我将ascii-Data放在一个名为'Testdata_interpolate.csv'的文件中，方式如下
x,y,z,v
val11,val12,val13,val14
val21,val22,val23,val24
...
其中x，y，z表示坐标，v表示空间中此点的标量值。 x，y，z是随机分布的。
对于进一步的计算，v应作为V插值到规则网格（X，Y，Z）上。因此我尝试在该代码中使用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()
尝试此操作会导致错误。
ValueError: Buffer has wrong number of dimensions (expected 1, got 3)
由于失败，我试图用
生成新网格X=np.linspace(0,1,10)
Y=np.linspace(0,1,10)
Z=np.linspace(0,1,10)
使用这种表示法执行插值，但我只有一个直线（正确）插值从音量的一个角到另一个。
要插入的X，Y，Z网格的正确生成应该是什么？
EDIT1： 我找到了一个适合我的工作。使用以下代码生成新网格X，Y，Z：
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]
有人知道这是否可以简化和/或如何以良好的python风格书写？
答案 0 :(得分：1)
以下是完整的解决方案：
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()
使用随机点计算示例数据
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)
产生了这个示例数据：
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
0.0510125536,0.8540123724,0.1709006799,0.2063127017
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
0.7309012385,0.7477130298,0.308651145,0.4770633087
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
0.7301135939,0.7515287406,0.5509963591,0.5213232105
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
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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
产生以下结果：
original values at random points interpolated values using griddata with linear interpolation
答案 1 :(得分：0)
据我了解，您只需要将新网格转换为1D。
# 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.flatten(),Y.flatten(),Z.flatten()), method='nearest')