matplotlib 曲面图时，对象不能广播到单个形状

Question

我正在使用 X=lat、Y=long 和 Z=depth(Elevation)with csv 绘制 3D 曲面图。我很确定所有变量的长度都相同 43 但我收到以下错误，程序指的是什么值？ ValueError: shape mismatch: objects cannot be broadcast to a single shape.

这是我的 python sn-p。

import matplotlib.pyplot as plt
%matplotlib inline
import plotly.graph_objects as go
import pandas as pd
import numpy as np
# Read data from a csv
z_data = pd.read_csv('bathy_bedford.csv')
z = z_data.values
sh_0, sh_1 = z.shape
x, y = np.linspace(44.66875, 44.74791667, sh_0), np.linspace(-63.69791667, -63.52708333, sh_0)

#plotting
fig = plt.figure(figsize=(8,6))

ax = fig.add_subplot(1,1,1, projection='3d')

ax.plot_surface(x, y, z, rstride=4, cstride=4, alpha=0.25)
cset = ax.contour(x, y, z, zdir='z', offset=-np.pi, cmap=matplotlib.cm.coolwarm)
cset = ax.contour(x, y, z, zdir='x', offset=-np.pi, cmap=matplotlib.cm.coolwarm)
cset = ax.contour(x, y, z, zdir='y', offset=3*np.pi, cmap=matplotlib.cm.coolwarm)

ax.set_xlim3d(-np.pi, 2*np.pi);
ax.set_ylim3d(0, 3*np.pi);
ax.set_zlim3d(-np.pi, 2*np.pi);

这里是变量长度的输出：

len(x)
43
len(y)
43
len(z)
43

Answer 1

进口

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.cm import coolwarm

从 csv 加载数据

df = pd.read_csv('bathy_bedford.csv', index_col=0)

# column names from str to float
df.columns = [float(x) for x in df.columns]

# df.head()

            44.668750  44.672917  44.677083   44.681250   44.685417  44.689583  44.693750  44.697917  44.702083  44.706250  44.710417  44.714583  44.718750  44.722917  44.727083  44.731250  44.735417  44.739583  44.743750  44.747917
-63.697917  76.949219  77.085938  94.507813  109.914060  111.292970  88.378906  51.730469  47.687500  59.089844  53.457031  46.628906  39.363281  44.792969  52.582031  41.074219  32.304688  31.945313  37.171875  31.265625  35.207031
-63.693750  74.859375  75.480469  88.718750  104.511720  102.984380  72.757813  51.261719  57.562500  68.406250  64.949219  46.140625  35.457031  41.566406  46.582031  44.464844  43.144531  48.738281  41.949219  28.066406  41.425781
-63.689583  76.234375  75.566406  80.800781   85.156250   76.046875  55.621094  57.980469  73.234375  78.527344  77.343750  51.320313  30.578125  33.000000  44.218750  57.890625  63.746094  67.226563  60.589844  43.121094  37.597656
-63.685417  78.855469  77.718750  70.976563   61.859375   52.851563  54.816406  72.570313  84.394531  86.800781  80.457031  56.378906  30.882813  33.000000  52.328125  71.847656  84.273438  86.675781  79.574219  53.785156  28.941406
-63.681250  75.035156  67.761719  68.031250   65.218750   56.160156  68.144531  88.738281  93.468750  92.445313  79.990234  49.884766  24.603516  39.861328  64.859375  78.960938  86.257813  86.726563  84.812500  65.078125  40.824219

指定x、y 和z

• 使用numpy.meshgrid 正确塑造x 和y 以匹配z。
• x、y 和z 中的shape 将分别为(43, 20)。

x = df.columns
y = df.index

# make x & y match the shape of z
x, y = np.meshgrid(x, y)

z = df.values

绘制数据

fig = plt.figure(figsize=(16, 12))

ax = fig.add_subplot(111, projection='3d')

ax.plot_surface(x, y, z, rstride=4, cstride=4, alpha=0.25)
cset = ax.contour(x, y, z, zdir='z', offset=-np.pi, cmap=coolwarm)
cset = ax.contour(x, y, z, zdir='x', offset=-np.pi, cmap=coolwarm)
cset = ax.contour(x, y, z, zdir='y', offset=3*np.pi, cmap=coolwarm)

plt.show()