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speed_limit.py
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import numpy as np
import pylab as p
#0:mach, 1:capsule_radius, 2:tube_radius
data = np.array([
[0.69999999999999996, 0.70999999999999996, 0.71999999999999997, 0.72999999999999998, 0.73999999999999999, 0.75, 0.76000000000000001, 0.77000000000000002, 0.78000000000000003, 0.79000000000000004, 0.80000000000000004, 0.81000000000000005, 0.82000000000000006, 0.83000000000000007, 0.84000000000000008, 0.85000000000000009, 0.8600000000000001, 0.87000000000000011, 0.88000000000000012, 0.89000000000000012, 0.90000000000000013, 0.91000000000000014, 0.92000000000000015, 0.93000000000000016],
[76.929085596269772, 76.929180655650782, 76.92893154759993, 76.928625174314675, 76.928291042047164, 76.927926280389812, 76.927521921825758, 76.927373426115111, 76.927037462352516, 76.926680093006979, 76.926333125836067, 76.926088498481477, 76.925840539057262, 76.925420446687767, 76.925198202117997, 76.924981707956789, 76.924521823742353, 76.924240953088756, 76.923869718952886, 76.923406601513179, 76.922975652893427, 76.922651164033553, 76.922253692429081, 76.922020448822849],
[156.81475866356442, 163.06699238805285, 169.39639143715658, 176.17567076581227, 183.48993558360036, 191.40688842529258, 200.00123552289159, 209.60446824461877, 220.02372038597602, 231.5065877125065, 244.26303993238952, 258.63028292168445, 274.8267926914944, 292.93901895011976, 313.99136796898489, 338.34220682599886, 366.45335097633716, 400.29991204459554, 441.91861153248129, 491.97554075197723, 557.62439932580014, 646.71530896872468, 776.45804066298967, 992.88105048682928],
])
#0:mach, 1:capsule_radius, 2:tube_radius ---- Mach_c1_in = .65, c1_PR_des 13
data = np.array([
[0.69999999999999996, 0.70999999999999996, 0.71999999999999997, 0.72999999999999998, 0.73999999999999999, 0.75, 0.76000000000000001, 0.77000000000000002, 0.78000000000000003, 0.79000000000000004, 0.80000000000000004, 0.81000000000000005, 0.82000000000000006, 0.83000000000000007, 0.84000000000000008, 0.85000000000000009, 0.8600000000000001, 0.87000000000000011, 0.88000000000000012, 0.89000000000000012, 0.90000000000000013, 0.91000000000000014, 0.92000000000000015, 0.93000000000000016],
[76.92908559621543, 76.929180655653113, 76.928931547555152, 76.928625174279304, 76.928291042057836, 76.927926280416969, 76.927521921782386, 76.92737342610198, 76.92703746234973, 76.926680093013545, 76.926333125841651, 76.926088498444997, 76.925840539014729, 76.925420446529898, 76.925198197905161, 76.92498170892209, 76.924521823847513, 76.92424095316872, 76.923869719387554, 76.923406601553964, 76.922975652881306, 76.922651164028153, 76.922253692427603, 76.922020449164194],
[156.81475861600063, 163.0669923880096, 169.39639140732967, 176.17567073947652, 183.48993559762917, 191.40688843767666, 200.00123550617204, 209.60446822904663, 220.0237203915824, 231.50658773165995, 244.26303992772819, 258.63028289363103, 274.82679261741077, 292.93901871709897, 313.99136072483941, 338.3422083202567, 366.45335115502922, 400.29991217359503, 441.9186119743261, 491.97554081777361, 557.62439921482735, 646.7153090091789, 776.45804070888335, 992.88105221017327],
])
#0:mach, 1:capsule_radius, 2:tube_radius ---- Mach_c1_in = .65, c1_PR_des 15
data2 = np.array([
[0.69999999999999996, 0.70999999999999996, 0.71999999999999997, 0.72999999999999998, 0.73999999999999999, 0.75, 0.76000000000000001, 0.77000000000000002, 0.78000000000000003, 0.79000000000000004, 0.80000000000000004, 0.81000000000000005, 0.82000000000000006, 0.83000000000000007, 0.84000000000000008, 0.85000000000000009, 0.8600000000000001, 0.87000000000000011, 0.88000000000000012, 0.89000000000000012, 0.90000000000000013, 0.91000000000000014, 0.92000000000000015, 0.93000000000000016, 0.94000000000000017],
[155.93175216175968, 161.90775815558786, 168.16135931735269, 174.86816149320694, 182.09656156505773, 189.9066763043113, 198.63355972499093, 208.050898522587, 218.37651279956833, 229.77299314219405, 242.47206935679745, 256.81619737359483, 272.75628900411186, 290.56823116367968, 311.31544125085532, 335.48503571636502, 364.03768554760808, 397.33671923676832, 438.253060800215, 488.7354855968785, 553.98661765922532, 641.9473572566377, 770.39183611247699, 985.31963743906749, 1456.0149446680291],
])
#0:mach, 1:capsule_radius, 2:tube_radius ---- Mach_c1_in = .67, c1_PR_des 13
data2 = np.array([
[0.69999999999999996, 0.70999999999999996, 0.71999999999999997, 0.72999999999999998, 0.73999999999999999, 0.75, 0.76000000000000001, 0.77000000000000002, 0.78000000000000003, 0.79000000000000004, 0.80000000000000004, 0.81000000000000005, 0.82000000000000006, 0.83000000000000007, 0.84000000000000008],
[77.021469108162435, 77.02140802451072, 77.021140504859375, 77.020837159414228, 77.020510240499505, 77.020156201264314, 77.019768019730918, 77.019333669326016, 77.019205291571225, 77.018858764299353, 77.01849663548596, 77.018182577943008, 77.017987393090721, 77.017478467946376, 77.017261723730229],
[154.8510448032263, 160.88350066249996, 167.11573710362299, 173.80813129985762, 181.03102838200959, 188.85044969450243, 197.341545259236, 206.58957449445978, 217.02550145038671, 228.36667039796833, 240.93881953305544, 255.03595229814252, 271.07282668992588, 288.84902959115101, 309.58971082583338],
])
#0:mach, 1:capsule_radius, 2:tube_radius ---- Mach_c1_in = .7, c1_PR_des 16
data3 = np.array([
[0.69999999999999996, 0.70999999999999996, 0.71999999999999997, 0.72999999999999998, 0.73999999999999999, 0.75, 0.76000000000000001, 0.77000000000000002, 0.78000000000000003, 0.79000000000000004, 0.80000000000000004, 0.81000000000000005, 0.82000000000000006, 0.83000000000000007, 0.84000000000000008, 0.85000000000000009, 0.8600000000000001, 0.87000000000000011, 0.88000000000000012, 0.89000000000000012, 0.90000000000000013, 0.91000000000000014, 0.92000000000000015, 0.93000000000000016, 0.94000000000000017],
[76.190203147972539, 76.189894797961259, 76.189843484511485, 76.189566317339384, 76.189219159990486, 76.188828384244744, 76.188385031314269, 76.188201944684067, 76.187854799434476, 76.187486790856937, 76.18711667463397, 76.186801362480466, 76.18659219883962, 76.186166988793303, 76.18585517823621, 76.185515974523895, 76.185184137194582, 76.184865449258893, 76.184545999826184, 76.18388984961183, 76.183829811184594, 76.183257817824284, 76.182839632255067, 76.182526860902996, 76.181956393251227],
[150.26430351079694, 155.8316547653057, 162.1715893100295, 168.69827641315322, 175.6913336908913, 183.24414628539421, 191.42673694398539, 200.58173783678373, 210.54639698455193, 221.52667483219292, 233.71344815434932, 247.39228432093856, 262.94981169479934, 280.2934353903546, 300.31095056866445, 323.4397607887272, 350.57625627435954, 382.93376311655152, 422.56508467839666, 471.08536448968317, 533.84780804718889, 618.68556150184099, 742.74130384329032, 949.39840931728213, 1403.2136621120205],
])
#0:mach, 1:capsule_radius, 2:tube_radius ---- Mach_c1_in = .65, c1_PR_des 19
data4 = np.array([
[0.69999999999999996, 0.70999999999999996, 0.71999999999999997, 0.72999999999999998, 0.73999999999999999, 0.75, 0.76000000000000001, 0.77000000000000002, 0.78000000000000003, 0.79000000000000004, 0.80000000000000004, 0.81000000000000005, 0.82000000000000006, 0.83000000000000007, 0.84000000000000008, 0.85000000000000009, 0.8600000000000001, 0.87000000000000011, 0.88000000000000012, 0.89000000000000012, 0.90000000000000013, 0.91000000000000014, 0.92000000000000015, 0.93000000000000016, 0.94000000000000017],
[75.386941591996703, 75.386685528188963, 75.386345981344519, 75.385924594148761, 75.385778839225438, 75.385461508267696, 75.385121765669126, 75.384766536882069, 75.38441572633792, 75.384129986685309, 75.383957772359594, 75.383601202793059, 75.383319414236666, 75.382924919526175, 75.382598190639172, 75.382278719118887, 75.381969702220317, 75.381677136221086, 75.381425516371181, 75.380838540637086, 75.380239140723262, 75.380050841114283, 75.379722770875574, 75.379377093507259, 75.378939667657292],
[154.46502863325841, 160.29325688683195, 166.44145192503157, 173.01254260774533, 180.2706027709842, 188.09400151517707, 196.60062000705574, 205.89278023589173, 216.10471152036885, 227.440962692612, 240.16210250882185, 254.2042434396981, 270.04707152935248, 287.89870628653085, 308.42659953192708, 332.20321454437624, 360.11238268524846, 393.40970220674717, 433.94683369207934, 483.35552811587837, 547.23996013883414, 635.28005004211298, 762.93351362768135, 974.53893690665382, 1441.8124126980022],
])
#0:mach, 1:capsule_radius, 2:tube_radius ---- Mach_c1_in = .7, c1_PR_des 13
data5 = np.array([
[0.75, 0.76000000000000001, 0.77000000000000002, 0.78000000000000003, 0.79000000000000004, 0.80000000000000004, 0.81000000000000005, 0.82000000000000006, 0.83000000000000007, 0.84000000000000008, 0.85000000000000009, 0.8600000000000001, 0.87000000000000011, 0.88000000000000012, 0.89000000000000012, 0.90000000000000013, 0.91000000000000014, 0.92000000000000015, 0.93000000000000016, 0.94000000000000017],
[77.147637627818142, 77.147294201148227, 77.146899832599871, 77.14644407907501, 77.146312928358427, 77.145955838227465, 77.1455936204529, 77.145319855741931, 77.145012783933481, 77.144601277456346, 77.144340740672448, 77.144106073778119, 77.143667679627683, 77.143380519035233, 77.142844421654061, 77.142429187277003, 77.141913092234915, 77.141707703329317, 77.141327128215224, 77.140786075467517],
[185.14003407731957, 193.56970107355696, 202.67689708052842, 212.62021552750309, 223.92952144872282, 236.26955722826119, 250.04492518410032, 265.66194068717857, 283.35964040243834, 303.4285497775416, 326.95221777732576, 354.57881367704931, 386.99598952242286, 426.79158680786594, 475.64178335236096, 539.17943154433863, 624.90496361186388, 750.9362911676094, 959.50863835082941, 1430.9297166522156],
])
#0:mach, 1:capsule_radius, 2:tube_radius ---- Mach_c1_in = .75, c1_PR_des 13
data6 = np.array([
[0.78100000000000003, 0.79000000000000004, 0.80000000000000004, 0.81000000000000005, 0.82000000000000006, 0.83000000000000007, 0.84000000000000008, 0.85000000000000009, 0.8600000000000001, 0.87000000000000011, 0.88000000000000012, 0.89000000000000012, 0.90000000000000013, 0.91000000000000014, 0.92000000000000015, 0.93000000000000016, 0.94000000000000017],
[77.330274997066738, 77.329840355241757, 77.329456786344196, 77.329069474374577, 77.328697715545061, 77.328357787804009, 77.327831645980623, 77.327670287328658, 77.327374384266733, 77.32713410939246, 77.326690263964238, 77.326419660332178, 77.325728295315599, 77.32541847783871, 77.325108362734113, 77.324592671572617, 77.324193754335397],
[207.85433069968857, 217.5376116077482, 229.37767015039438, 242.72603490164545, 257.76402446270475, 274.71309792366827, 294.2259682686776, 317.16542726644639, 343.89014269110925, 375.86284204201951, 414.08563015196063, 462.31646648045103, 523.38207474237447, 606.89624971748492, 729.0066984405679, 930.8090511801779, 1377.1634709833593],
])
#0:mach, 1:capsule_radius, 2:tube_radius ---- Mach_c1_in = .61, c1_PR_des 13
data7 = np.array([
[0.69999999999999996, 0.70999999999999996, 0.71999999999999997, 0.72999999999999998, 0.73999999999999999, 0.75, 0.76000000000000001, 0.77000000000000002, 0.78000000000000003, 0.79000000000000004, 0.80000000000000004, 0.81000000000000005, 0.82000000000000006, 0.83000000000000007, 0.84000000000000008, 0.85000000000000009, 0.8600000000000001, 0.87000000000000011, 0.88000000000000012, 0.89000000000000012, 0.90000000000000013, 0.91000000000000014, 0.92000000000000015, 0.93000000000000016, 0.94000000000000017],
[76.728017085303236, 76.727721028153269, 76.727424931156492, 76.727099814623443, 76.726743297030083, 76.726346171158383, 76.726259084957675, 76.725938029364514, 76.725590875584814, 76.725253271602412, 76.725023073025838, 76.724851777239593, 76.72443076194574, 76.724236065256335, 76.724030820938069, 76.723558566746874, 76.723283874432937, 76.722999038356747, 76.722297026415248, 76.722139000134163, 76.72177168150904, 76.721421229869961, 76.721121124178325, 76.72050242424605, 76.720432900189294],
[161.74094961960913, 167.79713899788146, 174.26388171838252, 181.22083202522188, 188.72770146676629, 196.84925744170883, 205.91686354404632, 215.68068602207111, 226.38566953351491, 238.21051036802135, 251.44151824933499, 266.32350200631549, 282.76020020416496, 301.70187326432375, 323.33749004079459, 348.00774764411909, 377.23826680844394, 412.19851386198843, 453.4372761849142, 506.34255806340957, 573.98110834652186, 665.61933433004526, 799.50693487416845, 1022.3784074064833, 1511.1360793444894],
])
#0:mach, 1:capsule_radius, 2:tube_radius ---- Mach_c1_in = .55, c1_PR_des 13
data8 = np.array([
[0.69999999999999996, 0.70999999999999996, 0.71999999999999997, 0.72999999999999998, 0.73999999999999999, 0.75, 0.76000000000000001, 0.77000000000000002, 0.78000000000000003, 0.79000000000000004, 0.80000000000000004, 0.81000000000000005, 0.82000000000000006, 0.83000000000000007, 0.84000000000000008, 0.85000000000000009, 0.8600000000000001, 0.87000000000000011, 0.88000000000000012, 0.89000000000000012, 0.90000000000000013, 0.91000000000000014, 0.92000000000000015, 0.93000000000000016, 0.94000000000000017],
[76.383930307437893, 76.383714325343874, 76.383496014082866, 76.38325462489091, 76.38299149379759, 76.382698877616349, 76.382578432312556, 76.382299136681098, 76.381937341629154, 76.38164088246684, 76.381428076206859, 76.381096737480689, 76.380887221275259, 76.380661239416739, 76.380392869077056, 76.380016289409554, 76.379704442526503, 76.379369492112701, 76.379054315469588, 76.378758441190982, 76.378431369120705, 76.378091356136764, 76.377729977464426, 76.377365160011067, 76.377129723793402],
[-169.00026986905911, -175.30664848493868, -182.10694166195665, -189.4280559404375, -197.33586462797501, -205.90057339373914, -215.43233885250876, -225.67057375287627, -236.82954496582198, -249.19477555182917, -263.0470428325238, -278.40056650997246, -295.89396428628015, -315.63824895202657, -338.03823476833298, -364.06938357527105, -394.54967896708246, -430.81201684238351, -474.89187256554533, -529.89735797389187, -600.73317759210192, -696.59535352143939, -836.31403010916938, -1068.0631819812488, -1581.2019140503339],
])
p.tick_params(axis='both', which='major', labelsize=15)
p.xlabel('Max Pod Mach', fontsize=18)
p.ylabel('Radius (cm)', fontsize=18)
p.title('Tube and Pod Radius vs Max Pod Mach', fontsize=20)
p.plot(data8[0],-1*data8[2], label="Tube (c1MN = .55)", lw=3) #'--',
p.plot(data[0],1*data[2], label="Tube (c1MN = .65)", lw=3)
p.plot(data6[0],1*data6[2], label="Tube (c1MN = .75)", lw=3) #':',
p.plot(data[0],1*data[1], label="Pod (c1MN = .55,.65,.75)", lw=3)
p.xlim([0.79,0.92])
p.ylim([0,1000])
p.legend(loc="best")
p.gcf().set_size_inches(11,5.5)
#p.gcf().savefig('test2png.png',dpi=130)
p.show()