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python - 如何在 matplotlib 中根据 x、y、z 坐标绘制等高线图? (plt.contourf 或 plt.contour)

转载 作者:行者123 更新时间:2023-12-01 18:28:54 68 4
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这些meshgrid对我来说使用起来有点困惑。我正在尝试使用 xy 坐标绘制散点图,并在散点图上叠加等高线图,并为 z 连续展开> 坐标。类似于高程图。

如果我将 meshgrid 与 x、y 和 z 坐标一起使用,那么我会得到每个 3D 数组,这仍然是不正确的输入。

df_xyz = pd.read_table("https://pastebin.com/raw/f87krHFK", sep="\t", index_col=0)
x = df_xyz.iloc[:,0].values
y = df_xyz.iloc[:,1].values
z = df_xyz.iloc[:,2].values

XX, YY = np.meshgrid(x,y)
with plt.style.context("seaborn-white"):
fig, ax = plt.subplots(figsize=(13,8))
ax.scatter(x,y, color="black", linewidth=1, edgecolor="ivory", s=50)
ax.contourf(XX,YY,z)
# TypeError: Input z must be a 2D array.

XX, YY, ZZ = np.meshgrid(x,y,z)
with plt.style.context("seaborn-white"):
fig, ax = plt.subplots(figsize=(13,8))
ax.scatter(x,y, color="black", linewidth=1, edgecolor="ivory", s=50)
ax.contourf(XX,YY,ZZ)
# TypeError: Input z must be a 2D array.

这是我当前的输出: enter image description here

我正在尝试做类似的事情: enter image description here

最佳答案

import pandas as pd
import numpy as np
from scipy.interpolate import griddata
import matplotlib.pyplot as plt
%matplotlib inline

df_xyz = pd.read_table("https://pastebin.com/raw/f87krHFK", sep="\t", index_col=0)
x = df_xyz.iloc[:,0].values
y = df_xyz.iloc[:,1].values
z = df_xyz.iloc[:,2].values

def plot_contour(x,y,z,resolution = 50,contour_method='linear'):
resolution = str(resolution)+'j'
X,Y = np.mgrid[min(x):max(x):complex(resolution), min(y):max(y):complex(resolution)]
points = [[a,b] for a,b in zip(x,y)]
Z = griddata(points, z, (X, Y), method=contour_method)
return X,Y,Z

X,Y,Z = plot_contour(x,y,z,resolution = 50,contour_method='linear')

with plt.style.context("seaborn-white"):
fig, ax = plt.subplots(figsize=(13,8))
ax.scatter(x,y, color="black", linewidth=1, edgecolor="ivory", s=50)
ax.contourf(X,Y,Z)

关于python - 如何在 matplotlib 中根据 x、y、z 坐标绘制等高线图? (plt.contourf 或 plt.contour),我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/52935143/

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