双线性插值的python内置函数

在Python中，可以使用SciPy库中的griddata函数来进行双线性插值。griddata函数提供了三种插值方法，分别是线性插值、三次插值和临近插值。可以通过指定method参数来选择使用的插值方法。以下是一个使用griddata函数进行双线性插值的Python代码示例：

import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import griddata

# 插值函数
def func(x, y):
    return x*(1-x)*np.cos(4*np.pi*x) * np.sin(4*np.pi*y**2)**2

rng = np.random.default_rng()
pts = rng.random((1000, 2))
values = func(pts[:,0], pts[:,1])

xs, ys = np.indices([100,100])/100
zs = func(xs, ys)

grids = {}
keys = ["nearest", "linear", "cubic"]

for key in keys:
    grids[key = griddata(pts, values, (xs,ys), method=key)

fig = plt.figure()
ax = fig.add_subplot(2,2,1)
ax.imshow(zs.T, extent=(0,1,0,1), origin='lower')
plt.scatter(pts[:,0], pts[:,1], marker='.', c='black')
ax.set_title('Original')
plt.axis('off')

for i,key in zip([2,3,4], keys):
    ax = fig.add_subplot(2,2,i,projection='3d')
    ax.plot_surface(xs, ys, grids[key])
    ax.set_title(key)

plt.show()

这段代码通过生成随机的点集pts和对应的函数值values，然后使用griddata函数进行插值，并绘制原始数据和插值结果的图表。可以根据需要选择不同的插值方法来得到双线性插值的结果。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* [双线性插值的python实现](https://blog.csdn.net/weixin_44359479/article/details/123794413)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v92^chatsearchT3_1"}}] [.reference_item style="max-width: 50%"]
- *2* *3* [Python双线性插值和双三次插值](https://blog.csdn.net/m0_37816922/article/details/128138833)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v92^chatsearchT3_1"}}] [.reference_item style="max-width: 50%"]
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