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In numpy I have an array like
[0 + 0.5j, 0.25 + 1.2352444e-24j, 0.25+ 0j, 2.46519033e-32 + 0j] what is the fastest and easiest way to set the super low value to zero to get
[0 + 0.5j, 0.25 + 0j, 0.25+ 0j, 0 + 0j] efficiency is not the paramount.
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You can use numpy. nan_to_num : numpy. nan_to_num(x) : Replace nan with zero and inf with finite numbers.
The zeros() function is used to get a new array of given shape and type, filled with zeros. Shape of the new array, e.g., (2, 3) or 2. The desired data-type for the array, e.g., numpy. int8.
To reduce a multi-dimensional array, use the np. ufunc. reduce() method in Python Numpy.
Hmmm. I'm not super-happy with it, but this seems to work:
>>> a = np.array([0 + 0.5j, 0.25 + 1.2352444e-24j, 0.25+ 0j, 2.46519033e-32 + 0j]) >>> a array([ 0.00000000e+00 +5.00000000e-01j, 2.50000000e-01 +1.23524440e-24j, 2.50000000e-01 +0.00000000e+00j, 2.46519033e-32 +0.00000000e+00j]) >>> tol = 1e-16 >>> a.real[abs(a.real) < tol] = 0.0 >>> a.imag[abs(a.imag) < tol] = 0.0 >>> a array([ 0.00+0.5j, 0.25+0.j , 0.25+0.j , 0.00+0.j ]) and you can choose your tolerance as your problem requires. I usually use an order of magnitude or so higher than
>>> np.finfo(np.float).eps 2.2204460492503131e-16 but it's problem-dependent.
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