Python中的3D曲面颜色映射
我现在面临另一个问题。我有一个如下所示的数据文件：Python中的3D曲面颜色映射,python,numpy,surface,colormap,mplot3d,Python,Numpy,Surface,Colormap,Mplot3d,我现在面临另一个问题。我有一个如下所示的数据文件： # 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
# 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
#!/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()
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
回溯（最近一次呼叫最后一次）：
文件“/barplot.py”，第31行，在
线宽=0，抗锯齿=假）
文件“/usr/lib/python2.7/dist packages/mpl_工具包/mplot3d/axes3d.py”，第1586行，在plot_曲面中
十、 Y，Z=np.广播_数组（X，Y，Z）
文件“/home/microbio/.local/lib/python2.7/site packages/numpy/lib/stride\u tricks.py”，第250行，广播数组
形状=_广播_形状（*args）
文件“/home/microbio/.local/lib/python2.7/site packages/numpy/lib/stride\u tricks.py”，第185行，呈广播形状
b=np.广播（*args[：32]）
ValueError:形状不匹配：无法将对象广播到单个形状
您能帮忙吗？检查
绘图曲面的文档字符串，它说明您需要以二维数组的形式提供数据。使用两行额外的代码，您可以使用numpy.meshgrid
获得基本网格，并使用numpy.reshave
以正确的格式获得z值
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()
检查plot_surface
的文档字符串，它说明您需要以2D数组的形式提供数据。使用两行额外的代码，您可以使用numpy.meshgrid
获得基本网格，并使用numpy.reshave
以正确的格式获得z值
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()
它显示了%matplotlib inline语句的语法错误。oops，忘记删除该行。我在一个jupyter笔记本上测试了代码，因此这一行。在您的情况下，只需删除该行，我将进行相应调整。它显示%matplotlib inline语句的语法错误。oops，忘记删除该行。我在一个jupyter笔记本上测试了代码，因此这一行。对你来说，只要把那条线移走，我会相应地调整。