2017-11-11 113 views
0

我有ASCII的数据进行插值3D如何在以下列方式称为“Testdata_interpolate.csv”文件中使用蟒蛇的GridData

x,y,z,v 
val11,val12,val13,val14 
val21,val22,val23,val24 
... 

其中x,在这个Y,Z是代表坐标和VA标量值点在太空中。 x,y,z是随机分布的。

对于进一步的计算,v应插入V到规则网格(X,Y,Z)。因此,我试图在该代码中使用Phytons griddata:

from mpl_toolkits.mplot3d import Axes3D 
import matplotlib.pyplot as plt 
import numpy as np 
import pandas as pd 
from scipy.interpolate import griddata as gd 

#read values 
f=open('Testdata_interpolate.csv','r') 
headers = ["x","y","z","v"] 
data = pd.read_csv(f, delimiter = ",",header=1,names=headers) 
x=data.x 
y=data.y 
z=data.z 
v=data.v 

#generate new grid 
X,Y,Z=np.mgrid[0:1:10j, 0:1:10j, 0:1:10j] 

#interpolate "data.v" on new grid "inter_mesh" 
V = gd((x,y,z), v, (X,Y,Z), method='nearest') 

#Plot values 
fig = plt.figure() 
ax=fig.gca(projection='3d') 
sc=ax.scatter(X, Y, Z, c=V, cmap=plt.hot()) 
plt.colorbar(sc) 
plt.show() 

试试这会导致错误。

ValueError: Buffer has wrong number of dimensions (expected 1, got 3) 

因为,失败的我试图生成

X=np.linspace(0,1,10) 
Y=np.linspace(0,1,10) 
Z=np.linspace(0,1,10) 

新的网格使用这个符号进行内插,但我只有(正确)插值直线距离的一个角落体积到另一个。

什么应该是正确生成的X,Y,Z栅格插值?

EDIT1: 我找到了解决办法,这对我很有用。新电网的产生X,Y,Z与此代码完成的:

xi,yi,zi=np.ogrid[0:1:10j, 0:1:10j, 0:1:20j] 
X1=xi.reshape(xi.shape[0],) 
Y1=yi.reshape(yi.shape[1],) 
Z1=zi.reshape(zi.shape[2],) 
ar_len=len(X1)*len(Y1)*len(Z1)+1 
X=np.arange(ar_len,dtype=float) 
Y=np.arange(ar_len,dtype=float) 
Z=np.arange(ar_len,dtype=float) 
l=0 
for i in range(0,len(X1)): 
    for j in range(0,len(Y1)): 
     for k in range(0,len(Z1)): 
      l=l+1 
      X[l]=X1[i] 
      Y[l]=Y1[j] 
      Z[l]=Z1[k] 

是否有人知道这是否可以简化和/或如何在一个良好的Python风格写?

回答

0

下面是完整的解决方案:

x,y,z,v 
0.6306633776,0.5399341613,0.0223922509,0.3692590427 
0.6201798089,0.713310769,0.2586306574,0.5222175648 
0.0133608796,0.5962421199,0.1336130457,0.2493248448 
0.625635438,0.2813785867,0.1412831986,0.4275538206 
0.4478070925,0.2533920221,0.8647321435,0.4226642295 
0.9496287584,0.5412782144,0.0181683893,0.2056985056 
0.353725578,0.8192529518,0.9945807043,0.2594541163 
0.6531066992,0.7549814415,0.8023866946,0.441885203 
0.2034177743,0.7157782351,0.5439790104,0.4966266806 
0.3936962193,0.5198892568,0.2851481802,0.6254897914 
0.7136407618,0.6884816985,0.6673836339,0.5355938576 
0.2679115187,0.1078689156,0.0960926992,0.2571141146 
0.9544471861,0.304474494,0.791678864,0.2917179703 
0.2513473134,0.8128617403,0.0276061489,0.2472632705 
0.5621786268,0.0105501017,0.3463567749,0.3492724404 
0.0236232636,0.7537357578,0.0049020726,0.1336057239 
0.5544935236,0.3265725226,0.4704909401,0.6818585809 
0.9169167232,0.1903847884,0.0384998546,0.1712855118 
0.6509500724,0.3350786431,0.4485415479,0.6366066805 
0.4892437129,0.4890809474,0.8062363257,0.5594057524 
0.2930552054,0.0103116581,0.5241043581,0.3338582869 
0.956388208,0.4938978335,0.6916433722,0.3709954906 
0.0722823141,0.0776713405,0.0164990656,0.094613018 
0.3340717987,0.1857906293,0.8615063092,0.3591264906 
0.9461038462,0.4192224785,0.1804858052,0.311387711 
0.6194368915,0.7870312196,0.1767282374,0.4175204154 
0.2598983985,0.0137079303,0.5218954065,0.3232472126 
0.1628694471,0.3909106815,0.4686704314,0.5103021533 
0.405955568,0.7681639045,0.5503229067,0.5774277084 
0.2701853069,0.2277688606,0.8209407311,0.3865178029 
0.8193266613,0.5615418105,0.7331885243,0.4658581901 
0.0428746114,0.5050042546,0.402704194,0.3986335582 
0.3438295609,0.7730675675,0.9674056518,0.3026222215 
0.9552899428,0.2841943004,0.072097092,0.2049944433 
0.3554257909,0.4985691797,0.5750067732,0.7031458004 
0.7771471706,0.9535270029,0.804036098,0.2537058423 
0.6300111088,0.673049493,0.9747896229,0.344226399 
0.0862787074,0.2732020325,0.2496220012,0.331898295 
0.4397787892,0.8737769038,0.5699767084,0.4810156535 
0.9463236536,0.1280889919,0.8617868226,0.1816188677 
0.0510125536,0.854,0.1709006799,0.2063127017 
0.2290255208,0.4523648438,0.0913191637,0.3733629408 
0.3739807131,0.5281513263,0.1555660222,0.4981828792 
0.381137688,0.3792761887,0.6625859691,0.6312133829 
0.45184571,0.3788999769,0.0564337434,0.4037104947 
0.9339652646,0.1587308487,0.284979922,0.2735525196 
0.3656675596,0.0956345971,0.5860244642,0.431333798 
0.3683657888,0.1729067734,0.6504682948,0.4826740608 
0.2295411313,0.4762862368,0.6254150038,0.5669614216 
0.7909070767,0.3906802474,0.8597905841,0.3906024342 
0.5232714652,0.4474381413,0.9464774509,0.4158627396 
0.5506931653,0.0136374333,0.4617536616,0.3755347049 
0.6163667089,0.4232951417,0.0508881645,0.3957847346 
0.2410430499,0.2445243536,0.8316876458,0.3737417434 
0.4896791476,0.4842978234,0.7950337037,0.5703939361 
0.6027538624,0.6198397316,0.5598951721,0.6971842087 
0.9190498489,0.3987407853,0.0112006215,0.214273122 
0.3530254834,0.520464629,0.0603594208,0.4020166232 
0.009953508,0.9841304369,0.5740347477,0.1731985942 
0.2196149815,0.1010876149,0.9944747656,0.1715816383 
0.671561366,0.6656832779,0.0124795495,0.3232911773 
0.9913571495,0.4012237578,0.6751740861,0.3351068085 
0.2136069964,0.2133224305,0.8533352507,0.3283909734 
0.9030364749,0.3387038174,0.8082359016,0.3336118149 
0.1987606561,0.4216349495,0.7621533491,0.4590725787 
0.5490367836,0.5750362376,0.5902005466,0.7388594425 
0.83009432,0.3577592844,0.7607350977,0.4219787031 
0.6959727876,0.7086074705,0.8989627659,0.3750124723 
0.3955585478,0.9522785004,0.3537629556,0.3793539211 
0.1740074199,0.90613637,0.5406577303,0.3436548798 
0.862232854,0.1641101963,0.0871443357,0.2222207748 
0.5892735408,0.028922464,0.4544688624,0.3844063692 
0.2092728323,0.5754606259,0.5228441169,0.5647971751 
0.6361810902,0.0517793576,0.8129563089,0.3026528052 
0.8755833265,0.0690054237,0.4029022524,0.2861572011 
0.3604205895,0.7272419375,0.6232588424,0.5722328712 
0.7245441675,0.8508843923,0.1043419443,0.2914949546 
0.378985798,0.705913034,0.3252829289,0.5701019853 
0.2450076354,0.4675920206,0.7503598065,0.5072059285 
0.3954747342,0.7524948808,0.538161918,0.5900987324 
0.8431238907,0.89791946,0.2850646916,0.2983361509 
0.0075750139,0.2701604665,0.9613816069,0.1531568568 
0.3915120547,0.5508184589,0.6184136179,0.6975797599 
0.7743560073,0.7732019407,0.7936674234,0.3800716254 
0.5731431141,0.0463964108,0.1102689808,0.2635335793 
0.3454076014,0.7799256704,0.4952835517,0.5462138412 
0.3992618618,0.039215973,0.1443308299,0.2752880251 
0.7309,0.7477130298,0.308651145,0.4770633087 
0.8798876626,0.0781818882,0.5140124665,0.2981860736 
0.6337635873,0.7414852159,0.5548462313,0.5845721823 
0.8779629321,0.7226434117,0.0649765363,0.2482297412 
0.3596338067,0.1629595478,0.9852069955,0.2587982938 
0.1470704943,0.7341692382,0.3173635881,0.4047765262 
0.5495922826,0.5831609034,0.4090942864,0.7332135854 
0.8372890817,0.6471977771,0.5592359801,0.4932788256 
0.8986365935,0.6013925502,0.3293837716,0.4207148601 
0.7529078043,0.7790015346,0.3484371136,0.4600998905 
0.5088906595,0.0117897109,0.9908112199,0.1736931253 
0.3322010292,0.8568216281,0.5259938139,0.4708623397 
0.0764332382,0.9636281886,0.1978059367,0.1691171237 
0.3475818783,0.3942058018,0.8030983983,0.5106490747 
0.6377423069,0.8868203075,0.3145786878,0.4154879953 
0.0045190767,0.8130553286,0.5093343537,0.2798580946 
0.7140381144,0.7407960293,0.027507628,0.2941477212 
0.3959185294,0.8889921485,0.7900283924,0.3697751519 
0.6414700936,0.0623275826,0.3796580266,0.3905749616 
0.4647447769,0.5080850862,0.2602779063,0.6235898833 
0.9261314441,0.9800868873,0.2389269791,0.1730387161 
0.4068127254,0.8521595996,0.0881227537,0.3161680425 
0.6252986387,0.1626255445,0.8149288615,0.3877979894 
0.0148919826,0.4288521749,0.5650029395,0.371437511 
0.6999084446,0.5193721698,0.6204045802,0.6318546752 
0.1939084605,0.4495516764,0.2238104075,0.4506728368 
0.6700975735,0.5135514273,0.8026774478,0.5185625092 
0.901180391,0.0469020734,0.9142602429,0.1326394304 
0.0953740531,0.8537873632,0.9959291318,0.1347023842 
0.4603053633,0.7629654546,0.8185415223,0.4510613138 
0.657618954,0.7520074762,0.1866926524,0.4341538988 
0.36567682,0.744729749,0.7568356825,0.4866839553 
0.061796768,0.4784516747,0.6231214956,0.4103442592 
0.0582960593,0.7570687738,0.3419325419,0.3310748949 
0.56401618,0.7113661579,0.2757130117,0.5512582246 
0.3284323403,0.7378651794,0.6241919629,0.5475305858 
0.4275464981,0.3531682925,0.5295711182,0.6996417769 
0.9765874392,0.742617911,0.4658176337,0.3301453126 
0.072844224,0.7883095643,0.9160671954,0.2036838205 
0.804863217,0.3942617685,0.7601831764,0.4515167308 
0.007844326,0.474396331,0.7450142246,0.3156575944 
0.2441903849,0.9658714967,0.1156812242,0.2101475706 
0.8654428603,0.810985844,0.7199050951,0.338181743 
0.4064994813,0.7387058858,0.1379702581,0.4224170394 
0.9703619592,0.2396814054,0.3980043184,0.3188423792 
0.72785583,0.7091076345,0.5463329126,0.5533098448 
0.4026269424,0.0984237189,0.6391085789,0.4300251531 
0.7936799405,0.5424635891,0.5265214701,0.5681085493 
0.4642213171,0.1198021551,0.9364229437,0.2861154959 
0.4620715394,0.6806879169,0.4046251749,0.6582200893 
0.6011675165,0.7123237968,0.3787300339,0.6014074419 
0.7100177282,0.8298857084,0.4942637179,0.474918089 
0.7301135939,0.7515287406,0.5509963591,0.5 
0.6994698314,0.3896948785,0.3934400219,0.6144097 
0.4389661969,0.690571303,0.8227801091,0.4862497522 
0.4713839427,0.9078846897,0.3451963795,0.4288149742 
0.6821897594,0.3855670615,0.7383466417,0.5449379897 
0.1437000915,0.1926245144,0.6224994888,0.3797791301 
0.6098303414,0.5698709236,0.9622570089,0.3857898448 
0.2782655851,0.8486976961,0.4988174602,0.4527970984 
0.2702581113,0.2774259285,0.836997639,0.4013879522 
0.6564572272,0.0922870446,0.1510823881,0.3070511487 
0.2726379978,0.8572610917,0.1478971792,0.3152937412 
0.5602773549,0.1056130207,0.4101895268,0.4570750766 
0.4259546883,0.3321364383,0.8096145661,0.5061336658 
0.530420481,0.5331294727,0.3407970744,0.7005910085 
0.1333708394,0.2706074738,0.9213914799,0.262197074 
0.3853847516,0.7872972615,0.5643746353,0.5500816346 
0.6383190772,0.3975758359,0.6680134941,0.6255022018 
0.3074904762,0.1474470852,0.8589877068,0.3272994548 
0.164294517,0.4569216795,0.2065183679,0.418046184 
0.3177061073,0.5843665436,0.646404561,0.6174633944 
0.6420002836,0.3850971295,0.7164241147,0.5828185048 
0.2020011383,0.7824059389,0.6739242807,0.4201486368 
0.1840347406,0.0619099449,0.1514663014,0.2231936368 
0.9046038843,0.7000077552,0.715691632,0.3657951714 
0.1403577243,0.1693091849,0.804109877,0.2905414345 
0.3889402016,0.7763260252,0.9926312481,0.2903728197 
0.3241593493,0.882831795,0.7627518547,0.3695189538 
0.7666204442,0.1088284545,0.602145248,0.3817369659 
0.8106734194,0.5358877461,0.3051675919,0.4975616049 
0.930311342,0.4840485226,0.5832080611,0.4274528532 
0.987676861,0.2516205517,0.9622990862,0.1496166405 
0.21015611,0.4693912954,0.2686613362,0.4939179586 
0.6057100715,0.9904309815,0.3821192914,0.3506682187 
0.6056677665,0.9920663947,0.9869230643,0.1657475416 
0.1820842028,0.2265129565,0.334319282,0.4151199621 
0.8876163042,0.1416437103,0.8254844324,0.2458595023 
0.6232990447,0.5307597364,0.7508213738,0.584848987 
0.7259092082,0.3400181764,0.0396656887,0.328869158 
0.112385463,0.2929171231,0.7552510665,0.3578114192 
0.5559131078,0.487973381,0.5951903189,0.754975361 
0.7368538344,0.03127444,0.4817180756,0.3405374228 
0.8190162682,0.1239617304,0.3222973147,0.3418551987 
0.97988197,0.2996560477,0.1457110446,0.2367837425 
0.38865937,0.2980691287,0.5831884996,0.6208863837 
0.7511422474,0.7001080603,0.5557206437,0.5401106076 
0.6946423471,0.6442451245,0.6779334003,0.5654383213 
0.6962270452,0.838215151,0.7650459673,0.3936443009 
0.4959681755,0.183628186,0.1338019599,0.3820745954 
0.1016190418,0.1526950048,0.5857433221,0.3306002785 
0.5497103268,0.1446169499,0.2261669635,0.4146355935 
0.5969970797,0.8211549471,0.6187163732,0.5101567331 
0.8423643133,0.7705178764,0.7524700778,0.3619079724 
0.1934005817,0.8919879179,0.3949489795,0.3574062457 
0.1128752324,0.2121278716,0.6453188962,0.3621866106 
0.984939352,0.4026293467,0.02108349,0.1775428384 
0.0369142757,0.2954519021,0.9591341697,0.182583747 
0.694564745,0.2295872361,0.7298591792,0.4612862963 
0.9867100492,0.6877417272,0.720915026,0.2995123176 
0.3844537931,0.6819663068,0.5866166998,0.6337214748 
0.5320925768,0.1904548845,0.0721293763,0.3369494332 
0.3250763989,0.8926740442,0.7442687657,0.3715980571 
0.0884456354,0.9474986999,0.9294405386,0.121679209 
0.2715253571,0.1475996259,0.3489222973,0.4196946314 
0.6080823056,0.1631524074,0.5568934452,0.5077169479 
0.9537259699,0.777563684,0.588825372,0.3267677648 
0.6059857589,0.105086105,0.9913446577,0.2267996704 
0.9239502157,0.3577242379,0.805413884,0.3244962555 
0.2808765186,0.4074664948,0.6179072143,0.6005453464 
0.3651996099,0.4394979137,0.4216764531,0.6987943492 
0.3838798791,0.2186392358,0.1964127432,0.4361265964 
0.6355936999,0.2846375207,0.3993498098,0.5923520405 
0.9409695841,0.5761777789,0.8722091025,0.2839628263 
0.5727240253,0.5160758496,0.6947885946,0.6574832896 
0.3439960505,0.532515792,0.9814805574,0.3588587289 
0.844665446,0.0352556573,0.9347137247,0.1423144144 
0.2812371739,0.2012480763,0.0445996914,0.2790847534 
0.8722510531,0.4501423568,0.8145664254,0.3761186095 
0.5294358074,0.8459800522,0.3094731357,0.4699583017 
0.9270235896,0.6212266353,0.2540890089,0.3585638203 
0.3562878568,0.3831062196,0.3007920287,0.5939938765 
0.9597124545,0.7427999057,0.3312525342,0.3194331954 
0.5537447884,0.4097215465,0.7437895008,0.6005597334 
0.7381427777,0.1960822498,0.650809103,0.4515119939 
0.3400718119,0.4290469985,0.2536068172,0.5638318329 
0.7990917316,0.0754560353,0.6559687782,0.3237895679 
0.0624729146,0.5030904655,0.6033139733,0.4164552996 
0.4639588027,0.4688854972,0.3431048342,0.7020645144 
0.9235925954,0.6130429817,0.5084196649,0.4275276349 
0.1875197874,0.9535069139,0.6924070234,0.2826448532 
0.6484220238,0.0996337414,0.4432496125,0.4352785585 
0.7373545477,0.9727624131,0.0764032472,0.1883265846 
0.4487495478,0.2451071694,0.7262175529,0.5213932156 
0.7541094572,0.9352042695,0.1495581423,0.252198174 
0.7621058298,0.450101799,0.4171894081,0.5866566678 
0.9890595462,0.102434163,0.7846070734,0.1744771259 
0.8505146717,0.5804425551,0.2416315574,0.4232096499 
0.698750175,0.874646647,0.775899879,0.3600782866 
0.5626629528,0.4913736361,0.8220569456,0.5378155075 
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0.7407627309,0.863310113,0.494226838,0.4301420934 
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0.1247621915,0.1237534864,0.525949755,0.3340120188 
0.9565921184,0.3952253068,0.8622134365,0.2738660735 
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0.3055876407,0.3310139194,0.9801331168,0.3211581991 
0.6631613025,0.276597142,0.5381954648,0.586759686 
0.1150634772,0.9736670898,0.2515394538,0.2070342949 
0.8232522632,0.4894588125,0.8950760974,0.3554489991 
0.4975627763,0.5231172818,0.4102074437,0.7732727653 
0.6119855439,0.1610955916,0.4975586061,0.5090899468 
0.682962104,0.0776563653,0.419031434,0.3986869975 
0.6726203319,0.7876597129,0.9536890857,0.3017739741 
0.0913773174,0.9450365906,0.7027720889,0.2287290962 
0.602766628,0.7837863102,0.8969436441,0.3673672982 
0.6155271592,0.9020927012,0.2874681981,0.3967761824 
0.8478364911,0.5960036262,0.5907624708,0.4939437637 
0.246004391,0.3808873582,0.0403679333,0.3275430665 
0.4305861098,0.0345780167,0.2477172503,0.3320942318 
0.5289812609,0.0837675568,0.7474209829,0.3809413873 
0.9523989413,0.4010429585,0.1831988612,0.304936944 
0.114349951,0.133078384,0.5551008282,0.3308679187 

生产下列结果::

from mpl_toolkits.mplot3d import Axes3D 
import matplotlib.pyplot as plt 
import numpy as np 
import pandas as pd 
from scipy.interpolate import griddata as gd 
import time 

#read values 
print("Read original data...") 
start_time=time.clock() 
f=open('Daten.csv','r') 
headers = ["x","y","z","V"] 
data = pd.read_csv(f, delimiter = ",",header=1,names=headers) 
x=data.x 
y=data.y 
z=data.z 
v=data.V 
print ('time needed: ', time.clock()-start_time, ' seconds') 
print("") 

#generate new grid X,Y,Z 
print("Generate new grid...") 
start_time=time.clock() 
xi,yi,zi=np.ogrid[0:1:11j, 0:1:11j, 0:1:11j] 
X1=xi.reshape(xi.shape[0],) 
Y1=yi.reshape(yi.shape[1],) 
Z1=zi.reshape(zi.shape[2],) 
ar_len=len(X1)*len(Y1)*len(Z1) 
X=np.arange(ar_len,dtype=float) 
Y=np.arange(ar_len,dtype=float) 
Z=np.arange(ar_len,dtype=float) 
l=0 
for i in range(0,len(X1)): 
    for j in range(0,len(Y1)): 
     for k in range(0,len(Z1)): 
      X[l]=X1[i] 
      Y[l]=Y1[j] 
      Z[l]=Z1[k] 
      l=l+1 
print ('time needed: ', time.clock()-start_time, ' seconds') 
print("") 

#interpolate "data.v" on new grid "X,Y,Z" 
print("Interpolate...") 
start_time=time.clock() 
V = gd((x,y,z), v, (X,Y,Z), method='linear') 
print ('time needed: ', time.clock()-start_time, ' seconds') 
print("") 

#Plot original values 
fig1 = plt.figure() 
ax1=fig1.gca(projection='3d') 
sc1=ax1.scatter(x, y, z, c=v, cmap=plt.hot()) 
plt.colorbar(sc1) 
ax1.set_xlabel('X') 
ax1.set_ylabel('Y') 
ax1.set_zlabel('Z') 

#Plot interpolated values 
fig2 = plt.figure() 
ax2=fig2.gca(projection='3d') 
sc2=ax2.scatter(X, Y, Z, c=V, cmap=plt.hot()) 
plt.colorbar(sc2) 
ax2.set_xlabel('X') 
ax2.set_ylabel('Y') 
ax2.set_zlabel('Z') 

#Show plots 
plt.show() 

实施例的数据与随机点

def func(x,y,z): 
    return 0.5*(3)**(1/2)-((x-0.5)**2+(y-0.5)**2+(z-0.5)**2)**(1/2) 
x = np.random.rand(10) 
y = np.random.rand(10) 
z = np.random.rand(10) 
v = func(x,y,z) 

导致在这个例子中数据计算

original values at random points interpolated values using griddata with linear interpolation