Understanding the difference between ndgrid and meshgrid (from Numpy)
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Hello everyone,
I am just trying to understand a diffrance between ngrid. Please observe the code below when I check the size of the Ngrid2 I get two cells of $10 \times 10$ and three cells of 15 × 15 × 15, but when I do something similar in numpy meshgrid function I get
. Can someone please explain to me the diffrance? Also please advise if there is any way to make Matlab ngrid behave as meshgrid from numpy.

Thank you very much in advance for the consideration!
%Matlab
x = linspace(0,1,5);
[Ngrid2{:}] = ndgrid(x,x);
[Ngrid3{:}] = ndgrid(x,x);
%Python Numpy
x = np.linspace(0,1,5)
z = np.array(np.meshgrid(x,x))
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Stephen23
2021년 6월 15일
편집: Stephen23
2021년 6월 15일
If you must replicate numpy.meshgrid (with the default indexing='xy') then do not use ndgrid, unless you want to waste time permuting all of the output arrays. The correct way to get the same behavior is to use meshgrid:
x = linspace(0,1,5);
C = cell(1,2);
[C{:}] = meshgrid(x);
Checking:
C{:}
[C{:}] = ndgrid(x); % Note the order of the first two dimensions!
C{:}
And for comparison (by default the same order as meshgrid):
x = np.linspace(0,1,5)
z = np.array(np.meshgrid(x,x))
print(z[0])
print(z[1])
[[0. 0.25 0.5 0.75 1. ]
[0. 0.25 0.5 0.75 1. ]
[0. 0.25 0.5 0.75 1. ]
[0. 0.25 0.5 0.75 1. ]
[0. 0.25 0.5 0.75 1. ]]
[[0. 0. 0. 0. 0. ]
[0.25 0.25 0.25 0.25 0.25]
[0.5 0.5 0.5 0.5 0.5 ]
[0.75 0.75 0.75 0.75 0.75]
[1. 1. 1. 1. 1. ]]
"Can someone please explain to me the diffrance?"
Look at the order of the first two dimensions in the output arrays.
- MATLAB's ndgrid corresponds to np.meshgrid(... indexing='ij')
- MATLAB's meshgrid corresponds to np.meshgrid(... indexing='xy') # default
See the "Notes" here: https://numpy.org/doc/stable/reference/generated/numpy.meshgrid.html
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Stephen23
2021년 6월 16일
편집: Stephen23
2021년 6월 16일
Rather than creating special cases for the first two dimensions you should consider writing your algorithm to use the consistent NDGRID order/orientation instead (just to be clear: this means changing the rest of your code to suit, and not permuting the arrays).
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