07-EindimKalmTeil2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Eindimensionaler Kalman-Filter, Teil 2 Simulationen\n",
    "8.4.2024, S. Mack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# display plots inside this notebook\n",
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from collections import namedtuple\n",
    "gaussian = namedtuple('Gaussverteilung', ['mean', 'var'])\n",
    "gaussian.__repr__ = lambda s: '𝒩(μ={:.3f}, 𝜎²={:.3f})'.format(s[0], s[1])\n",
    "\n",
    "def gaussian_multiply(g1, g2):\n",
    "    mean = (g1.var * g2.mean + g2.var * g1.mean) / (g1.var + g2.var)\n",
    "    variance = (g1.var * g2.var) / (g1.var + g2.var)\n",
    "    return gaussian(mean, variance)\n",
    "\n",
    "def predict(pos, movement):\n",
    "    return gaussian(pos.mean + movement.mean, pos.var + movement.var)\n",
    "\n",
    "def update(prior, likelyhood):\n",
    "    posterior = gaussian_multiply(likelyhood, prior)\n",
    "    return posterior"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die beiden Funktionen ``update()`` und ``predict()`` aus dem letzten Notebook sind  oben aufgeführt. Dort findet sich auch der Quellcode für das Rechnen mit Gaußverteilungen. Mit diesem Code lässt sich nun ein Kalman-Filter realisieren, der einen Zustandswert mit der bekannten Vorhersage-/Korrektur-Iteration trackt.  \n",
    "Damit der Filter getestet werden kann, müssen Messdaten simuliert werden. Daher wird im nächsten Abschnitt noch Code für die Simulation der Bewegung des Roboterfahrzeugs entwickelt. Die Algorithmen hierfür werden durch die Klasse ``RoboSim`` implementiert.\n",
    "\n",
    "## Klasse RoboSim für Simulation von Messdaten \n",
    "Es soll ein Roboterfahrzeug simuliert werden, welches im Wesentlichen geradlinig entlang einer geraden Wand fährt. Dabei muss seine Fahrspur nicht parallel zur Wand sein.  \n",
    "Von Interesse ist nicht mehr die Position entlang des Flurs sondern um die Position in Bezug auf die Wand: Es wird der Abstand zur Wand gemessen - diese Messgröße ist somit die zu filternde \"Zustandsgröße\". Auf diese Messgröße beziehen sich die nachfolgenden Variablen wie ``x0``, ``velocity`` usw.  \n",
    "Es wird also **nicht der zurück gelegte Weg sondern der Abstand bzw. die Abständsänderung zur Wand** betrachtet. Fährt das Fahrzeug beispielsweise geradlinig schräg von der Wand weg, dann besitzt es eine konstante Geschwindigkeit ``velocity``, mit der es sich von der Wand weg bewegt.\n",
    "\n",
    "Der Konstruktor ``RoboSim(x0,velocity,measurement_var,process_var)`` erhält folgende Variablen übergeben: \n",
    "* ``x0``: Startposition des Roboters, also der anfängliche Abstand zur Wand. \n",
    "* ``velocity``: Die (mittlere) Positionsänderung pro Iteration, also der Erwartungswert der Abstandsänderung zur Wand.\n",
    "* ``process_var``: Die Varianz des gaussförmigen Prozessrauschens, mit dem ``velocity`` beaufschlagt wird. Der Roboter bewegt sich realitätsnah *nicht* mit absolut konstanter Geschwindigkeit relativ zur Wand. D.h. er fährt beispielsweise leichte Schlangenlinien im Gegensatz zu einer idealen Fahrspur in Form einer Geraden.\n",
    "* ``measurement_var``: Die Varianz der Messunsicherheit des Positionssensors, also des Messrauschens.  \n",
    "  \n",
    "Die Methode `sense_position()` schlägt auf den schon durch das Prozessrauschen verrauschten wahren Positionswert zusätzlich ein Messrauschen auf."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import copy # needed for deepcopy() ín move_and_sense()\n",
    "class RoboSim(object):\n",
    "\n",
    "    def __init__(self, x0=0, velocity=1, process_var=0.0, measurement_var=0.0):\n",
    "        ''' x0 - initial position, velocity - (+=right, -=left), process_variance - variance \n",
    "        velocity of process (m/s)^2, measurement_variance - variance in measurement m^2 '''\n",
    "        self.x = x0\n",
    "        self.velocity = velocity\n",
    "        self.measurement_noise = np.sqrt(measurement_var)\n",
    "        self.process_noise = np.sqrt(process_var)\n",
    "\n",
    "    def move(self, dt=1.0):\n",
    "        '''compute new position of the robot assuming `dt` seconds have passed since the last update\n",
    "        based on (velocity + process noise).'''\n",
    "        velocity = self.velocity + np.random.randn() * self.process_noise * dt\n",
    "        self.x += velocity * dt\n",
    "\n",
    "    def sense_position(self):\n",
    "        # simulates measuring the position with measurement noise\n",
    "        return self.x + np.random.randn() * self.measurement_noise\n",
    "\n",
    "    def move_and_sense(self, dt=1.0):\n",
    "        self.move(dt)\n",
    "        x = copy.deepcopy(self.x)\n",
    "        return x, self.sense_position() # true noisy position and measured position of robot\n",
    "\n",
    "    def run_simulation(self, dt=1, count=1):\n",
    "        ''' simulate the robot moving over a period of time and returns data: np.array[float, float],2D array, \n",
    "        column 0: true actual position of robot, column 1: measurement of robot position\n",
    "        '''\n",
    "        return np.array([self.move_and_sense(dt) for i in range(count)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Im Quellcode oben ist es wichtig, den Unterschied zwischen dem Prozessrauschen und dem Messrauschen zu erkennen: \n",
    ">Das Prozessrauschen bewirkt, dass der Roboter sich nicht absolut gleichförmig bewegt. Diese schon \"verrauschte\" wahre Position wird von einem Sensor mit Messunsicherheit (= Messrauschen) gemessen und dadurch zusätzlich \"verrauscht\".\n",
    "\n",
    "Der Rückgabewert der Funktion ``run_simulation`` ist ein Array aus Tupeln. Jedes Arrayelement entspricht einem Zeitschritt. In zugehörigen Tupel befindet sich an Position 0 der wahre Abstandswert und an Position 1 der gemessene Abstandswert des Roboters.\n",
    "\n",
    "Nachfolgend wird mit der Klasse ``RoboSim`` eine Fahrt des Roboterfahrzeugs simuliert und grafisch dargestellt.\n",
    "\n",
    "### Beispiel Simulation Roboterfahrzeug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(13) # to make the simulation reproducible\n",
    "\n",
    "process_var = 1. # variance of robot velocity, same as later in Kalman filter\n",
    "sensor_var = 2. # variance distance sensor, same as later in Kalman filter\n",
    "\n",
    "n = 10 # number of date to be simulated\n",
    "x_start = 0\n",
    "velocity = 1\n",
    "dt = 1. # time step in seconds\n",
    "process_model = gaussian(velocity, process_var) # for simulation same parameters as later in Kalman filter\n",
    "  \n",
    "# simulate robot and get measurements\n",
    "robot = RoboSim(x0=x_start,velocity=process_model.mean,measurement_var=sensor_var,process_var=process_model.var)\n",
    "\n",
    "# create list of measurements\n",
    "zs = [robot.move_and_sense() for _ in range(n)] # column 0: true position, column 1: measurend position\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,4))\n",
    "time = np.linspace(dt,n*dt, n)\n",
    "ax.plot(time, [z[0] for z in zs], color='r', mfc='None', marker = 'v', linestyle='None',label='wahrer Wert')\n",
    "ax.plot(time, [z[1] for z in zs], color='k', mfc='None', marker = 'o', linestyle=':', linewidth = 1, label='Messwert')\n",
    "ax.set_xlabel('Zeit (s)')\n",
    "ax.set_ylabel('Wandabstand Roboterfahrzeug (a.u.)')\n",
    "ax.legend()    \n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Auf die oben simulierten Daten des Roboterfahrzeugs wird nun ein Kalman-Filter angewendet und dessen Wirkung untersucht. Dafür werden zuerst aber noch zwei Hilfsfunktionen benötigt.\n",
    "\n",
    "## Hilfsfunktionen\n",
    "### Plotfunktion für Ergebnisse Simulation mit Kalman-Filter\n",
    "Nachfolgend wird eine Plotfunktion erstellt, die für die kommenden Simulationen den Verlauf der Vorhersagewerte, Messwerte und Schätzwerte (=Filteroutput) grafisch darstellt.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_meas_pred_est(measurements, predictions, estimates, est_vars):\n",
    "    plt.figure(figsize=(10,4))\n",
    "    n = len(measurements)\n",
    "    time = range(1,n+1)\n",
    "    meas, = plt.plot(time, measurements, color='k', mfc='None', marker = 'o', linestyle='None') # mfc = hollow markers\n",
    "    pred, = plt.plot(time, predictions, color='k', mfc='None', marker = '+', linestyle='None')\n",
    "    est, = plt.plot(time, estimates, marker = 'o', linestyle=':', linewidth = 2, markersize=3)\n",
    "    est_var_min = np.asarray(estimates) - np.asarray(est_vars) # conversion to np.array because adding lists not possible\n",
    "    est_var_max = np.asarray(estimates) + np.asarray(est_vars)\n",
    "    plt.fill_between(time, est_var_min, est_var_max, facecolor='C0', alpha=0.2)\n",
    "    \n",
    "    plt.legend([meas, pred, est], ['Messwerte','Vorhersagewerte','Schätzwerte (Filter) $\\pm \\sigma$'], loc=\"upper left\", \n",
    "               bbox_to_anchor=(1,1))\n",
    "    plt.xlabel('Zeit (s)')\n",
    "    plt.ylabel('Zustandswert (a.u.)')\n",
    "    plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Auch für den Kalmanfilter wird eine Funktion erstellt, damit die Quellcodes in diesem Notebook überschaubar bleiben. Die nachfolgende Funktion ``kal_filt_for_plot()`` filtert die übergebenen Messdaten aus ``zs`` (Position 0 der einzelnen Tupel dieser Liste) und gibt Arrays für die grafische Darstellung zurück.\n",
    "\n",
    "### Funktion für Anwendung des Kalman-Filters auf Simulationsdaten inkl. Ergebnisplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def kal_filt_for_plot(meas_data, est_start, est_start_var, process_model, sensor_var):\n",
    "    posterior = gaussian(est_start, est_start_var)  # first initial guess of Robot position\n",
    "    priors=[] # needed for subsequent plotting\n",
    "    measurements=[]\n",
    "    posteriors=[]\n",
    "    post_vars=[]\n",
    "\n",
    "    print('Wahr \\t Vorhers. +/-Var      Mess +/-Var        Korr. +/-Var')\n",
    "\n",
    "    # perform Kalman filter on measurement z, process_model and sensor_var values same as for simulation.\n",
    "    for z in meas_data:    \n",
    "        prior = predict(posterior, process_model)\n",
    "        likelihood = gaussian(z[1], sensor_var)\n",
    "        posterior = update(prior, likelihood)\n",
    "        print('{:6.3f}\\t {:6.3f} +/-{:7.3f}    {:6.3f} +/-{:6.3f}\\t  {:6.3f} +/-{:6.3f}'.format\n",
    "              (z[0],prior.mean, prior.var,z[1],sensor_var,posterior.mean,posterior.var))\n",
    "        priors.append(prior.mean)\n",
    "        posteriors.append(posterior.mean)\n",
    "        post_vars.append(np.sqrt(posterior.var))\n",
    "        measurements.append(z[1])\n",
    "    \n",
    "    #plot_meas_pred_est(measurements,priors,posteriors,post_vars)\n",
    "    print()\n",
    "    print('final estimate:        {:10.3f}'.format(posterior.mean))\n",
    "    print('actual final position: {:10.3f}'.format(robot.x))\n",
    "    return measurements,priors,posteriors,post_vars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vorbemerkung zu den nachfolgenden Simulationen\n",
    "In den nachfolgend gezeigten Simulationsbeispielen erhält der Kalman-Filter für sein physikalisches Modell die selbe Geschwindigkeit ``velocity`` und Varianz ``process_var`` wie sie für die Simulation der Roboterbewegung verwendet wurde. Das Gleiche gilt auch für das Sensorrauschen: Das im Kalman-Filter angenommene Messwertrauschen ``sensor_var`` ist gleich zu dem simulierten Sensorrauschen ``measurement_var``.  \n",
    "Diese Tatsache hat ein wenig den Beigeschmack einer Täuschung, denn bei einem realen, nicht simulierten Fahrzeug liegen diese Werte nicht vor. Letztlich kann man aber sowohl das Fahrzeug als auch den Sensor empirisch untersuchen, und kann damit die beiden Varianz-Parameter näherungsweise bestimmen. \n",
    "\n",
    "**Der Parameter Geschwindigkeit im physikalischen Modell ist hier unveränderlich auf den Wert 1 festgelegt.**\n",
    "Damit er ähnlich wie beim g-h-Filter dynamisch angepasst werden kann, muss der Kalman-Filter diesen Zustandswert ebenfalls schätzen. Dafür ist aber ein *zweidimensionaler* Kalmanfilter nötig, der erst im nächsten Jupyter\n",
    "Notebook entwickelt wird.   \n",
    "\n",
    "## Simulation mit zehn Iterationen zur Verdeutlichung der Filterwirkung"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wahr \t Vorhers. +/-Var      Mess +/-Var        Korr. +/-Var\n",
      " 0.288\t  1.000 +/-401.000     1.354 +/- 2.000\t   1.352 +/- 1.990\n",
      " 1.243\t  2.352 +/-  2.990     1.882 +/- 2.000\t   2.070 +/- 1.198\n",
      " 3.588\t  3.070 +/-  2.198     4.341 +/- 2.000\t   3.736 +/- 1.047\n",
      " 5.938\t  4.736 +/-  2.047     7.156 +/- 2.000\t   5.960 +/- 1.012\n",
      " 8.417\t  6.960 +/-  2.012     6.939 +/- 2.000\t   6.949 +/- 1.003\n",
      " 8.628\t  7.949 +/-  2.003     6.844 +/- 2.000\t   7.396 +/- 1.001\n",
      "10.191\t  8.396 +/-  2.001     9.847 +/- 2.000\t   9.122 +/- 1.000\n",
      "12.105\t 10.122 +/-  2.000    12.553 +/- 2.000\t  11.338 +/- 1.000\n",
      "13.232\t 12.338 +/-  2.000    16.273 +/- 2.000\t  14.305 +/- 1.000\n",
      "14.838\t 15.305 +/-  2.000    14.800 +/- 2.000\t  15.053 +/- 1.000\n",
      "\n",
      "final estimate:            15.053\n",
      "actual final position:     14.838\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(13) # to make the simulation reproducible\n",
    "est_start = 0\n",
    "est_start_var = 400\n",
    "\n",
    "process_var = 1. # variance of robot velocity, same as later in Kalman filter\n",
    "sensor_var = 2. # variance distance sensor, same as later in Kalman filter\n",
    "\n",
    "x_start = 0\n",
    "velocity = 1\n",
    "dt = 1. # time step in seconds\n",
    "process_model = gaussian(velocity, process_var) # for simulation same parameters as later in Kalman filter\n",
    "  \n",
    "# simulate robot and get measurements\n",
    "robot = RoboSim(x0=x_start,velocity=process_model.mean,measurement_var=sensor_var,process_var=process_model.var)\n",
    "\n",
    "# create list of measurements\n",
    "zs = [robot.move_and_sense() for _ in range(10)] # column 0: true position, column 1: measurend position\n",
    "# perform Kalman tracking and plot results, * needed to unpack arguments out of the list\n",
    "plot_meas_pred_est(*kal_filt_for_plot(zs,est_start,est_start_var,process_model,sensor_var))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die wahren Roboterpositionen wurden mit der Klasse ``RoboSim`` simuliert. Die Startposition ist 0, die Geschwindigkeit 1. Mit der selben Klasse wurden auch die Messwerte simuliert: Sie streuen relativ zu den wahren Abstandswerten (die auch schon streuen) mit einer Varianz von 2.    \n",
    "Der Startpostionswert des Filters beträgt 0 und hat eine Varianz von 400. Dieser Wert ist der Schätzwert zur Zeit 0, welcher nicht im Plot und auch nicht oben in der Tabelle dargestellt ist. Die große Varianz bedeutet, dass das Roboterfahrzeug am Anfang praktisch überall vermutet wird.    \n",
    "Der erste Vorhersagewert ist gleich 1, denn im Prozessmodell ist eine feste Geschwindigkeit von 1/s angenommen und der Zeitschritt beträgt 1 s. Die Varianz dieses ersten Vorhersagewerts ist mit 401 sogar noch um 1 höher als die Varianz des Startwerts.  \n",
    "Der Schätzwert für die Zeit 1 liegt deshalb sehr nahe am Messwert (und nicht am Vorhersagewert), da dessen Varianz mit (konstant) 2 wesentlich kleiner ist als die des Vorhersagewerts (401).  \n",
    "Bei den nächsten Iterationen (=Zeitschritten) liegt der Schätzwert wie erwartet immer zwischen dem Vorhersagewert und dem Messwert.\n",
    "  \n",
    "Die Standardabweichung (*also die Wurzel der Varianz!*) des Schätzwertes ist durch das hellblaue Band markiert und engt sich rasch auf den Wert 1 ein: Die Varianz des Vorhersagewerts nähert sich rasch dem Wert 2. Denn zur  Varianz 1 des vorherigen Schätzwerts wird bei der Vorhersage die Varianz 1 des Prozessmodells hinzuaddiert. Anschließend werden bei der Korrektur die Varianz 2 der Vorhersage mit der Varianz 2 des Messwerts kombiniert, was wie bei einer Mittelwertbildung die Varianz 1 ergibt. Und das Spiel geht von vorne los... \n",
    "  \n",
    "> Die Varianz des des Filter-Outputs (Korrekturwert=Schätzwert) ist schon bei der zweiten Iteration deutlich kleiner als die Varianz des Messwerts. Der Kalman-Filter zeigt also seine Wirkung."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kalman-Filter für Simulation mit hohem Prozessrauschen\n",
    "\n",
    "```python\n",
    "process_var = 40\n",
    "sensor_var = 2\n",
    "```\n",
    "Hier ist die Varianz des Messwerts auf lange Sicht gleich ``sensor_var``, denn die Vorhersage wird wegen ihrer ungleich höheren Unsicherheit nicht mehr ernst genommen. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wahr \t Vorhers. +/-Var      Mess +/-Var        Korr. +/-Var\n",
      "-3.506\t  1.000 +/-440.000    -2.440 +/- 2.000\t  -2.424 +/- 1.991\n",
      "-2.787\t -1.424 +/- 41.991    -2.148 +/- 2.000\t  -2.115 +/- 1.909\n",
      " 6.720\t -1.115 +/- 41.909     7.473 +/- 2.000\t   7.082 +/- 1.909\n",
      "16.259\t  8.082 +/- 41.909    17.477 +/- 2.000\t  17.049 +/- 1.909\n",
      "26.612\t 18.049 +/- 41.909    25.133 +/- 2.000\t  24.810 +/- 1.909\n",
      "22.622\t 25.810 +/- 41.909    20.837 +/- 2.000\t  21.064 +/- 1.909\n",
      "27.181\t 22.064 +/- 41.909    26.837 +/- 2.000\t  26.620 +/- 1.909\n",
      "33.960\t 27.620 +/- 41.909    34.409 +/- 2.000\t  34.100 +/- 1.909\n",
      "35.765\t 35.100 +/- 41.909    38.807 +/- 2.000\t  38.638 +/- 1.909\n",
      "40.600\t 39.638 +/- 41.909    40.562 +/- 2.000\t  40.520 +/- 1.909\n",
      "\n",
      "final estimate:            40.520\n",
      "actual final position:     40.600\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(13) # to make the simulation reproducible\n",
    "est_start = 0\n",
    "est_start_var = 400\n",
    "\n",
    "process_var = 40.\n",
    "sensor_var = 2.\n",
    "\n",
    "x_start = 0\n",
    "velocity = 1\n",
    "dt = 1. # time step in seconds\n",
    "process_model = gaussian(velocity, process_var) # for simulation same parameters as later in Kalman filter\n",
    "  \n",
    "# simulate robot and get measurements\n",
    "robot = RoboSim(x0=x_start,velocity=process_model.mean,measurement_var=sensor_var,process_var=process_model.var)\n",
    "\n",
    "# create list of measurements\n",
    "zs = [robot.move_and_sense() for _ in range(10)] # column 0: true position, column 1: measurend position\n",
    "# perform Kalman tracking and plot results, * needed to unpack arguments out of the list\n",
    "plot_meas_pred_est(*kal_filt_for_plot(zs,est_start,est_start_var,process_model,sensor_var))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kalman-Filter für Simulation mit hohem Sensorrauschen\n",
    "\n",
    "```python\n",
    "process_var = 2\n",
    "sensor_var = 40\n",
    "```\n",
    "Hier ist die Varianz des Korrekturwerte auf lange Sicht etwa 8. Es stellt sich ein Gleichgewicht ein: Die Vorhersagewerte liegen bei einer Varianz von ca. 10, was mit der konstanten Varianz 40 der Messwerte zu einer resultierenden Varianz von 8 kombiniert wird."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wahr \t Vorhers. +/-Var      Mess +/-Var        Korr. +/-Var\n",
      "-0.007\t  1.000 +/-402.000     4.760 +/-40.000\t   4.420 +/-36.380\n",
      " 0.930\t  5.420 +/- 38.380     3.787 +/-40.000\t   4.620 +/-19.587\n",
      " 3.832\t  5.620 +/- 21.587     7.199 +/-40.000\t   6.173 +/-14.020\n",
      " 6.741\t  7.173 +/- 16.020    12.188 +/-40.000\t   8.608 +/-11.439\n",
      " 9.832\t  9.608 +/- 13.439     3.221 +/-40.000\t   8.001 +/-10.059\n",
      " 9.717\t  9.001 +/- 12.059     1.738 +/-40.000\t   7.319 +/- 9.266\n",
      "11.513\t  8.319 +/- 11.266     9.974 +/-40.000\t   8.682 +/- 8.790\n",
      "13.805\t  9.682 +/- 10.790    15.812 +/-40.000\t  10.985 +/- 8.498\n",
      "14.985\t 11.985 +/- 10.498    28.585 +/-40.000\t  15.436 +/- 8.315\n",
      "16.842\t 16.436 +/- 10.315    16.673 +/-40.000\t  16.484 +/- 8.201\n",
      "\n",
      "final estimate:            16.484\n",
      "actual final position:     16.842\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(13) # to make the simulation reproducible\n",
    "est_start = 0\n",
    "est_start_var = 400\n",
    "\n",
    "process_var = 2.\n",
    "sensor_var = 40.\n",
    "\n",
    "x_start = 0\n",
    "velocity = 1\n",
    "dt = 1. # time step in seconds\n",
    "process_model = gaussian(velocity, process_var) # for simulation same parameters as later in Kalman filter\n",
    "  \n",
    "# simulate robot and get measurements\n",
    "robot = RoboSim(x0=x_start,velocity=process_model.mean,measurement_var=sensor_var,process_var=process_model.var)\n",
    "\n",
    "# create list of measurements\n",
    "zs = [robot.move_and_sense() for _ in range(10)] # column 0: true position, column 1: measurend position\n",
    "# perform Kalman tracking and plot results, * needed to unpack arguments out of the list\n",
    "plot_meas_pred_est(*kal_filt_for_plot(zs,est_start,est_start_var,process_model,sensor_var))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kalmanfilter mit unpassendem Prozessmodell\n",
    "Nachfolgend wird untersucht, wie ein Kalman-Filter reagiert, wenn die Messdaten ganz und gar nicht zum Prozessmodell passen.\n",
    "Dazu wird die Klasse RoboSim so modifiziert, dass der Roboter sich nun nicht mehr gleichförmig sondern mit konstanter Beschleunigung bewegt. Dazu wird eine Klasse RoboSimAcc erstellt, die von der Klasse RoboSim erbt.    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RoboSimAcc(RoboSim):\n",
    "    def __init__(self, x0=0, velocity=1, accel = 1, process_var=0.0, measurement_var=0.0):\n",
    "        ''' x0 - initial position, velocity - (+=right, -=left), process_variance - variance in process (m/s)^2, \n",
    "        measurement_variance - variance in measurement m^2 '''\n",
    "        super().__init__(x0=0, velocity=1, process_var=0.0, measurement_var=0.0)\n",
    "        self.accel = accel\n",
    "        \n",
    "    def move(self, dt=1.0):\n",
    "        '''compute new position of the robot assuming `dt` seconds have passed since the last update.'''\n",
    "        # computes new position based on velocity and acceleration + process noise added\n",
    "        self.velocity = self.velocity + self.accel * dt\n",
    "        velocity = self.velocity + np.random.randn() * self.process_noise * dt\n",
    "        self.x += velocity * dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Das Prozessmodell des Kalman-Filters basiert jedoch anders als die im Code oben erzeugten Simulationswerte immer noch auf einer gleichförmigen Bewegung. **Im Gegensatz zu den vorangegangenen Abschnitten verwendet die Simulation der Roboterbewegung also einen ganz anderen Geschwindigkeitsverlauf als das Prozessmodell des Kalman-Filters.** \n",
    "Wie der nachfolgende Plot darstellt, kann nun der Kalman-Filter der beschleunigten Bewegung nicht folgen, da er ja von einer gleichförmigen Bewegung ausgeht. Das Gleiche wurde auch beim gh-Filter gezeigt. Der wahre Wert liegt ab der dritten Iteration immer außerhalb des 68 % Vertrauensintervalls des Filter-Outpus.\n",
    "Nur wenn das Prozessrauschen sehr viel größer als das Sensorrauschen gewählt wird, folgen die Schätzwerte dem Parabelverlauf. Dann aber findet jedoch fast gar keine Filterwirkung statt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wahr \t Vorhers. +/-Var      Mess +/-Var        Korr. +/-Var\n",
      " 2.000\t  1.000 +/-401.000     2.000 +/- 2.000\t   1.995 +/- 1.990\n",
      " 5.000\t  2.995 +/-  2.990     5.000 +/- 2.000\t   4.196 +/- 1.198\n",
      " 9.000\t  5.196 +/-  2.198     9.000 +/- 2.000\t   7.188 +/- 1.047\n",
      "14.000\t  8.188 +/-  2.047    14.000 +/- 2.000\t  11.128 +/- 1.012\n",
      "20.000\t 12.128 +/-  2.012    20.000 +/- 2.000\t  16.075 +/- 1.003\n",
      "27.000\t 17.075 +/-  2.003    27.000 +/- 2.000\t  22.041 +/- 1.001\n",
      "35.000\t 23.041 +/-  2.001    35.000 +/- 2.000\t  29.022 +/- 1.000\n",
      "44.000\t 30.022 +/-  2.000    44.000 +/- 2.000\t  37.011 +/- 1.000\n",
      "54.000\t 38.011 +/-  2.000    54.000 +/- 2.000\t  46.006 +/- 1.000\n",
      "65.000\t 47.006 +/-  2.000    65.000 +/- 2.000\t  56.003 +/- 1.000\n",
      "\n",
      "final estimate:            56.003\n",
      "actual final position:     65.000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(13) # to make the simulation reproducible\n",
    "est_start = 0\n",
    "est_start_var = 400\n",
    "\n",
    "process_var = 1. # variance of robot velocity, same as later in Kalman filter 1\n",
    "sensor_var = 2. # variance distance sensor, same as later in Kalman filter 2\n",
    "\n",
    "x_start = 0\n",
    "velocity = 1\n",
    "dt = 1. # time step in seconds\n",
    "process_model = gaussian(velocity, process_var) # for simulation same parameters as later in Kalman filter\n",
    "  \n",
    "# simulate robot and get measurements\n",
    "robot = RoboSimAcc(x0=x_start,velocity=process_model.mean,accel = 1,measurement_var=sensor_var,process_var=process_model.var)\n",
    "\n",
    "# create list of measurements\n",
    "zs = [robot.move_and_sense() for _ in range(10)] # column 0: true position, column 1: measurend position\n",
    "# perform Kalman tracking and plot results, * needed to unpack arguments out of the list\n",
    "plot_meas_pred_est(*kal_filt_for_plot(zs,est_start,est_start_var,process_model,sensor_var))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Allgemeine Notation und Bezeichnungen für den Kalman-Filter in der Literatur\n",
    "\n",
    "Es gibt viele, auch gute Lehrbücher zum Kalman-Filter. Sie haben aber fast alle gemeinsam, dass sie zuerst die Theorie behandeln und erst dann den tapferen Leser mit praktischen Beispielen belohnen.  \n",
    "In diesen Notebooks wird bewusst auf die \"reine Theorie\" verzichtet. Der Kalman-Filter wurde hier Schritt für Schritt intuitiv mit vielen Beispielen und Simulationen entwickelt.  \n",
    "\n",
    "Will man sich näher mit dem Kalman-Filter befassen und auch die Theorie dahinter verstehen, dann ist es wichtig, die Notationen und Bezeichnungen aus der Literatur den Bezeichnungen in diesen Notebooks zuordnen zu können. Dies ist der Sinn und Zweck dieses Abschnitts.  \n",
    "Generell gilt im vorliegenden Jupyter Notebook: Ein Dach $\\hat \\quad$ über einer Größe bezeichnet deren Wert nach der Korrektur. Ein Balken $\\bar \\quad$ über einer Größe bezeichnet deren Wert nach der Vorhersage.\n",
    "\n",
    "Im Korrekturschritt sind sowohl der Vorhersagewert als auch der Messwert Gaußverteilungen, die jeweils über einen Erwartungswert $\\bar\\mu$ bzw. $\\mu_z$ und eine Varianz $\\bar\\sigma^2$ bzw. ${\\sigma_z}^2$ definiert werden.  \n",
    "\n",
    "Der Korrekturwert (auch Posterior oder Schätzwert genannt) wird über folgende Formel berechnet:\n",
    "\n",
    "$$\n",
    "\\hat\\mu=\\frac{\\bar\\sigma^2\\, \\mu_z + \\sigma_z^2 \\, \\bar\\mu} {\\bar\\sigma^2 + \\sigma_z^2}\n",
    "$$\n",
    "\n",
    "Dies kann umgeformt werden zu:\n",
    "\n",
    "$$\\hat\\mu = \\left( \\frac{\\bar\\sigma^2}{\\bar\\sigma^2 + \\sigma_z^2}\\right) \\mu_z + \\left(\\frac{\\sigma_z^2}{\\bar\\sigma^2 + \\sigma_z^2}\\right)\\bar\\mu$$\n",
    "\n",
    "Letztlich sieht man oben wieder wie beim g-h-Filter einen gewichteten Mittelwert aus Vorhersagewert und Messwert. Aus den beiden Gewichtungsfaktoren in den Klammern wird nun wie beim beim g-h-Filter ein Gain berechnet, der dem Parameter $g$ des g-h-Filters entspricht: \n",
    "\n",
    "Mit \n",
    "$$K=\\frac{\\bar\\sigma^2}{\\bar\\sigma^2 + \\sigma_z^2}$$ \n",
    "ergibt sich:\n",
    "\n",
    "$$\\begin{aligned}\n",
    "\\hat\\mu &= K \\mu_z + (1-K) \\bar\\mu\\\\\n",
    "&= \\bar\\mu + K(\\mu_z - \\bar\\mu)\\\\\n",
    "&= \\bar\\mu + Ky \n",
    "\\end{aligned}$$\n",
    "\n",
    "> $K$ wird in der Literatur als *Kalman Gain* bezeichnet. Er ist das Verhältnis der Gewichtung des Vorhersagewerts im Vergleich zum Messwert.\n",
    "\n",
    "Die Differenz aus Messwert und Vorhersagewert $y =\\mu_z - \\bar\\mu$ wird Residuum (englich residual) genannt.\n",
    "\n",
    "Der dem g-h-Filter entsprechende Parameter $h$ wird hier übrigens noch nicht implementiert - im phyiskalischen Modell dieses *eindimensionalen* Kalman-Filters wird die Abständsänderung nicht dem Messwertverlauf angepasst sondern immer gleich 1 angenommen."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neuimplementierung der Kalman-Filter-Iterationsschritte \"update\" und \"predict\"\n",
    "Nachfolgend sind neue Implementierungen der Funktionen ``update()`` and ``predict()`` dargestellt. Vom Algorithmus her sind sie identisch zu denen am Beginn dieses Notebooks. Hier werden lediglich die Bezeichnungen denen der in der Literatur angepasst."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update(prior, measurement):\n",
    "    x, P = prior        # mean and variance of prior\n",
    "    z, R = measurement  # mean and variance of measurement\n",
    "    \n",
    "    y = z - x        # residual\n",
    "    K = P / (P + R)  # Kalman gain\n",
    "\n",
    "    x = x + K*y      # posterior\n",
    "    P = (1 - K) * P  # posterior variance, product of two gaussians\n",
    "    return gaussian(x, P)\n",
    "\n",
    "def predict(posterior, movement):\n",
    "    x, P = posterior # mean and variance of posterior\n",
    "    dx, Q = movement # mean and variance of movement\n",
    "    x = x + dx\n",
    "    P = P + Q # prior, sum of two gaussions\n",
    "    return gaussian(x, P)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zum besseren Verständnis ist nachfolgend nochmals eine Iteration aus Vorhersage und Korrektur grafisch dargestellt.  \n",
    ">Der Kalman Gain $K$ bestimmt, wo der neue Schätzwert entlang des Residuums zu liegen kommt.  \n",
    "\n",
    "<img src=\"grafikKalman_VorhersKorr.png\" width=\"800\">  \n",
    "\n",
    "In der vorherigen Notation war $z = \\mu_z$ und $\\bar x_t = \\bar \\mu$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die im Quellcode verwendeten Bezeichnungen ``z``, ``P``, ``Q`` und ``R`` findet man sehr oft in Literatur zum Kalman-Filter. Sie sind eine Standardnotation für den Kalman-Filter.\n",
    "+ $z$ steht meistens für den Messwert.\n",
    "+ $R$ steht für Varianz des Messwerts.\n",
    "+ $Q$ steht für Varianz des Prozesses.\n",
    "+ $P$ steht für die Varianz des Zustands, mit Balken $\\bar P$ des Priors (Vorhersagewert) bzw. mit Dach $\\hat P$ des Posteriors (Korrekturwert)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Korrespondenz der Bezeichnungen zum Buch \"Kalman-Filter\" von R. Marchthaler  und S. Dingler\n",
    "Dieses aktuelle Buch bietet die Möglichkeit, sich tiefer in den Thematik des Kalman-Filters einzuarbeiten. Hier sind auch die nötigen theoretischen Grundlagen erklärt.  \n",
    "Damit der Umstieg von dieser Notebookreihe auf das Fachbuch gelingt, sind in der Tabelle die Begrifflichkeiten verglichen.\n",
    "\n",
    "\n",
    "| Parameter in diesem Notebook | entsprechender Parameter Marchthaler |            Bemerkung            |\n",
    "| :--------------------------- | :----------------------------------: | -------------------------------:|\n",
    "| Prior, Vorhersagewert $\\bar x$ | prädizierter Zustandsvektor $\\hat {\\underline x}(k)$ | Ausgang predict()<br/>selbe Messgröße wie z|\n",
    "| Varianz Prior $\\bar P$ | prädizierte Kovarianz des Schätzfehlers $\\hat{\\underline P}(k)$ | Ausgang predict() |\n",
    "| Posterior, Korrekturwert $\\hat x$ | Korrigierter Zustandsvektor $\\tilde {\\underline x}(k)$ | Ausgang update() |\n",
    "| Varianz Posterior $\\hat P$ | korrigierte Kovarianz des Schätzfehlers $\\tilde{\\underline P}(k)$ | Ausgang update() |\n",
    "| Messwert z | Ausgangsvektor ${\\underline y}(k)$| hier Abstand |\n",
    "| Varianz Messwert R | Varianz Messrauschen ${\\underline R}(k)$ | hier const. |\n",
    "| - | prädizierter Messwert $\\hat {\\underline y}(k)$| = $\\underline C \\cdot \\hat {\\underline x}(k)$ meist = $\\hat {\\underline x}(k)$ |\n",
    "| Bewegung dx | steckt in Systemmatrix ${\\underline {A_d}}(k)$| hier: v = const., im Buch: $f(v,a) \\neq $const.|\n",
    "| Varianz Prozess Q | Systemrauschen ${\\underline Q}(k)$ | Zunahme Varianz Prior x durch Prädiktion |\n",
    "| Kalman Gain K | Kalmanverstärkung ${\\underline K}(k)$ | Input $\\hat {\\underline P}(k) , {\\underline R}(k)$ |\n",
    "| Residuum y | $\\Delta \\underline y(k)$ | engl. \"residual\" |\n",
    "\n",
    "In der Einleitung dieses Buchs ist am Beispiel der Mondlandefähre der Kalmanfilter erklärt. Hier handelt es sich aber im Gegensatz zu diesem Notebook um einen zweidimensionalen Kalmanfilter, bei dem der Zustandsvektor nicht aus dem einen Element einer Ortskoordinate sondern aus der Ortskoordinate h, der Geschwindigkeit v und der Beschleunigung a besteht. Messwerte existieren nur für h und a. v ist durch die Matrix C immer gleich Null. Das bedeutet, dass v zwar eine Zustandsgröße aber kein Messwert ist.  \n",
    "Die Rolle des Eingangsvektors $\\underline u(k)$ ist im Buch ebenfalls unklar. Vermutlich hat dieser Vektor rein gar nichts mit dem Filteralgorithmus selbst zu tun. Auch dem Autor dieses Notebooks fällt es übrigens sehr schwer, die reine Theorie eines Kalmanfilters aus dem Buch von Marchthaler et al. zu verstehen ;-)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Die Gleichungen und Implementierungen für einen Kalman-Filter\n",
    "\n",
    "Zu Übersicht sind hier nochmals die verschiedenen Gleichungen aufgeführt, die in diesem und den voran gegangenen Notebook im Zusammenhang mit dem Kalman-Filter aufgetaucht sind:\n",
    "\n",
    "\n",
    "<u>Vorhersage</u>\n",
    "\n",
    "$\\begin{array}{|l|l|l|}\n",
    "\\hline\n",
    "\\text{Gleichung} & \\text{Implementierung} & \\text{Kalman-Form}\\\\\n",
    "\\hline\n",
    " \\bar x = \\hat x + f_x & \\bar\\mu = \\hat\\mu + \\mu_{f_x} & \\bar x = \\hat x + dx\\\\\n",
    "& \\bar\\sigma^2 = \\sigma^2 + \\sigma_{f_x}^2 & \\bar P = \\hat P + Q\\\\\n",
    "\\hline\n",
    "\\end{array}$\n",
    "\n",
    "\n",
    "<u>Korrektur</u>\n",
    "\n",
    "$\\begin{array}{|l|l|l|}\n",
    "\\hline\n",
    "\\text{Gleichung} & \\text{Implementierung}& \\text{Kalman-Form}\\\\\n",
    "\\hline\n",
    " \\hat x = \\| \\mathcal L \\circ  \\bar x\\| & y = \\mu_z - \\bar\\mu & y = z - \\bar x\\\\\n",
    " & K = \\frac {\\bar\\sigma^2} {\\bar\\sigma^2 + \\sigma_z^2} & K = \\frac {\\bar P}{\\bar P+R}\\\\\n",
    " & \\hat\\mu = \\bar \\mu + Ky & \\hat x = \\bar x + Ky\\\\\n",
    " & \\sigma^2 = \\frac {\\bar\\sigma^2 \\sigma_z^2} {\\bar\\sigma^2 + \\sigma_z^2} & \\hat P = (1-K)\\bar P\\\\\n",
    "\\hline\n",
    "\\end{array}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Der generische Algorithmus eines Kalman-Filters\n",
    "\n",
    ">Ein Kalman-Filter erstellt eine Vorhersage, macht eine Messung und berechnet daraus einen Korrekturwert (auch Schätzwert genannt), der zwischen dem Vorhersagewert und dem Messwert liegt.  \n",
    "Egal welches Lehrbuch über Kalman-Filter man aufschlägt, zugrunde liegt immer dieser Algorithmus. Er wird nur unterschiedlich mathematisch implementiert. So unterscheiden sich die verschiedenen Lehrbücher und Implementierungen im Wesentlichen in der verwendeteten Mathematik *jedoch nicht im Algorithmus*.\n",
    "\n",
    "Dieser generische Kalman-Filter-Algorithmus lässt sich in neun Schritten beschreiben: \n",
    "  \n",
    "**Initialisierung**\n",
    "\n",
    "1. Ein physikalisches Modell erstellen und daraus eine mathematische Berechnungsformel für die Vorhersage ableiten.\n",
    "2. Einen Startwert der Zustandsgröße (Wert, Unsicherheit) willkürlich festlegen.\n",
    "    \n",
    "**Vorhersage**\n",
    "\n",
    "1. Den Zustandswert (Prior) für den nächsten Zeitschritt mit Hilfe es Prozessmodells vorhersagen.\n",
    "2. Die Unsicherheit des Prozessmodells in die Unsicherheit des Vorhersagewerts einfließen lassen.\n",
    "    \n",
    "**Korrektur**\n",
    "\n",
    "1. Eine Messung durchführen und wenn möglich deren Unsicherheit bestimmen, falls diese nicht als konstant angenommen wird.\n",
    "2. Das Resuiduum von Messwert minus Vorhersagewert berechnen.\n",
    "3. Den Gewichtungsfaktor (Kalman Gain) über den Vergleich der Unsicherheit von Messwert und Vorhersagewert berechnen.\n",
    "4. Mit dem Gewichtungsfaktor den Korrekturwert zwischen Messwert und Vorhersagewert verorten.\n",
    "5. Die Unsicherheit des Korrekturwerts über die Unsicherheiten von Mess- und Vorhersagewert berechnen.\n",
    "    \n",
    "**Bemerkung:** Oben wurde bewusst der Begriff \"Unsicherheit\" statt \"Varianz\" verwendet, obwohl damit Letzteres gemeint ist. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neuimplementierung der Funktion kal_filt_for_plot()\n",
    "Nachfolgend wird die Funktion ``kal_filt_for_plot()`` nochmals neu mit den aktualisierten Funktionen ``update()`` und ``predict()`` implementiert. Dadurch wird der Code übersichtlicher, kann besser mit Codebeispielen aus der Literatur verglichen werden und später für Übungen wiederverwendet werden.  \n",
    "Im Array ``zs`` befinden sich nach wie vor die wahren Werte (Spalte 0) und und die Messwerte (Spalte 1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def kal_filt(z_vals, z_var, x_start, process_model, do_print=False):\n",
    "    x = x_start  # first initial guess of Robot position\n",
    "    priors=[] # needed for subsequent plotting\n",
    "    measurements=[]\n",
    "    x_vals=[]\n",
    "    x_vars=[]\n",
    "\n",
    "    if do_print: print('Wahr \\t Vorhers. +/-Var      Mess +/-Var        Korr. +/-Var')\n",
    "\n",
    "    # perform Kalman filter on measurements zs\n",
    "    for z in z_vals:    \n",
    "        prior = predict(x, process_model)\n",
    "        x = update(prior, gaussian(z[1], z_var))\n",
    "        if do_print: print('{:6.3f}\\t {:6.3f} +/-{:7.3f}    {:6.3f} +/-{:6.3f}\\t  {:6.3f} +/-{:6.3f}'.format\n",
    "              (z[0],z[1],sensor_var,prior.mean, prior.var,x.mean,x.var))\n",
    "        priors.append(prior.mean)\n",
    "        x_vals.append(x.mean)\n",
    "        x_vars.append(np.sqrt(x.var))\n",
    "        measurements.append(z[1])\n",
    "    print('Letzter Schätzwert:  {:10.3f}'.format(x.mean))\n",
    "    return measurements,priors,x_vals,x_vars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Einführung Design eines Kalman-Filters\n",
    "\n",
    "Die Auslegung eines Kalmanfilters hängt immer vom Sensorverhalten und vom zugrunde liegenden physikalischen Modell (Prozessmodell) ab.  \n",
    "Angenommen, nun soll nicht mehr ein Roboterfahrzeug sondern ein Thermometer getrackt werden:  \n",
    "Die Zustandsgröße ist die Temperatur, welche mit einem integrierten Temperatursensor gemessen wird. Der Sensor gibt die Temperatur in °C als Spannungswert aus. Der Sensorhersteller spezifiziert das Rauschen dieses Sensors mit 0,13 V (Standardabweichung).\n",
    "\n",
    "Somit werden für den Temperatursensor mit folgender Funktion verrauschte Messwerte simuliert:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def volt(voltage, std): # simulation of thermometer readings\n",
    "    return voltage, voltage + (np.random.randn() * std) # true value (without noise), noisy value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die Varianz der Messwerte ist über das Sensordatenblatt bekannt. Für das Prozessmodell wird angenommen, dass die Temperatur weitgehend konstant bleibt, da sich das Thermometer beispielsweise in einem klimatisierten Raum befindet. Der wahre Temperaturwert ändert sich also im Mittel nicht. Daher sollte eigentlich auch der Erwartungswert des Prozessmodells, also die angenommene Temperaturänderung gleich 0 sein. Würde man nun auch noch eine Varianz von 0 für das Prozessmodell annehmen - also annehmen die Temperatur ändere sich nie, dann würde der Filter so träge, dass er selbst kleinsten Temperaturschwankungen beispielsweise durch kurzes Lüften nicht mehr folgt. Ein solches Lüften wird nachfolgend zur Zeit 20 während 8 Sekunden simuliert. \n",
    "In diesem Fall macht es also Sinn, einen Wert für ``process_var`` größer Null zu wählen, damit der vorübergehende Temperaturabfall durch das Lüften vom Filter überhaupt registriert wird."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Letzter Schätzwert:      16.305\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(10)\n",
    "\n",
    "true_voltage = 16.3 # true value of constant temperature\n",
    "voltage_std = 0.13 # try different values, nominal value 0.13\n",
    "process_mean = 0.0 # mean of movement, try different values, nominal value 0.0\n",
    "process_var = 0.005 # variance of movement, try different values, nominal value 0.005\n",
    "\n",
    "x_start = gaussian(18., 1000.) # initial state\n",
    "process_model = gaussian(process_mean, process_var)\n",
    "z_vars = voltage_std**2\n",
    "\n",
    "N = 50 # must be greater than 10\n",
    "z_vals = [volt(true_voltage , voltage_std) for i in range(N)]\n",
    "for i in range(N//2-4, N//2+4):\n",
    "    z_vals[i] = volt(true_voltage*0.95 , voltage_std) # intermediate small temperature drop due to open window\n",
    "plot_meas_pred_est(*kal_filt(z_vals, z_vars, x_start, process_model))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Das Festlegen des Parameters ``process_var`` ist demnach die schwerste Aufgabe in diesem Beispiel:  \n",
    "**Frage:** Welche Schwankungen sind z.B. durch das Lüften zu erwarten? Wie groß darf man diesen Parameter wählen, damit der Filter nicht dem Sensorrauschen folgt?  \n",
    "**Antwort:** Entscheidend ist für den Filter die Information über das Sensorrauschen ``voltage_std``: Nur wenn dieser Wert stimmt, dann werden die Messwerte auch mit einer angemessenen Signifikanz in den Filter mit einbezogen und ``process_var`` kann passend gewählt werden, sodass der Filter einer Störung wie dem Lüften angemessen folgt. Ist der Wert ``process_var`` zu hoch, dann wird dem Sensor zu wenig vertraut. Ist der Wert zu klein, dann wird das Sensorrauschen fälschlicherweise als echte Temperaturschwankung interpretiert."
   ]
  }
 ],
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       "step": 0.5,
       "style": "IPY_MODEL_c12f994b4fac4f8e8beddda188143729",
       "value": 7.5
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     },
     "03b0305ffb5842d8b0358424a5ebcc03": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "04e32ea18c164e4cb2d4e80bb3c18ff3": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "0576c9a9355b44f8864d34874e241d06": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "0678934f34814a3bbc9030402b5689a1": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "FloatSliderModel",
      "state": {
       "continuous_update": false,
       "description": "process_noise",
       "layout": "IPY_MODEL_20e981e0333242d7bffe378f81ecada1",
       "max": 40,
       "step": 0.1,
       "style": "IPY_MODEL_eaf994a16d8d4dca96970ab80128a3a0",
       "value": 0.1
      }
     },
     "06cf3abfc4ef4985bf38f8b0f4f039a6": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "FloatSliderModel",
      "state": {
       "continuous_update": false,
       "description": "velocity",
       "layout": "IPY_MODEL_a5dd0be268c44ae492d17c5b2ff2589a",
       "max": 2,
       "min": -2,
       "step": 0.1,
       "style": "IPY_MODEL_c941e50422f2413bb40f3e2aaf518239",
       "value": 1
      }
     },
     "0900d2edeba643fc8458d7552f46eddd": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_d63b2db028054739835f3970a9df990e",
       "outputs": [
        {
         "data": {
          "image/png": 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KosiwQUKICmHnlZ3ojXoaVm8oN1P+jYvOhcfrPA7A1ktbVU4jhBD2xeJiPSQk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          "text/plain": "<matplotlib.figure.Figure at 0x256353de6a0>"
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KosiwQUKICmHnlZ3ojXoaVm8oN1P+jYvOhcfrPA7A1ktbVU4jhBD2xeJiPSQk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\n",
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      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
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     },
     "a5dd0be268c44ae492d17c5b2ff2589a": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "a7ee41632f4b4dc081590db1b7d7f017": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "a827ad9b2bd246b1a8aa72b856c0ee06": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "aa02a7f96ffd421980335a86bb5dfd6b": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "FloatSliderModel",
      "state": {
       "continuous_update": false,
       "description": "process_noise",
       "layout": "IPY_MODEL_3bcb1f6d26fb4f5682fb0ff544c70206",
       "max": 40,
       "step": 0.1,
       "style": "IPY_MODEL_03b0305ffb5842d8b0358424a5ebcc03",
       "value": 0.1
      }
     },
     "aa8a404be50544f799076ddbb0442785": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "start_pos",
       "layout": "IPY_MODEL_2b2ee57ef23d455c8f303cd70cb78216",
       "max": 10,
       "min": -10,
       "style": "IPY_MODEL_758bd052a8f84708b064fd72639185ca"
      }
     },
     "aab2f6e001d44b8a9da145fa31edbe0d": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_efa8e1510bb142bda72d60e26167d486",
       "outputs": [
        {
         "data": {
          "image/png": 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KosiwQUKICmHnlZ3ojXoaVm8oN1P+jYvOhcfrPA7A1ktbVU4jhBD2xeJiPSQk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          "text/plain": "<matplotlib.figure.Figure at 0x1669dbe6a90>"
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M2kueyUh2aV/JPgBiA9VJMjpFx7SfpjEmdAyLhjXfeEpq212/Xt42YVLPScwZMIcabQ33b7r/stdv7HMj0/pM43TtaR5NvLgo5bJrl7U5ZnZ2NkuWLGH06NHMnz+f//73vwC4uro21SqLjo7mP//5D2PHjuWZZ57h+eef55133uGuu+5qev7xxx9vc6ySkhIWLFjA1q1biYiIoLy8HD8/PxYuXIinp2eLTcssiTyTkezShO4T+GTiJ/Ty6aXK+BqhwcfZpynZSdYjPDyc0aNHA3Dbbbc1JZabb74Z0NfyOnPmDGPHjgVg3rx5bN269bLnb7/99jbHSkpKYsyYMURERABcVgjTGsgzGckuuTu5N00lVktMYAxfHfyK+sZ6nB2cVY3FWrV25uHm6Nbq676uvgaduVzq0hp35x+3VeBSUZQW6+M5Ojqi0+maHp9vEdDae6yFPJOR7E5VQxX/2fMf8iryVI0jNiiWBl0DB8pljz9rUlBQwM6dOwFYsWIFV1xxxUWv+/j44Ovr23Sv5IsvvmDs2LF06dIFHx+fpjOf5cuXN72ne/fu7N27F51OR2FhIbt26csdjRw5ki1btpCbmwtAeXk5AF5eXlRWVpr2BzUSmWQku7O/dD8fpX/EsXPtb/RkTOcrDew9tVfVOKT2GThwIJ999hnR0dGUl5dz3333XbbNZ599xuOPP050dDR79+7lmWeeAWDZsmU88MADjBw5Ejc3t6btR44cSUREBFFRUSxatIihQ4cCEBgYyEcffcSMGTOIiYlpuiR3ww03sGrVKnnjX5Is0fkP9ajAKFXjCHQPZH7kfAb5D1I1Dql9NBoNH3zwwUXP5eXlXfQ4NjaWpKSky94bFxfHvn3/uw/33HPPUVVVhRDiojObC02ePJnJkydf9Fy/fv1IT0/v4E9gXjLJSHZnX8k++nTpg7ezt9qh8EjcI2qHIEkmJS+XSXZFp+jYV7LP7EUxW9Koa+TQ6UOcrT+rdiiSAXr27ElGRobaYVgVmWQku3Kq+hQ6RWcxSeZg+UFuWn0TO45ZR4kQSWovmWQkuxLiEcL2Odu5rtd1aocCQD+/frg6uMr1MpLNkklGsjsOGgeLWZfipHFicMBgOcPMQBqNpmkNiWR+tbW1aDTtSxvyxr9kVx5NfJThIcOZM2CO2qE0iQmM4fPMz6nV1uLq6Kp2OBbNz8+P8vJyq1kjYoiysjIA/P39VY6kbRqNpt1VB2SSkezG2fqzbMzfSH/f/mqHcpHYwFiWKkvJLMskLjhO7XAsmhDCKj6M2yM/Px+AAQMGqByJacjLZZLdyCzNBNRfH3OpuJA4Fk9YbHHJT5KMQZ7JSHYjo1Q/9TQyoGONokzF29mbMWFj1A5DkkzCoDMZIcS1QohsIcQRIcSTrWw3UwihCCHijReiJBlHRmkGPbx7WMQizEtll2fz1YGv1A5DkoyuzSQjhHAAFgOTgUHAXCHEZXUwhBBewMOAbFwuWaRgj2DGhY1TO4xmbT++nVd2vcLp2tNqhyJJRmXImcxw4IiiKDmKotQDXwM3NrPdi8BrgJxfKFmkv4/4u8U2CIsK0N8nOn9JT5JshSH3ZEKBwgseFwEXNeIQQgwBwhVF+VkI0eZfcWpqaruCtFTW/nNodQqFZ7XknNbiqIHevk509XLAoRP9Kyz1mDQqjTgI0/Zdb4khx6S2sRaBYEP6BtyK3drc3uooCs41J/E9k41jfQUHTmdS490bnYOcsn2epf7ttKVv376tvm5IkmnuE0dpelEIDfA2cGd7ApPUk3aijh8OVpFzuoEG3cWvuToK+vk5MXuQBwMCLGPBojH8eOpHkiqSeLXvq6olm9a4ObjR1aUruTW5aodiVC5Vxwg9uASv0j04NVxcn00RGmo8e3Kq51RKu08BC/x3kTrPkCRTBIRf8DgMOH7BYy8gEkj8s4NbCLBaCDFVUZRmU3N8vHXPCzj/jcPafo7C8mqeX5PFpgNniAjw4M7RYUSFdSEq1IeGRh3pRRXsLzrD+syTPJ14mplxYTw5eQABni5t7tvSj8lHGz4iwCuAEcPM1w2zvcckvjaelOIUiz2G7VJfDX+8DdvfAQcXiLwRug0lq8IVrXMXooNAHN+D+9Hf6bn/HXqWbYEpb0KY/a0TsvS/nbZUVFS0+rohSSYF6CuEiACOAXOAW86/qChKBRBw/rEQIhFY1FKCkdTx6fZcXll3EAeN4KnJA7hrdATOjhffkusX7MXMuDCeuHYA//7tMEu25bIhs5j/NyOK66O7qRR55ymKQkZZBhN7TFQ7lFYtil+Ep5On2mF0XlEqfHcXnCmAqNkw8UXwCgGg+vwloQHxMOA6uOofkPE9rP8HfDIBht8D174CGnlWYyvaTDKKomiFEA8C6wEHYKmiKJlCiBeAVEVRVps6SKlz3vvtMG9sOMSEAUG8ND2Srj6tX/P3cHHkqckDmRUXxhPfpfPwij3UNei4KS7MTBEbV0FlAZX1lU031y2Vr6uv2iF0XkESfDkT3P3gzrXQ84rWtxcComZC34nw24uw60OoKYdpH4CDXMZnCwz6V1QU5Rfgl0uee6aFbcd1PizJGBRF4d3Nh3ln02GmDwnl9ZnRODoYXuShT5AXy/+SwF8+T2HRd/to1CnMHhbe9hstjKUuwmzO22lvE+oZyuz+s9UOpf3ytsPyWeDdFeatAe92nP26esOU18GrK2x+HnRamPExODiZLl7JLGRZGRulKApvbjjEO5sOMzMujDdmxbQrwZzn5uzAknnDuKJPAE98n85XyQUmiNa0enj34NaBt9K7S2+1Q2lT8olk1uetVzuM9svdBstngk+o/gymPQnmQlc+Cte8CJmr9JfctPXGjVMyO5lkbNTK1ELe+/0Ic4aF89pN0ThoOj4t2dXJgY/viOeq/oH848f9bD1UYsRITS8yIJInhz+Jo8byL79EBkSSWZaJTtG1vbGlKDsKX98CXbrrE8yf9186bPTDMOkVOLAGNjxtnBgl1cgkY4MOFp/lmZ8yGd3Hn5enR6HpRII5z9XJgf/eGkffIE8e+WYvJ89ax5pbrU7LwfKDaHVatUMxSGRAJFUNVeRV5KkdimEaauHbO0Fo4NZvwTPIOPsdeT8k3K+/R5Mlb/taM5lkbExVnZYHlu/G282Jd24e0qkzmEu5OTuw+JahVNc38vCKPWgbLf/b9tEzR5m1ZpbVXIKK9NffN8oos5KV/xuehuJ0mP6B/kzGmK5+HroNhZ8ehHLbWj9kT2SSsSGKovD0jxnkllbx7pxYAr3aXt/SXn2DvXhpWiTJueX8e/Nho+/f2M7f9Lf0mWXnRfhE0N2rO7VaKzhTzFwFKR/DyAeh/2Tj79/RGWYt0y8H/+4u0NYZfwzJ5GSSsSHfpRWxas8x/jqhH6N6B7T9hg66KS6MWXFh/Of3I2w/UmqycYwhoywDb2dvwr2sY1acg8aBtTPWWv7sstN58NNDEDYMrn7OdOP49oQb/wvH98AmE44jmYxMMjai7FwdL609wPAIPx4c38fk471wYyQR/h78fdV+ahsaTT5eR2WWZjLYfzCiE/XYpEsoCvzyBKDAzKWmn2Y88HoY9hdIeh+O7TbtWJLRySRjI15dd5CqOi0vT4s06n2Ylrg5O/DCjZHkl1Xz4ZYck4/XEXWNdRw+fZjBAYPVDqVd9p7ay/Wrrudg+UG1Q2le9i9weD2Me8r492FaMuEZ/aSCtY+BznK/1EiXk0nGBqTmlfNtWhF3XxlB32Avs417Rd8ArovuyuLEIxSfs7zZWxo0vDfhPW7ofYPaobSLn6sf+Wfzm9pFW5T6Klj3JAQNghH3mm9cVx+Y+DIc3w27PzPfuFKnySRj5bSNOp7+MYOuPq48PL71ktum8M/rBuGkESzdW4miKG2/wYycHJwYHTqaXj691A6lXcK9wvFy9iKzzAKTzNY3oKIArnvT/Kvxo2ZCzyth0/NQZdn3AqX/kUnGyn2+M5+DxZU8e8MgPFzMv9gwxMeVR67px+7ielKOW9bsn8TCRFKKU9QOo92EEAz2H2x5SabkEOz4D8TMhR6jzD++EDDlDag/BxufNf/4UofIJGPFys7V8dbGQ4ztF8ikwZ1cZd0J80b1pLu3I0v3VVrUJIB3d7/L0oylaofRIYP9B3Po9CHqGy2orMqvT4KTO1zzgnoxBA2AkQ/A3i/11Z4liyeTjBV7P/Eo1fVa/nn9IFVnTzk5aLgzxpPSah0rdllGbbPqhmpyKnIY7G9dN/3PS+iWwNTeU6lqqFI7FL3cbXB0M4x93Hir+jtqzBPgHgCbVUx2ksFkkrFSJypq+Dwpn5uGhtEnSP0eJFFBzkQGOrH49yNU16s/CSD7dDY6RWe9SaZrAs+Pet4yyv8rir4Mv1dX/VRitbl4wpWPQe4WyNmidjRSG2SSsVL/3nwERVF4eIL5b/Y3RwjB3EhPSs/Vs2x7ntrhNM3MsrbpyxfSKTrKa8vVDgMOb4TCZBjzODi13ovIbOLng3eoPvlZ2IQT6WIyyVihvNIqvk0tZO7w7oT7uasdTpP+/s5MGBDEh1uOUlHToGosB8oPEOgWSJC7ypd2OuHRxEdZsGGBukHodPDbC/qV90NuVzeWCzm5wtgnoCgFDv2qdjRSK2SSsULvbDqEo4PgwatMv7K/vR6b2J+ztVo+3qruAs3nRj3H55M/VzWGzurdpTdHzxylRlujXhAHfoLi/TDu7/paYpYk9lbw6wW/vaRPhpJFkknGymQXV/LTvuPMG9WTIG9XtcO5zKBu3lwf3ZWl23MpPafelGYnjRNhXtbZLvq8wf6DaVQayS7PVieARi389jIEDtCvUbE0Dk765HcyAzJ/UDsaqQUyyViZf/92GA9nRxaOsdwuj49e04/ahkbVzmYOlB3gleRXOFV9SpXxjeX8pAXV1stkroKyw3DVP0DjoE4MbYm8SV99YMtr8mzGQskkY0VyS6tYt/8EtyX0wNfDwi5dXKBXoCfXRXdjeXKBKvdmdhXv4quDX+EgLPSD0UBB7kEEuAWQVZZl/sEVBf54W38WM+B6849vKI1GP9OsNBsOrVM7GqkZMslYkY+25uDooGH+6J5qh9Kme8f04lydli+T8s0+dmZpJl09uuLv5m/2sY1JCMEjcY+oU3vt8EY4lQmj/0//QW7JBk2DLj30SVHONLM4Fv7bI5136mwt36cVMTMuzCLvxVwqMtSHMf0CWbY9z+xVADLLMq12fcylpvaeSkLXBPMP/Mfb4B1mmfdiLuXgCKMe0s80y9+hdjTSJWSSsRJLt+eh1em450rrKfZ439jelJ6r47u0IrONWVFXQUFlgVWvj7lQQ2MDaSfTKK4qNt+gBUlQsEP/wW3uIpgdNeQ2fRWAP95WOxLpEjLJWIGztQ0sT8pnSlRXegZ4qB2OwRJ6+REb3oWPtuagbTTPTdniqmKC3INs5kzmbP1Z7vz1TtbnrTffoH+8A25+MNSC1sW0xckNEu6DIxv1U64liyGTjBX4MimfyjotC8da7oyy5gghuG9cbwrKq/klwzzfxPv79WfzrM3qXGIyAX83f7p6dCWjNMM8A57M0t9AH7EQnK3nCw2gL3nj7KVPkpLFkEnGwtVpG1n6Rx5j+gUSGeqjdjjtds3AYHoHevBB4lGz9puxpXbLkQGR5ksyO/4NTh4wXOVKAx3h1gXi79KvmTmdp3Y00p8MSjJCiGuFENlCiCNCiCebeX2hEGK/EGKvEOIPIcQg44dqn9bsO0HpuToWXBmhdigdotEIFlzZi6wTZ0nONX0drnnr5rH8wHKTj2NOg/0HU3SuiIq6CtMOVHkS9n8HQ24Fdz/TjmUqCfeB0MCuj9WORPpTm0lGCOEALAYmA4OAuc0kka8URYlSFCUWeA14y+iR2iFFUVi2PZd+wZ5c0SdA7XA6bNqQUHzdnVi2Pdek45TWlLL71G4abawH/PlJDCZvx5y6FHRa/aUya+XdDQbdCLs/h7pKtaORMOxMZjhwRFGUHEVR6oGvgRsv3EBRlLMXPPQA5GR1I9iVW07m8bPcNTrCqi//uDo5cMuI7mzIOklhebXJxjl/SSkyINJkY6ghOiCaLyZ/wdDgoaYbRFsHqUug3yTwt657f5dJuB/qzsLeFWq0lsz2AAAgAElEQVRHIgGG9OsNBQoveFwEjLh0IyHEA8CjgDMwvrUdpqbaRkc7U/8cb+08g6ezoLvuJKmp1lEipaVjEu3eiAZ4bVUy82K8TDL2xlMbEQhq8mtILbSc3zFj/Z5kFJjuvox/4XoiqkrI9h1PpRn+Pk39tzOgy0Act75DhiZWf/nMCljr52Lfvq23GzHk6Df3FfqyMxVFURYritIb+BvwtEHRSS06VdVIyrE6rolww8XRes9izvN3c2BkmCubc2uoaTDNdObcmlxCXUJx0biYZP9qyqnO4adTP5lm54pCcO4P1Hj1pDLAhGdLZnQyYgauVcfwObVL7VDsniFnMkVA+AWPw4DjrWz/NfB+azuMj483YFjLdf4bhyl/jpfXZiE0ZTwxI4GuPhbSKKoVhhyTx4JOM/2/O8hRgpgX39PoMQwTw3B3dCc+xjJ+v4z5e5J9IJsfc3/koXEPGb9HTt52OHsEbniX+Lhhxt33JczxtwPAkBg4soy+ZRvhugdNO1Ynme2YmEhFResTUgw5k0kB+gohIoQQzsAcYPWFGwghLjxfug443M44pQtU1Wn5OqWQKVFdrSLBGGpId1+GdO/Cpzvy0OmMf9vukbhHuDfmXqPv1xKcX1xqkqnMSf/VL76Mvtn4+1aLgxMM/wvkJOrX/kiqaTPJKIqiBR4E1gMHgJWKomQKIV4QQkz9c7MHhRCZQoi96O/LzDNZxHbgh91FVNZqucsKCmG2112jI8gtrSLxkHHvMVU3VNvcrLIL9ffrj4NwMH6SOVMA2b9A3J2W01rZWOLuAkdX2PWh2pHYNYPuiCmK8ouiKP0URemtKMrLfz73jKIoq//8778qijJYUZRYRVGuUhRFpQYY1k9RFD7fmU90mA9Du/uqHY7RTY4MIcjLhS92Grc68/v73ueqlVfZbKJxc3SjT5c+xu8tk7pU///D7jbufi2Bu5++wGf6Sqg5o3Y0dss6pl3YkaSccg6fOsftCT3UDsUknBw0zB3encRDJRSUGW86c0ZpBmFeYThYanMtI4gMiKSo0ojFRrV1+vUk/aeAj3V3EW3RsAXQUA37vlY7Erslk4yF+TIpny7uTtwQ003tUExm7vDuaIRgebJxzmYadY1klWUxyN+2C008MewJfp7+s/F2mPkjVJfpa37Zqm6xEBoPKZ/IXjMqkUnGgpw8W8v6zGJmx4fj6mS738hDfFyZNDiYb1ILjdJrJv9sPtXaaptbhHkpdyd34y7KTfkE/PtAxFjj7dMSDV+gbyOdu0XtSOySTDIW5KvkAhoVhVtHdFc7FJO7LaEHZ6obWLOvtdnwhsko+3Olv79tJxmAl5Je4vPMzzu/oxP7oGiX/izG0jtfdtagaeDuL+uZqcTGf7usR0OjjhW7ChjbL5Ae/lZWYr0DRvbyp2+Qp1HaMw/wG8DCmIVE+FhnEdH2OFB+gM0Fmzu/o5RPwMkdYuZ2fl+WzskVhtyun0VXcUztaOyOTDIWYkPmSU5V1nHHSNu84X8pIQS3j+zBvqIK9hV2buZPP99+PBD7gE3f9D8vOiCarLIsGnQNHd9JzWlI/xaiZunL49uD+Pn6ezJpy9SOxO7IJGMhvkjKI8zXjbH9jLya24JNHxKKh7MDn3diOnNdYx0pxSlUN5iu8KYliQmMobaxlsOnO7Heee8K0NbY9g3/S/n20Bf/TPsMtPVqR2NXZJKxAEdOVZKUU86tI3rgoLH+OmWG8nJ1YtqQUH5OP86Z6o794WeVZTF//XySTyQbOTrLFB0YDcD+kg62GFYUfbXlsOHQNdqIkVmBYX+BqlNw0Igz9KQ2ySRjAZYnF+DkIJgdb6NrFVpx64ge1Gl1fL+7Y9fK00vSAYgKjDJmWBarq0dXhgYNRdPRm/V526DsiG0uvmxL7wnQpfv/FqBKZiGTjMpq6hv5Pq2IyZFd8fe0verBbRnUzZuh3buwPDm/Q+2Z95XsI9QzlAA3623q1h5CCD6b/Bmz+s3q2A5SloCbr37Glb3RaPSlZvK2QckhtaOxGzLJqGxN+nHO1mrtYtpyS24d0YOckip25pS1+73pJelEB9jZZR/05Yd0SjtbJlSe1F8qir1VP+PKHg25DTROcgKAGckko7LlyQX0DfJkeISV9lQ3guuiu+Lj5sTy5IJ2ve9k1UlOVp9suk9hLw6dPsS4lePYfmx7+9645wt9e+W4u0wTmDXwDIKBN8De5dBQo3Y0dkEmGRVlHNNP3711RHerbq/cWa5ODsyKC2N9RjGnKmsNfp+fmx9fTfmKiT0nmjA6yxPmGcaZujPsL23HzX9dI6R9ql/dH9DHZLFZhWF3Q20FZPygdiR2QSYZFS1PLsDVScP0ofZ3w/9Sc0d0R6tT+DbV8AKQThonogKjjN/Ey8K5O7nTp0ufpkkPBjmyCSoK7fOG/6V6jIaAfnICgJnIJKOSytoGftp7jKkx3fBxc1I7HNX1DvRkVG9/fWkdAxuafXXgK7uZunyp6MBo0kvTDb8vk7oUPEP0FZftnRD6xZnHUvXldSSTkklGJT/uOUZ1fSO3jrCPFf6GuC2hB8fO1LDFgIZmWp2Wt9PeJrEw0fSBWaDogGgq6yvJO5vX9sZnCuDQehh6u75jpKQvp+PoJs9mzEAmGRUoisLy5AKiQn2ICbeTsh4GuGZQMIFeLixPansCwOHTh6ltrCUqwD7Wx1wqPjieeYPm4eJgwLT3tM/0396Hyoa1Tdy6QORN+vI6tWfVjsamySSjgrT80xwsrrTracvNcXLQcHN8OL9ln6LodOtlYs7fj7C3mWXnhXuHs2jYIkI9Q1vfsLFB35is7yToEm6e4KxF/HxoqIL9K9WOxKbJJKOC5ckFeLk4MjXWdhuTddTcEd0RwDcpha1ul16ajp+rX9sfsjasvrGe7PLs1jc6uFZfSiV+vnmCsiahQ6FrDKQslQ3NTEgmGTMrr6pn7f4TzBgairuzo9rhWJzQLm5c1T+Ir1MKaWhs+aZ2wdkCogOi7Xrq90fpHzH759mtFwdNXaIvpdJngvkCsxbnJwCcyoTCXWpHY7NkkjGz79IKqdfquEXe8G/RrQndKamsY2PWyRa3+Xzy57w65lUzRmV5YgJj0Ck6Mkozmt+g9DDkboW4O8EO2iB0SORMcPaSEwBMSCYZM9LpFL5KLmBYT1/6h3ipHY7FGtsviNAubixPbrkFgBACDyfbb+7WmpigGASCtFNpzW+Q9iloHPUNu6TmuXhCzM2QuQqqy9WOxibJJGNGO46WkVdWzW0J8iymNQ4awS0jurP9SBk5Jecue31ZxjKe3/m8CpFZFm9nb/r59mP3yd2Xv9hQA3u+1JdQ8bSvxartFj8fGuv0pWYko5NJxoy+TMrHz8OZayND1A7F4s2KD8NRI5qtZ7YxfyM5Z3JUiMryDA0eyr6SfWh12otfyPwRas/IG/6GCB4M4Qn6S2a6dhYdldokk4yZnKioYeOBk8yKC8PFUV4fb0uQlyuTIkP4Lq2ImvrGpuerG6o5UHaAuOA4FaOzHLP7zWbxhMUILpkAkfIJ+PeFnleqE5i1GXY3lOdAbqLakdgcmWTMZMWuQnSKIlf4t8PtCT2oqGlgTfrxpufSS9PRKlqGBA1RMTLL0ce3D8NChuFw4Y3943v0JVOG/UU/g0pq26Abwd1f329HMiqZZMygXqtjxa4CxvULpLu/u9rhWI0REX70C/bki53/a2i25+QeBILYoFiVo7McKcUprDm65oInPgEnd4idq15Q1sbRBYbeAdm/wJnW12hJ7WNQkhFCXCuEyBZCHBFCPNnM648KIbKEEOlCiM1CCPl1/QIbsoopqazjjpE91Q7FqgghuD2hB/uPVbCvqAIAT2dPru5xNV7OcnbeeT8e+ZE3Ut/QJ+Ka07D/O4ieDa4+aodmXeLn6xdlpn2qdiQ2pc0kI4RwABYDk4FBwFwhxKBLNtsDxCuKEg18B7xm7ECt2Rc78wn3c2NMv0C1Q7E604aE4uHswBc79dOZbx90O2+Ne0vlqCzLkKAhlNeW64tl7v0KtLX6S2VS+3TpDv2uhd2fgbZe7WhshiFLzocDRxRFyQEQQnwN3Ahknd9AUZTfL9g+CbittR2mpqa2P1ILZMjPUVChJTm3nNuiPNmzu4X1DDbEFP+2V4Q5s3pvEZO6naOLixMaYV1XeU39++5Spy+SuSr5B+7fu4QG30iyi+qgyHL/ziz1M8C7yxj6HVpHztp3KA8db9axLfWYtKVv376tvm7IX2socOFFyqI/n2vJ3cA6A/ZrF9bnVOOkgfE93dQOxWpN6u1Ogw6W5W7g4eyHqW5svXimvQlxDsHLwYvc00m4Vh+jpOdUtUOyWmcD46l170Zg/k9qh2IzDDmTaW56SrPV5IQQtwHxwNjWdhgfH2/AsJbr/DeOtn6Oc3Va/li9iRtiQxk/2rZvVBt6TDoiHvj6yE6yq48QHODLmBFjjD6GKZjymFxq+LnhHC/cDh6B9Lru/+jlaEALABWY85h0WMP9uG54mvgwVwiJNPlwVnFMWlFRUdHq64acyRQBF9YIDwOOX7qREOJq4B/AVEVR6toRo836Pq2IqvpGbpcr/DvtthHdqXM8SlfXS28HSgDPD7qbr3OP6GdIWWiCsRqxt4KjK+z6SO1IbIIhSSYF6CuEiBBCOANzgNUXbiCEGAJ8iD7BtN3W0A7odAqf7cgjJrwLQ7r7qh2O1RvYvQ6NYxXFJ2W1hOb47PsGDQLi71Y7FOvn7qefnZe+UtYzM4I2k4yiKFrgQWA9cABYqShKphDiBSHE+Yu/rwOewLdCiL1CiNUt7M5ubD1cQk5pFfNH91Q7FJuwr1Rfn+tAXiCHT1aqHI2Fqa+C3Z/zep8hfFIgb4caxYiFoK3RN3yTOsWgaTqKovyiKEo/RVF6K4ry8p/PPaMoyuo///tqRVGCFUWJ/fN/dn/n8dMdeQR6uTA5sqvaodiEwf6DmTdwAU66ID7bmad2OJZl39dQW8FR7yBWH7X773fGETxYX5In5RNo1La9vdQi65oLaiWOlpwjMbuE20b0wNlRHmJjGOg/kEXDH+bGmFC+TztGRXWD2iFZBkWB5A+haywjIyaRW5FLcVWx2lHZhhELoaIQsteqHYlVk5+AJvD5jjycHTTcMqK72qHYhOKqYtJOptGga+DO0T2paWhkZaos/QFATiKUZsOIhSR0GwlA8olkdWOyFf0n6xdoJn+odiRWTSYZIztb28B3aUVcH9OVQC85y8cYfs39lTt/vZPTtacZ3M2H4RF+fLYzj0ad7MtO8gfgEQiRM+jr2xc/Vz+STiSpHZVt0DjA8HsgfzucSFc7Gqslk4yRfZeqn7Z816gItUOxGUknkujt05sgd33zrbtG9aTodA2bDrTcntkulB2FQ+sh7i5wdEEjNEyJmIK/q7/akdmOIbfpi43Ks5kOk0nGiLSNOpbtyCWuhy9RYbI4oTHUN9aTdjKNhG4JTc9dMyiY0C5uLPkjV8XILEDyB/r2yhc0Jvvb8L+xaNgiFYOyMW6+EDMX9q+ESjv/UtNBMskY0frMkxSW17Dgyl5qh2Iz9pXso7axloSu/0syjg4a7hrdk1255ewtPKNidCqqLte3V46aBd4Xz2BUFIVz9Ze3rZY6aOQD0NgAKR+rHYlVkknGSBRF4aOtR4kI8OCaQcFqh2Mzkk4k4SAciA++uOTGnOHd8XJ15ONtdtqGOXUJNFTDqIcue+mu9Xfx1LanVAjKRvn3hgHX6acz11epHY3VkUnGSFLyTrOvqIK7r4jAQSO7ERrLwuiFrLhuBZ7Onhc97+niyK0jerBu/wkKyuysYGZDLSR/BH2uhuDLy+z09ulNyskUGnRymrfRjHpY36tnz3K1I7E6MskYyUdbc/DzcOamoWFqh2JTnBycGOg/sNnX7hzVEweNYOl2O7s3s38lVJ1q9iwGIKFbAlUNVWSUZpg5MBvWfQSEDYed74GuUe1orIpMMkZwtOQcmw6c5PaEHrg5O7T9BskgKcUpvJn6Jmfrzzb7eoiPK1NjQvkmpZAz1XbSZEqngx3vQUg0RDRf7Hx4yHAEgqTjciqzUY16CM7kw4E1bW8rNZFJxgg+2ZaDi6OGO0bKasvGtCFvA99kf4ObQ8u9eBaMiaCmoZHlyQVmjExFhzfoF1+OehhE85dlfVx8GOw/WK6XMbYB14FfL9jxb32lBckgMsl00qnKWr7ffYyZcWH4e8rFl8a088RO4oLjcHJwanGbASHejO0XyLLtedQ22MFljB3/Bu8wGDyt1c0WRC9g3uB5ZgrKTmgc9DPNjqVB/g61o7EaMsl00pJtuWgbdXLaspHlVOSQfzafsWGt9r8DYOHY3pSeq7P9UjP5O/Srz0c9CK0kXoDx3cczvrt52wfbhZhb9BUWtr2hdiRWQyaZTiivqueLpHymxnSjZ4CH2uHYlMTCRADGhY9rc9uEXn7E9/Dlg8Sj1Gt1pg1MTVtf13/ADTXsDOXw6cNsyt9k4qDsjLM7jHwQjv4GRWlqR2MVZJLphGXbc6lpaOSBq/qoHYrNqWusY3jIcEI82m5SJoTgwfF9OF5Ryw+7i8wQnQqK0vQfbCMf1H/QGeDzrM95ZsczciqzsQ27W18JYOvrakdiFWSS6aCqeh2fbs9jcmQIfYO91A7H5twXcx+fTPzE4O3H9gskOsyH/yYeRdtog2czW1/Xf7ANM7zz5bjwcVTWV7L75G4TBmaHXLwg4X44tE4WzjSATDIdtO5oNZV1WnkWYwI12hpAf4ZiKCEED17Vh4LyalbvO26q0NRxIl3/gZZwv/4DzkAju47ExcGl6dKjZETD7wEXb3lvxgAyyXRATYOOnw9Xc/XAIAZ3k4Uwje1vW//GPRvuaff7rh4YzIAQL977/YhttQHY9ob+A214+46Ju5M7CV0T+L3wdxQ55da43Lro/z2yVsOpg2pHY9FkkumA9Tk1nKtXeHB8X7VDsTm12lp2Ht9JT5+e7X6vRqO/N5NTUsUv+08YPzg1nDqo/yAbfo/+g62dxoWPo6S6hBNVNnI8LEnC/fo2APJsplUyybTT2doGfsyuIjbYmdjw9v/RS61LOpFEbWOtQbPKmjM5siv9gj15e+Mh27g38/tL4OypX5/RAVMiprBtzja6eXYzcmASHv4wfAHs/w5OZqodjcWSSaadPt6aw7l6hVsiPdveWGq3xMJEPJ08GRY8rEPvd9AIFk3sT05pFd+lWflMs6I0fQmT0Q+Du1+HduHu5I67k2Gz0aQOGP1X/aXMzS+qHYnFkkmmHUoq61jyRy6jwlzo5dv6Yjip/XSKjsTCRK4IvaLVVf5tuWZQMEO6d+HdzYetuwrA5ufBPQAS7uvUbvae2ssta2/hVPUpIwUmNXH3038JOLQOCpLVjsYiySTTDot/P0KdVsfcwfIsxhQalUaeGPYEcwfM7dR+hBA8Pqk/Jypq+TIp30jRmVlOIuRugTGL2jWjrDmeTp7sL90vZ5mZSsJ94BEEm1+QNc2aIZOMgQrLq1menM/s+DC6ejmqHY5NctI4MaXXFIYGD+30vkb1DuDKvgEs/v0IlbVWthhRUWDT8+ATflFr5Y7q3aU3ET4RrM1Za4TgpMs4e8CYxyH/Dzi6We1oLI5MMgZ6Z9NhNELw8AQ5o8wUGnQNfJrxKSXVJUbb5+OT+nO6uoGPt1lZv5kDa+D4bhj3FDh2vuiqEILre13P7lO7OXbumBEClC4Tdyd06a7/cqCzgQknRiSTjAEyjlXww54i5o3qSVeflsvOSx23/dh23kx7k6yyLKPtMzqsC1OiQvhkWw7FFbVG269Jaev192IC+kPMHKPt9rpe1wHIsxlTcXSGq/4Bxemw/1u1o7EoBiUZIcS1QohsIcQRIcSTzbw+RgixWwihFULMNH6Y6lEUhefXZOLr7ixX95vQmqNr8HXxZVToKKPu92/XDkDbqPCvX61kwVzyB1B2BCa9rC8tbyShnqHcMegO+nSRv8MmEzUbug2BTc9C3Tm1o7EYbSYZIYQDsBiYDAwC5gohLm0sXgDcCXxl7ADVtib9BCl5p1k0sT8+bnJGmSmcrT9LYmEi10Zci5PGuMe4h78Hf7kyglV7jpGWf9qo+za6ypOw5TXoOwn6XmP03T8+7HFZ/t+UNBqY/BpUnoBtb6odjcUQbZWbEEKMBJ5TFGXSn4+fAlAU5ZVmtv0U+FlRlO8ufa2ioqJpoMOHD3cuajOp1Sr8dX0p3i4aXp3gh0M7amlJhttyegufHv+Uf0b8k17uxu/LU6PV8ddfy/B10/DKeD80Fvrv2HPf6/gVbSJz7BLqPMNMMkaFtoITdScY4DHAJPuXIGLPK/ie2ELm2KXUedj+Iti+ff93n9rHx+eyPy5DLpeFAhd2gyr68zmb92N2FWU1OubHeskEY0Kn6k/RzaUbEW4RJtm/m6OG26I9OXpay+95lnlvxv3MQQIKf+VUxAyTJRiA5SeWs7hwMVpFa7Ix7F3RwAUowoGwrPfVDsUiGDIXt7lP105NBo+Pj+/M282isLyaNT9uYWpMN26/dshFr6WmpgLW8XOYS2eOSTzxNDQ2dGoBZlvi4hS2n9zJt9lV3HdDAt6upr/0afAx0elg6ZPgEUTI7DcJcfU2WUx3BN/BQ789RF1IHQnhCSYbpyV287ej/A3fzc8T36UC+kxodVNrPyYVFRWtvm7ImUwREH7B4zDAxmqpX0xRFJ75KQONEDw1RV5WMKW6xjoAkyYY0E/jfe6GwZRV1fP6r9kmHavddn8GRSlw9XNgwgQDMDp0NL4uvvyc87NJx7F7Ix8A3wj45XFoqFE7GlUZkmRSgL5CiAghhDMwB1ht2rDU9ePeY/yeXcLjk/rLKcsm1Khr5KbVN/FmqnlukkaF+XDXqAi+SMonOafMLGO2qeIYbPgn9LwSYm8x+XBOGicmR0zmt4LfKKuxkGNgixxd4IZ3ofwo/P7/1I5GVW0mGUVRtMCDwHrgALBSUZRMIcQLQoipAEKIYUKIImAW8KEQwmpLkpaeq+P5NVkM7d6FeaN6qh2OTUssSiT/bD5RAVFmG3PRpH6E+7nx5A/71a9rpijw8yOg08LUf4OZ7vvNGTAHraIl+YSstWVSvcbC0Dtg53twzH67kxq0TkZRlF8URemnKEpvRVFe/vO5ZxRFWf3nf6coihKmKIqHoij+iqIMNmXQpvTs6kyq6xr5103ROGjkzX5T+iLrC7p5dDPrtFp3Z0denRFNbmkVb286ZLZxm7X/Ozi8Hib8E/yMP6uuJRE+EWycuZEpvaaYbUy7dc2L4BkMPz2oX2hrh+SK/wuszyxmbfoJHhrfh77BnStKKLUuqyyLtJNp3DLwFhw15q0FN7pPADfHh/Px1hzSi86Ydewm50pg3RMQNgxGLDT78EHuQQDUN9rnB5/ZuHWB696CU5nwx9tqR6MKmWT+VHqujqd/zGBgV28Wjuutdjg2b/mB5bg7ujOj7wxVxv/7dQMJ9HJh0bf7zH/ZTFFg7SNQfw6mvmfUlf3t8Wbqm9z2y22yNbOpDZgCkTfB1tfhxD61ozE7mWQAnU7hsZX7qKhp4K3ZMTg5yMNiaoviF/H2uLfxclbnjNHHzYnXZsZw6OQ5XvjZePXSDJK6RF8Ec/w/IUi92Yu9fHpxoPwAu4p3qRaD3Zj8OngEwrd3QV2l2tGYlfw0BT7elsOWQyX88/pBDOxq2imkkp6vq/HrlLXX2H6B3Du2F18lF7A2/YR5Bi3eD7/+HfpcAyMfNM+YLZjSawp+rn58kfWFqnHYBQ9/uOljOJ0Lax+zq74zdp9k9hSc5vX12UyODOG2Ed3VDsfm1WhrWLhpIbtPWsZsm0UT+xMb3oUnv0+nsLzatIPVndN/k3Xzhekf6GtdqcjFwYXZ/WezpWgLuRVW1g7BGvW8Asb+DdK/gX0r1I7GbOw6yVTUNPDQij0Ee7vy6k3RCFk6xuSWH1jO9mPbLeZYOzlo+M/cISDgwRV7qNeaqBeIougX5pUd0X+j9QgwzTjtdHP/m3F1cGXJ/iVqh2IfxjyuXxO19jEoUXl2o5nYbZLRNup4aMUeiitq+c8tQ2SFZTMory1nyf4ljAsbx5CgIW2/wUzC/dx57aZo9hWe4Z8/ZpjmRviuj2DfV/oPmYgxxt9/BwW4BfDmuDd5NP5RtUOxDxoHmPExOLnB13OhxsIrgxuB3SaZF3/OYuuhEl6aFsnQ7r5qh2MXPtz3ITXaGh6Je0TtUC4zOaorD17Vh29SC/nE2J00D2+EX5+E/lNg3GXtmFQ3JmwMfq5+6BSdnGlmDt5d4eYv4XQ+rLwDobPtYqV2mWQ+25HHZzvzWXBlBHOGy/sw5lB4tpCV2SuZ0XcGvbqYb+Fhezx6TT+mRIXw/9YdYENmsVH26Xo2V38fJniw/husStOV21JcVcwta29hS9EWtUOxDz1GwdT/QO5Wumf826YnAthdkknMPsXzazK5emAwT04eqHY4diPEM4SnRjzF/bH3qx1KizQawZuzYokO9eGvX+8l41jr1WXb4lhXTt+Uf4CzB8z9Blw8jRSp8fm7+VPVUMVbaW+htfFv1hYjdi5c+RiBBWsJzrHdls12lWRS8sq5f/lu+od48+6cWFk2xoycNE7M7j+bADfLuOHdEjdnBz6+Ix5fdyfuXJbCkVMdbKNbXU6/5CdxrDsDc1eAj2W3YHLSOPF/cf9HbkUuq46sUjsc+3HV05R3HUPYgY9gz3K1ozEJu0kyafmnuXPpLkJ8XPls/jA8XMxbysRe1TfWs3DjQtbmrFU7FIMFebvy+d0jALjl4yRyS6vat4Oa0/DFNFzPFXB02AsQOtQEURrf+PDxDA0ayr93/5uS6hK1w7EPGg25sU9xNmAo/PQA7PtG7YiMzi6SzN7CM9y5dBdB3q6sWJBAkJer2iHZjff2vsf249vxcPJQO5R26RPkyYoFI2jUKcz9KBbm8JUAABQISURBVIn8MgMTTW0FfDEDTh3gaNzznA20nkZUQgieHfUstdpalmYsVTscu6E4OHNk2IsQcSX8uFBfONWG2HySScsv5/Ylyfh6OPPVghEEe8sEYy4pxSl8mvEps/rNYlz4OLXDabe+wV4sXzCCOm0jcz9KavvSWVWZPsEUp8Psz6kIHmGeQI2ol08vPp74MY/GySnN5qQ4uMDcr6H7SPjhHps6o7HpJLM2/QRzP07G/88EIxuQmc/Z+rP8/Y+/0927O4viF6kdTocNCPHmy7+MoL5Rx03v7yCppWZnZUdhydX6sjGzPoP+k80bqBHFBsXi5ODE2fqzFFUWqR2O/XD2gFtW6meerboHtrxuE7PObDLJKIrCB1uO8sBXu4kO9WHV/aMJ83VXOyy7siFvAyXVJbxyxSu4O1n3sR/cTf87FOjlwu1Lklm155IP3oIk+ORqqDkD89bAwOvVCdSIFEVhwYYFPJr4qGwHYE4unnDbDxA9B35/SX+fxsr70Nhckqmpb+SJ79J5dd1Bbojpxpd/GYGvh7PaYdmdmf1m8sONPxAVaL6ul6YU7ufO9wtHEdfDl0e+2cfr6w+i1TbC7i/gs6n6emR/2QTdre8SWXOEECyMXsiB8gM8vf1pdIqJyu1Il3N01te2G/sk7F0OX86ASuOs21KDTSWZrONnueG9P/hudxEPje/DuzfH4upkmYvfbNVPR34is0zffbuXj2UuuuwoH3cnPp8/gjnDwvn89/0kvT4dVj+oTyx/2QT+ttWH6KruV/HXoX9lXe46Xt31qqwGYE5CwFVPwfQPoSgV3h+trxxhhWwiyeh0Cp/tyGPaf7dTUdPAF/NH8NjE/mjkOhizSj2byjM7nmFZxjK1QzEZZ0cNrw6vJcnvWRJqt/Euc/g59r/g7qd2aCZxd+TdzBs0jxUHV7DioP1UDrYYMXPgnkR9C+flM/VtIhpq1Y6qXax+sUjm8Qqe+SmTtPzTXNU/kDdmxeDv6aJ2WHYnvTKdD4s+JDogmhdHv6h2OKZRXQ6/vQipy/DwCad41k/8tsWBt1eks2pPMc9NHUy4n3Xff7qUEILH4h/Dx8WHyRHWO5nBqgUNgAWbYcM/IWkxHFqnb4LW92q1IzOI1Z7JnK1t4LnVmdzwnz/ILa3itZuiWXrnMJlgzExRFD7P/Jx3Ct6hq0tX3pvwHm6ONjaLT6fT33t5Lx7SPoURC+G+PwiJHMN3C0fy9ykD2JlTxtVvbeHdTYfN387ZxIQQLIhegK+rL/WN9SzLWEZDY4PaYdkXJze47g24fRUIDSy/Cb65Dc4UqB1Zm6zuTKaipoFl23NZ+kculXVabh3RnUUT+9PFXd7cV8ueU3uI847j7m534+Pio3Y4xqNrhMxV+t7sJQchPEH/hx7yv8kMTg4a7hnTmxtiuvHy2gO8vekQy5PzWTi2N/2dFFwcbOuS7dairbyV9haJhYm8Oe5Niy8TZHN6j4f7dsDO9/RTnA+thyG3w5WPgk+Y2tE1S5jrZl5FRUXTQD4+Hfsg+mnvMZ7+MYPKWi3XDArm/67uy+Bu5v9QS01NBSA+3npWcxtbVlkWzhpn+vj2oa6xjn2796ERGts4Jg01+uTyx9tQeggCB+j7wAye0WY3y6ScMt7ZdIiknHK6uGq4oa87j00faVNfgn7J+YVndzyLj4sP/0z4J2PDxxr8Xvm3c7kOH5MzhbDtjf/VPBtyGyTcB4H9jRxh6yoq/ldI1sfH57JvVQ7PPfecWQKpq6trGsjVtWOr7qvqtJyqrOPfc4dw9xW9VCsPc/z4cQC6deumyvhqOlN7htdSXuOFnS9wqvoUkyMm46hx5MSJE4CVH5NTB2HrG7BqIWR8r7/ZOvk1mPIGhAzWz/hpQ5ivOzPjwkno5c/enJNsyq1h6fa8/9/emQfXVZ0H/Pfd+1Y9PS3WYslPsmV5w8VgEzyAWTLpJMSkSUkyDQklzTCE/kFmGqZtaBuaTtJlhtAhM21mQptpSQqhgZkESEKKQ2AoxTFxXczmDW/Ywn6yZelps7a33Hu//nGfZcnXsrw9P8k6v5kzd/nOe+/T0b3nO989534fB3pGqKuMsKA6NmOygp4vy2qXcUvqFl5Lv8ZTu5/CVZfrm89u6fZcvnem4rzbJFbtv/S7+g+hMOovd97yfTiwEewIzFsCdukfVuVyuZMqxWJ/d6p8VnkyM4W5OBrLjGV4Zu8zPLnrSUYKI9y18i6+svorJCNJYJa2iSp074L3fumXYzvACsPK34e19/hpci/AIGzdupWOgQLvDif5+dudDOUcUjVxblvVxG2rmrh2Ye2sXgFZ8Ao89d5TrFuwjuW1y0kPpXE8h7bqtik/MyuvkxJz0dpkuMc3NG8+Dv0HIZKE5R/3r+elt5Ys1cR0nsysm5MxXDocz8FTj4gd4YUDL/DoO49yc+pmvnbt11hau7Tc6p07qjCYho5N0PEbOLgRBg8D4seMWv9tuOoOqGy4aD/ZVhPmcx9bxYO/dwUbtnfxq+1HeXLzB/xg00FqK8KsW1LHuvY61i2po72+clYZnbAV5u4r7x4/fmz7Yzy771luWnATn1/xeW5K3UTUNgtxLhmVDXDzn8KN90PHRtjxHOx+wffK7Qi0Xu8PnBbfAqlrIXRp/jdn5cmIyG3AdwEbeExVHz5FHgV+BFwL9AJfUNWOiXWMJzPzUVWOjhzl7e632ZjeyKbOTTyw9gE+u+yzDOWHyIxlWFy9+LSfnXFtUhjz44ll9vreypF34Og7MFIMYR+fB203w5LfhSs+BZWNF12FqdpkKFvg1T09vLanh83vZzgy6L/3kIyGuDJVxVWpalY2V7GkoZL2hgTJWPii61YKTni7P93zU7rHuomH4nyy/ZN8a923AP/6evPNN4EZdJ3MAEp673iuH/ZozwZ/YHV0G6BghaBxJTSvgbVfvqB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       "description": "start_pos",
       "layout": "IPY_MODEL_f85d3e52220643b1a5e4060eb33282a9",
       "max": 10,
       "min": -10,
       "style": "IPY_MODEL_c5b876f5345b404f9144d7bdffdb63b8"
      }
     },
     "e3ace564a0e94faf90ef4845b258927d": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "e5e17299d1b84799856e6527020faedf": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "FloatSliderModel",
      "state": {
       "continuous_update": false,
       "description": "process_noise",
       "layout": "IPY_MODEL_a177ba1c72984991bf99b609b1579ce9",
       "max": 40,
       "step": 0.1,
       "style": "IPY_MODEL_e2875dd42d454b6496a13fb1c6226570",
       "value": 0.1
      }
     },
     "e82f62af573c4cc19388471a8808b080": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "e83744fb3f4c45c5a80510fbaceb2510": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "e847e07509ca47eb9ef0f25c17cd268d": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "e84e3f5ad8244ac8b5a1a522d4fdb707": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_38c8d26f9b544ed5ad86a0ef8b8e58a2",
       "outputs": [
        {
         "data": {
          "image/png": 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KosiwQUKICmHnlZ3ojXoaVm8oN1P+jYvOhcfrPA7A1ktbVU4jhBD2xeJiPSQk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          "text/plain": "<matplotlib.figure.Figure at 0x229ce74fda0>"
         },
         "metadata": {},
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     "eca9816c3069401a9eeda3327720961f": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "start_pos",
       "layout": "IPY_MODEL_f353a9386bb6474c92048520af0af1a8",
       "max": 10,
       "min": -10,
       "style": "IPY_MODEL_305137f4ac924fe699a466a3d0c3c39c"
      }
     },
     "ecec53a966eb4ce7a866ee51db918da9": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "FloatSliderModel",
      "state": {
       "continuous_update": false,
       "description": "sensor_noise",
       "layout": "IPY_MODEL_32a4368d324e482b982adcb55526e0fa",
       "step": 0.1,
       "style": "IPY_MODEL_a237b7d10c78449d8023ef7cffcc34c4",
       "value": 5
      }
     },
     "ef9dd4712cb24e248a1aff4f5f15a71f": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "efa8e1510bb142bda72d60e26167d486": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "f05bb041bec8447292eec1114847a0c5": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "start_pos",
       "layout": "IPY_MODEL_e3ace564a0e94faf90ef4845b258927d",
       "max": 10,
       "min": -10,
       "style": "IPY_MODEL_413485a0b43d456791570633e4fd1d30"
      }
     },
     "f0e21b9df58b4f2c94223f5120159fd8": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_49c177b6614c4627ae21b6debace3486",
       "outputs": [
        {
         "data": {
          "image/png": 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