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compmech-project02/02_Analyze-data_project.ipynb

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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",
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"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": 1191,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import math\n",
"\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": "markdown",
"metadata": {},
"source": [
"# 1. Statistical Analysis"
]
},
{
"cell_type": "code",
"execution_count": 1192,
"metadata": {},
"outputs": [],
"source": [
"target = pd.read_csv(\"../data/target_data.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 1193,
"metadata": {},
"outputs": [
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" <th>throw #</th>\n",
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" <th>y position (m)</th>\n",
" <th>picture x position (pixel)</th>\n",
" <th>picture y position (pixel)</th>\n",
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" <td>1203.326705</td>\n",
" <td>2055.169256</td>\n",
" <td>1508.331047</td>\n",
" <td>1</td>\n",
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" <tr>\n",
" <th>8</th>\n",
" <td>8</td>\n",
" <td>-0.192565</td>\n",
" <td>-0.044575</td>\n",
" <td>1727.218750</td>\n",
" <td>1432.417614</td>\n",
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],
"text/plain": [
" throw # x position (m) y position (m) picture x position (pixel) \\\n",
"0 0 -0.466403 -0.304000 1260.855114 \n",
"1 1 -0.206978 -0.448126 1702.673295 \n",
"2 2 -0.091677 -0.457734 1899.036932 \n",
"3 3 0.153336 -0.510580 2316.309659 \n",
"4 4 0.364720 -0.597055 2676.309659 \n",
"5 5 -0.120502 -0.227133 1849.946023 \n",
"6 6 -0.091677 -0.255959 1899.036932 \n",
"7 7 -0.096482 -0.179092 1890.855114 \n",
"8 8 -0.192565 -0.044575 1727.218750 \n",
"9 9 -0.082069 -0.025358 1915.400568 \n",
"\n",
" picture y position (pixel) target x position (pixel) \\\n",
"0 990.599432 2055.169256 \n",
"1 745.144886 2055.169256 \n",
"2 728.781250 2055.169256 \n",
"3 638.781250 2055.169256 \n",
"4 491.508523 2055.169256 \n",
"5 1121.508523 2055.169256 \n",
"6 1072.417614 2055.169256 \n",
"7 1203.326705 2055.169256 \n",
"8 1432.417614 2055.169256 \n",
"9 1465.144886 2055.169256 \n",
"\n",
" target y position (pixel) image # \n",
"0 1508.331047 1 \n",
"1 1508.331047 1 \n",
"2 1508.331047 1 \n",
"3 1508.331047 1 \n",
"4 1508.331047 1 \n",
"5 1508.331047 1 \n",
"6 1508.331047 1 \n",
"7 1508.331047 1 \n",
"8 1508.331047 1 \n",
"9 1508.331047 1 "
]
},
"execution_count": 1193,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"target[:10]"
]
},
{
"cell_type": "code",
"execution_count": 1194,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['throw #', ' x position (m)', ' y position (m)',\n",
" 'picture x position (pixel)', ' picture y position (pixel)',\n",
" 'target x position (pixel)', ' target y position (pixel)', ' image #'],\n",
" dtype='object')"
]
},
"execution_count": 1194,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"target.columns"
]
},
{
"cell_type": "code",
"execution_count": 1195,
"metadata": {},
"outputs": [],
"source": [
"#using x position (m) and y position (m) as the hit locations\n",
"xpos = target[' x position (m)']\n",
"ypos = target[' y position (m)']\n",
"xpos = xpos.values\n",
"ypos = ypos.values"
]
},
{
"cell_type": "code",
"execution_count": 1196,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The first quartile for ypos is -0.21785495681409592\n",
"The second quartile for ypos is -0.070851135369002\n",
"The third quartile for ypos is 0.13222390283592378\n"
]
}
],
"source": [
"Q1_ypos = np.percentile(ypos, q=25)\n",
"Q2_ypos = np.percentile(ypos, q=50)\n",
"Q3_ypos = np.percentile(ypos, q=75)\n",
"\n",
"print('The first quartile for ypos is {}'.format(Q1_ypos))\n",
"print('The second quartile for ypos is {}'.format(Q2_ypos))\n",
"print('The third quartile for ypos is {}'.format(Q3_ypos))"
]
},
{
"cell_type": "code",
"execution_count": 1221,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(5,5));\n",
"plt.hist(xpos, bins=20, color='g', histtype='bar', edgecolor='w');\n",
"plt.title('Frequency vs. X Position');\n",
"plt.xlabel('x position (m)');\n",
"plt.ylabel('Frequency');"
]
},
{
"cell_type": "code",
"execution_count": 1198,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(5,5));\n",
"plt.hist(ypos, bins=20, color='m', histtype='bar', edgecolor='w');\n",
"plt.title('Frequency vs. Y Position');\n",
"plt.xlabel('y position (m)');\n",
"plt.ylabel('Frequency');"
]
},
{
"cell_type": "code",
"execution_count": 1236,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.boxplot(xpos, labels=[''],vert=False);\n",
"plt.xlabel('X Position');\n",
"plt.title('Horizontal Distribution of Class Throws(X)');"
]
},
{
"cell_type": "code",
"execution_count": 1237,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.boxplot(ypos, labels = ['']);\n",
"plt.ylabel('Y Position');\n",
"plt.title('Vertical Distribution of Class Throws (Y)');"
]
},
{
"cell_type": "code",
"execution_count": 1201,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Statistical Analysis: \n",
"Mean of X Position (m) is: 0.012 Std deviation of X Position is: 0.257 \n",
"Mean of Y Position (m) is: -0.047 Std deviation of Y position is: 0.255\n"
]
}
],
"source": [
"print('Statistical Analysis: \\nMean of X Position (m) is:',round(np.mean(xpos),3),'Std deviation of X Position is:',\n",
" round(np.std(xpos),3),'\\nMean of Y Position (m) is:',round(np.mean(ypos),3), 'Std deviation of Y position is:',\n",
" round(np.std(ypos),3))"
]
},
{
"cell_type": "code",
"execution_count": 1202,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(6,6));\n",
"plt.scatter(xpos, ypos, color='b',label='raw data');\n",
"plt.scatter(np.mean(xpos),np.mean(ypos),color='r',label='mean')\n",
"plt.xlabel('x position (m)');\n",
"plt.ylabel('y position (m)');\n",
"plt.title('Raw data and mean location');\n",
"plt.axhline(lw ='0.5')\n",
"plt.axvline(lw='0.5')\n",
"plt.legend();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2. Monte Carlo Model"
]
},
{
"cell_type": "code",
"execution_count": 1204,
"metadata": {},
"outputs": [],
"source": [
"#variables\n",
"g = 9.81 #m/s^2\n",
"d = 3 #m distance to target\n",
"y0 = 0.3 #m initital height is 0.3 m above bull's eye\n",
"\n",
"#theta should (maybe) be between 0.5236 and 0.7854 radians"
]
},
{
"cell_type": "code",
"execution_count": 1205,
"metadata": {},
"outputs": [],
"source": [
"#range 1 below is 4<vel<6.3, 30<theta<45\n",
"#range 2 below is 10<vel<12, 0<theta<45\n",
"\n",
"#can be multiple correct ranges ^, because we have 2 independent variables, I used range 1"
]
},
{
"cell_type": "code",
"execution_count": 1206,
"metadata": {},
"outputs": [],
"source": [
"def montecarlo_robot(N):\n",
" '''Create random v0 and theta0 values and plug these values into eqn 2, \n",
" which yields random yimpact locations\n",
" \n",
" '''\n",
" \n",
" #velocity extreme range was given, below is a tightened range for more precision\n",
" vmin = 4\n",
" vmax = 6.3\n",
" \n",
" #theta range is found by trial and error (measured from horizontal(0) to vertical(90))\n",
" thetamin = math.radians(30) #convert degrees to radians\n",
" thetamax = math.radians(45) #convert degrees to radians\n",
"\n",
" #generate random velocities\n",
" random=np.random.rand(N) # random numbers 0 to 1, size N\n",
" v0=vmin+(vmax-vmin)*random # scales to numbers btwn vmin and vmax, size N\n",
" v0 = np.mean(v0) # averages velocities from array size N to single value\n",
" \n",
" #generate random thetas\n",
" theta0=thetamin+(thetamax-thetamin)*random # scales to numbers btwn thetamin and theta max, size N\n",
" theta0 = np.mean(theta0) # averages thetas from array size N to single value\n",
" \n",
" #plug in values below\n",
" yloc = (d / math.cos(theta0)) * (math.sin(theta0)-(g*d)/(2*(v0**2)*math.cos(theta0))) + y0\n",
" return yloc\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 1207,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mean value for yloc is -0.057650\n",
"standard deviation is 0.241536\n"
]
}
],
"source": [
"#use this as testing cell to call function N number of times\n",
"test_robot=np.zeros(100)\n",
"for i in range(0,100):\n",
" test_robot[i]=montecarlo_robot(10);\n",
"print('mean value for yloc is %f'%np.mean(test_robot))\n",
"print('standard deviation is %f'%np.std(test_robot))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3. Comparison"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Histograms"
]
},
{
"cell_type": "code",
"execution_count": 1208,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.subplot(1, 2, 1);\n",
"plt.hist(ypos, bins=20, color='m', histtype='bar', edgecolor='w',range=(-.5, .5));\n",
"plt.title('Frequency vs. Y Position (CLASS)',fontsize=12);\n",
"plt.xlabel('y position (m)');\n",
"plt.ylabel('Frequency');\n",
"\n",
"plt.subplot(1, 2, 2);\n",
"plt.hist(test_robot, bins=20, color='g', histtype='bar', edgecolor='w',range=(-.5, .5));\n",
"plt.title('Frequency vs. Y Position (ROBOT)',fontsize=12);\n",
"plt.xlabel('y position (m)');\n",
"plt.ylabel('Frequency');\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 1209,
"metadata": {},
"outputs": [],
"source": [
"# In order to get a similar standard deviation return from our Monte Carlo function,\n",
"# I needed to use a sample size of N=10, and called the function 100 times using\n",
"# my test_robot loop"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Standard deviation"
]
},
{
"cell_type": "code",
"execution_count": 1210,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Statistical Analysis (CLASS):\n",
"Mean of Y Position (m) is: -0.047 \n",
"Std deviation of Y position is: 0.255\n",
"\n",
"Statistical Analysis (ROBOT):\n",
"Mean of Y Position (m) is: -0.058 \n",
"Std deviation of Y position is: 0.242\n"
]
}
],
"source": [
"#CLASS\n",
"print('Statistical Analysis (CLASS):\\nMean of Y Position (m) is:',round(np.mean(ypos),3), \n",
" '\\nStd deviation of Y position is:',round(np.std(ypos),3))\n",
"print()\n",
"\n",
"#ROBOT\n",
"print('Statistical Analysis (ROBOT):\\nMean of Y Position (m) is:',round(np.mean(test_robot),3), \n",
" '\\nStd deviation of Y position is:',round(np.std(test_robot),3))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Quartiles"
]
},
{
"cell_type": "code",
"execution_count": 1211,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Class Quartiles:\n",
"The first quartile for ypos is -0.2179\n",
"The second quartile for ypos is -0.0709\n",
"The third quartile for ypos is 0.1322\n",
"\n",
"Robot Quartiles:\n",
"The first quartile for ypos is -0.1675\n",
"The second quartile for ypos is -0.0288\n",
"The third quartile for ypos is 0.1046\n"
]
}
],
"source": [
"#CLASS\n",
"Q1_ypos = np.percentile(ypos, q=25)\n",
"Q2_ypos = np.percentile(ypos, q=50)\n",
"Q3_ypos = np.percentile(ypos, q=75)\n",
"print('Class Quartiles:')\n",
"print('The first quartile for ypos is {:.4f}'.format(Q1_ypos))\n",
"print('The second quartile for ypos is {:.4f}'.format(Q2_ypos))\n",
"print('The third quartile for ypos is {:.4f}'.format(Q3_ypos))\n",
"print()\n",
"\n",
"#ROBOT\n",
"Q1_test_robot = np.percentile(test_robot, q=25)\n",
"Q2_test_robot = np.percentile(test_robot, q=50)\n",
"Q3_test_robot = np.percentile(test_robot, q=75)\n",
"print('Robot Quartiles:')\n",
"print('The first quartile for ypos is {:.4f}'.format(Q1_test_robot))\n",
"print('The second quartile for ypos is {:.4f}'.format(Q2_test_robot))\n",
"print('The third quartile for ypos is {:.4f}'.format(Q3_test_robot))"
]
},
{
"cell_type": "code",
"execution_count": 1212,
"metadata": {},
"outputs": [],
"source": [
"#quartile values are extremely close between class and robot"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Box Plots"
]
},
{
"cell_type": "code",
"execution_count": 1213,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"data = [ypos,test_robot]\n",
"fig, ax = plt.subplots();\n",
"ax.boxplot(data,labels = ('Class','Robot'));\n",
"plt.title('Boxplots of Class and Robot hit locations (y)');"
]
},
{
"cell_type": "code",
"execution_count": 1214,
"metadata": {},
"outputs": [],
"source": [
"#Above boxplots seem to show that the robot is slightly more accurate, \n",
"#with a smaller box and whisker size compared to the class"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Overall Scatter Plot Robot vs. Class"
]
},
{
"cell_type": "code",
"execution_count": 1215,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"x_robot = np.zeros(len(test_robot))\n",
"plt.figure(figsize=(6,6));\n",
"plt.scatter(xpos, ypos, color='b',label='class',facecolors='none');\n",
"plt.scatter(np.mean(xpos),np.mean(ypos),color='r',label='class mean')\n",
"plt.scatter(x_robot[::3],test_robot[::3],color='g',label='robot',facecolors='none')\n",
"plt.scatter(0,np.mean(test_robot),color='#00ffe4',label='robot mean');\n",
"plt.xlabel('x position (m)');\n",
"plt.ylabel('y position (m)');\n",
"plt.title('Hit data from Robot and Class, with means');\n",
"plt.axhline(lw ='0.5')\n",
"plt.axvline(lw='0.5')\n",
"plt.legend(loc= 'best',prop={'size': 10});"
]
},
{
"cell_type": "code",
"execution_count": 1216,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"length of ypos is: 54 \n",
"length of test_robot is: 100\n"
]
}
],
"source": [
"print('length of ypos is:',len(ypos),'\\nlength of test_robot is:',len(test_robot))"
]
},
{
"cell_type": "code",
"execution_count": 1217,
"metadata": {},
"outputs": [],
"source": [
"#plotted every 3rd value from test_robot array (because test_robot array is almost\n",
"#twice as large, see above)"
]
},
{
"cell_type": "code",
"execution_count": 1218,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"x_robot = np.zeros(len(test_robot))\n",
"x_class = np.zeros(len(ypos))\n",
"plt.figure(figsize=(6,6));\n",
"plt.scatter(x_class[::3], ypos[::3], color='b',label='class',facecolors='none');\n",
"plt.scatter(0,np.mean(ypos),color='r',label='class mean');\n",
"plt.scatter(x_robot[::5],test_robot[::5],color='g',label='robot',facecolors='none');\n",
"plt.scatter(0,np.mean(test_robot),color='#00ffe4',label='robot mean');\n",
"plt.xlabel('x position (m)');\n",
"plt.ylabel('y position (m)');\n",
"plt.title('Hit data from Robot and Class, with means, only Y variation');\n",
"plt.axhline(lw ='0.5')\n",
"plt.axvline(lw='0.5')\n",
"plt.legend(loc= 'best',prop={'size': 12});"
]
},
{
"cell_type": "code",
"execution_count": 1219,
"metadata": {},
"outputs": [],
"source": [
"#replotted above graph to eliminate x positions from class data\n",
"#can now see the direct comparison between y hits from class and robot, as well as\n",
"#mean values from class and robot, all normalized to x=0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"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
}