Converting Find() In Matlab To Python
I am converting a code from Matlab to Python. The code in Matlab is: x = find(sEdgepoints > 0 & sNorm < lowT); sEdgepoints(x)=0; Both arrays are of same size, and I am b
Solution 1:
Try np.argwhere()
(and note the importance of the () around the inequalities):
>>X=np.array([1,2,3,4,5])
>>Y=np.array([7,6,5,4,3])
>>ans = np.argwhere((X>3) & (Y<7))
>>ans
array([[3],
[4]])
Solution 2:
and
does boolean operation and numpy expects you to do bitwise operation, so you have to use &
i.e
x = np.nonzero((dstc > 0) & ( dst < 60))
Solution 3:
You could implement it yourself like:
x = [[i,j] for i, j in zip(sEdgepoints , sNorm ) if i > 0 and j < lowT]
Will give you a list of of lists corresponding to your the matching constraints. I guess this might not be exactly what you are looking for.
Maybe look at the pandas module, it makes masking more comfortable than plain python or numpy: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.mask.html
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