slight reduction of code doubling
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@ -103,8 +103,8 @@ def divergence(size: _FloatSequence,
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k_s = _ks(size,f.shape[:3],True)
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f_fourier = _np.fft.rfftn(f,axes=(0,1,2))
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divergence_ = (_np.einsum('ijkl,ijkl ->ijk', k_s,f_fourier)*2.0j*_np.pi if n == 3 else # vector, 3 -> 1
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_np.einsum('ijkm,ijklm->ijkl',k_s,f_fourier)*2.0j*_np.pi) # tensor, 3x3 -> 3
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divergence_ = (_np.einsum('ijkl,ijkl ->ijk' if n==3 else
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'ijkm,ijklm->ijkl', k_s,f_fourier)*2.0j*_np.pi) # vector 3 -> 1, tensor 3x3 -> 3
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return _np.fft.irfftn(divergence_,axes=(0,1,2),s=f.shape[:3])
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@ -131,8 +131,8 @@ def gradient(size: _FloatSequence,
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k_s = _ks(size,f.shape[:3],True)
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f_fourier = _np.fft.rfftn(f,axes=(0,1,2))
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gradient_ = (_np.einsum('ijkl,ijkm->ijkm', f_fourier,k_s)*2.0j*_np.pi if n == 1 else # scalar, 1 -> 3
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_np.einsum('ijkl,ijkm->ijklm',f_fourier,k_s)*2.0j*_np.pi) # vector, 3 -> 3x3
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gradient_ = (_np.einsum('ijkl,ijkm->ijkm' if n==1 else
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'ijkl,ijkm->ijklm',f_fourier,k_s)*2.0j*_np.pi) # scalar 1 -> 3, vector 3 -> 3x3
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return _np.fft.irfftn(gradient_,axes=(0,1,2),s=f.shape[:3])
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