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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Computational Mechanics Project #02 - Create specifications for a projectile robot\n", | |
| "\n", | |
| "On the first day of class, we threw $2\"\\times~2\"$ dampened paper (spitballs) at a target on the whiteboard. Now, we are going to analyze the accuracy of the class with some cool Python tools and design a robot that has the same accuracy and precision as the class, but we will have the robot move farther away from the target and use a simpler projectile i.e. a tennis ball so we don't need to worry about knuckle-ball physics. \n", | |
| "\n", | |
| "The goal of this project is to determine the precision of necessary components for a robot that can reproduce the class throwing distibution. We have generated pseudo random numbers using `numpy.random`, but the class target practice is an example of truly random distributions. If we repeated the exercise, there is a vanishingly small probability that we would hit the same points on the target, and there are no deterministic models that could take into account all of the factors that affected each hit on the board. \n", | |
| "\n", | |
| "<img src=\"../images/robot_design.png\" style=\"height: 250px;\"/>\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Now, we ask ourselves some questions:\n", | |
| "\n", | |
| "1. How do we quantify the class accuracy and precision?\n", | |
| "\n", | |
| "2. If we design a robot, what design components can we control?\n", | |
| "\n", | |
| "3. How can we relate the controlled components to the class accuracy, and specify the component precision?\n", | |
| "\n", | |
| "The first question, we have some experience from our work in [02_Seeing_Stats](../notebooks/02_Seeing_Stats.ipynb). We can define the mean, standard deviation, measure the first, second, and third quartiles, etc. \n", | |
| "\n", | |
| "The second question is a physical question. We cannot control the placement of the robot or the target those are chosen for us. We cannot control temperature, mechanical vibrations, etc. We *can* control the desired initial velocity. The initial velocity will have some speed and direction, and both will be subject to random noise. Once the speed and direction are set, the location on the target is determined by kinematic equations for an object in freefall, as such\n", | |
| "\n", | |
| "$x_{impact} = \\frac{v_x}{v_y}d + x(0)~~~~~~~~~~~~~~~~~~~~(1.a)$\n", | |
| "\n", | |
| "$z_{impact} = d\\left(\\frac{v_z(0)}{v_y}-\\frac{g}{2v_y^2}d\\right)+ z(0)~~~~~(1.b)$.\n", | |
| "\n", | |
| "Where the location of impact is at a $y$-distance of $d$ at a point on the target with coordinates $(x_{impact},~z_{impact})$, and the initial velocity is $\\bar{v}=v_x\\hat{i}+v_y\\hat{j}+v_z(0)\\hat{k}$, the object is released at an initial location $\\bar{r}(0)=x(0)\\hat{i}+0\\hat{j}+z(0)\\hat{k}$, and the only acceleration is due to gravity, $\\bar{a}=-g\\hat{k}$. Equation (1) becomes much easier to evaluate if we assume that $v_x=0$, resulting in an evalution of the accuracy of the height of the impact, $z_{impact}$, as such\n", | |
| "\n", | |
| "$x_{impact} = x(0)~~~~~~~~~~~~~~~~~~~~(2.a)$\n", | |
| "\n", | |
| "$z_{impact} = \\frac{d}{\\cos{\\theta}}\\left(\\sin{\\theta}-\\frac{g}{2v_0^2\\cos{\\theta}}d\\right)+ z(0)~~~~~(2.b)$.\n", | |
| "\n", | |
| "Where $\\theta$ is the angle of the initial velocity and $v_0$ is the initial speed. Equation (2) restricts the analysis to height accuracy. You can incorporate the 2D impact analysis if you finish the 1D analysis. " | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "The third question, is how we can relate equation (2) to the measured points of impact? For this, we can use Monte Carlo methods *(There are other methods, but Monte Carlo is one of the most straight-forward)*. Our Monte Carlo approach is as such, if we have a desired initial speed, $v_0$, and desired angle, $\\theta$, we can propagate the uncertainty of our actual speeds and angles into the $z_{impact}$ locations. Then, we can choose distributions in speed and angles that match the distributions in $z_{impact}$ locations. Here are the steps:\n", | |
| "\n", | |
| "1. Generate random $\\theta_i$ and $v_{0~i}$ variables\n", | |
| "\n", | |
| "2. Plug into eqn 2 for random $z_{impact~i}$ locations\n", | |
| "\n", | |
| "3. Compare to our measured $z_{impact}$ location statistics\n", | |
| "\n", | |
| "4. Repeat 1-3 until the predicted uncertainty matches the desired uncertainty, we can use a number of comparison metrics:\n", | |
| " \n", | |
| " - standard deviation\n", | |
| " \n", | |
| " - first, second, and third quartiles\n", | |
| " \n", | |
| " - visually, with box plots and histograms" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Project Deliverables\n", | |
| "\n", | |
| "1. Statistical analysis of class accuracy and precision (x- and z-locations) data is in the csv file [../data/target_data.csv](../data/target_data.csv) _Note: if you want to see how I turned the images into data check out the jupyter notebook [process_target_practice](./process_target_practice.ipynb)\n", | |
| "\n", | |
| "2. A Monte Carlo model to generate impact heights based upon uncertainty in $\\theta_0$ and $v_0$. \n", | |
| "\n", | |
| "3. The precision required to recreate the class accuracy and precision with a robot. \n", | |
| "**You must show some validation of your work**\n", | |
| "\n", | |
| "4. [BONUS] Repeat 2-3 taking into account the variation in $x_{impact}$ due to misalignment. \n", | |
| "\n", | |
| "Given constants and constraints:\n", | |
| "\n", | |
| "- $d=$3 m, distance to target\n", | |
| "\n", | |
| "- $g=$9.81 m/s$^2$, acceleration due to gravity\n", | |
| "\n", | |
| "- $z(0)=$0.3 m, the initial height is 0.3 m above the bull's eye\n", | |
| "\n", | |
| "- 4 m/s$<v_0<$12 m/s, the initial velocity is always higher than 9 mph and less than 27 mph" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "import matplotlib.pyplot as plt\n", | |
| "import numpy as np\n", | |
| "import pandas as pd\n", | |
| "\n", | |
| "#Import rcParams to set font styles\n", | |
| "from matplotlib import rcParams\n", | |
| "\n", | |
| "#Set font style and size \n", | |
| "#rcParams['font.family'] = 'sans'\n", | |
| "#rcParams['font.size'] = 16\n", | |
| "#rcParams['lines.linewidth'] = 3" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
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\n", | |
| "text/plain": [ | |
| "<Figure size 720x720 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "target_data = pd.read_csv('Cooper_data/target_data.csv')\n", | |
| "x_data = target_data[' x position (m)']\n", | |
| "y_data = target_data[' y position (m)']\n", | |
| "x_data1 = x_data[:29]\n", | |
| "y_data1 = y_data[:29]\n", | |
| "x_data2 = x_data[30:]\n", | |
| "y_data2 = y_data[30:]\n", | |
| "\n", | |
| "x_mean = np.mean(x_data)\n", | |
| "y_mean = np.mean(y_data)\n", | |
| "x_std = np.std(x_data)\n", | |
| "y_std = np.std(y_data)\n", | |
| "mean_std = (x_std+y_std)/2\n", | |
| "\n", | |
| "Q1_x = np.percentile(x_data, q=25)\n", | |
| "Q2_x = np.percentile(x_data, q=50)\n", | |
| "Q3_x = np.percentile(x_data, q=75)\n", | |
| "Q1_y = np.percentile(y_data, q=25)\n", | |
| "Q2_y = np.percentile(y_data, q=50)\n", | |
| "Q3_y = np.percentile(y_data, q=75)\n", | |
| "\n", | |
| "plt.figure(figsize=(10,10))\n", | |
| "plt.scatter(x_data1,y_data1,label = 'Target 1 Hits')\n", | |
| "plt.scatter(x_data2,y_data2,label = 'Target 2 Hits')\n", | |
| "plt.vlines(0,.8,-.8,linewidth=1)\n", | |
| "plt.hlines(0,.8,-.8,linewidth=1)\n", | |
| "plt.grid()\n", | |
| "plt.xlim(-.8,.8)\n", | |
| "plt.ylim(-.8,.8)\n", | |
| "\n", | |
| "theta = np.linspace(0, 2*np.pi, 100)\n", | |
| "x1_mean = x_std*np.cos(theta)+x_mean\n", | |
| "x2_mean = y_std*np.sin(theta)+y_mean\n", | |
| "plt.plot(x_mean,y_mean,'s',color='black',label='Mean ({:.3},{:.3})'.format(x_mean,y_mean),markersize='8')\n", | |
| "plt.plot(x1_mean,x2_mean,color='black',label='Standard Deviation Limit')\n", | |
| "\n", | |
| "plt.hlines(Q1_x,.8,Q1_y,linewidth=.75)\n", | |
| "plt.vlines(Q1_y,.8,Q1_x,linewidth=.75)\n", | |
| "\n", | |
| "plt.hlines(Q2_x,.8,Q2_y,linewidth=.75)\n", | |
| "plt.vlines(Q2_y,.8,Q2_x,linewidth=.75)\n", | |
| "\n", | |
| "plt.hlines(Q3_x,.8,Q3_y,linewidth=.75)\n", | |
| "plt.vlines(Q3_y,.8,Q3_x,linewidth=.75)\n", | |
| "\n", | |
| "#x1_Q1 = Q1_x*np.cos(theta)+x_mean\n", | |
| "#x2_Q1 = Q1_y*np.sin(theta)+y_mean\n", | |
| "#plt.plot(x1_Q1,x2_Q1,color='red',label='First Quartile Limit')\n", | |
| "\n", | |
| "#x1_Q2 = Q2_x*np.cos(theta)+x_mean\n", | |
| "#x2_Q2 = Q2_y*np.sin(theta)+y_mean\n", | |
| "#plt.plot(x1_Q2,x2_Q2,color='orange',label='Second Quartile Limit')\n", | |
| "\n", | |
| "#x1_Q3 = Q3_x*np.cos(theta)+x_mean\n", | |
| "#x2_Q3 = Q3_y*np.sin(theta)+y_mean\n", | |
| "#plt.plot(x1_Q3,x2_Q3,color='yellow',label='Third Quartile Limit')\n", | |
| "\n", | |
| "plt.title('Target Distribution')\n", | |
| "plt.xlabel('x-Position [m]')\n", | |
| "plt.ylabel('z-Position [m]')\n", | |
| "plt.legend();" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "$x_{impact} = x(0)~~~~~~~~~~~~~~~~~~~~(2.a)$\n", | |
| "\n", | |
| "$z_{impact} = \\frac{d}{\\cos{\\theta}}\\left(\\sin{\\theta}-\\frac{g}{2v_0^2\\cos{\\theta}}d\\right)+ z(0)~~~~~(2.b)$.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "def Imp_Height(theta_std,v_0_std,theta_mean=(1/12)*np.pi,v_0_mean=6.383192,N=100):\n", | |
| " d = 3 #m\n", | |
| " g = 9.81 #m/s^2\n", | |
| " z_0 = 0.3 #m\n", | |
| " z_0 = 0.3 #m\n", | |
| " v_0 = np.random.normal(v_0_mean,v_0_std,N)\n", | |
| " theta = np.random.normal(theta_mean,theta_std,N)\n", | |
| " z_imp_mean = (d/np.cos(theta_mean))*(np.sin(theta_mean - ((g*d)/(2*np.cos(theta_mean)*v_0_mean**2)))) + z_0\n", | |
| " z_imp_std = []\n", | |
| " for i in range(N):\n", | |
| " z_imp_std.append((d/np.cos(theta[i]))*(np.sin(theta[i] - ((g*d)/(2*np.cos(theta[i])*v_0[i]**2)))) + z_0)\n", | |
| " z_std = np.std(z_imp_std)\n", | |
| " quartiles = np.percentile(z_imp_std, q=[25, 50, 75])\n", | |
| " return z_imp_mean,z_imp_std,z_std,quartiles" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "a,b,c,d = Imp_Height(.1,.1)\n", | |
| "data = [y_data,b]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 576x576 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=(8,8))\n", | |
| "plt.boxplot(data)\n", | |
| "plt.xticks([1, 2], ['Actual Results', 'Robot Results'])\n", | |
| "\n", | |
| "plt.hlines(a,1,1,color='orange',label='Mean = {:.3}'.format(a))\n", | |
| "plt.hlines(c,1,1,color='white',label='STD = {:.4}'.format(c))\n", | |
| "plt.hlines(a,1,1,color='white',label='Q1 = {:.4}'.format(d[0]))\n", | |
| "plt.hlines(c,1,1,color='white',label='Q2 = {:.4}'.format(d[1]))\n", | |
| "plt.hlines(c,1,1,color='white',label='Q3 = {:.4}'.format(d[2]))\n", | |
| "\n", | |
| "plt.hlines(0,1,1,color='white',label='Actual Results')\n", | |
| "plt.hlines(a,1,1,color='orange',label='Mean = {:.3}'.format(y_mean))\n", | |
| "plt.hlines(c,1,1,color='white',label='STD = {:.4}'.format(y_std))\n", | |
| "plt.hlines(a,1,1,color='white',label='Q1 = {:.4}'.format(Q1_y))\n", | |
| "plt.hlines(c,1,1,color='white',label='Q2 = {:.4}'.format(Q2_y))\n", | |
| "plt.hlines(c,1,1,color='white',label='Q3 = {:.4}'.format(Q3_y))\n", | |
| "\n", | |
| "plt.ylabel('Height [m]')\n", | |
| "plt.title('Boxplot of Robot Throw Distribution Along Y-axis')\n", | |
| "plt.legend(title='Robot Results');" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Therefore, with an inital release angle of $\\frac{1}{12}\\pi$ +/- 0.10 radians and an initial velocity of 6.38 +/- 0.10 $\\frac{m}{s}$ , the robot can recreate the results from the actual class's results. These results are similar in the mean y-position, standard deviation, and in their quartiles. All of this information can be seen in the legend of the boxplot." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "def Imp_x(x_0_std,x_0_mean=x_mean,N=100):\n", | |
| " x_0 = np.random.normal(x_0_mean,x_0_std,N)\n", | |
| " x_0_mean = np.mean(x_0)\n", | |
| " x_0_std = np.std(x_0)\n", | |
| " quartiles = np.percentile(x_0, q=[25, 50, 75])\n", | |
| " return x_0_mean,x_0_std,quartiles,x_0" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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t2zl48CD3338/3bp147PPPqNVq1Y8//zz7Ny5k0qVKtGlSxeWL1/O9OnTefvtt4mPj8+xDWvWrOHhhx8G4NChQyxevJgtW7ZQsmRJRo0axcKFCwkMDCQ5OZn9+/cD5HhbKU3t2rUZOXIk5cqVY8yYMQAEBwezdu1aatasmety7hTaM6KUUup3I63XonLlypw/f57OnTsDsHnzZgYNGoS3tzfVqlWjffv2fPfddwCEhYVRt25dvL29GTRoEJs3b+a7774jPDyce++9lxIlShAREcHXX3+dqzZ06NCBqlWr8tVXXzF48GAAYmNj2blzJ82aNcNutxMbG8vx48epW7cux48f58knn2TNmjVUqFAh3/veunVrhg0bxv/93/+Rmpqa73I8QYMRpZRSvxtpY0ZOnDjBb7/95hwzYozJMk/GX4eISLbpcxIXF8eJEycIDAxk0qRJzvqHDh1KfHw88fHxHDlyhMmTJ1OpUiX27NlDeHg477zzDo8//jgAJUqUcN4uyu3PaN9//31effVVTp48id1u59y5c/neh9tNgxGllFK/OxUrVmTWrFnMmDGDlJQU2rVrx+LFi0lNTeXnn3/m66+/JiwsDHDcpvn++++5desWixcvpk2bNjRv3pyNGzdy9uxZUlNTiYmJoX379gCULFmSlJSUbOsvXbo0b731FtHR0Zw/f55OnTqxZMkSfvrpJwDOnz/PiRMnOHv2LLdu3aJ///688sor7Nq1C3Dchtm5cycAS5YscVtH+fLl+eWXX5zv//vf/9K8eXNefvllqlSpwsmTJwt2EG8jDUaUUkr9LjVu3JhGjRqxaNEi+vbtS0hICI0aNaJjx468/vrr/PGPfwQcA03Hjx9PUFAQderUoW/fvlSvXp1p06bRoUMHGjVqRJMmTejTpw8ATzzxBCEhITkOYK1evTqDBg3inXfeoWHDhrz66qt06dKFkJAQOnfuzOnTp0lOTiY8PBy73c6wYcOYNm0aAGPGjOG9996jVatWnD171m35vXr1YtmyZc4BrGPHjnUOuG3Xrh2NGjUqxKNZtKQgXVEqa6GhoSZtJLRSSqn/OXToEA0aNPB0M1QBuPsMRWSnMSY0P+Vpz4hSSimlPEqDEaWUUkp5lAYjSimllPIoDUaUUkop5VEajCillFLKozQYUUoppZRHaTCilFKq2BERHn30Uef7mzdvcu+999KzZ08PtiqzqKgo/P398ff3Jyoqym2aGzduMGDAAPz8/GjevDmJiYnObd26deMPf/hDpv2KjY2lSZMm2O122rRpw7FjxwB44403sNvt2O12goKC8Pb25vz580W2f2k0GFFKKVXslC1blv3793Pt2jUA1q1bR82aNT3cqvTOnz/PlClT2LZtG9u3b2fKlClcuHAhU7q5c+dSqVIljh07xujRo50zDAOMHTuWBQsWZMrz97//nYULFxIfH8/gwYN59dVXnenTHlk/bdo02rdvj6+vb9HtpEWDEaWUUsVS9+7d+eKLLwCIiYlh0KBBzm1Xr15l+PDhNGvWjMaNG7NixQoAEhMTadu2LU2aNKFJkyZs3boVgA0bNhAeHs4jjzxC/fr1iYiIKND8NgBr166lc+fO+Pr6UqlSJTp37syaNWsypVuxYgVDhw4F4JFHHiE2NtZZd6dOnShfvnymPCLC5cuXAbh06RI1atTIlCbjMSlKJW5LLUoppdQdZuDAgbz88sv07NmTvXv3Mnz4cDZt2gTA1KlT6dixI/PmzePixYuEhYXx4IMPUrVqVdatW4ePjw8JCQkMGjSItKdt7969mwMHDlCjRg1at27Nli1baNOmTbo633jjDRYuXJipLe3atWPWrFnp1iUnJ1OrVi3ne5vNRnJycqa8rulKlChBxYoVOXfuHFWqVMly3+fMmUOPHj0oXbo0FSpU4Ntvv023/ddff2XNmjW8/fbb2R3CQqPBiFJKKc/6Krxwy3twQ66ShYSEkJiYSExMDD169Ei37csvv2TlypXMmDEDcMyc+8MPP1CjRg0iIyOJj4/H29ubo0ePOvOEhYVhs9kAsNvtJCYmZgpGxo4dy9ixY3PVPnc9KxlnGM5LOlczZ85k9erVNG/enDfeeINnn32WOXPmOLd//vnntG7d+rbcogENRpRSShVjvXv3ZsyYMWzYsIFz58451xtjWLp0KQEBAenST548mWrVqrFnzx5u3bqFj4+Pc1upUqWcr729vbl582am+vLSM2Kz2diwYYPzfVJSEuHh4Zny2mw2Tp48ic1m4+bNm1y6dCnbIOLnn39mz549NG/eHIABAwbQrVu3dGkWLVp0227RgAYjSimlPC2XPRlFYfjw4VSsWJHg4OB0F/6uXbsye/ZsZs+ejYiwe/duGjduzKVLl7DZbHh5eREVFUVqamqe6stLz0jXrl158cUXnYNWv/zyS+esvq569+5NVFQULVu2ZMmSJXTs2DHbnpFKlSpx6dIljh49Sr169Vi3bl26Se8uXbrExo0b+fe//52nfSsIDUaUUkoVWzabjaeffjrT+okTJ/LMM88QEhKCMYbatWuzatUqRo0aRf/+/fn000/p0KEDZcuWLbK2+fr6MnHiRJo1awbApEmTnD0ekyZNIjQ0lN69ezNixAgeffRR/Pz88PX1ZdGiRc4y2rZty+HDh7ly5Qo2m425c+fStWtX/u///o/+/fvj5eVFpUqVmDdvnjPPsmXL6NKlS5HuW0ZS0NG+yr3Q0FCTNqhJKaXU/7ibfl7dXdx9hiKy0xgTmp/y9Ke9SimllPIoDUaUUkop5VEajCillFLKozQYUUoppZRHaTCilFJKKY/SYEQppZRSHqXBiFJKqWJn6tSpBAYGEhISgt1uZ9u2bfTt2xe73Y6fnx8VK1bEbrdjt9vZunUr4eHhBAQEEBISQv369YmMjOTixYsFbocxhqeeego/Pz9CQkLYtWuX23QREREEBAQQFBTE8OHDSUlJcW7bsGEDdrudwMBA2rdvny5famoqjRs3pmfPns51Y8eOpX79+oSEhNC3b99C2Y+C0mBEKaVUsfLNN9+watUqdu3axd69e/nqq6+oVasWy5YtIz4+njlz5tC2bVvi4+OJj4+nVatWACxcuJC9e/eyd+9eSpUqRZ8+fQrclv/85z8kJCSQkJDAhx9+yN///ne36SIiIjh8+DD79u3j2rVrznlkLl68yKhRo1i5ciUHDhzg008/TZfvX//6V6bngXTu3Jn9+/ezd+9e6tWr5/aprrebBiNKKaWKldOnT1OlShXnXDJVqlShRo0auc5/zz338Prrr/PDDz+wZ8+eArVlxYoVDBkyBBGhRYsWXLx4kdOnT2dK16NHD0QEESEsLIykpCQAPv74Y/r168d9990HQNWqVZ15kpKS+OKLL3j88cfTldWlSxdKlHA8gL1FixbOsjxJgxGl7jK+vr7OP0q3Y2FyxdtSz+2aHVSpLl26cPLkSerVq8eoUaPYuHFjnsvw9vamUaNGHD58ONO2AQMGOG/xuC7R0dGZ0iYnJ1OrVi3ne5vNRnJycpb1pqSksGDBAufEdkePHuXChQuEh4fTtGnTdHU888wzvP7663h5ZX2pnzdvHt27d8/VPhclnZtGqbvMhQsX3E4ZXmQmV7wt9eU05blShaVcuXLs3LmTTZs2ERcXx4ABA5g+fTrDhg3LUzlZ/b9YvHhxgcrI7v/CqFGjaNeuHW3btgXg5s2b7Ny5k9jYWK5du0bLli1p0aIFR48epWrVqjRt2jTdBICupk6dSokSJYiIiMh1e4uKBiNKKaWKHW9vb8LDwwkPDyc4OJioqKg8BSOpqans27fP7Rw7AwYM4MiRI5nWP/vsswwZMiTdOpvNxsmTJ53vk5KSsrxlNGXKFH7++Wc++OCDdPmrVKlC2bJlKVu2LO3atWPPnj3s2rWLlStXsnr1aq5fv87ly5f5y1/+4pyJNyoqilWrVhEbG3tHfBHI020aEUkVkXgR2S8in4vIH3JIHy4iq/JYx4vZbEsUkX0isldENorI/XkpOxd1O9trvW5VmOWr9O6E/wBK/V7o/6fcO3LkCAkJCc738fHx3H9/7i8nKSkpvPDCC9SqVYuQkJBM2xcvXuwc/Oq6ZAxEAHr37k10dDTGGL799lsqVqxI9erVM6WbM2cOa9euJSYmJt1tlz59+rBp0yZu3rzJr7/+yrZt22jQoAHTpk0jKSmJxMREFi1aRMeOHZ2ByJo1a3jttddYuXIlZcqUyfV+F6W8jhm5ZoyxG2OCgPPAP4qgTVkGI5YOxpgQYAPw/4qg/jThgAYjSqm7RlBQEN7e3gQFBRETE5PvcmJiYpxl1apVi1q1ahVKuXeKK1euMHToUBo2bEhISAgHDx5k8uTJOeaLiIggJCSEoKAgrl69yooVKwrclh49elC3bl38/Pz461//yrvvvptu26lTpwAYOXIkZ86coWXLltjtdl5++WUAGjRoQLdu3QgJCSEsLIzHH3+coKCgbOuMjIzkl19+oXPnztjtdkaOHFng/SgwY0yuF+CKy+uRwLvWawHeAPYD+4AB1vpw4GtgGXAQeB/wsrYNstLuB16z1k0HUoF4YKGb+hOBKtbrbsBql21/AbZbeT8AvK1lvku7RltpNwCh1usqQKJLe1cBtYEfgWSrvLbAn6xy9gBf53SsmjZtalT2HKefyqvbftxeqnBbqtHzIf8+/vhjA5j169eb3377zaxfv97UqVPHfPzxx/kqq06dOmb9+vUmOjraVK9e3fzxj3800dHRBSrX1cGDBwuUX3meu88Q2GHyEFO4LvkKRqyL/KdAN+t9f2Cdtb4a8ANQ3bq4XwfqWtvWAY8ANaw09+IYt7IeeNi1jizqdw1G3gKesF43AD4HSlrv3wWGAE2BdS75/2ByEYxYrycDY1zy7gNqupaT3aLBSM704pM/GoyojAIDAzMdv/Xr15vAwMB8lbV+/fp0r13Lym+5rjQYufsVdjCS1wGspUUkHkfPwU4ruABoA8QYY1KBMyKyEWgGXAa2G2OOA4hIjJU2BdhgjPnZWr8QaAcsz0Ub4kSkGvAT/7tN0wlH4PGddd+0tLX9c6CuiMwGvgC+zOP+utoCzBeRT4DP3CUQkSeAJwDnb75V9vQ+t3Kl50PhadOmDYcOHcpzvkOHDtGmTRu3rwtSrlLZydeYEeB+4B7+N2Yku78gGX+3ZHJIn5MOVv0HgJdd6o8yjvEsdmNMgDFmsjHmAtAIR0/IP4A5Vvqb/G/ffXJTqTFmJI7gpxYQLyKV3aT50BgTaowJvffee/O3d8VMfqPo4rz8nnn62N6tS2BgYKZjuXnzZre/9MhJgwYN2Lx5c7rXrmXlt1ylspOvh54ZYy4BTwFjRKQkjnEhA0TEW0TuxdHLsd1KHiYidUTECxgAbAa2Ae1FpIqIeOMYP5L21JkUq8zs6r8GPAMMERFfIBZ4RESqAoiIr4jcLyJVcIxRWQpMBJpYRSTi6EkBx20jd34Byqe9EZEHjDHbjDGTgLM4ghKllPK4CRMmABAXF0dKSgpxcXGMGDHCuT6vZY0YMYK4uDief/55IiIiGDx4MM8//3yBylUqO/l+zogxZreI7AEGAv8GWuIY3GmAccaYH0WkPvANjoGpwViDWY0xt0TkBSAOR6/GamNM2rDkD4G9IrLLGJPlk1iMMaet2z7/MMa8IiL/D/jSCnpScPSEXAM+stYBvGD9OwP4REQexTFexZ3PgSUi0gd4EhgtIv5We2OtfVVKKY8bNGgQgwcP5sknn+TQoUM0aNCAqVOnMmjQoHyVBTjLSnvmxbBhwwpUrlLZkd97t6+nhIaGmh07dni6GXc0Efnd33YoCrf9uE2uCJMvFXk1ej4UzN10/NICJnX3cvcZishOY0xofsrTuWmUx9wtfziVuhvo/6e8SUpKok+fPvj7+1O3bl0iIyO5ceMGAOfOnaNDhw6UK1eOyMjIIqn//PnzdO7cGX9/fzp37syFCxfcphs+fDhVq1bN9OyQyZMnU7NmTee8N6tXr3ZumzZtGn5+fgQEBLB27Vrn+gkTJlCrVi3KlStXJPtUEBqMKKWUKlaMMfTr14+HH36YhIQEEhISuHbtGuPGjQPAx8eHV155hRkzZhRZG6ZPn0aYliMAACAASURBVE6nTp1ISEigU6dOTJ8+3W26YcOGsWbNGrfbRo8e7Xy6a48ePQA4ePAgixYt4sCBA6xZs4ZRo0aRmpoKQK9evdi+fbvbsjxNgxGl7kK3ddbe21RfpUqVPHxUVXGxfv16fHx8eOyxxwDHPDUzZ84kOjqaK1euULZsWdq0aYOPT65+bJkvK1asYOjQoQAMHTqU5cvdP9miXbt2eZrResWKFQwcOJBSpUpRp04d/Pz8nAFIixYt3D5q/k6gwYhSd5nb/lPiyZduSz3nz5/39KFVxcSBAwdo2rRpunUVKlSgdu3aHDt2LNfljB492nmbxHXJqpfD1ZkzZ5yBQfXq1fnpp5/ythPA22+/TUhICMOHD3fe5klOTqZWrf/92NNms5GcnJznsm83nbVXKaVUsWKMcfuAvbyOu5k5c2ZhNSnP/v73vzNx4kREhIkTJ/Lcc88xb948t/twNzxMUHtGlFJKFSuBgYFk/LXj5cuXOXPmDAEBAbkuJy89I4899hh2u905tqNatWqcPn0agNOnT1O1atU87UO1atXw9vbGy8uLv/71r85bMTabjZMnTzrTJSUlOX+efSfTnhGllFLFSqdOnRg/fjzR0dEMGTKE1NRUnnvuOSIjIyldunSuy8lLz8hHH32U7n3v3r2Jiopi/PjxREVF0adPn1yXBY4AJu02z7Jly5y/tunduzeDBw/m2Wef5dSpUyQkJBAWFpansj1Be0aUUkoVKyLCsmXLWLJkCf7+/lSuXBkvL690T5atXbs2zz77LPPnz8dms3Hw4MFCbcP48eNZt24d/v7+rFu3jvHjxwNw6tQpZ+8JOB5C17JlS44cOYLNZmPu3LkAjBs3juDgYEJCQoiLi3MGRoGBgfz5z3+mYcOGdOvWjXfeeQdvb29nHpvNxq+//orNZmPy5MmFuk8FoQ89KyL60DOllHLvTnvo2datWxk0aBCfffZZpoGtyr3CfuiZ3qZRSilVrLVq1YoTJ054uhnFmt6mUUoppZRHaTCilFJKKY/SYEQppZRSHqXBiFJKKaU8SoMRpZRSSnmUBiNKKaWKnaSkJPr06YO/vz9169YlMjKSGzduALBu3TqaNm1KcHAwTZs2Zf369YVS57Rp0/Dz8yMgIIC1a9e6TXP+/Hk6d+6Mv78/nTt3ds45k13+3377jSeeeIJ69epRv359li5dCsDXX39NkyZNKFGiBEuWLElXj7e3t/OJsb1793auN8YwYcIE6tWrR4MGDZg1a1ah7HtONBhRSilVrBhj6NevHw8//DAJCQkkJCRw7do1xo0bB0CVKlX4/PPP2bdvH1FRUTz66KMFrvPgwYMsWrSIAwcOsGbNGkaNGkVqamqmdNOnT6dTp04kJCTQqVMn56Pls8s/depUqlatytGjRzl48CDt27cH4L777mP+/PkMHjw4Uz2lS5cmPj6e+Ph4Vq5c6Vw/f/58Tp48yeHDhzl06BADBw4s8L7nhgYjSimlipX169fj4+PDY489Bjh6CWbOnEl0dDRXrlyhcePGzvlcAgMDuX79urPXJL9WrFjBwIEDKVWqFHXq1MHPz885n0zGdEOHDgVg6NChLF++PMf88+bN44UXXgDAy8uLKlWqAI6nyIaEhODllftL/XvvvcekSZOcefI6Z05+aTCilFKqWDlw4ECmJ61WqFCB2rVrc+zYsXTrly5dSuPGjSlVqlSmcvIyUV5ycjK1atVyvrfZbCQnJ2dKd+bMGeecM9WrV+enn37KNv/FixcBmDhxIk2aNOFPf/oTZ86cyfEYXL9+ndDQUFq0aOEMeAD++9//snjxYkJDQ+nevTsJCQk5llUY9AmsSimlihVjDCLidr2rAwcO8Pzzz/Pll1+6LScvE+W5m3rFXRvymv/mzZskJSXRunVr3nzzTd58803GjBnDggULsi3vhx9+oEaNGhw/fpyOHTsSHBzMAw88wI0bN/Dx8WHHjh189tlnDB8+nE2bNuW6nfmlPSNKKaWKlcDAQDLOHXb58mXOnDlDQEAA4Bjg2rdvX6Kjo3nggQfclpOXnhGbzcbJkyed75OSkpy3glxVq1aN06dPA46ZedNuk2SVv3LlypQpU4a+ffsC8Kc//Yldu3bleAzS6q5bty7h4eHs3r3bWU///v0B6Nu3L3v37s2xrMKgwYhSSqlipVOnTvz6669ER0cDkJqaynPPPUdkZCSlS5fm4sWLPPTQQ0ybNo3WrVtnWc7MmTOdg0Bdl7QZeF317t2bRYsWcePGDb7//nsSEhIICwtzmy4qKgqAqKgo+vTpk21+EaFXr15s2LABgNjYWBo2bJjt/l+4cME5Bubs2bNs2bLFmefhhx92/npo48aN1KtXL9uyCo0xRpciWJo2bWqUUkpldvDgQU83wfzwww+mV69exs/Pz1SsWNE88cQTzm2vvPKKKVOmjGnUqJFzOXPmTIHrfPXVV03dunVNvXr1zOrVq53rR4wYYb777jtjjDFnz541HTt2NH5+fqZjx47m3LlzOeZPTEw0bdu2NcHBwaZjx47mxIkTxhhjtm/fbmrWrGnKlCljfH19TcOGDY0xxmzZssUEBQWZkJAQExQUZObMmeMs68KFC6ZHjx4mKCjItGjRwsTHx7vdF3efIbDD5POaKcbNfShVcKGhoSZjN6BSSin308970tatWxk0aBCfffZZpoGtyj13n6GI7DTGhOanPB3AqpRSqlhr1aoVJ06c8HQzijUdM6KUUkopj9JgRCmllFIepcGIUkoppTxKgxGllFJKeZQGI0oppZTyKA1GlFJKFTtJSUn06dMHf39/6tatS2RkpPNBYNu3b3c+TbVRo0YsW7asUOqcNm0afn5+BAQEsHbtWrdpPv30UwIDA/Hy8sr0lFhwPMa9XLlyzJgxw7lu8eLFhISEEBgY6Jx5GBwz8N57773OfZkzZ06h7EdR0GBEKaVUsWKMoV+/fjz88MMkJCSQkJDAtWvXnBfyoKAgduzYQXx8PGvWrOFvf/sbN2/eLFCdBw8eZNGiRRw4cIA1a9YwatQoUlNTM6ULCgris88+o127dm7LGT16NN27d3e+P3fuHGPHjiU2NpYDBw5w5swZYmNjndsHDBjgfDLs448/XqB9KEoajCillCpW1q9fj4+PD4899hgA3t7ezJw5k+joaK5cuUKZMmUoUcLxGK7r16/naUK7rKxYsYKBAwdSqlQp6tSpg5+fH9u3b8+UrkGDBs75cTJavnw5devWJTAw0Lnu+PHj1KtXj3vvvReABx98kKVLlxa4vbebBiNKKaWKlQMHDmR60mqFChWoXbs2x44dA2Dbtm0EBgYSHBzM+++/7wxOXOVlorzk5GRq1arlfG+z2UhOTs51m69evcprr73GSy+9lG69n58fhw8fJjExkZs3b7J8+fJ0E+otXbqUkJAQHnnkkXTr7zT6BFallFLFijHGbW+H6/QozZs358CBAxw6dIihQ4fSvXt3fHx80qWfOXNmnurMKC89Li+99BKjR4+mXLly6dZXqlSJ9957jwEDBuDl5UWrVq04fvw4AL169WLQoEGUKlWK999/n6FDhzonwbvTaDCilFKqWAkMDMx0K+Py5cucOXMm0y2SBg0aULZsWfbv309oaPppV0aPHk1cXFym8gcOHJhp5l6bzZauZyIpKYkaNWrkus3btm1jyZIljBs3josXL+Ll5YWPjw+RkZH06tWLXr16AfDhhx/i7e0NQOXKlZ35//rXv/L888/nur7bTYMRpZRSxUqnTp0YP3480dHRDBkyhNTUVJ577jkiIyMpXbo033//PbVq1aJEiRKcOHGCI0eOULt27Uzl5KVnpHfv3gwePJhnn32WU6dOkZCQQFhYWK7zb9q0yfl68uTJlCtXjsjISAB++uknqlatyoULF3j33Xf55JNPADh9+jTVq1cHYOXKlXfU5IQZ6ZgRpZRSxYqIsGzZMpYsWYK/vz+VK1fGy8uLCRMmALB582YaNWqE3W6nb9++vPvuu1SpUqVAdQYGBvLnP/+Zhg0b0q1bN9555x1nD8bjjz/u/BnvsmXLsNlsfPPNNzz00EN07do1x7KffvppGjZsSOvWrRk/fjz16tUDYNasWQQGBtKoUSNmzZrF/PnzC7QPRUnc3cdSBRcaGmrc/UZcKaWKO3fTz3vS1q1bGTRoEJ999lmmga3KPXefoYjsNMaEZpElW3qbRimlVLHWqlUrTpw44elmFGt6m0YppZRSHqXBiFJKKaU8SoMRpZRSSnmUBiNKKaWU8igNRpRSSinlURqMKKWUKpaWLVuGiHD48OEc086fP59Tp07lu64NGzbQs2dPt+srVqxI48aNqV+/PmPGjMl3HVkZNmwYS5YsAeCtt97i119/LfQ6CkqDEaWUUsVSTEwMbdq0YdGiRTmmLWgwkp22bduye/dudu/ezapVq9iyZUuR1AMajCillFJ3jCtXrrBlyxbmzp2bKRh5/fXXCQ4OplGjRowfP54lS5awY8cOIiIisNvtXLt2jdq1a3P27FkAduzYQXh4OADbt2+nVatWNG7cmFatWnHkyJFct6l06dLY7XbnbL5Xr15l+PDhNGvWjMaNG7NixQrAMetwWFgYdrudkJAQEhISSExMJCgoyFnWjBkzmDx5crryZ82axalTp+jQoQMdOnQgNTWVYcOGERQURHBwcJ4eb1/Y9KFnSimlip3ly5fTrVs36tWrh6+vL7t27aJJkyb85z//Yfny5Wzbto0yZcpw/vx5fH19efvtt5kxY0amyfIyql+/Pl9//TUlSpTgq6++4sUXX8w0KV9WLly4QEJCAu3atQNg6tSpdOzYkXnz5nHx4kXCwsJ48MEHef/993n66aeJiIjgt99+IzU1lTNnzuRY/lNPPcWbb75JXFwcVapUYefOnSQnJ7N//34ALl68mKt2FgUNRpRSShU7MTExPPPMM4Bjlt2YmBiaNGnCV199xWOPPUaZMmUA8PX1zVO5ly5dYujQoSQkJCAipKSk5Jhn06ZNhISEcOTIEcaPH88f//hHAL788ktWrlzJjBkzALh+/To//PADLVu2ZOrUqSQlJdGvXz/8/f3z1MY0devW5fjx4zz55JM89NBDdOnSJV/lFAYNRpRSShUr586dY/369ezfvx8RITU1FRHh9ddfxxiDiORYRokSJbh16xbgCBLSTJw4kQ4dOrBs2TISExOdt2+y07ZtW1atWsXRo0dp06YNffv2xW63Y4xh6dKlBAQEpEvfoEEDmjdvzhdffEHXrl2ZM2cO9erVc7YnY5uyUqlSJfbs2cPatWt55513+OSTT5g3b16O+YqCjhlRSilVrCxZsoQhQ4Zw4sQJEhMTOXnyJHXq1GHz5s106dKFefPmOQd5nj9/HoDy5cvzyy+/OMuoXbs2O3fuBEh3G+bSpUvUrFkTIM+z5NarV48XXniB1157DYCuXbsye/Zs0ia03b17NwDHjx+nbt26PPXUU/Tu3Zu9e/dSrVo1fvrpJ86dO8eNGzdYtWqV2zpc9+Ps2bPcunWL/v3788orr7Br1648tbcwaTCilFKqWImJiaFv377p1vXv35+PP/6Ybt260bt3b0JDQ7Hb7c5bJMOGDWPkyJHOAawvvfQSTz/9NG3btsXb29tZzrhx43jhhRdo3bo1qampeW7byJEj+frrr/n++++ZOHEiKSkphISEEBQUxMSJEwFYvHgxQUFB2O12Dh8+zJAhQyhZsiSTJk2iefPm9OzZk/r167st/4knnqB79+506NCB5ORkwsPDsdvtDBs2jGnTpuW5vYVF0iIuVbhCQ0PNjh07PN0MpZS647ibfl7dXdx9hiKy0xiT/QjfLGjPiFJKKaU8SoMRpZRSSnmUBiNKKaWU8igNRpRSSinlURqMKKWUUsqjNBhRSimllEdpMKKUUqrYEREeffRR5/ubN29y77330rNnTw+2KrOoqCj8/f3x9/cnKirKbZobN24wYMAA/Pz8aN68OYmJic5t3bp14w9/+EOm/YqIiCAgIICgoCCGDx/ufGz94cOHadmyJaVKlXI+Y+V20GBEKaVUsVO2bFn279/PtWvXAFi3bp3zyal3ivPnzzNlyhS2bdvG9u3bmTJlChcuXMiUbu7cuVSqVIljx44xevRonn/+eee2sWPHsmDBgkx5IiIiOHz4MPv27ePatWvMmTMHcMzFM2vWLMaMGVN0O+aGBiNKKaWKpe7du/PFF18AjqeyDho0yLnt6tWrDB8+nGbNmtG4cWNWrFgBQGJiIm3btqVJkyY0adKErVu3ArBhwwbCw8N55JFHqF+/PhERERT0oaJr166lc+fO+Pr6UqlSJTp37syaNWsypVuxYgVDhw4F4JFHHiE2NtZZd6dOnShfvnymPD169EBEEBHCwsJISkoCoGrVqjRr1oySJUsWqO15pcGIUkqpYmngwIEsWrSI69evs3fvXpo3b+7cNnXqVDp27Mh3331HXFwcY8eO5erVq1StWpV169axa9cuFi9ezFNPPeXMs3v3bt566y0OHjzI8ePH2bJlS6Y633jjDex2e6bFtZw0ycnJ1KpVy/neZrORnJycbboSJUpQsWJFzp07l6tjkJKSwoIFC+jWrVuu0hcVnbVXKaWUZ30VXrjlPbghV8lCQkJITEwkJiaGHj16pNv25ZdfsnLlSue4ievXr/PDDz9Qo0YNIiMjiY+Px9vbm6NHjzrzhIWFYbPZALDb7SQmJtKmTZt05Y4dO5axY8fmqn3uelbczSic23TujBo1inbt2tG2bdtcpS8qGowopZQqtnr37s2YMWPYsGFDut4EYwxLly4lICAgXfrJkydTrVo19uzZw61bt/Dx8XFuK1WqlPO1t7c3N2/ezFTfG2+8wcKFCzOtb9euHbNmzUq3zmazsWHDBuf7pKQkwsPDM+W12WycPHkSm83GzZs3uXTpEr6+vjnu+5QpU/j555/54IMPckxb1DQYUUop5Vm57MkoCsOHD6dixYoEBwenu/B37dqV2bNnM3v2bESE3bt307hxYy5duoTNZsPLy4uoqKg8z8ybl56Rrl278uKLLzoHrX755ZduZ9bt3bs3UVFRtGzZkiVLltCxY8cce0bmzJnD2rVriY2NxcvL8yM2PN8CpZRSykNsNhtPP/10pvUTJ04kJSWFkJAQgoKCmDhxIuC4rREVFUWLFi04evQoZcuWLbK2+fr6MnHiRJo1a0azZs2YNGmSs8dj0qRJrFy5EoARI0Zw7tw5/Pz8ePPNN5k+fbqzjLZt2/KnP/2J2NhYbDYba9euBWDkyJGcOXOGli1bYrfbefnllwH48ccfsdlsvPnmm7z66qvYbDYuX75cZPuYRgo62le5Fxoaanbs2OHpZiil1B3H3fTz6u7i7jMUkZ3GmND8lKc9I0oppZTyKA1GlFJKKeVRGowopZRSyqM0GFFKKaWUR2kwopRSSimP0mBEKaWUUh6lwYhSSqliZ+rUqQQGBhISEoLdbmfbtm307dsXu92On58fFStWdM4bs3XrVsLDwwkICCAkJIT69esTGRnJxYsXC9wOYwxPPfUUfn5+hISEsGvXLrfpIiIiCAgIICgoiOHDh5OSkgI4JuhzbWva80KOHDmSbu6bChUq8NZbbwGOB6/Vr1+fkJAQ+vbtWyj7UWDGGF2KYGnatKlRSimV2cGDBz1a/9atW02LFi3M9evXjTHG/PzzzyY5Odm5PS4uzjz00EPp8rRv39589913xhhjbty4YZ599lnTrl27Arfliy++MN26dTO3bt0y33zzjQkLC8sy3a1bt8ytW7fMwIEDzbvvvptlWzO6efOmqVatmklMTDTGGLN27VqTkpJijDFm3LhxZty4cXlut7vPENhh8nnN1J4RpZRSxcrp06epUqWKcy6ZKlWqUKNGjVznv+eee3j99df54Ycf2LNnT4HasmLFCoYMGYKI0KJFCy5evMjp06czpevRowcigogQFhZGUlJSruuIjY3lgQce4P777wegS5culCjhmA2mRYsWeSqrqGgwopRSqljp0qULJ0+epF69eowaNYqNGzfmuQxvb28aNWrE4cOHM20bMGBAulskaUt0dHSmtMnJydSqVcv53mazkZycnGW9KSkpLFiwgG7dujnXffPNNzRq1Iju3btz4MCBTHkWLVrEoEGD3JY3b948unfvnu2+3g46UZ5SSqlipVy5cuzcuZNNmzYRFxfHgAEDmD59OsOGDctTOSaL6VQWL15coDKym+Ru1KhRtGvXjrZt2wLQpEkTTpw4Qbly5Vi9ejUPP/wwCQkJzvS//fYbK1eudDvB3tSpUylRogQRERG5bm9R0WBEKaVUsePt7U14eDjh4eEEBwcTFRWVp2AkNTWVffv2uZ1jZ8CAARw5ciTT+meffZYhQ4akW2ez2Th58qTzfVJSUpa3jKZMmcLPP//MBx984FxXoUIF5+sePXowatQozp49S5UqVQD4z3/+Q5MmTahWrVq6sqKioli1ahWxsbE5zvB7O2gwopRSqlg5cuQIXl5e+Pv7AxAfH+8cT5EbKSkpTJgwgVq1ahESEpJpe156Rnr37s3bb7/NwIED2bZtGxUrVqR69eqZ0s2ZM4e1a9cSGxuLl9f/Rlj8+OOPVKtWDRFh+/bt3Lp1i8qVKzu3x8TEZLpFs2bNGl577TU2btxImTJlct3WoqTBiFJKqWLlypUrPPnkk1y8eJESJUrg5+fHhx9+mGO+iIgISpUqxY0bN3jwwQdZsWJFgdvSo0cPVq9ejZ+fH2XKlOGjjz5Kt23OnDnUqFGDkSNHcv/999OyZUsA+vXrx6RJk1iyZAnvvfceJUqUoHTp0ixatMjZ0/Hrr7+ybt26dD0pAJGRkdy4cYPOnTsDjkGs77//foH3pSAkq3teqmBCQ0PNjh07PN0MpZS647ibfl7dXdx9hiKy0xgTmp/y9Nc0SimllPIoDUaUugv4+vo6nzHg6YXJFYu8Dl9fX08fcqXUbaRjRpS6C1y4cCHLnxHedpMrFnlb7oTR/Uqp20d7RpRSSinlURqMKKWUUsqjchWMiEhfETEiUj8XaYeJSO4f8p85f7iIrMpi/SUR2S0ih0VkRn7ryKbu+SLyiPX6GRG5M36ArbKlXfpKOej/BXW3ym3PyCBgMzAwF2mHAfkORnKwyRjTGGgM9BSR1kVUD8AzQLEORmJiYggKCsLb25ugoCBiYmI83SSllCoUSUlJ9OnTB39/f+rWret89gbAuXPn6NChA+XKlSMyMrJI6j9//jydO3fG39+fzp07c+HCBbfphg8fTtWqVQkKCsq0bfbs2QQEBBAYGMi4ceMAxwPZhg4dSnBwMA0aNEj3GPjw8HACAgKcc+X89NNPRbJv+ZFjMCIi5YDWwAgyBCMiMk5E9onIHhGZbvUqhAILRSReREqLSKKIVLHSh4rIBut1mIhstXo6topIQG4bbYy5BsQDNa2yyorIPBH5ziqvj7U+UES2W23ZKyL+IlJbRPa77MMYEZmcYb+ewhFQxYlInIh4W70m+639HZ3btt6tYmJimDBhArNnz+b69evMnj2bCRMmaECilLrrGWPo16+fcx6XhIQErl275ryg+/j48MorrzBjRqF3wDtNnz6dTp06kZCQQKdOnZg+fbrbdMOGDWPNmjWZ1sfFxbFixQr27t3LgQMHGDNmDACffvopN27cYN++fezcuZMPPviAxMREZ76FCxcSHx9PfHw8VatWLZJ9y4/c9Iw8DKwxxhwFzotIEwAR6W5ta26MaQS8boxZAuwAIowxditoyMphoJ3V0zEJ+GduGy0ilQB/4Gtr1QRgvTGmGdABeENEygIjgX8ZY+w4gqRczZNsjJkFnAI6GGM6AHagpjEmyBgTDHyUbQG/A1OnTmXu3Ll06NCBkiVL0qFDB+bOncvUqVM93TSllCqQ9evX4+Pjw2OPPQY45qmZOXMm0dHRXLlyhbJly9KmTRt8fHyKrA0rVqxg6NChAAwdOpTly5e7TdeuXTu3P3V/7733GD9+PKVKlQJwBhYiwtWrV7l58ybXrl3jnnvuSTd/zZ0qN8HIIGCR9XqR9R7gQeAjY8yvAMaY83msuyLwqdVLMRMIzEWetiKyF/gRWGWM+dFa3wUYLyLxwAbAB7gP+AZ4UUSeB+7PITjKznGgrojMFpFuwGV3iUTkCRHZISI7fv7553xWdWc4dOgQbdq0SbeuTZs2HDp0yEMtyp6nn71R1Etx5OljfjcuKncOHDhA06ZN062rUKECtWvX5tixY7kuZ/To0c5bHq5LVr0crs6cOeOcg6Z69ep5vmVy9OhRNm3aRPPmzWnfvj3fffcdAI888ghly5alevXq3HfffYwZMyZdMPPYY49ht9t55ZVX7pzHBZDDc0ZEpDLQEQgSEQN4A0ZExgEC5GZPbvK/oMc1zHwFiDPG9BWR2jiCiJxsMsb0FJF6wGYRWWaMibfa0t8Yk3GaxEMisg14CFgrIo8DR0kfhOUY+hpjLohII6Ar8A/gz8BwN+k+BD4Ex+Pgc7E/d6wGDRqwefNmOnTo4Fy3efPmO/YRznfSf6qiUBwvNL/3z7QoFMfzJD+MMW6PVV7PuZkzZxZWk/Ls5s2bXLhwgW+//ZbvvvuOP//5zxw/fpzt27fj7e3NqVOnuHDhAm3btuXBBx+kbt26LFy4kJo1a/LLL7/Qv39/FixYkGkWYU/JqWfkESDaGHO/Maa2MaYW8D3QBvgSGC7WL05EJC30+gUo71JGIpAWgvZ3WV8RSLZeD8tLo61bRtOA561Va4EnxTq7RKSx9W9d4Lh122UlEAKcAaqKSGURKQX0zKIa536IY8yLlzFmKTARaJKX9t6NJkyYwIgRI4iLiyMlJYW4uDhGjBjBhAkTPN00pZQqkMDAQDLOHXb58mXOnDlDQECuhy/mqWckrUeiR48eAFSrVo3Tp08DcPr06TyP37DZFWyssAAAFA1JREFUbPTr1w8RISwsDC8vL86ePcvHH39Mt27dKFmyJFWrVqV169bOfa1ZsyYA5cuXZ/DgwWzfvj1PdRalnIKRQcCyDOuWAoONMWtwXOB3iOP2yBhr+3zgfbEGsAJTgH+JyCYg1aWc14FpIrIFR49LXr0PtBOROjh6WUoCe8Vx2+cVK80AYL/Vvvo4AqsU4GVgG7AKx9gVdz4E/iMicTgGym6wypkPvJCP9t5VBg0axNSpU3nyySfx8fHhySefZOrUqZmmolZKqbtNp06d+PXXX4mOjgYgNTWV5557jsjISEqXLp3rcmbOnOkcDOq6jB8/PlPajz76iPj4eFavXs3/b+/eg6su7zyOf75clgArLkuqEIIETAZJTIAkitKKSOpya7mNRajtMoyMbh1aLbWUgXbttM7EtTubGVeE6bgdQrcLI2ki9rJcVqqUpQgoEQgUw2KBRBYEHLkGPfDdP3JyFnKB3E4eQt6vmTM5l9/v+T3nOU+Szzy/33keSZo8ebIKCwslSYWFhZoyZUqT3sPUqVO1ceNGSdWnbD777DMlJibqjjvu0MaNG+XuOnfunLZu3aq77rpLkUhEJ06ckFT9jZvf/va39X5DJxh35xaHW05OjgOtpfpX9QbxXK+4H+KGer9odXv37g1dBT98+LB/9atf9dTUVL/11lv9iSeeuOr1gQMHeu/evb1nz57ev39/Lysra9XjnzhxwseOHeupqak+duxYP3nypLu7V1ZW+oQJE2LbzZw50/v27etdunTx/v37+6uvvuru7hcvXvTHHnvMMzIyfMSIEf7mm2+6u/uZM2f8kUce8fT0dB86dKi/+OKL7u5+9uxZz87O9szMTE9PT/fvfOc7HolEml3/+j5DSTu8mf8zzTkvGxe5ubleexgQaC4zu3GuofjxrdKPP43rIW6o94tWV9/y8yFt2bJFs2bNUnFxcZ0LW1G/+j5DM3vX3XObUx4L5QEAOrRRo0bp0KFDoavRoRFGgHbiRvmmhD/XK+516d27d1zLB3BjIYwA7cCNdsrCfxy6BgBuJqzaCwAAgiKMAACAoAgjAAAgKMIIAKDDqaio0JQpU5SWlqbBgwdr3rx5unjxoiRpw4YNysnJUWZmpnJycmKTi7VUfn6+UlNTNWTIEK1bt67ebU6dOqWHH35YaWlpevjhh/XJJ59ct04rV65UZmamsrKyNH78+NjkZjWKiopkZlfNOtu5c+fYjLGTJ09ulffXEoQRAECH4u6aPn26pk6dqvLycpWXl+vChQtasGCBJCkxMVG/+c1vtHv3bhUWFuqb3/xmi4+5d+9erVq1SmVlZVq7dq2eeuopXbp0qc52L7zwgvLy8lReXq68vLzY1PIN1SkSiejpp5/WH/7wB+3atUtZWVl6+eWXY+WdOXNGL730kkaOHHnVcbp37x6bMfaNN95o8ftrKcIIAKBD2bhxoxISEjRnzhxJ1aMEBQUFWrFihc6ePasRI0YoKSlJUvU6NlVVVbFRk+Zas2aNZs6cqW7dumnQoEFKTU2td22YNWvWaPbs2ZKk2bNn6/XXX5ekButUM4PpuXPn5O46ffp0bDtJ+tGPfqQFCxYoIeG6a8IGRRgBAHQoZWVldWZa7dWrl1JSUnTgwIGrnv/1r3+tESNGqFu3bnXKacpCeZWVlRowYEDscXJysiorK+tsd+zYMfXr10+S1K9fPx0/frzONlfWqWvXrlq6dKkyMzOVlJSkvXv36vHHH5ck7dy5U0eOHNFXvlJ3Pdiqqirl5ubqvvvuiwWekJhnBADQobh7vRP31Z7Pp6ysTD/4wQ+0fv36esspKCho0jFra87kgbXr9Pnnn2vp0qXauXOnBg8erG9/+9vKz8/XokWL9N3vflfLly+vt5zDhw8rKSlJBw8e1NixY5WZmak777yzyfVpLYyMAAA6lIyMDNVeO+z06dM6duyYhgwZIqn6Atdp06ZpxYoVDf6TbsrISHJyso4cORJ7XFFRcdXplBq33367jh49Kkk6evSobrvttqv2qV2n0tJSSdKdd94pM9OMGTO0ZcsWnTlzRnv27NGYMWOUkpKirVu3avLkybH3XXPswYMHa8yYMdq5c2fjGi9emrvCHjdW7QWA5gi9au/ly5c9JyfHCwsL3d09Eon43Llz/fnnn3d3908++cSzsrK8qKio1Y65Z88ez8rK8qqqKj948KAPGjSo3lVzn332Wc/Pz3d39/z8fP/+979/zTpVVlZ63759/fjx4+7u/sMf/tDnz59fp9wHH3zQt2/f7u7up06d8qqqKnd3//jjjz01NbXJqxK39qq9jIwAADoUM1NJSYmKioqUlpamPn36qFOnTlq8eLEk6eWXX9aBAwf005/+NDbaUd+1G02RkZGhGTNmKD09XePHj9eSJUvUuXNnSdLcuXNjIxYLFy7Uhg0blJaWpg0bNmjhwoXXrFNSUpKee+45jR49WllZWSotLdWiRYuuWZd9+/YpNzdXw4YN00MPPaSFCxcqPT29Re+vpcxvsDUvbha5ubleexgQAFD/8vMhbdmyRbNmzVJxcXGdC1tRv/o+QzN7191zm1MeF7ACADq0UaNG6dChQ6Gr0aFxmgYAAARFGAEAAEERRgAAQFCEEQAAEBRhBAAABEUYAQB0OBUVFZoyZYrS0tI0ePBgzZs3L7YY3rZt22JzeQwbNkwlJSWtcsz8/HylpqZqyJAhWrduXb3brF69WhkZGerUqVOdWWJ37dql+++/XxkZGcrMzFRVVZXOnDlz1eyviYmJeuaZZyRJmzZtUnZ2trp06aKioqJWeQ/xQhgBAHQo7q7p06dr6tSpKi8vV3l5uS5cuKAFCxZIku6++27t2LFDpaWlWrt2rZ588klFIpEWHXPv3r1atWqVysrKtHbtWj311FO6dOlSne3uvvtuFRcXa/To0Vc9H4lE9I1vfEPLli1TWVmZ3nrrLXXt2lW33HKLSktLY7eBAwdq+vTpkqQ77rhDy5cv19e//vUW1b0tEEYAAB3Kxo0blZCQoDlz5kiSOnfurIKCAq1YsUJnz55Vjx491KVL9TRcVVVVzVrQrrY1a9Zo5syZ6tatmwYNGqTU1FRt27atznZDhw6NrY9zpfXr1ysrK0vDhg2TJPXp0yc2g2uN8vJyHT9+XA888IAkKSUlRVlZWerU6cb/V3/j1xAAgFZUVlZWZ6bVXr16KSUlRQcOHJAkvfPOO7HTIcuWLYuFkys1ZaG8yspKDRgwIPY4OTlZlZWVja7zBx98IDPTuHHjlJ2drRdffLHONitXrtSjjz7aKuGprTEDKwCgQ3H3ev9hX7k8ysiRI1VWVqZ9+/Zp9uzZmjBhghISEq7avqCgoEnHrK0poSESiWjz5s3avn27evTooby8POXk5CgvLy+2zapVq/TLX/6y0WXeSBgZAQB0KBkZGXUuDj19+rSOHTtW5xTJ0KFD1bNnT+3Zs6dOOU0ZGUlOTtaRI0dijysqKpSUlNToOicnJ+vBBx9UYmKievTooYkTJ+q9996Lvf7+++8rEom027V1CCMAgA4lLy9P58+f14oVKyRJly5d0ve+9z3NmzdP3bt314cffhi7YPXQoUPav3+/UlJS6pRTUFBw1cWjNbealXavNHnyZK1atUoXL17Uhx9+qPLyct17772NrvO4ceO0a9cunT9/XpFIRG+//fZVK+2uXLlSs2bNamJL3DgIIwCADsXMVFJSoqKiIqWlpalPnz7q1KmTFi9eLEnavHmzhg0bpuHDh2vatGl65ZVXlJiY2KJjZmRkaMaMGUpPT9f48eO1ZMmS2AWoc+fOjY3UlJSUKDk5WX/60580adIkjRs3TpLUu3dvzZ8/X/fcc4+GDx+u7OxsTZo0KVb+a6+9VieMbN++XcnJyVq9erWefPJJZWRktOg9xJPVdx4LLZebm+u1hwEBAPUvPx/Sli1bNGvWLBUXF7fb0xxtrb7P0Mzedffc5pTHBawAgA5t1KhROnToUOhqdGicpgEAAEERRgAAQFCEEQBAm+N6xfYrHp8dYQQA0KYSEhJ08uRJAkk75O46efJknQngWooLWAEAbSo5OVkVFRX6+OOPQ1cFzZCQkKDk5ORWLZMwAgBoU127dtWgQYNCVwM3EE7TAACAoAgjAAAgKMIIAAAIijACAACCIowAAICgCCMAACAowggAAAiKMAIAAIIijAAAgKAIIwAAICjCCAAACIowAgAAgiKMAACAoAgjAAAgKMIIAAAIijACAACCIowAAICgCCMAACAowggAAAiKMAIAAIIijAAAgKAIIwAAICjCCAAACIowAgAAgiKMAACAoAgjAAAgKMIIAAAIijACAACCIowAAICgCCMAACAowggAAAiKMAIAAIIijAAAgKAIIwAAICjCCAAACIowAgAAgiKMAACAoAgjAAAgKMIIAAAIijACAACCIowAAICgCCMAACAowggAAAiKMAIAAIIijAAAgKAIIwAAICjCCAAACIowAgAAgiKMAACAoAgjAAAgKMIIAAAIijACAACCIowAAICgCCMAACAowggAAAiKMAIAAIIijAAAgKAIIwAAICjCCAAACIowAgAAgiKMAACAoAgjAAAgKMIIAAAIijACAACCIowAAICgCCMAACAowggAAAiKMAIAAIIijAAAgKAIIwAAICjCCAAACIowAgAAgiKMAACAoAgjAAAgKMIIAAAIijACAACCIowAAICgCCMAACAowggAAAiKMAIAAIIijAAAgKAIIwAAIChz99B1uCmZ2RlJ+0PX4yaXKOlE6Erc5Gjj+KON2wbtHH9D3P2W5uzYpbVrgpj97p4buhI3MzPbQRvHF20cf7Rx26Cd48/MdjR3X07TAACAoAgjAAAgKMJI/Pw8dAU6ANo4/mjj+KON2wbtHH/NbmMuYAUAAEExMgIAAIIijLQCM/uamZWZ2WUza/BqbTMbb2b7zeyAmS1syzreDMzsb81sg5mVR3/2bmC7v5jZbjMrbcnV3R3J9fqmVXsp+vouM8sOUc/2rBFtPMbMPo3221Iz+8cQ9WzPzOwXZnbczPY08Dr9uIUa0cbN6seEkdaxR9J0SZsa2sDMOktaImmCpHRJs8wsvW2qd9NYKOlNd0+T9Gb0cUMecvfhfJXv+hrZNydISovenpC0tE0r2c414ff/j9F+O9zdf9Kmlbw5LJc0/hqv049bbrmu3cZSM/oxYaQVuPs+d7/eBGf3Sjrg7gfd/TNJqyRNiX/tbipTJBVG7xdKmhqwLjeTxvTNKZJWeLWtkv7GzPq1dUXbMX7/24C7b5J06hqb0I9bqBFt3CyEkbbTX9KRKx5XRJ9D493u7kclKfrztga2c0nrzexdM3uizWrXfjWmb9J/W6ax7Xe/mb1vZv9pZhltU7UOhX7cNprcj5mBtZHM7L8k9a3npcXuvqYxRdTzHF9lquVa7dyEYr7o7h+Z2W2SNpjZn6NpHvVrTN+k/7ZMY9rvPUkD3f2smU2U9LqqTyeg9dCP469Z/Zgw0kju/uUWFlEhacAVj5MlfdTCMm8612pnMztmZv3c/Wh0aPV4A2V8FP153MxKVD1EThhpWGP6Jv23Za7bfu5++or7vzezV8ws0d1ZT6X10I/jrLn9mNM0bWe7pDQzG2RmfyVppqQ3AtepvXlD0uzo/dmS6oxImVlPM7ul5r6kv1P1BcZoWGP65huS/j76bYT7JH1ac8oMjXLdNjazvmZm0fv3qvrv88k2r+nNjX4cZ83tx4yMtAIzmybpXyV9QdLvzKzU3ceZWZKkV919ortHzGyepHWSOkv6hbuXBax2e/SCpNfM7HFJhyV9TZKubGdJt0sqif4udJH0H+6+NlB924WG+qaZ/UP09WWSfi9poqQDks5LmhOqvu1RI9v4EUnfMrOIpAuSZjqzUjaJma2UNEZSoplVSHpOUleJftxaGtHGzerHzMAKAACC4jQNAAAIijACAACCIowAAICgCCMAACAowggAAAiKMAIAAIIijACIGzO7FF1GfI+ZrTazHs0o49WaFW7NbFGt17a0Uj3fMrP9Zja5Cft0j763z8wssTXqAXRUzDMCIG7M7Ky7/3X0/q8kvevu/9Ia5bUmM3tL0rPuvqMZ+/5FUi7TtgPNx8gIgLbyR0mpkmRm86OjJXvM7Jnocz3N7HfR1T73mNmj0effMrNcM3tBUs1oxK+ir52N/jQz+1l0v91X7Dsmun+Rmf3ZzH5VM1X1tUT3KTCzTWa2z8zuMbNiMys3s+fj0zxAx8V08ADizsy6SJogaa2Z5ah6Gu6Rql5F9R0ze1vSYEkfufuk6D63XlmGuy80s3nuPryeQ0yXNFzSMEmJkrabWc3iiCMkZah6QbT/lvRFSZsbUe3P3H20mT2t6nWQciSdkvQ/Zlbg7qwbA7QSRkYAxFN3MyuVtEPV6wn9m6QvSSpx93PuflZSsaQHJO2W9GUz+ycze8DdP23Ccb4kaaW7X3L3Y5LelnRP9LVt7l7h7pcllUpKaWSZNQvZ7ZZU5u5H3f2ipIO6euVXAC3EyAiAeLpQeySjodMk7v5BdNRkoqR8M1vv7j9p5HGuderl4hX3L6nxf/dq9rtcq4zLTSgDQCMwMgKgrW2SNNXMephZT0nTJP0xuvryeXf/d0n/LCm7nn0/N7OuDZT5qJl1NrMvSBotaVuc6g+glZHuAbQpd3/PzJbr/8PCq+6+08zGSfqZmV2W9Lmkb9Wz+88l7TKz99z9sSueL5F0v6T3JbmkBe7+v2Z2V9zeCIBWw1d7AXR4fLUXCIvTNABQ/S2Z5c2Z9ExSV1VfRwKgmRgZAQAAQTEyAgAAgiKMAACAoAgjAAAgKMIIAAAIijACAACC+j9cJuTu/Ph7aAAAAABJRU5ErkJggg==\n", | |
| "text/plain": [ | |
| "<Figure size 576x360 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "ax,bx,cx,dx = Imp_x(0.2575)\n", | |
| "data = [x_data,dx]\n", | |
| "plt.figure(figsize=(8,5))\n", | |
| "plt.boxplot(data, 0,'o', 0)\n", | |
| "plt.yticks([1, 2], ['Actual Results', 'Robot Results'])\n", | |
| "\n", | |
| "plt.hlines(ax,1,1,color='orange',label='Mean = {:.3}'.format(np.mean(ax)))\n", | |
| "plt.hlines(bx,1,1,color='white',label='STD = {:.4}'.format(bx))\n", | |
| "plt.hlines(ax,1,1,color='white',label='Q1 = {:.4}'.format(cx[0]))\n", | |
| "plt.hlines(bx,1,1,color='white',label='Q2 = {:.4}'.format(cx[1]))\n", | |
| "plt.hlines(bx,1,1,color='white',label='Q3 = {:.4}'.format(cx[2]))\n", | |
| "\n", | |
| "plt.hlines(0,1,1,color='white',label='Actual Results')\n", | |
| "plt.hlines(ax,1,1,color='orange',label='Mean = {:.3}'.format(x_mean))\n", | |
| "plt.hlines(cx,1,1,color='white',label='STD = {:.4}'.format(x_std))\n", | |
| "plt.hlines(ax,1,1,color='white',label='Q1 = {:.4}'.format(Q1_x))\n", | |
| "plt.hlines(cx,1,1,color='white',label='Q2 = {:.4}'.format(Q2_x))\n", | |
| "plt.hlines(cx,1,1,color='white',label='Q3 = {:.4}'.format(Q3_x))\n", | |
| "\n", | |
| "plt.xlim(-1,1.5)\n", | |
| "plt.xlabel('Position [m]')\n", | |
| "plt.title('Boxplot of Robot Throw Distribution Along X-axis')\n", | |
| "plt.legend(title='Robot Results');" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Therefore, with an input of the standard deviation of the x(0) value, the robot can recreate the results from the actual class's results. These results are similar in the mean x-position, standard deviation, and in their quartiles. All of this information can be seen in the legend of the boxplot." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 720x720 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=(10,10))\n", | |
| "plt.scatter(dx,b,label = 'Robot Hits')\n", | |
| "plt.vlines(0,.8,-.8,linewidth=1)\n", | |
| "plt.hlines(0,.8,-.8,linewidth=1)\n", | |
| "plt.grid()\n", | |
| "plt.xlim(-.8,.8)\n", | |
| "plt.ylim(-.8,.8)\n", | |
| "\n", | |
| "theta = np.linspace(0, 2*np.pi, 100)\n", | |
| "x1_mean = bx*np.cos(theta)+ax\n", | |
| "x2_mean = c*np.sin(theta)+a\n", | |
| "plt.plot(ax,a,'s',color='black',label='Mean ({:.3},{:.3})'.format(ax,a),markersize='8')\n", | |
| "plt.plot(x1_mean,x2_mean,color='black',label='Standard Deviation Limit')\n", | |
| "\n", | |
| "plt.hlines(cx[0],.8,d[0],linewidth=.75)\n", | |
| "plt.vlines(d[0],.8,cx[0],linewidth=.75)\n", | |
| "\n", | |
| "plt.hlines(cx[1],.8,d[1],linewidth=.75)\n", | |
| "plt.vlines(d[1],.8,cx[1],linewidth=.75)\n", | |
| "\n", | |
| "plt.hlines(cx[2],.8,d[2],linewidth=.75)\n", | |
| "plt.vlines(d[2],.8,cx[2],linewidth=.75)\n", | |
| "\n", | |
| "plt.title('Target Distribution')\n", | |
| "plt.xlabel('x-Position [m]')\n", | |
| "plt.ylabel('z-Position [m]')\n", | |
| "plt.legend();" | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 3", | |
| "language": "python", | |
| "name": "python3" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.7.3" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 4 | |
| } |