{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Original Tweet:\n",
"https://twitter.com/akpn_aopn/status/615839348525629440/photo/1
\n",
"\n",
"Original functions\n",
"
\n",
"\n",
"
\n"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(-10, 10)"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"plt.figure(1)\n",
"\n",
"\n",
"#I \n",
"y0=np.arange(2,6.1,0.1)\n",
"x0=y0*0-6\n",
"\n",
"x=np.arange(-7,-4.9,0.1)\n",
"y=x*0+6\n",
"x0=np.append(x0,x)\n",
"y0=np.append(y0,y)\n",
"\n",
"x=np.arange(-7,-4.9,0.1)\n",
"y=x*0+2\n",
"x0=np.append(x0,x)\n",
"y0=np.append(y0,y)\n",
"\n",
"#L in love\n",
"y=np.arange(2,6.1,0.1)\n",
"x=y*0-3\n",
"x0=np.append(x0,x)\n",
"y0=np.append(y0,y)\n",
"\n",
"# O in love\n",
"theta=np.arange(-1*np.pi,1*np.pi,2*np.pi/60)\n",
"x=1*np.cos(theta)-1;\n",
"y=1*np.sin(theta)+3;\n",
"x0=np.append(x0,x)\n",
"y0=np.append(y0,y)\n",
"\n",
"\n",
"# v in love\n",
"x=np.arange(1,2.1,0.1)\n",
"y=-2*x+6\n",
"x0=np.append(x0,x)\n",
"y0=np.append(y0,y)\n",
"\n",
"x=np.arange(2,3.1,0.1)\n",
"y=2*x-2\n",
"x0=np.append(x0,x)\n",
"y0=np.append(y0,y)\n",
"########################\n",
"\n",
"# e in love\n",
"x=np.arange(4,6.1,0.1)\n",
"y=x*0+3\n",
"x0=np.append(x0,x)\n",
"y0=np.append(y0,y)\n",
"\n",
"#wrong function\n",
"x=np.arange(4,6.1,0.1)\n",
"y=1*(x-5)**2+4\n",
"x0=np.append(x0,x)\n",
"y0=np.append(y0,y)\n",
"\n",
"#wrong function\n",
"x=np.arange(4,5.1,0.1)\n",
"y=-1*(x-5)**2+2\n",
"x0=np.append(x0,x)\n",
"y0=np.append(y0,y)\n",
"########################\n",
"\n",
"# y in you\n",
"x=np.arange(-4,-2.9,0.1)\n",
"y=-2*x-8\n",
"x0=np.append(x0,x)\n",
"y0=np.append(y0,y)\n",
"\n",
"x=np.arange(-4,-1.9,0.1)\n",
"y=2*x+4\n",
"x0=np.append(x0,x)\n",
"y0=np.append(y0,y)\n",
"\n",
"# O in you\n",
"theta=np.arange(-1*np.pi,1*np.pi,2*np.pi/60)\n",
"x=1*np.cos(theta);\n",
"y=1*np.sin(theta)+1;\n",
"x0=np.append(x0,x)\n",
"y0=np.append(y0,y)\n",
"\n",
"# u in you\n",
"x=np.arange(2,4.1,0.1)\n",
"y=2*(x-3)**2-2\n",
"x0=np.append(x0,x)\n",
"y0=np.append(y0,y)\n",
"\n",
"y=np.arange(-2,0.1,0.1)\n",
"x=y*0+4\n",
"x0=np.append(x0,x)\n",
"y0=np.append(y0,y)\n",
"\n",
"# dot at 5,-2\n",
"x0=np.append(x0,5)\n",
"y0=np.append(y0,-2)\n",
"\n",
"plt.plot(x0,y0,'b.')\n",
"plt.axis('equal')\n",
"plt.ylim((-10,10))\n",
"plt.xlim((-10,10))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Corrected Version"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(-10, 10)"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"plt.figure(1)\n",
"\n",
"\n",
"#I \n",
"y0=np.arange(2,6.1,0.1)\n",
"x0=y0*0-6\n",
"\n",
"x=np.arange(-7,-4.9,0.1)\n",
"y=x*0+6\n",
"x0=np.append(x0,x)\n",
"y0=np.append(y0,y)\n",
"\n",
"x=np.arange(-7,-4.9,0.1)\n",
"y=x*0+2\n",
"x0=np.append(x0,x)\n",
"y0=np.append(y0,y)\n",
"\n",
"#L in love\n",
"y=np.arange(2,6.1,0.1)\n",
"x=y*0-3\n",
"x0=np.append(x0,x)\n",
"y0=np.append(y0,y)\n",
"\n",
"# O in love\n",
"theta=np.arange(-1*np.pi,1*np.pi,2*np.pi/60)\n",
"x=1*np.cos(theta)-1;\n",
"y=1*np.sin(theta)+3;\n",
"x0=np.append(x0,x)\n",
"y0=np.append(y0,y)\n",
"\n",
"\n",
"# v in love\n",
"x=np.arange(1,2.1,0.1)\n",
"y=-2*x+6\n",
"x0=np.append(x0,x)\n",
"y0=np.append(y0,y)\n",
"\n",
"x=np.arange(2,3.1,0.1)\n",
"y=2*x-2\n",
"x0=np.append(x0,x)\n",
"y0=np.append(y0,y)\n",
"########################\n",
"\n",
"# e in love\n",
"x=np.arange(4,6.1,0.1)\n",
"y=x*0+3\n",
"x0=np.append(x0,x)\n",
"y0=np.append(y0,y)\n",
"\n",
"#corrected\n",
"x=np.arange(4,6.1,0.1)\n",
"y=-1*(x-5)**2+4\n",
"x0=np.append(x0,x)\n",
"y0=np.append(y0,y)\n",
"\n",
"#corrected\n",
"x=np.arange(4,5.1,0.1)\n",
"y=1*(x-5)**2+2\n",
"x0=np.append(x0,x)\n",
"y0=np.append(y0,y)\n",
"########################\n",
"\n",
"# y in you\n",
"x=np.arange(-4,-2.9,0.1)\n",
"y=-2*x-8\n",
"x0=np.append(x0,x)\n",
"y0=np.append(y0,y)\n",
"\n",
"x=np.arange(-4,-1.9,0.1)\n",
"y=2*x+4\n",
"x0=np.append(x0,x)\n",
"y0=np.append(y0,y)\n",
"\n",
"# O in you corrected\n",
"theta=np.arange(-1*np.pi,1*np.pi,2*np.pi/60)\n",
"x=1*np.cos(theta);\n",
"y=1*np.sin(theta)-1;\n",
"x0=np.append(x0,x)\n",
"y0=np.append(y0,y)\n",
"\n",
"# u in you\n",
"x=np.arange(2,4.1,0.1)\n",
"y=2*(x-3)**2-2\n",
"x0=np.append(x0,x)\n",
"y0=np.append(y0,y)\n",
"\n",
"y=np.arange(-2,0.1,0.1)\n",
"x=y*0+4\n",
"x0=np.append(x0,x)\n",
"y0=np.append(y0,y)\n",
"\n",
"# dot at 5,-2\n",
"x0=np.append(x0,5)\n",
"y0=np.append(y0,-2)\n",
"\n",
"plt.plot(x0,y0,'b.')\n",
"plt.axis('equal')\n",
"plt.ylim((-10,10))\n",
"plt.xlim((-10,10))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.9"
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"nbformat": 4,
"nbformat_minor": 0
}