Re: [Numpy-discussion] FFT and reconstruct
Maybe I found the problems; 1. t0=1.0, t1=3.0, y['1'] = cos(1.0*omega*t): I have to reconstruct the signal using > yRec += a * cos(omega*i*(t-t0) + f) not > yRec += a * cos(omega*i*t + f) 2. t0=2, t1=3, y['Signal'] = 1.0*cos(1.0*omega*t) + ... + 5.0*cos(5.0*omega*t) + 1.0: starting point and end point must not be the same, so to generate the signal I have to use > t = linspace(t0, t1, 1000, endpoint=False) not > t = linspace(t0, t1, 1000) Thanks ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] f2py: ram usage
Using order='F' solved the problem. Thanks for reply. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] f2py: ram usage
Hi all, I am trying to write some code to do calculation onto an array: for each row I need to do some computation and have a number as return. To speed up the process I wrote a fortran subroutine that is called from python [using f2py] for each row of the array, so the input of this subroutine is a row and the output is a number. This method works but I saw some speed advantage if I pass the entire array to fortran and then, inside fortran, call the subroutine that does the math; so in this case I pass an array and return a vector. But I noticed that when python pass the array to fortran, the array is copied and the RAM usage double. Is there a way to "move" the array to fortran, I don't care if the array is lost after the call to fortran. The pyd module is generated using: python f2py.py -c --opt="-ffree-form -Ofast" -m F2PYMOD F2PYMOD.f90 Thanks Vasco ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
