqKvUXkF\nDz1E4BtvQH5+yb6guXNxdOmC9ZZbSvYlJibidEJWVu1ixn7ppZgSE3VnuYuZVq7E0aMHWtu2nj9A\nUXB06KC79HZDXmrbX0nNotAjcSH0SFwIb/LayoFt2rQpM5Ocnp5OmzZtyMnJqbA/KipK9x733HMP\nsUUJW0REBN26dSv5FUtx4PvLdsFPP7H7//6PrgChoeRHRPDHF1/Qs6j3r6/HV5ttQ0oKe00mDicm\n6h633H03gT16sOODD+h2++269zv+2muc6tOHJkX1FOWP/3ryJH06dqTJokUU3nMPe19+mW7LlmFJ\nSgJFKTlf0+Cxx4LYufMUjzzyW40/37odOxjQsSNBa9Zgu+oq3fP7v/kmyh131Pjr1zssjCYHDuDo\n27fMcXX/flKDgvizkq+nbHu+vWPHDr8aj2z7x3YxfxmPbPvHtny/kO1iiYmJpKamAjB58mRqQtG0\n8ktGuO/w4cOMGzeOHTt2YLVaiY+PJykpCYvFwogRI9i3b1+l+8tbs2YNvXv3rulQ6ld+Pk06d+bM\nvn0lL0qFTJqE9ZprsF1zjY8HV3uh48dj+fvfsY8aVek55o8+wrxkCbnffKNbDxx20UUUPPss9sGD\nK72HYedOQq+/npzvviPs8svJXbQIR7m+zf/8ZyArV5r45ptcIiJqHKqAqzVeyL33Yu/TB9vll2Mb\nPbpkZl3JzCS8f3/O7tyJW0sL6gh89lkALLNnl9kfPHUq9gEDsN56a63GL4QQQgjv2Lp1KyM9WK+h\nWI1LNaZOncqgQYNITk4mJiaGVatWMX/+fAYPHszIkSNZsGABAGazWXd/Q2bcuhVH165lugucSy3p\n1NTUaluOWW++GfXECYyrV1e8/uBB1PR07NUsXuK44ALsffoQdsklFP7tb2WS5owMhYcfDuLrr818\n8UXtk2YA21VXcebPPymcPBnD9u2EjRxJ2NChBD77LIELFmC74ooaJ83gekHQoPOCoJRqCCGEEOeG\nGifOr7/+OseOHcNqtXLkyBHGjRvH9ddfz969e9m7dy9jxowpObey/Q2VcfPmkvrmYo6EhHMjcXY6\nUY8cqb5Xr9FIwdy5BD/xRIXabtO337oWLjEYqn2cZdYsbJdeimXGjJJ9mZkKgwaFo6rw+OM/0rJl\n7ZPmEiEh2MaMIf/11zm7Zw/5zz+PYrFg+uUXCidNqtWtHXq9nDXtnOjh7G/K/2peCJC4EPokLoQ3\nycqBNWAsWviktHOlJZ2Sno4WEUGVK4oUsY0ahbNFC8yffVZmv/mbb7BedZVbz3MkJJD/5ptgNJbs\na9VKY+vWbObPL6BpU6tnH8ATBgOOAQMoeOopstevr1Am4iln8eqBpaqflBMnQFXRSrVuFEIIIUTD\nJImzp5xODP/7X4XE2dmuHerJk1WviNcAuFOmUUJRKJg7l6D58yEvr+R69cgR7IMGuXULm01/f3Fp\nRnFxf0OgNWuGZjC4kuUihv37Zba5DjSkuBD1R+JC6JG4EN4kibOH1ORktKZN0Vq1KnvAYMDRpUuD\nL9cwHD5c0sPZHY4+fbAPGEDgm28Crt7NtiuuKDODXF5ODnz0kZlRo8KYO/fcWoXOGRdXpiWdundv\ng18xUAghhBAukjh7SK++udi5UOespqTg8CBxBih4/HEC3noLJSvLVaahs+iJpkFSkoF77w2me/cI\nVq828dBDFp58sqDKeze02jRHx45lXhA07N+Po3NnH47o3NTQ4kLUD4kLoUfiQnhT5dOCQpdx8+YK\nZRrFzoXOGmpqarXdMMpztm+PdcIEgh98EPXgQexDh1Y459QphQcfDOGGGwrZtKmA1q29+MKfH3GW\ne0FQ3bcP+8CBPhyREEIIIbxFZpw9VHrFwPIcCQkYG/gLgqWX2/aEZcYMTL/+iu3yy8FkqnC8eXON\n9euzue++Qo+S5oZWm+YofkGwiGH/finVqAMNLS5E/ZC4EHokLoQ3yYyzB5TMTJSTJ3HGx+seL5lx\n1jTdRUEaAkMNE2eteXPy3nwTR/v2dTCqhqPMjHNhIWpaGs5G/jURQgghzhUy4+wB4//+h6NvX1D1\nv2xas2ZooaGoR47U88i8xGpFycrC2bZtjS63XXEFycYEnngiiJqvR1lWQ6tNcxQvgqJpqIcO4YyJ\n0Z2BF7XT0OJC1A+JC6FH4kJ4kyTOHtDr31xeQ35BUD16FGdkZJUdMfRoGmzfbmD27CBGjw4jKsrp\ntcS5wQkPRwsKQklPlxUDhRBCiHOMJM4ecCtxbsALoagetqID+OQTMwMGhDNpUgjBwRo//5zNXXcV\nVjYp77GGWJvmjIvDcPCgq4ez1DfXiYYYF6LuSVwIPRIXwpukxtldFguGXbuw9+5d5WmOhARM339f\nT4PyLo8WPykSHe3ktdfy6NfP0VDLur3O0aED6oEDro4abi4EI4QQQgj/JzPObjL88YerH29ISJXn\n2RMSGuyMsyElBWe7dh5dc/HFdvr3r7ukuSHWppXMOO/bJx016khDjAtR9yQuhB6JC+FNkji7qaqF\nT0pzduzoejmwoPKFPZQzZzAtXerN4XlFZYuf/PijkeuuC63qI4lSHEWdNdR9+3DK4idCCCHEOUMS\nZze5U98MgNmMIy4Ow969lZ4S9PTThNx/P9jtXhxh7ZUv1dizR2XChFAefTSYyZMLCQys/zE1xNo0\nZ4cOGDdvBqMRrVkzXw/nnNQQ40LUPYkLoUfiQniTJM7u0DS3Z5yhqLNGJeUahu3bMS1fjrNZM7/r\nvlG8+MmZMwoPPxzElVeGMWKEjfXrsxk92iY1zG5ytG+PmpmJUzpqCCGEEOcUeTmwmN1O6A03gMWC\nFhaGFh4ORf/UFAUtKAjNzf7GlSbOmkbQrFkUPPooxq1bMW7ejKN7dy9/kBrKyUHJz0dr1Yo/fjHg\ndMKmTdk0a+bbvnKJiYkNb7YgLAxn69ZS31yHGmRciDoncSH0SFwIb5LEuYgxMRHlxAkK/vlPlOxs\nlJwclOxsV0KZnU3BnDlu38vRtSumtWsr7Dd9+SVKQQHWW28FoxHjr7/C5Mne/Bg1ZkhNdS3WoShc\nfLGdiy/2rzKShsYRFyc9nIUQQohzjCTORcxLl2IdP94r7cN0Z5xzcwl+4gly33sPDAbsF15I4Asv\n1PpZ3qLWcKntutZQZwmsf/sb9oQEXw/jnNVQ40LULYkLoUfiQniTJM4ANhumFSso+Plnr9xOi4wE\nhwMlMxOtVSsAAl9+GdvQoTgGDABc3TeU3FyU48fRoqIA+P57E+npCs2ba/TubadtW63KuuL0dIW1\na02sWWNi82YD2dkK553n5Jdfciqcu327geuvDyU21klsrJPzznPQqpWrDGPKlEJXRw0PW9GJylkn\nTPD1EIQQQgjhZfJyIGD8+WecHTqgRUd754aKUmbWWT14kIAPP+TUw0/wxhsBJCUZQFGw9+uH8X//\nK7lszx4DO3YY+e9/zVxySTjx8RHceGMIx45VzJ7PnlUYOjScVatMDBtm4+ulOWzbls2PP1ZMmgEu\nuMDBTz9l89RT+YwaZSMwEHbvNhAb63SNMSXF48VP6oP03xR6JC6EHokLoUfiQniTzDgD5q+/xnrN\nNV69Z/HS2/bhwzHPeoy1fR/gljHx9Otn55JLbK5z+vfHmJSE7corAXjgAUvJ9ZoGaWkKv/9upGnT\nii/oRURoJCefRVVBycwk7OqryV65EkzhuuNRVWjTRqNNGwcDBjgqHk9NxS6/zhJCCCGEqJTMOBcW\nYvr+e6xXXeXV2zoSEmD7blbev5Zjaw/yuvF+vvgil48+yqNzZ9csr71/f1e/Xx2KAtHRGuPG2QgK\n0n+GWvS3Z/z9dwx79hD47rs1Hq9BapxFAyJxIfRIXAg9EhfCmxp94mz66SccXbuW1Bl7i6NrV4zb\n/+Dirx/BMv+fvPeJnYSEsjO99l69MPz5Z5WrDLrDsH071jFjCHjrLcjO9vwGmoaamqq7aqAQQggh\nhHCRxHnpUmxeLtMAcMTHY9qXTPNBHWhzxwj9k4KDcXTpgmHbtlo9y7B9O9ZrrsE2YgSB77zj8fXK\niRNoJhOE65d5+JLUpgk9EhdCj8SF0CNxIbypcSfOBQWYfvgB67hxNb7FunVGrr02lC++MJU9EBKC\n9dZbKXj22Sqvr6pcw12G7dtx9OiB5aGHajTr7K+t6IQQQggh/EmjTpxNP/6Io2fPkpZxnlq+3MTk\nySHcdJOVMWNsFY7nL1iAs337Ku9R28RZOXUK9fRpnO3b4+zUCdvIkR7POvtrRw2Q2jShT+JC6JG4\nEHokLoQ3NerE2bx0Kdarr67RtV9+aWLGjGAWL85lwgRrpS/wVackcdZqtrS1YccO7N26lbwpWJNZ\nZ0NqKk7p4SyEEEIIUaXGmzjn5WFaswZbDco0li838Y9/BPPVVzn06FGxtZsntLZtISAA9eDBGl1v\n2L4dR7duJds1mXVWU1L89sVAqU0TeiQuhB6JC6FH4kJ4U6NNnE2rVmHv1w+teXOPr+3f386yZTl0\n7er0ylhqU65hLKpvLs3TWWd/LtUQQgghhPAXjTZxrs2iJ61aaXTq5J2kGWqXOBu2b8fRvXuZfZ7O\nOqupqX77cqDUpgk9EhdCj8SF0CNxIbypcSbO2dmYfvkF25gxvh4JAPYLL6xZ4pybi5qWhqNz5wqH\nLDNmuDfr7HCgpqXhjInx/PlCCCGEEI1Io0yczStXYhs0CK1Jk2rPTUlRa7s+SbUcCQmoR46gnD3r\n0XWGXbtwdOkCJlOFY86OHbFdcgmBb79d5T3UY8dc5SqBgR49u75IbZrQI3Eh9EhcCD0SF8KbGmXi\n7M6iJ9nZMHduECNHhrFtm6GOB2TC3rMnhi1bPLrMqFOmUZrloYcIWLiwyllnqW8WQgghhHBPo0uc\nlTNnMG3YgPXyy3WP2+3wwQdm+veP4ORJhcTEbAYMqF3nDHfY+/fHmJTk0TWG7duxV5E4l8w6v/FG\npeeoKSk4/LgVndSmCT0SF0KPxIXQI3EhvKnRJc6mFSuwXXSR7vLSp08rDBsWztKlZhYvzuW11/KJ\njKxZf2VP2fv3x/i//3l0jd6LgeUVPPYYAe+9h7p7t+5xmXEWQgghhHBP40qcnU4CPvgA6/jxuoeb\nNNF48cV8li3LpXv3up9lLs3Rty/G335zTXm7o7AQw/79OLp2rfI0LTqagjlzCLnnHrBVXN3Qnztq\ngNSmCX0SF0KPxIXQI3EhvKlRJc7mL74ATcM2dqzucUWBgQPtKEo9DwzQmjXDGRWF4c8/3TrfsGeP\na7U/N5YstE6ciNa6NYEvvljxPikpfp04CyGEEEL4i8aTOOflEfTUU+TPmweqSmqq/310T9rSVVff\nXIaikLdgAQEffIDhjz/KHPLnVQNBatOEPokLoUfiQuiRuBDe5H/ZYx0JfPVV7AMGcDBqELfdFsKN\nN4a6XRVRX+z9+2PwIHGurr65NC0qioJnnnGVbBQWunYWFKCcOoUWFVWT4QohhBBCNCr1ljgvXryY\nzp0706VLF5YvX15fjwUgc8tRtH+/x/gDzzNiRBjnn+9gzZpsjMZ6HUa1POmsUV0rOj3W8eNxdOxI\n0Pz5AKhHjuBs2xYMddxurxakNk3okbgQeiQuhB6JC+FN9ZI6Wq1WZs2aRVJSEhaLheHDhzO2kjrj\nCjSN2hYd59zzNCs73MWtj7XknWFnMZtrdbs64+zYESUnB+X48apngR0ODLt3Y+/WzbMHKAr5L75I\n+LBhWEePRjl7VuqbhRBCCCHcVC8zzklJSSQkJNCyZUtiYmKIiYlh27Ztbl0bMmkSpmpmqDMzFdas\nMbJpU8WZU0NSEr3z1zP8+7u55BK73ybNAKgq9n79qm1Lp+7fj7NVK92WetXRWrYk/1//ImTqVNcL\nhn6eOEttmtAjcSH0SFwIPRIXwpvqJXHOyMggKiqKhQsXsmTJEiIjIzl+/Hj1F2oaxg0bCJo3Dxx/\ntYfbscPA/fcHc911oZx/fgQDBoTz6quBHDhQLnF2OgmePZuCOXMgJMTLn6pu2IcMwbRiRZXnGLdv\nx+HpbHMptnHjsPfqRdDzz/v1i4FCCCGEEP6kXl8OnDJlChMmTABAqaL8wmKB77838dmr2eB0ooWF\nYfr665LjISEavXrZmTLFwurV2Rw4cJZly3K55RZrmfuYP/8cVLXSvs3+qHDSJEw//4xaRVs6w/bt\nOHr0qNVzCp57Di00FGdcXK3uU9ekNk3okbgQeiQuhB6JC+FN9VLjHBUVVWaGOT09nSidGt4rr3yf\nkydHcPBgF9q0yeL+Hj/i7NKFgocfhunTSWzRgiEXXURcnJNjx34CIDra9SuY4v8win8ls3H1aob/\n4x/kf/YZqGqF4/68bbn3XvIfeogts2frHjds385vI0aQlZhY4+et27UL8/PP079o6XF/+vylt4v5\ny3hk2z+2d+zY4VfjkW3/2C7mL+ORbf/Yrur7hdVqZc+ePRiNRpo0aQLA2bNnAYiIiJDtBrqtaRpN\nmjShRYsWbC7VrSwxMZHU1FQAJk+eTE0omqbV+ZrSVquV+Pj4kpcDR4wYwb59+8qcs2Y6bWA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E1cUU+8Jx277GxJmIiIj8mv34ZLGwUN/15eVQBg8GAOzbF9vqtMC2DAOjkBhS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\nq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c0gJHXh4Mmzb5PFd2quhjKcIld6Rrv9+64my1CvjXv4KUj2uzIfLFF2F76CE0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WJSoKxvhkVP1dPVFX01rYiDMdzpW4fFUxcSYiIiKvJp47yEIsL3eqf0B7PQIz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\nT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/CGLV2ecj9tVXOH79ao5RgPrRJ0PPb/Jkw2OteVOpeWcE9nLu6CDmvgeIf+AB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\nNOS91CGan/RJTsYXn0DdvobBz+vqgu5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Pd/tuwZ49CsSOqIRnmOXttgm5nFHiTGwa1aYR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Pf6h8hhdAZAvpBTWcKjonR77Bg+0uwxZcb91sdud30UVeLV9epF/9qkx33FFf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\nzwhveHGBVjc6VXEN4+wuJeI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yxNEjDAAA0BcVFxersLAwqGv77Ixw5cqt2hE/niIYAAAAHeqzhTA7ygWPXimz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\nsHtzibx5eb39sgAAAMAxer0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H7RGWpC1btmjGjBkqLy/Xu+++q4ULF2rZsmWSpLPOOksPPvigxo5t3ZbQmT3C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\nzLxwvgUAAEBUa2ho0O23365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xmMc85nHpbe5Sj6ccdw0KguHJJ3HkgQeQ2a0b7vlrudbe66lUO/HWW2q0aNHJ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\n7rdo0SJmzJiBw+Fg0aJF7mtPPvkkMTExADz88MM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SFWExznIKNzk5WQkJCUpKSip2+ZNPPimHw6Fx48aVuM0nn3yijh07VqgQAED5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\nMhYtkiIifF0SAABAjUbz6yM/b0qW7ep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Hl+rEdT3U8OUJMnXvrDN79ip32rRy2xRLrBgDAACgiMw//6yMfyQqZMVS1a7dUTnPPqfa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\nsM672vXOnPlR06ad0uDBbbz/fDgu9eN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4xw42OEDT2WxsjwwAAAIvoA3w/fez/AN8HS67ddppbu3YYdcXXziCNCKEAzJ9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\nrbLl6FCzooGRna3qbt1ktG2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L0vW/vWn69rtI3XlnqU45pe61IRedadOMtoYc1d1KmVau3KPk5G5+/TGg9SED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\n7NkunXlmaDfAZLdghrqAGeoCZqgLWIE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