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DAMASK_EICMD
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further simplifcation of scripts

This commit is contained in:
Philip Eisenlohr 2018-01-29 14:30:26 -05:00
parent b45b43c5ac
commit 1d71a52133
2 changed files with 66 additions and 72 deletions
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73
processing/post/addCurl.py
View File

@ -10,50 +10,46 @@ scriptName = os.path.splitext(os.path.basename(__file__))[0]
scriptID = ' '.join([scriptName,damask.version]) scriptID = ' '.join([scriptName,damask.version])
def merge_dicts(*dict_args): def merge_dicts(*dict_args):
""" """Given any number of dicts, shallow copy and merge into a new dict, with precedence going to key value pairs in latter dicts."""
Given any number of dicts, shallow copy and merge into a new dict, result = {}
precedence goes to key value pairs in latter dicts. for dictionary in dict_args:
""" result.update(dictionary)
result = {} return result
for dictionary in dict_args:
result.update(dictionary)
return result
def curlFFT(geomdim,field): def curlFFT(geomdim,field):
shapeFFT = np.array(np.shape(field))[0:3] """Calculate curl of a vector or tensor field by transforming into Fourier space."""
grid = np.array(np.shape(field)[2::-1]) shapeFFT = np.array(np.shape(field))[0:3]
N = grid.prod() # field size grid = np.array(np.shape(field)[2::-1])
n = np.array(np.shape(field)[3:]).prod() # data size N = grid.prod() # field size
n = np.array(np.shape(field)[3:]).prod() # data size
if n == 3: dataType = 'vector' field_fourier = np.fft.rfftn(field,axes=(0,1,2),s=shapeFFT)
elif n == 9: dataType = 'tensor' curl_fourier = np.empty(field_fourier.shape,'c16')
field_fourier = np.fft.rfftn(field,axes=(0,1,2),s=shapeFFT) # differentiation in Fourier space
curl_fourier = np.empty(field_fourier.shape,'c16') TWOPIIMG = 2.0j*math.pi
einsums = {
3:'slm,ijkl,ijkm->ijks', # vector, 3 -> 3
9:'slm,ijkl,ijknm->ijksn', # tensor, 3x3 -> 3x3
}
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 # Nyquist freq=0 for even grid (Johnson, MIT, 2011)
# differentiation in Fourier space k_sj = np.where(np.arange(grid[1])>grid[1]//2,np.arange(grid[1])-grid[1],np.arange(grid[1]))/geomdim[1]
TWOPIIMG = 2.0j*math.pi if grid[1]%2 == 0: k_sj[grid[1]//2] = 0 # Nyquist freq=0 for even grid (Johnson, MIT, 2011)
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)
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)
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')
k_s = np.concatenate((ki[:,:,:,None],kj[:,:,:,None],kk[:,:,:,None]),axis = 3).astype('c16')
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, 2, 1] = e[2, 1, 0] = e[1, 0, 2] = -1.0
if dataType == 'tensor': # tensor, 3x3 -> 3x3
curl_fourier = np.einsum('slm,ijkl,ijknm->ijksn',e,k_s,field_fourier)*TWOPIIMG
elif dataType == 'vector': # vector, 3 -> 3
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]) 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')
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, 2, 1] = e[2, 1, 0] = e[1, 0, 2] = -1.0
curl_fourier = np.einsum(einsums[n],e,k_s,field_fourier)*TWOPIIMG
return np.fft.irfftn(curl_fourier,axes=(0,1,2),s=shapeFFT).reshape([N,n])
# -------------------------------------------------------------------- # --------------------------------------------------------------------
@ -135,7 +131,8 @@ for name in filenames:
table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:])) table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
for data in active: for data in active:
table.labels_append(['{}_curlFFT({})'.format(i+1,data['label']) for i in range(np.prod(np.array(data['shape'])))]) # extend ASCII header with new labels table.labels_append(['{}_curlFFT({})'.format(i+1,data['label'])
for i in range(np.prod(np.array(data['shape'])))]) # extend ASCII header with new labels
table.head_write() table.head_write()
# --------------- figure out size and grid --------------------------------------------------------- # --------------- figure out size and grid ---------------------------------------------------------

65
processing/post/addGradient.py
View File

@ -10,46 +10,42 @@ scriptName = os.path.splitext(os.path.basename(__file__))[0]
scriptID = ' '.join([scriptName,damask.version]) scriptID = ' '.join([scriptName,damask.version])
def merge_dicts(*dict_args): def merge_dicts(*dict_args):
""" """Given any number of dicts, shallow copy and merge into a new dict, with precedence going to key value pairs in latter dicts."""
Given any number of dicts, shallow copy and merge into a new dict, result = {}
precedence goes to key value pairs in latter dicts. for dictionary in dict_args:
""" result.update(dictionary)
result = {} return result
for dictionary in dict_args:
result.update(dictionary)
return result
def gradFFT(geomdim,field): def gradFFT(geomdim,field):
shapeFFT = np.array(np.shape(field))[0:3] """Calculate gradient of a vector or scalar field by transforming into Fourier space."""
grid = np.array(np.shape(field)[2::-1]) shapeFFT = np.array(np.shape(field))[0:3]
N = grid.prod() # field size grid = np.array(np.shape(field)[2::-1])
n = np.array(np.shape(field)[3:]).prod() # data size N = grid.prod() # field size
n = np.array(np.shape(field)[3:]).prod() # data size
if n == 3: dataType = 'vector' field_fourier = np.fft.rfftn(field,axes=(0,1,2),s=shapeFFT)
elif n == 1: dataType = 'scalar' grad_fourier = np.empty(field_fourier.shape+(3,),'c16')
field_fourier = np.fft.rfftn(field,axes=(0,1,2),s=shapeFFT) # differentiation in Fourier space
grad_fourier = np.empty(field_fourier.shape+(3,),'c16') TWOPIIMG = 2.0j*math.pi
einsums = {
1:'ijkl,ijkm->ijkm', # scalar, 1 -> 3
3:'ijkl,ijkm->ijklm', # vector, 3 -> 3x3
}
# differentiation in Fourier space k_sk = np.where(np.arange(grid[2])>grid[2]//2,np.arange(grid[2])-grid[2],np.arange(grid[2]))/geomdim[0]
# Question: why are grid[0,1,2] normalized by geomdim[2,1,0]?? if grid[2]%2 == 0: k_sk[grid[2]//2] = 0 # Nyquist freq=0 for even grid (Johnson, MIT, 2011)
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]
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]
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_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 # Nyquist freq=0 for even grid (Johnson, MIT, 2011)
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')
if dataType == 'vector': # vector, 3 -> 3x3
grad_fourier = np.einsum('ijkl,ijkm->ijklm',field_fourier,k_s)*TWOPIIMG
elif dataType == 'scalar': # scalar, 1 -> 3
grad_fourier = np.einsum('ijkl,ijkm->ijkm',field_fourier,k_s)*TWOPIIMG
return np.fft.irfftn(grad_fourier,axes=(0,1,2),s=shapeFFT).reshape([N,3*n]) k_si = np.arange(grid[0]//2+1)/geomdim[2]
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')
grad_fourier = np.einsum(einsums[n],field_fourier,k_s)*TWOPIIMG
return np.fft.irfftn(grad_fourier,axes=(0,1,2),s=shapeFFT).reshape([N,3*n])
# -------------------------------------------------------------------- # --------------------------------------------------------------------
@ -131,7 +127,8 @@ for name in filenames:
table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:])) table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
for data in active: for data in active:
table.labels_append(['{}_gradFFT({})'.format(i+1,data['label']) for i in range(coordDim*np.prod(np.array(data['shape'])))]) # extend ASCII header with new labels table.labels_append(['{}_gradFFT({})'.format(i+1,data['label'])
for i in range(coordDim*np.prod(np.array(data['shape'])))]) # extend ASCII header with new labels
table.head_write() table.head_write()
# --------------- figure out size and grid --------------------------------------------------------- # --------------- figure out size and grid ---------------------------------------------------------