improved readability
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d35c9dd431
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@ -38,9 +38,9 @@ def curlFFT(geomdim,field):
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e[0, 1, 2] = e[1, 2, 0] = e[2, 0, 1] = 1.0 # Levi-Civita symbols
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e[0, 2, 1] = e[2, 1, 0] = e[1, 0, 2] = -1.0
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if dataType == 'tensor':
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if dataType == 'tensor': # tensor, 3x3 -> 3x3
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curl_fourier = np.einsum('slm,ijkl,ijknm->ijksn',e,k_s,field_fourier)*TWOPIIMG
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elif dataType == 'vector':
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elif dataType == 'vector': # vector, 3 -> 3
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curl_fourier = np.einsum('slm,ijkl,ijkm->ijks',e,k_s,field_fourier)*TWOPIIMG
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return np.fft.irfftn(curl_fourier,axes=(0,1,2),s=shapeFFT).reshape([N,n])
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@ -15,6 +15,9 @@ def divFFT(geomdim,field):
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N = grid.prod() # field size
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n = np.array(np.shape(field)[3:]).prod() # data size
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if n == 3: dataType = 'vector'
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elif n == 9: dataType = 'tensor'
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field_fourier = np.fft.rfftn(field,axes=(0,1,2),s=shapeFFT)
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div_fourier = np.empty(field_fourier.shape[0:len(np.shape(field))-1],'c16') # size depents on whether tensor or vector
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@ -30,9 +33,9 @@ def divFFT(geomdim,field):
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kk, kj, ki = np.meshgrid(k_sk,k_sj,k_si,indexing = 'ij')
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k_s = np.concatenate((ki[:,:,:,None],kj[:,:,:,None],kk[:,:,:,None]),axis = 3).astype('c16')
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if n == 9: # tensor, 3x3 -> 3
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if dataType == 'tensor': # tensor, 3x3 -> 3
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div_fourier = np.einsum('ijklm,ijkm->ijkl',field_fourier,k_s)*TWOPIIMG
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elif n == 3: # vector, 3 -> 1
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elif dataType == 'vector': # vector, 3 -> 1
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div_fourier = np.einsum('ijkl,ijkl->ijk',field_fourier,k_s)*TWOPIIMG
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return np.fft.irfftn(div_fourier,axes=(0,1,2),s=shapeFFT).reshape([N,n/3])
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@ -33,9 +33,9 @@ def gradFFT(geomdim,field):
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kk, kj, ki = np.meshgrid(k_sk,k_sj,k_si,indexing = 'ij')
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k_s = np.concatenate((ki[:,:,:,None],kj[:,:,:,None],kk[:,:,:,None]),axis = 3).astype('c16')
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if n == 3: # vector, 3 -> 3x3
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if dataType == 'vector': # vector, 3 -> 3x3
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grad_fourier = np.einsum('ijkl,ijkm->ijklm',field_fourier,k_s)*TWOPIIMG
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elif n == 1: # scalar, 1 -> 3
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elif dataType == 'scalar': # scalar, 1 -> 3
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grad_fourier = np.einsum('ijkl,ijkl->ijkl',field_fourier,k_s)*TWOPIIMG
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return np.fft.irfftn(grad_fourier,axes=(0,1,2),s=shapeFFT).reshape([N,3*n])
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