Plot contourf 有 3 个向量

Question

我正在尝试 plot contourf 的 3 个不同的相等向量。 我了解到我必须对它们进行插值，这样我才能获得连接它们的 function。 但我不知道如何插值或使用 contourf。 我正在使用这段代码：

from scipy.interpolate import griddata

[X, Y] = np.meshgrid(np.unique(x), np.unique(hf));
Z = griddata(x, hf, Sf, X, Y);
plt.contourf(X, Y, Z);

但它不起作用，它返回给我：“未知的插值方法数组......”。 我是 Python 的新手，非常感谢您的帮助。 谢谢 ！

我的载体样本：

hf=[1.09761983 1.09761983 1.09703788 1.097271   1.10385924 1.11355334
 1.12989642 1.14784403 1.16748527 1.18743366 1.20757876 1.22775466
 1.24794379 1.26813503 1.28832711 1.30851956 1.32871231 1.34890535
 1.36909866 1.38929224 1.40948608 1.42968017 1.44987451 1.47006907
 1.49026387 1.51045888 1.5306541  1.55084953 1.57104516 1.59124097
 1.61143697 1.63163315 1.6518295  1.67202601 1.69222269 1.71241952
 1.73261649 1.75281362 1.77301088 1.79320827 1.81340579 1.83360344
 1.85380121 1.8739991  1.8941971  1.9143952  1.93459341 1.95479173
 1.97499014 1.99518865 2.01538725 2.03558594 2.05578471 2.07598357
 2.09618251 2.11638152 2.13658061 2.15677978 2.17697901 2.19717832
 2.21737769 2.23757712 2.25777662 2.27797617 2.29817579 2.31837546
 2.33857519 2.35877497 2.3789748  2.39917468 2.41937461 2.43957459
 2.45977462 2.47997469 2.5001748  2.52037495 2.54057515 2.56077538
 2.58097566 2.60117597 2.6213763  2.64157661 2.66177664 2.68197522
 2.70216771 2.72233703 2.74242962 2.76229069 2.78156777 2.79947929
 2.81495902 2.82619235 2.83289165 2.8341084  2.8333398  2.83220409
 2.83188838 2.83287856 2.83369273 2.83369273]


sf=[-0.00000000e+00  9.41936214e-05  1.91152978e-04  2.98759982e-04
  3.99659269e-04  4.70022026e-04  5.16169870e-04  5.32537982e-04
  5.37949242e-04  5.35608036e-04  5.31283264e-04  5.26012479e-04
  5.20586048e-04  5.15141534e-04  5.09744458e-04  5.04407257e-04
  4.99135214e-04  4.93930450e-04  4.88794377e-04  4.83727997e-04
  4.78732027e-04  4.73806938e-04  4.68952988e-04  4.64170250e-04
  4.59458631e-04  4.54817895e-04  4.50247680e-04  4.45747516e-04
  4.41316836e-04  4.36954993e-04  4.32661270e-04  4.28434890e-04
  4.24275023e-04  4.20180801e-04  4.16151315e-04  4.12185633e-04
  4.08282794e-04  4.04441824e-04  4.00661733e-04  3.96941521e-04
  3.93280183e-04  3.89676711e-04  3.86130097e-04  3.82639334e-04
  3.79203420e-04  3.75821360e-04  3.72492166e-04  3.69214858e-04
  3.65988469e-04  3.62812041e-04  3.59684630e-04  3.56605303e-04
  3.53573142e-04  3.50587242e-04  3.47646715e-04  3.44750685e-04
  3.41898293e-04  3.39088693e-04  3.36321058e-04  3.33594572e-04
  3.30908438e-04  3.28261874e-04  3.25654112e-04  3.23084400e-04
  3.20552001e-04  3.18056194e-04  3.15596272e-04  3.13171543e-04
  3.10781329e-04  3.08424968e-04  3.06101811e-04  3.03811222e-04
  3.01552581e-04  2.99325279e-04  2.97128722e-04  2.94962327e-04
  2.92825525e-04  2.90717754e-04  2.88638441e-04  2.86586924e-04
  2.84562119e-04  2.82561312e-04  2.80576100e-04  2.78580803e-04
  2.76501340e-04  2.74154196e-04  2.71113974e-04  2.66628934e-04
  2.59338319e-04  2.47990830e-04  2.30648251e-04  2.08333235e-04
  1.80857088e-04  1.52747034e-04  1.25846945e-04  1.00061809e-04
  7.56242555e-05  5.11214342e-05  2.54090569e-05 -0.00000000e+00]


x=[ 0.          0.1010101   0.2020202   0.3030303   0.4040404   0.50505051
  0.60606061  0.70707071  0.80808081  0.90909091  1.01010101  1.11111111
  1.21212121  1.31313131  1.41414141  1.51515152  1.61616162  1.71717172
  1.81818182  1.91919192  2.02020202  2.12121212  2.22222222  2.32323232
  2.42424242  2.52525253  2.62626263  2.72727273  2.82828283  2.92929293
  3.03030303  3.13131313  3.23232323  3.33333333  3.43434343  3.53535354
  3.63636364  3.73737374  3.83838384  3.93939394  4.04040404  4.14141414
  4.24242424  4.34343434  4.44444444  4.54545455  4.64646465  4.74747475
  4.84848485  4.94949495  5.05050505  5.15151515  5.25252525  5.35353535
  5.45454545  5.55555556  5.65656566  5.75757576  5.85858586  5.95959596
  6.06060606  6.16161616  6.26262626  6.36363636  6.46464646  6.56565657
  6.66666667  6.76767677  6.86868687  6.96969697  7.07070707  7.17171717
  7.27272727  7.37373737  7.47474747  7.57575758  7.67676768  7.77777778
  7.87878788  7.97979798  8.08080808  8.18181818  8.28282828  8.38383838
  8.48484848  8.58585859  8.68686869  8.78787879  8.88888889  8.98989899
  9.09090909  9.19191919  9.29292929  9.39393939  9.49494949  9.5959596
  9.6969697   9.7979798   9.8989899  10.        ]

Answer 1

您可以参考下面的示例，它可能适合您，但您需要相应地进行更改。

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from matplotlib import cm
from matplotlib import rc, rcParams
from mpl_toolkits.mplot3d import Axes3D
from scipy.interpolate import griddata
import matplotlib.gridspec as gridspec

df = pd.read_csv('bathy_bedford.csv')
# 2D-arrays from DataFrame
x1 = np.linspace(df['x'].min(), df['x'].max(), len(df['x'].unique()))
y1 = np.linspace(df['y'].min(), df['y'].max(), len(df['y'].unique()))

"""
x, y via meshgrid for vectorized evaluation of
2 scalar/vector fields over 2-D grids, given
one-dimensional coordinate arrays x1, x2,..., xn.
"""

x2, y2 = np.meshgrid(x1, y1)

# Interpolate unstructured D-dimensional data.
z2 = griddata((df['x'], df['y']), df['z'], (x2, y2), method='cubic')

# Ready to plot

fig = plt.figure(211,figsize=(15,20))
ax = fig.add_subplot(211, projection='3d')
spec = gridspec.GridSpec(ncols=1, nrows=2,
                         height_ratios=[4, 1])

surf = ax.plot_surface(x2, y2, z2, rstride=1, cstride=1, cmap=cm.terrain,
                       linewidth=1, antialiased=False)
ax.view_init(45,-55)

cset = ax.contourf(x2, y2, z2, zdir='z2', offset=-80, cmap=cm.terrain, antialiased=True)

rcParams['legend.fontsize'] = 20

rc('text', usetex=True)
rc('axes', linewidth=2)
rc('font', weight='bold')

这是我所拥有的 plot。

附言：

但它不起作用，它返回给我：“未知的插值方法数组......”。

这是在抱怨Interpolate unstructured D-dimensional data 。 您需要指定方法。

请参阅此处的文档。 z2 = griddata((df['x'], df['y']), df['z'], (x2, y2), method='cubic')