Merge remote-tracking branch 'origin/development' into NoCoreModule
This commit is contained in:
Martin Diehl
commit
f04b737e4c
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@ -74,32 +74,30 @@ for name in filenames:
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}
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# ------------------------------------------ Evaluate condition ---------------------------------------
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if options.condition:
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if options.condition is not None:
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interpolator = []
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condition = options.condition # copy per file, since might be altered inline
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condition = options.condition # copy per file, since might be altered inline
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breaker = False
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for position,operand in enumerate(set(re.findall(r'#(([s]#)?(.+?))#',condition))): # find three groups
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for position,operand in enumerate(set(re.findall(r'#(([s]#)?(.+?))#',condition))): # find three groups
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condition = condition.replace('#'+operand[0]+'#',
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{ '': '{%i}'%position,
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's#':'"{%i}"'%position}[operand[1]])
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if operand[2] in specials: # special label
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if operand[2] in specials: # special label
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interpolator += ['specials["%s"]'%operand[2]]
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else:
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try:
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interpolator += ['%s(table.data[%i])'%({ '':'float',
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's#':'str'}[operand[1]],
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table.label_index(operand[2]))] # ccould be generalized to indexrange as array lookup
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table.label_index(operand[2]))] # could be generalized to indexrange as array lookup
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except:
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damask.util.croak('column "{}" not found.'.format(operand[2]))
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breaker = True
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if breaker: continue # found mistake in condition evaluation --> next file
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if breaker: continue # found mistake in condition evaluation --> next file
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evaluator_condition = "'" + condition + "'.format(" + ','.join(interpolator) + ")"
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else: condition = ''
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# ------------------------------------------ build formulae ----------------------------------------
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evaluator = {}
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@ -165,19 +163,19 @@ for name in filenames:
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for label in output.labels():
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oldIndices = table.label_indexrange(label)
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Nold = max(1,len(oldIndices)) # Nold could be zero for new columns
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Nold = max(1,len(oldIndices)) # Nold could be zero for new columns
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Nnew = len(output.label_indexrange(label))
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output.data_append(eval(evaluator[label]) if label in options.labels and
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(condition == '' or eval(eval(evaluator_condition)))
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else np.tile([table.data[i] for i in oldIndices]
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if label in tabLabels
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else np.nan,
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np.ceil(float(Nnew)/Nold))[:Nnew]) # spread formula result into given number of columns
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(options.condition is None or eval(eval(evaluator_condition)))
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else np.tile([table.data[i] for i in oldIndices]
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if label in tabLabels
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else np.nan,
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np.ceil(float(Nnew)/Nold))[:Nnew]) # spread formula result into given number of columns
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outputAlive = output.data_write() # output processed line
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outputAlive = output.data_write() # output processed line
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# ------------------------------------------ output finalization -----------------------------------
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table.input_close() # close ASCII tables
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output.close() # close ASCII tables
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table.input_close() # close ASCII tables
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output.close() # close ASCII tables
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@ -18,7 +18,7 @@ def curlFFT(geomdim,field):
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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.fftpack.rfftn(field,axes=(0,1,2),s=shapeFFT)
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field_fourier = np.fft.rfftn(field,axes=(0,1,2),s=shapeFFT)
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curl_fourier = np.empty(field_fourier.shape,'c16')
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# differentiation in Fourier space
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@ -56,7 +56,7 @@ def curlFFT(geomdim,field):
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curl_fourier[i,j,k,2] = ( field_fourier[i,j,k,1]*xi[0]\
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-field_fourier[i,j,k,0]*xi[1]) *TWOPIIMG
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return np.fft.fftpack.irfftn(curl_fourier,axes=(0,1,2),s=shapeFFT).reshape([N,n])
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return np.fft.irfftn(curl_fourier,axes=(0,1,2),s=shapeFFT).reshape([N,n])
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# --------------------------------------------------------------------
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@ -158,7 +158,7 @@ for name in filenames:
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# we need to reverse order here, because x is fastest,ie rightmost, but leftmost in our x,y,z notation
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stack.append(curlFFT(size[::-1],
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table.data[:,data['column'][i]:data['column'][i]+data['dim']].
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reshape([grid[2],grid[1],grid[0]]+data['shape'])))
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reshape(grid[::-1].tolist()+data['shape'])))
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# ------------------------------------------ output result -----------------------------------------
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@ -18,7 +18,7 @@ def cell2node(cellData,grid):
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datalen = np.array(cellData.shape[3:]).prod()
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for i in xrange(datalen):
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node = scipy.ndimage.convolve(cellData.reshape(tuple(grid)+(datalen,))[...,i],
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node = scipy.ndimage.convolve(cellData.reshape(tuple(grid[::-1])+(datalen,))[...,i],
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np.ones((2,2,2))/8., # 2x2x2 neighborhood of cells
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mode = 'wrap',
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origin = -1, # offset to have cell origin as center
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@ -35,14 +35,14 @@ def cell2node(cellData,grid):
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def displacementAvgFFT(F,grid,size,nodal=False,transformed=False):
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"""calculate average cell center (or nodal) displacement for deformation gradient field specified in each grid cell"""
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if nodal:
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x, y, z = np.meshgrid(np.linspace(0,size[0],1+grid[0]),
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x, y, z = np.meshgrid(np.linspace(0,size[2],1+grid[2]),
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np.linspace(0,size[1],1+grid[1]),
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np.linspace(0,size[2],1+grid[2]),
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np.linspace(0,size[0],1+grid[0]),
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indexing = 'ij')
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else:
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x, y, z = np.meshgrid(np.linspace(0,size[0],grid[0],endpoint=False),
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x, y, z = np.meshgrid(np.linspace(0,size[2],grid[2],endpoint=False),
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np.linspace(0,size[1],grid[1],endpoint=False),
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np.linspace(0,size[2],grid[2],endpoint=False),
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np.linspace(0,size[0],grid[0],endpoint=False),
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indexing = 'ij')
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origCoords = np.concatenate((z[:,:,:,None],y[:,:,:,None],x[:,:,:,None]),axis = 3)
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@ -69,7 +69,7 @@ def displacementFluctFFT(F,grid,size,nodal=False,transformed=False):
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#--------------------------------------------------------------------------------------------------
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# integration in Fourier space
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displacement_fourier = +np.einsum('ijkml,ijkl,l->ijkm',
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displacement_fourier = -np.einsum('ijkml,ijkl,l->ijkm',
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F if transformed else np.fft.rfftn(F,axes=(0,1,2)),
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k_s,
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integrator,
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@ -78,7 +78,7 @@ def displacementFluctFFT(F,grid,size,nodal=False,transformed=False):
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#--------------------------------------------------------------------------------------------------
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# backtransformation to real space
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displacement = np.fft.irfftn(displacement_fourier,grid,axes=(0,1,2))
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displacement = np.fft.irfftn(displacement_fourier,grid[::-1],axes=(0,1,2))
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return cell2node(displacement,grid) if nodal else displacement
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@ -186,8 +186,8 @@ for name in filenames:
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F_fourier = np.fft.rfftn(table.data[:,:9].reshape(grid[2],grid[1],grid[0],3,3),axes=(0,1,2)) # perform transform only once...
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displacement = displacementFluctFFT(F_fourier,grid,size,options.nodal,transformed=True)
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avgDisplacement = displacementAvgFFT (F_fourier,grid,size,options.nodal,transformed=True)
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fluctDisplacement = displacementFluctFFT(F_fourier,grid,size,options.nodal,transformed=True)
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avgDisplacement = displacementAvgFFT (F_fourier,grid,size,options.nodal,transformed=True)
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# ------------------------------------------ assemble header ---------------------------------------
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@ -203,18 +203,18 @@ for name in filenames:
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# ------------------------------------------ output data -------------------------------------------
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zrange = np.linspace(0,size[2],1+grid[2]) if options.nodal else xrange(grid[2])
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yrange = np.linspace(0,size[1],1+grid[1]) if options.nodal else xrange(grid[1])
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xrange = np.linspace(0,size[0],1+grid[0]) if options.nodal else xrange(grid[0])
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Zrange = np.linspace(0,size[2],1+grid[2]) if options.nodal else xrange(grid[2])
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Yrange = np.linspace(0,size[1],1+grid[1]) if options.nodal else xrange(grid[1])
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Xrange = np.linspace(0,size[0],1+grid[0]) if options.nodal else xrange(grid[0])
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for i,z in enumerate(zrange):
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for j,y in enumerate(yrange):
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for k,x in enumerate(xrange):
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for i,z in enumerate(Zrange):
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for j,y in enumerate(Yrange):
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for k,x in enumerate(Xrange):
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if options.nodal: table.data_clear()
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else: table.data_read()
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table.data_append([x,y,z] if options.nodal else [])
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table.data_append(list(avgDisplacement[i,j,k,:]))
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table.data_append(list( displacement[i,j,k,:]))
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table.data_append(list( avgDisplacement[i,j,k,:]))
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table.data_append(list(fluctDisplacement[i,j,k,:]))
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table.data_write()
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# ------------------------------------------ output finalization -----------------------------------
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@ -15,7 +15,7 @@ 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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field_fourier = np.fft.fftpack.rfftn(field,axes=(0,1,2),s=shapeFFT)
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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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# differentiation in Fourier space
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@ -42,7 +42,7 @@ def divFFT(geomdim,field):
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elif n == 3: # vector, 3 -> 1
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div_fourier[i,j,k] = sum(field_fourier[i,j,k,0:3]*xi) *TWOPIIMG
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return np.fft.fftpack.irfftn(div_fourier,axes=(0,1,2),s=shapeFFT).reshape([N,n/3])
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return np.fft.irfftn(div_fourier,axes=(0,1,2),s=shapeFFT).reshape([N,n/3])
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# --------------------------------------------------------------------
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@ -145,7 +145,7 @@ for name in filenames:
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# we need to reverse order here, because x is fastest,ie rightmost, but leftmost in our x,y,z notation
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stack.append(divFFT(size[::-1],
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table.data[:,data['column'][i]:data['column'][i]+data['dim']].
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reshape([grid[2],grid[1],grid[0]]+data['shape'])))
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reshape(grid[::-1].tolist()+data['shape'])))
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# ------------------------------------------ output result -----------------------------------------
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@ -18,7 +18,7 @@ def gradFFT(geomdim,field):
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if n == 3: dataType = 'vector'
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elif n == 1: dataType = 'scalar'
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field_fourier = np.fft.fftpack.rfftn(field,axes=(0,1,2),s=shapeFFT)
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field_fourier = np.fft.rfftn(field,axes=(0,1,2),s=shapeFFT)
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grad_fourier = np.empty(field_fourier.shape+(3,),'c16')
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# differentiation in Fourier space
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@ -46,7 +46,7 @@ def gradFFT(geomdim,field):
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grad_fourier[i,j,k,1,:] = field_fourier[i,j,k,1]*xi *TWOPIIMG # tensor field from vector data
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grad_fourier[i,j,k,2,:] = field_fourier[i,j,k,2]*xi *TWOPIIMG
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return np.fft.fftpack.irfftn(grad_fourier,axes=(0,1,2),s=shapeFFT).reshape([N,3*n])
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return np.fft.irfftn(grad_fourier,axes=(0,1,2),s=shapeFFT).reshape([N,3*n])
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# --------------------------------------------------------------------
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@ -148,7 +148,7 @@ for name in filenames:
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# we need to reverse order here, because x is fastest,ie rightmost, but leftmost in our x,y,z notation
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stack.append(gradFFT(size[::-1],
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table.data[:,data['column'][i]:data['column'][i]+data['dim']].
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reshape([grid[2],grid[1],grid[0]]+data['shape'])))
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reshape(grid[::-1].tolist()+data['shape'])))
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# ------------------------------------------ output result -----------------------------------------
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