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BrianChevalier/gist:81bf87c9498f78b37a43dd430101b463

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Import Packages"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from numpy import array\n",
"from numpy import matrix\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Input Physical Probelm Data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"gravity = 9.81 # acceleration of gravity\n",
"e3 = array([0, 0, 1]) # direction of gravity (e3)\n",
"mass = 2.0 # mass of particle"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Initial conditions and analysis start and end times "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"xo = array([0.0, 0.0, 55.0]) # initial position of particle\n",
"vo = array([25.0, 15.0, 55.0]) # initial velocity of particle\n",
"to = 0.0 # initial time (usually zero)\n",
"tf = 12.5 # final time (end of analysis)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#.... Numerical analysis parameters.\n",
"dt = 0.1 # analysis time step\n",
"beta = 0.5 # numerical integration parameter\n",
"\n",
"#.... Set time and output counters to get ready to start analysis\n",
"t = to; \n",
"out = 0; \n",
"iout = 0;\n",
" \n",
"#.... Initialize position and velocity (put in 'xold' and 'vold' arrays)\n",
"xold = xo;\n",
"vold = vo; \n",
"\n",
"#.... Compute initial acceleration from the equation of motion\n",
"aold = -gravity*e3;\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Note: All arguments are required.\n",
"# We're not fancy enough to implement all.\n",
"def frange(start, stop, step):\n",
" i = start\n",
" while i < stop:\n",
" yield i\n",
" i += step\n",
"\n",
"#for i in frange(0.5, 1, 0.1):\n",
" #print(i)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def update(xold, vold, anew, aold):\n",
" beta = 0.5\n",
" dt = 0.1\n",
" #....... Compute new velocity and position by trapezoidal rule \n",
" vnew = vold + dt*(beta*aold + (1-beta)*anew)\n",
" xnew = xold + dt*(beta*vold + (1-beta)*vnew)\n",
" \n",
" #....... Put current state (new) into old slot to get ready for new step\n",
" aold = anew\n",
" vold = vnew\n",
" xold = xnew\n",
" return (xold, vold, aold)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Set up the history matrix"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" xx xy xz\n",
"t \n",
"0.0 0.0 0.0 55.0\n"
]
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"#history = {'t' : [to], 'xold' : [xo], 'vold' : [vo], 'aold' : [aold]}\n",
"history = {'t' : [to], 'xx' : [xo[0]], 'xy' : [xo[1]], 'xz' : [xo[2]]}\n",
"\n",
"#new = {'t' : to+1, 'x': 1,'y': 1,'z': 3}\n",
"\n",
"#for item in history:\n",
"# history[item].append(new[item])\n",
"\n",
"df = pd.DataFrame(history)\n",
"print(df.set_index('t'))\n",
"\n",
"#for item in plt.style.available:\n",
"# plt.style.use(item)\n",
"# df.plot.line(['t'])\n",
"# plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Compute motion by numerical integration\n",
"- loop over time steps"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" xx xy xz\n",
"t \n",
"0.0 0 0 55\n",
"[0.0] [630.0] [378.0] [-1673.8712]\n",
"[0.1] [632.5] [379.5] [-1693.14145]\n",
"[0.2] [635.0] [381.0] [-1712.5098]\n",
"[0.30000000000000004] [637.5] [382.5] [-1731.97625]\n",
"[0.4] [640.0] [384.0] [-1751.5408]\n",
"[0.5] [642.5] [385.5] [-1771.20345]\n",
"[0.6] [645.0] [387.0] [-1790.9642]\n",
"[0.7] [647.5] [388.5] [-1810.82305]\n",
"[0.7999999999999999] [650.0] [390.0] [-1830.78]\n",
"[0.8999999999999999] [652.5] [391.5] [-1850.83505]\n",
"[0.9999999999999999] [655.0] [393.0] [-1870.9882]\n",
"[1.0999999999999999] [657.5] [394.5] [-1891.23945]\n",
"[1.2] [660.0] [396.0] [-1911.5888]\n",
"[1.3] [662.5] [397.5] [-1932.03625]\n",
"[1.4000000000000001] [665.0] [399.0] [-1952.5818]\n",
"[1.5000000000000002] [667.5] [400.5] [-1973.22545]\n",
"[1.6000000000000003] [670.0] [402.0] [-1993.9672]\n",
"[1.7000000000000004] [672.5] [403.5] [-2014.80705]\n",
"[1.8000000000000005] [675.0] [405.0] [-2035.745]\n",
"[1.9000000000000006] [677.5] [406.5] [-2056.78105]\n",
"[2.0000000000000004] [680.0] [408.0] [-2077.9152]\n",
"[2.1000000000000005] [682.5] [409.5] [-2099.14745]\n",
"[2.2000000000000006] [685.0] [411.0] [-2120.4778]\n",
"[2.3000000000000007] [687.5] [412.5] [-2141.90625]\n",
"[2.400000000000001] [690.0] [414.0] [-2163.4328]\n",
"[2.500000000000001] [692.5] [415.5] [-2185.05745]\n",
"[2.600000000000001] [695.0] [417.0] [-2206.7802]\n",
"[2.700000000000001] [697.5] [418.5] [-2228.60105]\n",
"[2.800000000000001] [700.0] [420.0] [-2250.52]\n",
"... ... ... ...\n",
"[9.599999999999982] [1815.0] [1089.0] [-21805.0778]\n",
"[9.699999999999982] [1817.5] [1090.5] [-21870.84745]\n",
"[9.799999999999981] [1820.0] [1092.0] [-21936.7152]\n",
"[9.89999999999998] [1822.5] [1093.5] [-22002.68105]\n",
"[9.99999999999998] [1825.0] [1095.0] [-22068.745]\n",
"[10.09999999999998] [1827.5] [1096.5] [-22134.90705]\n",
"[10.19999999999998] [1830.0] [1098.0] [-22201.1672]\n",
"[10.29999999999998] [1832.5] [1099.5] [-22267.52545]\n",
"[10.399999999999979] [1835.0] [1101.0] [-22333.9818]\n",
"[10.499999999999979] [1837.5] [1102.5] [-22400.53625]\n",
"[10.599999999999978] [1840.0] [1104.0] [-22467.1888]\n",
"[10.699999999999978] [1842.5] [1105.5] [-22533.93945]\n",
"[10.799999999999978] [1845.0] [1107.0] [-22600.7882]\n",
"[10.899999999999977] [1847.5] [1108.5] [-22667.73505]\n",
"[10.999999999999977] [1850.0] [1110.0] [-22734.78]\n",
"[11.099999999999977] [1852.5] [1111.5] [-22801.92305]\n",
"[11.199999999999976] [1855.0] [1113.0] [-22869.1642]\n",
"[11.299999999999976] [1857.5] [1114.5] [-22936.50345]\n",
"[11.399999999999975] [1860.0] [1116.0] [-23003.9408]\n",
"[11.499999999999975] [1862.5] [1117.5] [-23071.47625]\n",
"[11.599999999999975] [1865.0] [1119.0] [-23139.1098]\n",
"[11.699999999999974] [1867.5] [1120.5] [-23206.84145]\n",
"[11.799999999999974] [1870.0] [1122.0] [-23274.6712]\n",
"[11.899999999999974] [1872.5] [1123.5] [-23342.59905]\n",
"[11.999999999999973] [1875.0] [1125.0] [-23410.625]\n",
"[12.099999999999973] [1877.5] [1126.5] [-23478.74905]\n",
"[12.199999999999973] [1880.0] [1128.0] [-23546.9712]\n",
"[12.299999999999972] [1882.5] [1129.5] [-23615.29145]\n",
"[12.399999999999972] [1885.0] [1131.0] [-23683.7098]\n",
"[12.499999999999972] [1887.5] [1132.5] [-23752.22625]\n",
"\n",
"[505 rows x 3 columns]\n"
]
}
],
"source": [
"\n",
"\n",
"for t in frange(to, tf, dt): \n",
" \n",
" #new = {'t' : t, 'xold[0]': xold,'vold': vold,'aold': aold}\n",
" new = {'t' : [t], 'xx' : [xold[0]], 'xy' : [xold[1]], 'xz' : [xold[2]]}\n",
"\n",
" for item in history:\n",
" history[item].append(new[item])\n",
"\n",
" \n",
" #Compute new acceleration from equation of motion \n",
" anew = -gravity*e3\n",
" (xold, vold, aold) = update(xold, vold, anew, aold)\n",
"\n",
" \n",
" \n",
"df = pd.DataFrame(history)\n",
"print(df.set_index('t'))\n"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"ename": "TypeError",
"evalue": "float() argument must be a string or a number, not 'dict'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-29-7cb7283dadb7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;31m#for item in plt.style.available:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m# plt.style.use(item)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# df.plot.line('t')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m# plt.show()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/BrianBadahdah/anaconda/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 3316\u001b[0m mplDeprecation)\n\u001b[1;32m 3317\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3318\u001b[0;31m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3319\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3320\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/BrianBadahdah/anaconda/lib/python3.6/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1890\u001b[0m warnings.warn(msg % (label_namer, func.__name__),\n\u001b[1;32m 1891\u001b[0m RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1892\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1893\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1894\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/BrianBadahdah/anaconda/lib/python3.6/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1405\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1406\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1407\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1408\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1409\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/BrianBadahdah/anaconda/lib/python3.6/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36madd_line\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 1785\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1786\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1787\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_line_limits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1788\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1789\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'_line%d'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlines\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/BrianBadahdah/anaconda/lib/python3.6/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_update_line_limits\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 1807\u001b[0m \u001b[0mFigures\u001b[0m \u001b[0mout\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdata\u001b[0m \u001b[0mlimit\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mgiven\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mupdating\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataLim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1808\u001b[0m \"\"\"\n\u001b[0;32m-> 1809\u001b[0;31m \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1810\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvertices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1811\u001b[0m \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/BrianBadahdah/anaconda/lib/python3.6/site-packages/matplotlib/lines.py\u001b[0m in \u001b[0;36mget_path\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 987\u001b[0m \"\"\"\n\u001b[1;32m 988\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invalidy\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invalidx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 989\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 990\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_path\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 991\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/BrianBadahdah/anaconda/lib/python3.6/site-packages/matplotlib/lines.py\u001b[0m in \u001b[0;36mrecache\u001b[0;34m(self, always)\u001b[0m\n\u001b[1;32m 683\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myconv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 684\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 685\u001b[0;31m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myconv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 686\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 687\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/BrianBadahdah/anaconda/lib/python3.6/site-packages/numpy/core/numeric.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m 480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 481\u001b[0m \"\"\"\n\u001b[0;32m--> 482\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 483\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 484\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0masanyarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: float() argument must be a string or a number, not 'dict'"
]
}
],
"source": [
"plt.plot(history)\n",
"#for item in plt.style.available:\n",
"# plt.style.use(item)\n",
"# df.plot.line('t')\n",
"# plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2\n",
"3\n",
"4\n"
]
}
],
"source": [
"def f(x):\n",
" y0 = x + 1\n",
" y1 = x * 3\n",
" y2 = y0 ** 2\n",
" return (y0,y1,y2)\n",
"\n",
"(y0,y1,y2) = f(1)\n",
"print(y0)\n",
"print(y1)\n",
"print(y2)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(1.105, 1.1, 1)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"update(1,1,1,1)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['bmh', 'classic', 'dark_background', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark-palette', 'seaborn-dark', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'seaborn']\n"
]
}
],
"source": [
"print(plt.style.available)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import seaborn\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"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": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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