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I'm writing a program that is for a friend of mine that is currently studying Aeronautical Engineering. I'm trying to test if the math I've implemented works. For those who know, I'm trying to calculate the divergence (I think I'm not an engineer and I'm not going to pretend that I am).

He sent me a Stack overflow link to a how he thinks this should be done. (The thread can be found here. His version doesn't work for me as it gives me a Numpy error as seen below:

numpy.core._internal.AxisError: axis 1 is out of bounds for array of 
dimension 1

Now I've tried a different method that gives me a different error as seen below:

ValueError: operands could not be broadcast together with shapes (60,58) 
(60,59)

This method gives me the error above and I'm not entirely sure how to fix it. I've put the code that gives me the above error.

velocity = np.diff(c_flow)/np.diff(zex)
ucom = velocity.real
vcom = -(velocity.imag)
deltau = np.divide((np.diff(ucom)),(np.diff(x)))
deltav = np.divide((np.diff(vcom)),np.diff(y))
print(deltau + deltav)

Note: C_flow is defined earlier in the program and is the complex potential. zex is also defined earlier as an early form of the complex variable. x and y are two coordinate matrices from coordinate vectors.

The expected results from the print statement should be zero or a value that is very close to zero. (I'm not entirely sure what the value should be but as I've said, I'm not an engineer)

Thank you in advance

EDIT:

After following BenT's advice I used np.gradient and np.sum but this was adding the axis in the wrong direction so to counteract this the I separated the two functions as seen below:

velocity = np.diff(c_flow)/np.diff(z)
grad = (np.gradient(velocity))
divergence = np.sum(grad, axis=0)
print(np.average(divergence))
print(np.average(velocity))
DestinedPotato
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  • When taking the difference you get 1 value less than your original data, so you need to remove a value at the end by using `[:,:-1]` to get the same shape. You could also use np.gradient which will conserve shape but will not be a finite difference. – BenT May 03 '19 at 16:28
  • That you for your help @BenT. I used np.gradient and I asked my friend if it was correct. It was very nearly there. np.gradient wasn't adding the values in the correct axis. – DestinedPotato May 04 '19 at 11:42
  • All these functions have an axis parameter. If the arrays aren't 1D, use it. – Mad Physicist May 04 '19 at 11:51

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