英文:
Interpolation Scipy in Python with meshgrid
问题
以下是您的代码的中文翻译部分：
这是我在MATLAB中的代码：
x = linspace(-10,10,10);
y = linspace(-5,5,10);
DATA = rand(10,10);
[XX,YY] = ndgrid(x,y);
XX2 = XX;
YY2 = YY;
DATA2 = interpn(XX,YY,DATA,XX2,YY2);
我尝试在Python中实现相同的插值，但似乎很难使用网格格式的矩阵来做到这一点。
import numpy as np
import scipy.interpolate
x = np.linspace(-10,10,10)
y = np.linspace(-5,5,10)
DATA = np.random.rand(10,10)
[XX,YY] = np.meshgrid(x,y,indexing='ij')
XX2 = XX
YY2 = YY
DATA2 = scipy.interpolate.interpn(XX,YY,DATA,XX2,YY2) # 无法正常工作
有关如何解决这个问题的想法吗？
英文:
I would like to do the same interpolation as MATLAB in Python with scipy. Here is an example of my code.
This is what I have in MATLAB :
x = linspace(-10,10,10);
y = linspace(-5,5,10);
DATA = rand(10,10);
[XX,YY] = ndgrid(x,y);
XX2 = XX;
YY2 = YY;
DATA2 = interpn(XX,YY,DATA,XX2,YY2);
I try to to it in Python but seems to be difficult to do it with matrix in meshgrid format.
import numpy as np
import scipy.interpolate
x = np.linspace(-10,10,10)
y = np.linspace(-5,5,10)
DATA = np.random.rand(10,10)
[XX,YY] = np.meshgrid(x,y,indexing='ij')
XX2 = XX
YY2 = YY
DATA2 = scipy.interpolate.interpn(XX,YY,DATA,XX2,YY2) # NOT WORKING
Any ideas on how to solve this issue ?
答案1
得分: 0
我找到了解决方案。以下是使用Python和Scipy编写的代码：
import numpy as np
import scipy.interpolate
x = np.linspace(-10, 10, 10)
y = np.linspace(-5, 5, 10)
DATA = np.random.rand(10, 10)
[XX, YY] = np.meshgrid(x, y, indexing='ij')
XX2 = XX
YY2 = YY
gridInitial = (x, y)
gridToInterpolate = np.stack((XX2.ravel(), YY2.ravel()), axis=1)
DATA2 = scipy.interpolate.interpn(gridInitial, DATA, gridToInterpolate, method='linear', bounds_error=False, fill_value=0)
DATA2 = DATA2.reshape(XX2.shape)
gridToInterpolate仅是一个包含新网格所有点的向量。因此，最后只需重新整形你的数据即可。
英文:
I found the solution. Here the code in Python with Scipy :
import numpy as np
import scipy.interpolate
x = np.linspace(-10,10,10)
y = np.linspace(-5,5,10)
DATA = np.random.rand(10,10)
[XX,YY] = np.meshgrid(x,y,indexing='ij')
XX2 = XX
YY2 = YY
gridInitial = (x,y)
gridToInterpolate = np.stack((XX2.ravel(),YY2.ravel()),axis=1)
DATA2 = scipy.interpolate.interpn(gridInitial,DATA,gridToInterpolate,method='linear',bounds_error=False,fill_value=0)
DATA2 = DATA2.reshape(XX2.shape)
gridToInterpolate is just a vector with all the points of the new grid. So you just have to reshape your data at the end.
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