Pythonの3Dサーフェスカラーマップ

Question

私は今直面している別の問題があります。私は次のようなデータファイルを持っている：私は、x軸に時間の経過とともに3Dカラーマップをプロットしようとしている、のために

# Time THR-1 ALA-2 PRO-3 VAL-4 PRO-5 MET-6 PRO-7 ASP-8 LEU-9 LYS-10 ASN-11 VAL-12 LYS-13 SER-14 LYS-15 ILE-16 GLY-17 SER-18 THR-19 GLU-20 ASN-21 LEU-22 LYS-23 HIS-24 GLN-25 PRO-26 GLY-27 GLY-28 GLY-29 LYS-30 VAL-31 GLN-32 ILE-33 ILE-34 ASN-35 LYS-36 LYS-37 LEU-38 ASP-39 LEU-40 SER-41 ASN-42 VAL-43 GLN-44 SER-45 LYS-46 CYS-47 GLY-48 SER-49 LYS-50 ASP-51 ASN-52 ILE-53 LYS-54 HIS-55 VAL-56 PRO-57 GLY-58 GLY-59 GLY-60 SER-61 VAL-62 GLN-63 ILE-64 VAL-65 TYR-66 LYS-67 PRO-68 VAL-69 ASP-70 LEU-71 SER-72 LYS-73 VAL-74 THR-75 SER-76 LYS-77 CYS-78 GLY-79 SER-80 LEU-81 GLY-82 ASN-83 ILE-84 HIS-85 HIS-86 LYS-87 PRO-88 GLY-89 GLY-90 GLY-91 GLN-92 VAL-93 GLU-94 VAL-95 LYS-96 SER-97 GLU-98 LYS-99 LEU-100 ASP-101 PHE-102 LYS-103 ASP-104 ARG-105 VAL-106 GLN-107 SER-108 LYS-109 ILE-110 GLY-111 SER-112 LEU-113 ASP-114 ASN-115 ILE-116 THR-117 HIS-118 VAL-119 PRO-120 GLY-121 GLY-122 GLY-123 ASN-124 DA-1 DA-2 DA-3 DC-4 DA-5 DT-6 DG-7 DT-8 DT-9 DA-10 DA-11 DA-12 DC-13 DA-14 DT-15 DG-16 DT-17 DT-18 DT-19 DA-1 DA-2 DA-3 DC-4 DA-5 DT-6 DG-7 DT-8 DT-9 DT-10 DA-11 DA-12 DC-13 DA-14 DT-15 DG-16 DT-17 DT-18 DT-19 
      0.000   84.841   0.274   8.595   -4.939   1.713   -1.704   0.768  -127.825   5.554  108.207   5.297   8.390  212.124   2.830   39.479   8.168   0.458   8.848   6.897  -83.882   29.016   9.647  308.856   6.400   32.481   11.327   10.372   0.247   -3.669   45.391   7.648   -6.990   16.870   11.946   18.778   29.161  127.841   -1.885  -49.943   4.716   6.552   16.029   4.803   7.307   5.423   35.449   -1.362   0.703   0.817   5.544  -14.168   -2.450   0.138   10.984   2.680   -0.238   -0.204   -1.814   -0.273   0.971   -0.256   2.553   -1.172   0.337   0.659   -3.890   8.570   1.180   2.319  -10.711   0.433   0.320   7.904   -0.021   1.672   -0.895   -1.804   -0.317   0.233   0.013   1.462   -1.310   -3.139   -1.453   -4.536   0.559   59.050  -10.891   3.089   5.579   9.818   6.599   -1.635  -34.622   2.576   14.145   9.062  -82.518   51.319   -5.944  -42.734   -0.065   5.200  -18.819   -1.670   0.354   -0.142   -0.938   -4.108   -0.582   -0.511   -0.452   0.763  -21.291   2.587   -5.088   -0.458   5.958   -0.746   -0.587   0.600   6.134   9.432  -47.476   0.517   -0.958   -1.246   0.005   -1.422   -5.105   -2.815   -6.459   -1.618   56.055  117.408   92.845   60.554   -6.065   -9.293   -3.752   -5.407   -1.491   -4.924   -0.944   13.894   32.688   15.937   2.866   -0.934   25.169   1.291   -5.292   -8.727   5.852   -8.092  -40.334  -18.542   0.468   -6.011   -2.043   -1.305   -0.959 
     10.000  127.315   0.993   15.230   12.627   0.804   0.642   -2.810  -101.634   5.500  114.097   3.368   9.100  162.819  -10.033   39.935   6.920   9.887   9.732   4.997  -79.368   25.134   -5.714  307.359   5.781   34.996   8.885   7.234   -5.875   -0.094   31.674   3.963   -8.064   14.720   12.726   25.431   25.011  108.108   -0.293  -63.815   4.442   1.071   12.768   2.871   1.451   2.179   30.666   -2.066   0.995   1.496   3.384   -1.398   -0.776   -0.101   5.159   1.092   -0.829   -0.205   -0.125   1.054   0.574   -0.291   1.106   0.875   -1.106   -1.955   1.153   4.273   0.628   1.305   -5.547   0.755   0.126   3.704   0.925   0.074   -0.516   3.643   -0.133   -0.064   0.717   0.547   0.197   -0.408   -0.912   -1.296   0.508   35.027   -3.056   10.216   5.885   8.755   -0.792   -1.442  -28.498   2.122   6.803   1.344  -58.583   47.395   -2.332  -32.863   -2.826   5.311  -23.087   6.478   -0.205   0.288   -0.373   4.358   0.362   -1.010   -0.352   2.271  -13.406   -2.747   -4.616   -2.275   3.943   -4.391   -7.063   -0.599   3.081   12.778  -40.043   0.327   -1.940   -2.012   2.592   2.909   1.041   0.658   -0.868   -3.206   16.355  109.843  107.372   63.801   8.499   0.931   2.639   -0.884   0.214   1.880   -2.379   8.408   12.583   10.883   23.083   7.955   31.277   0.539   3.992   -0.887   12.925   -4.248  -31.420   -4.812   1.125   3.287   -0.532   -0.438   0.291 
     20.000   84.636   5.538   15.954   10.437   0.439   1.773   -1.913  -96.625   5.704  132.598   -0.572   6.877  174.628   -9.400   32.417   -0.264   3.812   6.175   5.056  -62.617   25.479   -1.171  288.031   8.114   37.636   10.461   4.612   -3.521   -0.335   37.957   6.596  -11.250   12.510   11.557   21.128   37.344  135.293   -2.163  -80.896   0.912   1.963   1.101   2.815   6.051   5.374   28.443   0.905   1.734   0.813   5.060   -1.365   1.653   -0.415   4.862   1.758   -0.572   -0.339   0.423   0.759   1.036   -0.543   0.783   0.102   -0.971   -1.529   -1.595   5.519   0.587   1.306   -2.813   0.605   0.761   4.542   0.698   0.767   -0.050   2.201   -0.084   0.563   0.357   0.422   0.642   0.588   -1.426   -1.375   1.455   31.332   -3.390   16.696   15.616   13.449   0.096   -2.711  -24.804   1.969   4.095   2.078  -58.303   47.776   -1.047  -22.013   -2.270   4.204  -11.059   3.952   0.382   -0.863   0.010   3.473   0.375   -1.301   -0.037   1.396  -14.392   -2.887   -5.915   -2.315   5.888   -3.365   -5.950   -2.439   4.814   7.125  -46.399   4.393   5.939   -0.508   2.461   2.562   -0.717   4.225   3.642   4.664   27.859  104.835  114.077   74.730   8.410   1.862   0.061   -1.288   -1.181   2.106   4.346   9.017   29.050   -5.088   14.618   4.149   5.062   1.369   15.083   9.537   18.306   -1.165   -8.966   3.864   3.523   7.232   4.275   1.888   4.708 
     30.000   91.953   11.008   15.794   12.043   0.596   4.611   1.048  -70.764   7.475   72.100   1.360   6.891  150.455   -7.180   11.932   4.845   9.519   6.184   4.684  -57.283   24.797   0.393  275.626   14.021   22.233   10.877   0.934   -7.551   -2.439   27.929   5.098   -6.797   12.784   12.140   19.698   25.762  108.882   0.267  -54.801   1.470   2.139   1.302   1.996   2.021   3.090   22.690   0.669   1.347   0.113   5.378   -1.570   0.585   -0.143   1.156   -0.050   -1.086   0.148   -0.017   -0.417   -0.201   -1.304   0.808   -0.950   -0.958   -1.741   0.200   2.846   0.633   1.279   -3.693   0.338   -1.058   3.651   0.009   0.202   -1.009   0.037   -0.245   -0.183   -0.615   0.192   -0.386   0.426   -1.800   -2.009   0.496   33.517   -4.213   15.421   16.942   14.559   0.109   -2.553  -25.113   1.199   2.074   -0.265  -56.399   40.657   -0.746  -24.020   -1.986   3.400   -9.631   1.384   0.502   -1.001   0.547   2.622   -0.201   -1.062   -0.916   0.493  -14.621   -2.660   -4.459   -1.066   3.788   -4.289   -7.086   2.460   5.341   8.759  -39.474   -0.051   2.116   0.498   1.267   0.728   1.071   1.155   0.824   3.214   32.413  124.028  144.011   80.795   11.199   5.365   1.969   0.659   2.780   2.311   1.671   14.244   33.170   -6.859   -6.106   13.690   4.742   0.645   17.301   12.245   15.829  -11.976  -22.289   3.100   1.725   5.538   5.041   3.517   -0.205 
     40.000  149.956   11.453   22.603   13.125   1.909   5.563   1.533  -90.126   5.479   90.590   4.141   6.652  173.681   -3.703   24.551   3.012   10.247   12.607   7.241  -64.707   21.636   -0.285  276.445   6.223   29.727   8.346   5.092   -5.591   -2.969   27.881   3.581   -6.824   13.884   11.709   21.034   25.732  104.610   -0.237  -54.221   1.960   1.674   2.394   1.727   6.499   3.453   25.335   0.636   0.754   -0.591   5.789   -3.344   1.182   -0.366   0.810   0.901   -0.625   -0.997   -0.241   0.214   0.311   -0.312   0.498   -1.336   -0.911   -1.210   -2.459   3.182   0.599   0.713   -4.273   0.326   0.522   3.207   0.312   0.830   -0.558   1.351   -0.017   0.569   -0.367   0.966   -0.637   -2.392   -2.722   -3.405   0.818   39.708   -2.537   16.297   14.229   10.427   0.837   -1.855  -24.033   0.996   5.579   -1.055  -65.068   48.891   -2.411  -21.785   -2.094   1.285   -3.668   1.264   0.463   0.070   -0.034   2.779   0.115   -0.947   1.107   0.337  -16.009   -3.881   -5.203   -1.503   0.358   -4.410   -8.007   -1.383   10.872   17.390  -47.147   1.140   -2.218   -0.597   -0.312   0.685   1.781   5.662   1.917   1.504   32.806  123.230  132.991   68.245   11.523   3.048   0.389   -0.890   0.170   2.100   1.166   11.693   31.756   2.595   19.844   24.565   30.414   11.828   18.563   22.426   20.596  -13.383  -18.574   -2.142   4.737   1.680   0.071   3.983   -0.001 

を、Y軸の列とそれぞれの値の数z軸。

私は、ファイルからデータを抽出し、それをプロットするために、次のコードを書かれている：

#!/usr/bin/python 

from mpl_toolkits.mplot3d import Axes3D 
import matplotlib.pyplot as plt 
from matplotlib import cm 
from matplotlib.ticker import LinearLocator, FormatStrFormatter 
import numpy as np 

data = np.loadtxt('contrib_pol.dat', skiprows=1) 

x = data[:,0] 
y = range(1,len(data[0,:])) 
z = [] 



fig=plt.figure() 

ax=fig.gca(projection='3d') 

for r, row in enumerate(data): 
    for c, col in enumerate(row[1:], start=1): 
     z.append(col) 

surf = ax.plot_surface(x, y, z, cmap=cm.coolwarm, 
         linewidth=0, antialiased=False) 

plt.show() 

をそして、私はエラーを抱えている：

Traceback (most recent call last): 
    File "./barplot.py", line 31, in <module> 
    linewidth=0, antialiased=False) 
    File "/usr/lib/python2.7/dist-packages/mpl_toolkits/mplot3d/axes3d.py", line 1586, in plot_surface 
    X, Y, Z = np.broadcast_arrays(X, Y, Z) 
    File "/home/microbio/.local/lib/python2.7/site-packages/numpy/lib/stride_tricks.py", line 250, in broadcast_arrays 
    shape = _broadcast_shape(*args) 
    File "/home/microbio/.local/lib/python2.7/site-packages/numpy/lib/stride_tricks.py", line 185, in _broadcast_shape 
    b = np.broadcast(*args[:32]) 
ValueError: shape mismatch: objects cannot be broadcast to a single shape 

あなたは助けることができます？

Answer 1

plot_surfaceのドキュメントストリングを確認するには、データを2D配列として提供する必要があると記載されています。さらに2行のコードを追加すると、numpy.meshgridを使ってベースグリッドを取得し、numpy.reshapeを使用してz値を適切な形式で取得できるようになります。

from mpl_toolkits.mplot3d import Axes3D 
import matplotlib.pyplot as plt 
from matplotlib import cm 
from matplotlib.ticker import LinearLocator, FormatStrFormatter 
import numpy as np 

data = np.loadtxt('contrib_pol.dat', skiprows=1) 

x = data[:,0] 
y = range(1,len(data[0,:])) 
z = [] 



fig=plt.figure() 

ax=fig.gca(projection='3d') 

for r, row in enumerate(data): 
    for c, col in enumerate(row[1:], start=1): 
     z.append(col) 

# generate the grid 
xx, yy = np.meshgrid(x, y) 
# reshaping your data to match the grid shape 
zz = np.reshape(z, (len(y), len(x))) 

surf = ax.plot_surface(xx, yy, zz, cmap=cm.coolwarm, 
         linewidth=0, antialiased=False) 

plt.show()