使用 2D 数组的 Python 中的颤动图

Question

您好，我正在尝试使用箭袋图来创建矢量场图。这是我的逻辑和方法，我首先通过 np.arange 创建位置的 x,y 坐标，并使用 0.1 的步长。然后我为 x,y 网格划分网格。然后我将函数 Fx 的 x 分量和 y 分量 Fy 作为 .dat 文件导入 python。 .dat 文件是每个二维数组（只是一个方阵）。然后我对网格化的 x,y 坐标和 Fx,Fy 2d 数组使用 quiver 命令。然而，就我所期望的而言，颤动图输出根本没有多大意义。

我忽略的代码是否存在问题？如果步长不是整数， np.arange 是否有效？我打印出所有数组以手动检查数据，一切似乎都很好。

会不会是我的四个二维数组的形状不一样？我导入的两个 .dat 文件都是 40x40 方阵。不确定这是否与我网格化的网格匹配良好。

除此之外，我不确定问题是什么。任何帮助或建议将不胜感激。如果有帮助，我可以在我的 .dat 文件中添加数据。谢谢！ （我已经检查了有关堆栈溢出的所有其他示例以解决此问题，看来我的代码在逻辑上是正确的，所以我非常卡住）

import numpy as np
import matplotlib.pyplot as plt

data = np.genfromtxt('file1.dat')
data2 = np.genfromtxt('file2.dat')

nx = 2
ny = 2
x=np.arange(-nx,nx,0.1)
y=np.arange(-ny,ny,0.1)
xi,yi=np.meshgrid(x,y)
Fx = data[::5] #picks out every 5 rows in the matrix
Fy = data2[::5] 
#print(Fx)
#print(Fy)
#print(xi)
#print(yi)

plt.axes([0.065, 0.065, 0.9, 0.9])
plt.quiver(xi,yi,Fx,Fy, alpha=.5)
plt.quiver(xi,yi,Fx,Fy, edgecolor='k',facecolor='none', linewidth=.5)

plt.show()

编辑：.dat 文件如下所示。如果有办法导入 .dat 文件，请告诉我，我意识到这是很多数字，而且格式很糟糕。首先列出 Fx，然后是 Fy 数组。我期待一个非常漂亮的颤动图，其中我有某种圆形图案/圆形流动。箭头应全部形成顺时针和/或逆时针流动。

  -30.9032192      0.512708426      0.511251688      0.508112907      0.503038108      0.495766401      0.486015081      0.473499298      0.457935333      0.439051390      0.416606665      0.390406251      0.360321403      0.326310992      0.288441181      0.246901810      0.202013552      0.154238343      0.104165822       5.24933599E-02   0.00000000      -5.24933599E-02 -0.104165822     -0.154238343     -0.202013552     -0.246901810     -0.288441181     -0.326310992     -0.360321403     -0.390406251     -0.416606665     -0.439051390     -0.457935333     -0.473499298     -0.486015081     -0.495766401     -0.503038108     -0.508112907     -0.511251688     -0.512708426       30.9032192    

  0.640149713      0.648661256      0.646115780      0.638335168      -13.4731970      -13.0613079      0.587181866      0.561966598      0.533295572      0.501472771      0.466741979      0.429292738      0.389282435      0.346857786      0.302170664      0.255400449      0.206771404      0.156560570      0.105099753       5.27719632E-02   2.10129052E-08  -5.27718328E-02 -0.105099864     -0.156560570     -0.206771582     -0.255400449     -0.302170008     -0.346857607     -0.389282405     -0.429292321     -0.466741502     -0.501472294     -0.533295095     -0.561966538     -0.587181747       13.0613060       13.4731960     -0.638335109     -0.646115661     -0.648661256     -0.640149713    

  0.799892545      0.824215114      0.801061392      0.776797950      0.753669202      0.730814993      0.707295001      0.682291210      0.655105412      -8.68122292      -8.12608242      0.554765701      0.513439834      0.467435867      0.416336209      0.359773695      0.297508597      0.229575798      0.156477526       7.93530941E-02   6.53175791E-10  -7.93530941E-02 -0.156477645     -0.229576021     -0.297508597     -0.359773695     -0.416336179     -0.467435598     -0.513440192     -0.554765582       8.12608242       8.68122387     -0.655105233     -0.682291508     -0.707294881     -0.730815291     -0.753669143     -0.776797950     -0.801061392     -0.824215114     -0.799892545    

  0.940612555      0.983826339      0.933131218      0.884394646      0.842061043      0.804476202      0.769944012      0.737089813      0.704840183      0.672395170      0.639202237      0.604933023      0.569452882      0.532750905      0.494812310      -2.68859553      -2.16188312      0.365726620      0.304749787      0.205249593       6.78142031E-09 -0.205249622     -0.304749817     -0.365726680       2.16188359       2.68859553     -0.494812399     -0.532750905     -0.569453001     -0.604932964     -0.639202118     -0.672395170     -0.704840362     -0.737089515     -0.769943893     -0.804476202     -0.842061162     -0.884394407     -0.933131695     -0.983826339     -0.940612555    

  0.999167860       1.05166125      0.986028075      0.923735499      0.870001256      0.822448075      0.778727889      0.736939847      0.695574820      0.653458953      0.609715879      0.563743949      0.515199065      0.463976830      0.410177410      0.354019582      0.295616359      0.234412342      0.167968050       9.07804966E-02  -8.54922577E-10  -9.07804891E-02 -0.167968005     -0.234412268     -0.295616418     -0.354019672     -0.410177410     -0.463976830     -0.515199006     -0.563743949     -0.609715819     -0.653458893     -0.695574880     -0.736939907     -0.778727889     -0.822448075     -0.870001316     -0.923735559     -0.986028075      -1.05166125     -0.999167860    


  0.940612555      0.983826339      0.932870448      0.884094179      0.841758013      0.804004610      0.768958390      0.735091329      0.701199591      0.666386902      0.630052805      0.591893077      0.551910400      0.510422051      0.468044579      0.425626040      0.384017974      0.343483299      0.302600116     -0.377980769       8.43500270E-10  0.377980769     -0.302600116     -0.343483359     -0.384017944     -0.425625950     -0.468044549     -0.510422230     -0.551910520     -0.591892898     -0.630052805     -0.666386902     -0.701199770     -0.735090971     -0.768958986     -0.804005086     -0.841758251     -0.884094059     -0.932870448     -0.983826339     -0.940612555    


 0.799892545      0.824215114      0.807587028      0.790868759      0.775763810      0.761242151      0.746228993      0.729784787      0.711097538      0.689466000      0.664264023      -6.33222771      -5.70436525      0.561126649      0.514991641      0.460934460      0.396892428      0.320130050      0.227872163      0.119494393      -1.02303694E-08 -0.119494416     -0.227872089     -0.320129842     -0.396892160     -0.460934043     -0.514991641     -0.561126769       5.70436525       6.33222771     -0.664264023     -0.689466000     -0.711097836     -0.729784369     -0.746228993     -0.761242330     -0.775764227     -0.790868759     -0.807587445     -0.824215114     -0.799892545 

  0.640149713      0.648661256      0.658376634      0.663496077      0.663335323      -12.7135134      -12.2490902      0.630356669      0.608760655      0.581994295      0.550120413      0.513214111      0.471384048      0.424800932      0.373717010      0.318486720      0.259573966      0.197552294      0.133099481       6.69753179E-02  -1.07370708E-08  -6.69753179E-02 -0.133099481     -0.197552368     -0.259573698     -0.318486512     -0.373717397     -0.424800485     -0.471384078     -0.513214111     -0.550120771     -0.581994355     -0.608760655     -0.630356669       12.2490902       12.7135134     -0.663335383     -0.663496077     -0.658376753     -0.648661256     -0.640149713    

 -30.9032192      0.512708426      0.511251688      0.508112907      0.503038108      0.495766401      0.486015081      0.473499298      0.457935333      0.439051390      0.416606665      0.390406251      0.360321403      0.326310992      0.288441181      0.246901810      0.202013552      0.154238343      0.104165822       5.24933599E-02   0.00000000      -5.24933599E-02 -0.104165822     -0.154238343     -0.202013552     -0.246901810     -0.288441181     -0.326310992     -0.360321403     -0.390406251     -0.416606665     -0.439051390     -0.457935333     -0.473499298     -0.486015081     -0.495766401     -0.503038108     -0.508112907     -0.511251688     -0.512708426       30.9032192   

现在 Fy 数组：

 -0.205083355     -0.525830388     -0.552687049     -0.580741763     -0.609929502     -0.640149713     -0.671258569     -0.703064799     -0.735320449     -0.767719150     -0.799892545     -0.831412077     -0.861791074     -0.890495777     -0.916961849     -0.940612555     -0.960886896     -0.977269113     -0.989315629     -0.996686459     -0.999167860     -0.996686459     -0.989315629     -0.977269113     -0.960886896     -0.940612555     -0.916961849     -0.890495777     -0.861791074     -0.831412077     -0.799892545     -0.767719150     -0.735320449     -0.703064799     -0.671258569     -0.640149713     -0.609929502     -0.580741763     -0.552687049     -0.525830388     -0.205083355    
 -0.495766401     -0.496165156     -0.509083092     -0.549605310       13.5129404       13.0519953     -0.646288395     -0.672055602     -0.695797563     -0.717920899     -0.738660455     -0.758110344     -0.776252687     -0.792979062     -0.808119476     -0.821464479     -0.832787395     -0.841867268     -0.848508835     -0.852558434     -0.853919387     -0.852558374     -0.848508716     -0.841867328     -0.832787514     -0.821464896     -0.808119833     -0.792978704     -0.776252151     -0.758110642     -0.738660395     -0.717920780     -0.695797503     -0.672055602     -0.646288335       13.0519953       13.5129395     -0.549605191     -0.509083092     -0.496165156     -0.495766401    

 -0.416606665     -0.387658477     -0.370003909     -0.412325561     -0.451486528     -0.484789789     -0.512974977     -0.536900580     -0.557342112       8.73137856       8.12754345     -0.604040861     -0.616312325     -0.627466083     -0.637651145     -0.646887839     -0.655064702     -0.661947429     -0.667217672     -0.670547307     -0.671688557     -0.670547426     -0.667217493     -0.661947429     -0.655064702     -0.646887779     -0.637651086     -0.627466381     -0.616312623     -0.604041040       8.12754345       8.73137951     -0.557341993     -0.536900103     -0.512975276     -0.484789670     -0.451485991     -0.412325561     -0.370003909     -0.387658477     -0.416606665   

 -0.246901810     -0.228335708     -0.217398927     -0.246074528     -0.271431714     -0.291785061     -0.307664692     -0.319617361     -0.328106791     -0.333535194     -0.336277753     -0.336733580     -0.335400879     -0.333002120     -0.330682963       2.81363893       2.24033999     -0.348281264     -0.372185618     -0.395866930     -0.403591305     -0.395866960     -0.372185677     -0.348281264       2.24033999       2.81363893     -0.330682874     -0.333002120     -0.335400909     -0.336733490     -0.336277664     -0.333535045     -0.328106642     -0.319617361     -0.307664692     -0.291785270     -0.271431714     -0.246074289     -0.217398927     -0.228335708     -0.246901810    
   0.00000000      -3.97699699E-02  -8.22334886E-02  -9.01840925E-02  -9.43243951E-02  -9.68469381E-02  -9.79287177E-02  -9.75681171E-02  -9.57226083E-02  -9.23085213E-02  -8.71856511E-02  -8.01347122E-02  -7.08276853E-02  -5.87978214E-02  -4.34263758E-02  -2.40071025E-02  -4.12676527E-05   2.79203784E-02   5.66387177E-02   7.90976062E-02   8.76100808E-02   7.90975988E-02   5.66387326E-02   2.79204026E-02  -4.12871887E-05  -2.40071043E-02  -4.34263758E-02  -5.87978400E-02  -7.08276406E-02  -8.01346377E-02  -8.71856511E-02  -9.23085883E-02  -9.57226381E-02  -9.75680798E-02  -9.79286432E-02  -9.68469679E-02  -9.43244398E-02  -9.01841149E-02  -8.22335258E-02  -3.97699960E-02   0.00000000    

 0.246901810      0.149554759       5.41899577E-02   6.69130459E-02   8.30149651E-02   9.62892994E-02  0.106718197      0.114569001      0.119987577      0.122970015      0.123354375      0.120809816      0.114815064      0.104622498       8.91864598E-02   6.69886991E-02   3.55363674E-02  -1.02187870E-02  -8.21609423E-02 -0.177876130     -0.191068053     -0.177876085      -8.21608678E-02  -1.02187609E-02   3.55363339E-02   6.69886544E-02   8.91865119E-02  0.104622573      0.114814982      0.120810024      0.123354279      0.122969493      0.119987287      0.114568666      0.106718197       9.62890834E-02   8.30147490E-02   6.69130459E-02   5.41902333E-02  0.149555355      0.246901810    

 0.416606665      0.324635506      0.239433557      0.271107137      0.304715306      0.333829224      0.358776420      0.380251735      0.398895025      0.415270001      0.429880798      -6.52393579      -5.84947205      0.467720896      0.479777455      0.492111117      0.504699171      0.516976655      0.527697802      0.535157621      0.537844956      0.535157681      0.527697802      0.516976714      0.504699290      0.492111027      0.479777277      0.467720628      -5.84947205      -6.52393579      0.429880500      0.415270001      0.398895413      0.380252063      0.358776003      0.333829224      0.304715246      0.271106362      0.239433587      0.324635804      0.416606665    

 0.495766401      0.468931794      0.452914894      0.491556555      0.528390408      -12.8101072      -12.3052654      0.617275119      0.641844690      0.664552093      0.685565233      0.704941750      0.722658634      0.738638997      0.752775729      0.764953554      0.775063336      0.783014059      0.788738489      0.792190075      0.793342948      0.792190075      0.788738668      0.783013999      0.775063157      0.764953852      0.752775729      0.738638759      0.722658694      0.704941571      0.685565174      0.664552152      0.641844690      0.617275119      -12.3052645      -12.8101072      0.528390408      0.491556555      0.452914953      0.468931794      0.495766401  

0.512708426      0.525830388      0.552687049      0.580741763      0.609929502      0.640149713      0.671258569      0.703064799      0.735320449      0.767719150      0.799892545      0.831412077      0.861791074      0.890495777      0.916961849      0.940612555      0.960886896      0.977269113      0.989315629      0.996686459      0.999167860      0.996686459      0.989315629      0.977269113      0.960886896      0.940612555      0.916961849      0.890495777      0.861791074      0.831412077      0.799892545      0.767719150      0.735320449      0.703064799      0.671258569      0.640149713      0.609929502      0.580741763      0.552687049      0.525830388      0.512708426   

Answer 1

沿着y=x 和y=-x 的行似乎存在异常大的值（可能是渐近奇点的指示？）。

您可以在您发布的the data 中看到这一点。例如，考虑第一行：

  -31.3490391       6.68895245E-02   6.68859407E-02   ... -6.68895245E-02   31.3490391    

第一个值是大的负数，后面是小的正数。在行尾附近，数字很小且为负，而最后一个值较大且为正。显然，就目前而言，这些数据不会产生平滑变化的颤动图。

如果我们删除这些异常大的值：

data[np.abs(data) > 1] = np.nan
data2[np.abs(data2) > 1] = np.nan

然后

import numpy as np
import matplotlib.pyplot as plt

data = np.genfromtxt('file1.dat')
data2 = np.genfromtxt('file2.dat')

data[np.abs(data) > 1] = np.nan
data2[np.abs(data2) > 1] = np.nan

N = 10
Fx = data[::N, ::N]
Fy = data2[::N, ::N]

nrows, ncols = Fx.shape
nx = 2
ny = 2
x = np.linspace(-nx, nx, ncols)
y = np.linspace(-ny, ny, nrows)
xi, yi = np.meshgrid(x, y, indexing='ij')

plt.axes([0.065, 0.065, 0.9, 0.9])
plt.quiver(xi, yi, Fx, Fy, alpha=.5)
plt.quiver(xi, yi, Fx, Fy, edgecolor='k', facecolor='none', linewidth=.5)

plt.show()

产量

---

data 是形状为 (301, 301) 的二维数组：

In [109]: data.shape
Out[109]: (301, 301)

如果我们使用data[::10] 对data 进行切片，则结果具有形状

In [113]: data[::10].shape
Out[113]: (31, 301)

请注意，只有第一个轴被切片。要对第一个轴和第二个轴进行切片，请使用 data[::10, ::10]：

In [114]: data[::10, ::10].shape
Out[114]: (31, 31)

有关多维切片的更多信息，请参阅the docs。 始终注意 NumPy 数组的形状。它通常是理解 NumPy 操作的关键。

---

虽然plt.quiver 有时可以接受不同形状的数组， 通过传递四个相同形状的数组，最容易使用plt.quiver。 为确保xi, yi, Fx, Fy 都具有相同的形状，将data 和data2 切片以形成Fx 和Fy 首先，然后构建xi 和yi 以符合到Fx 的Fy 的（常见）形状：

nrows, ncols = Fx.shape
x = np.linspace(-nx, nx, ncols)
y = np.linspace(-ny, ny, nrows)

np.linspace 的第三个参数表示 返回数组。

Answer 2

首先确保Fx 和Fy 的维度相同，以避免混淆。然后根据数据维度生成网格空间维度。您可以使用np.linspace 代替np.arange 作为：

x = np.linspace(-nx, nx, Fx.shape[1])
y = np.linspace(-ny, ny, Fx.shape[0])

更新： 完整代码如下：

import numpy as np
import matplotlib.pyplot as plt

# Fxdata.dat contain Fx data while Fydata.dat is Fy downloaded from the provided link
Fx = np.genfromtxt('Fxdata.dat')
Fy = np.genfromtxt('Fydata.dat')

# input data dimensions 
num_Fx = Fx.shape[0]    # number of lines for the data in file1.dat
length_Fx = Fx.shape[1]  # length of each row for file1.dat 

nx = 15
ny = 15

# you can generate the grid points based on the dimensions of the input data
x = np.linspace(-nx, nx, length_Fx)
y = np.linspace(-ny, ny, num_Fx)

# grid points 
xi,yi=np.meshgrid(x,y)

#
plt.axes([0.065, 0.065, 0.9, 0.9])
plt.quiver(xi,yi,Fx,Fy, alpha=.5)
#plt.quiver(xi,yi,Fx,Fy, edgecolor='k',facecolor='none', linewidth=.5)

plt.show()

不确定现在是否有意义，但结果图如下：