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DAMASK_EICMD
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using 3 way merge to have syntax as similar as possible

This commit is contained in:
Martin Diehl 2016-11-07 09:49:53 +01:00
parent cbe2fb8d57
commit 873c52cceb
3 changed files with 23 additions and 23 deletions
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14
processing/post/addCurl.py
View File

@ -12,8 +12,8 @@ scriptID = ' '.join([scriptName,damask.version])
def curlFFT(geomdim,field): def curlFFT(geomdim,field):
shapeFFT = np.array(np.shape(field))[0:3] shapeFFT = np.array(np.shape(field))[0:3]
grid = np.array(np.shape(field)[2::-1]) grid = np.array(np.shape(field)[2::-1])
N = grid.prod() # field size N = grid.prod() # field size
n = np.array(np.shape(field)[3:]).prod() # data size n = np.array(np.shape(field)[3:]).prod() # data size
if n == 3: dataType = 'vector' if n == 3: dataType = 'vector'
elif n == 9: dataType = 'tensor' elif n == 9: dataType = 'tensor'
@ -24,10 +24,10 @@ def curlFFT(geomdim,field):
# differentiation in Fourier space # differentiation in Fourier space
TWOPIIMG = 2.0j*math.pi TWOPIIMG = 2.0j*math.pi
k_sk = np.where(np.arange(grid[2])>grid[2]//2,np.arange(grid[2])-grid[2],np.arange(grid[2]))/geomdim[0] k_sk = np.where(np.arange(grid[2])>grid[2]//2,np.arange(grid[2])-grid[2],np.arange(grid[2]))/geomdim[0]
if grid[2]%2 == 0: k_sk[grid[2]//2] = 0 # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011) if grid[2]%2 == 0: k_sk[grid[2]//2] = 0 # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011)
k_sj = np.where(np.arange(grid[1])>grid[1]//2,np.arange(grid[1])-grid[1],np.arange(grid[1]))/geomdim[1] k_sj = np.where(np.arange(grid[1])>grid[1]//2,np.arange(grid[1])-grid[1],np.arange(grid[1]))/geomdim[1]
if grid[1]%2 == 0: k_sj[grid[1]//2] = 0 # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011) if grid[1]%2 == 0: k_sj[grid[1]//2] = 0 # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011)
k_si = np.arange(grid[0]//2+1)/geomdim[2] k_si = np.arange(grid[0]//2+1)/geomdim[2]
@ -35,12 +35,12 @@ def curlFFT(geomdim,field):
k_s = np.concatenate((ki[:,:,:,None],kj[:,:,:,None],kk[:,:,:,None]),axis = 3).astype('c16') k_s = np.concatenate((ki[:,:,:,None],kj[:,:,:,None],kk[:,:,:,None]),axis = 3).astype('c16')
e = np.zeros((3, 3, 3)) e = np.zeros((3, 3, 3))
e[0, 1, 2] = e[1, 2, 0] = e[2, 0, 1] = 1.0 # Levi-Civita symbols e[0, 1, 2] = e[1, 2, 0] = e[2, 0, 1] = 1.0 # Levi-Civita symbols
e[0, 2, 1] = e[2, 1, 0] = e[1, 0, 2] = -1.0 e[0, 2, 1] = e[2, 1, 0] = e[1, 0, 2] = -1.0
if dataType == 'tensor': # tensor, 3x3 -> 3x3 if dataType == 'tensor': # tensor, 3x3 -> 3x3
curl_fourier = np.einsum('slm,ijkl,ijknm->ijksn',e,k_s,field_fourier)*TWOPIIMG curl_fourier = np.einsum('slm,ijkl,ijknm->ijksn',e,k_s,field_fourier)*TWOPIIMG
elif dataType == 'vector': # vector, 3 -> 3 elif dataType == 'vector': # vector, 3 -> 3
curl_fourier = np.einsum('slm,ijkl,ijkm->ijks',e,k_s,field_fourier)*TWOPIIMG curl_fourier = np.einsum('slm,ijkl,ijkm->ijks',e,k_s,field_fourier)*TWOPIIMG
return np.fft.irfftn(curl_fourier,axes=(0,1,2),s=shapeFFT).reshape([N,n]) return np.fft.irfftn(curl_fourier,axes=(0,1,2),s=shapeFFT).reshape([N,n])

14
processing/post/addDivergence.py
View File

@ -12,30 +12,30 @@ scriptID = ' '.join([scriptName,damask.version])
def divFFT(geomdim,field): def divFFT(geomdim,field):
shapeFFT = np.array(np.shape(field))[0:3] shapeFFT = np.array(np.shape(field))[0:3]
grid = np.array(np.shape(field)[2::-1]) grid = np.array(np.shape(field)[2::-1])
N = grid.prod() # field size N = grid.prod() # field size
n = np.array(np.shape(field)[3:]).prod() # data size n = np.array(np.shape(field)[3:]).prod() # data size
if n == 3: dataType = 'vector' if n == 3: dataType = 'vector'
elif n == 9: dataType = 'tensor' elif n == 9: dataType = 'tensor'
field_fourier = np.fft.rfftn(field,axes=(0,1,2),s=shapeFFT) field_fourier = np.fft.rfftn(field,axes=(0,1,2),s=shapeFFT)
div_fourier = np.empty(field_fourier.shape[0:len(np.shape(field))-1],'c16') # size depents on whether tensor or vector div_fourier = np.empty(field_fourier.shape[0:len(np.shape(field))-1],'c16')
# differentiation in Fourier space # differentiation in Fourier space
TWOPIIMG = 2.0j*math.pi TWOPIIMG = 2.0j*math.pi
k_sk = np.where(np.arange(grid[2])>grid[2]//2,np.arange(grid[2])-grid[2],np.arange(grid[2]))/geomdim[0] k_sk = np.where(np.arange(grid[2])>grid[2]//2,np.arange(grid[2])-grid[2],np.arange(grid[2]))/geomdim[0]
if grid[2]%2 == 0: k_sk[grid[2]//2] = 0 # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011) if grid[2]%2 == 0: k_sk[grid[2]//2] = 0 # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011)
k_sj = np.where(np.arange(grid[1])>grid[1]//2,np.arange(grid[1])-grid[1],np.arange(grid[1]))/geomdim[1] k_sj = np.where(np.arange(grid[1])>grid[1]//2,np.arange(grid[1])-grid[1],np.arange(grid[1]))/geomdim[1]
if grid[1]%2 == 0: k_sj[grid[1]//2] = 0 # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011) if grid[1]%2 == 0: k_sj[grid[1]//2] = 0 # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011)
k_si = np.arange(grid[0]//2+1)/geomdim[2] k_si = np.arange(grid[0]//2+1)/geomdim[2]
kk, kj, ki = np.meshgrid(k_sk,k_sj,k_si,indexing = 'ij') kk, kj, ki = np.meshgrid(k_sk,k_sj,k_si,indexing = 'ij')
k_s = np.concatenate((ki[:,:,:,None],kj[:,:,:,None],kk[:,:,:,None]),axis = 3).astype('c16') k_s = np.concatenate((ki[:,:,:,None],kj[:,:,:,None],kk[:,:,:,None]),axis = 3).astype('c16')
if dataType == 'tensor': # tensor, 3x3 -> 3 if dataType == 'tensor': # tensor, 3x3 -> 3
div_fourier = np.einsum('ijklm,ijkm->ijkl',field_fourier,k_s)*TWOPIIMG div_fourier = np.einsum('ijklm,ijkm->ijkl',field_fourier,k_s)*TWOPIIMG
elif dataType == 'vector': # vector, 3 -> 1 elif dataType == 'vector': # vector, 3 -> 1
div_fourier = np.einsum('ijkl,ijkl->ijk',field_fourier,k_s)*TWOPIIMG div_fourier = np.einsum('ijkl,ijkl->ijk',field_fourier,k_s)*TWOPIIMG
return np.fft.irfftn(div_fourier,axes=(0,1,2),s=shapeFFT).reshape([N,n/3]) return np.fft.irfftn(div_fourier,axes=(0,1,2),s=shapeFFT).reshape([N,n/3])

12
processing/post/addGradient.py
View File

@ -12,8 +12,8 @@ scriptID = ' '.join([scriptName,damask.version])
def gradFFT(geomdim,field): def gradFFT(geomdim,field):
shapeFFT = np.array(np.shape(field))[0:3] shapeFFT = np.array(np.shape(field))[0:3]
grid = np.array(np.shape(field)[2::-1]) grid = np.array(np.shape(field)[2::-1])
N = grid.prod() # field size N = grid.prod() # field size
n = np.array(np.shape(field)[3:]).prod() # data size n = np.array(np.shape(field)[3:]).prod() # data size
if n == 3: dataType = 'vector' if n == 3: dataType = 'vector'
elif n == 1: dataType = 'scalar' elif n == 1: dataType = 'scalar'
@ -24,18 +24,18 @@ def gradFFT(geomdim,field):
# differentiation in Fourier space # differentiation in Fourier space
TWOPIIMG = 2.0j*math.pi TWOPIIMG = 2.0j*math.pi
k_sk = np.where(np.arange(grid[2])>grid[2]//2,np.arange(grid[2])-grid[2],np.arange(grid[2]))/geomdim[0] k_sk = np.where(np.arange(grid[2])>grid[2]//2,np.arange(grid[2])-grid[2],np.arange(grid[2]))/geomdim[0]
if grid[2]%2 == 0: k_sk[grid[2]//2] = 0 # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011) if grid[2]%2 == 0: k_sk[grid[2]//2] = 0 # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011)
k_sj = np.where(np.arange(grid[1])>grid[1]//2,np.arange(grid[1])-grid[1],np.arange(grid[1]))/geomdim[1] k_sj = np.where(np.arange(grid[1])>grid[1]//2,np.arange(grid[1])-grid[1],np.arange(grid[1]))/geomdim[1]
if grid[1]%2 == 0: k_sj[grid[1]//2] = 0 # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011) if grid[1]%2 == 0: k_sj[grid[1]//2] = 0 # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011)
k_si = np.arange(grid[0]//2+1)/geomdim[2] k_si = np.arange(grid[0]//2+1)/geomdim[2]
kk, kj, ki = np.meshgrid(k_sk,k_sj,k_si,indexing = 'ij') kk, kj, ki = np.meshgrid(k_sk,k_sj,k_si,indexing = 'ij')
k_s = np.concatenate((ki[:,:,:,None],kj[:,:,:,None],kk[:,:,:,None]),axis = 3).astype('c16') k_s = np.concatenate((ki[:,:,:,None],kj[:,:,:,None],kk[:,:,:,None]),axis = 3).astype('c16')
if dataType == 'vector': # vector, 3 -> 3x3 if dataType == 'vector': # vector, 3 -> 3x3
grad_fourier = np.einsum('ijkl,ijkm->ijklm',field_fourier,k_s)*TWOPIIMG grad_fourier = np.einsum('ijkl,ijkm->ijklm',field_fourier,k_s)*TWOPIIMG
elif dataType == 'scalar': # scalar, 1 -> 3 elif dataType == 'scalar': # scalar, 1 -> 3
grad_fourier = np.einsum('ijkl,ijkl->ijkl',field_fourier,k_s)*TWOPIIMG grad_fourier = np.einsum('ijkl,ijkl->ijkl',field_fourier,k_s)*TWOPIIMG
return np.fft.irfftn(grad_fourier,axes=(0,1,2),s=shapeFFT).reshape([N,3*n]) return np.fft.irfftn(grad_fourier,axes=(0,1,2),s=shapeFFT).reshape([N,3*n])