354 lines
12 KiB
Python
354 lines
12 KiB
Python
#!/usr/bin/python
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# -*- coding: iso-8859-1 -*-
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# This script is used for the post processing of the results achieved by the spectral method.
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# As it reads in the data coming from "materialpoint_results", it can be adopted to the data
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# computed using the FEM solvers. Until now, its capable to handle elements with one IP in a regular order
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# written by M. Diehl, m.diehl@mpie.de
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import os,sys,re,array,struct,numpy, time, postprocessingMath
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class vector:
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x,y,z = [None,None,None]
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def __init__(self,coords):
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self.x = coords[0]
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self.y = coords[1]
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self.z = coords[2]
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class element:
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items = []
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type = None
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def __init__(self,nodes,type):
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self.items = nodes
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self.type = type
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class element_scalar:
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id = None
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value = None
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def __init__(self,node,value):
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self.id = node
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self.value = value
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class MPIEspectral_result:
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file = None
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dataOffset = 0
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N_elemental_scalars = 0
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resolution = [0,0,0]
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dimension = [0.0,0.0,0.0]
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theTitle = ''
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wd = ''
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extrapolate = ''
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N_increments = 0
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increment = 0
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N_nodes = 0
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N_node_scalars = 0
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N_elements = 0
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N_element_scalars = 0
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N_element_tensors = 0
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theNodes = []
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theElements = []
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def __init__(self,filename):
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self.file = open(filename, 'rb')
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self.title = self._keyedString('load')
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self.wd = self._keyedString('workingdir')
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self.geometry = self._keyedString('geometry')
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self.N_increments = self._keyedInt('increments')
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self.N_element_scalars = self._keyedInt('materialpoint_sizeResults')
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self.resolution = self._keyedPackedArray('resolution',3,'i')
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self.N_nodes = (self.resolution[0]+1)*(self.resolution[1]+1)*(self.resolution[2]+1)
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self.N_elements = self.resolution[0]*self.resolution[1]*self.resolution[2]
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self.dimension = self._keyedPackedArray('dimension',3,'d')
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a = self.resolution[0]+1
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b = self.resolution[1]+1
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c = self.resolution[2]+1
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self.file.seek(0)
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self.dataOffset = self.file.read(2048).find('eoh')+7
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def __str__(self):
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return '\n'.join([
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'title: %s'%self.title,
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'workdir: %s'%self.wd,
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'extrapolation: %s'%self.extrapolate,
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'increments: %i'%self.N_increments,
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'increment: %i'%self.increment,
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'nodes: %i'%self.N_nodes,
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'resolution: %s'%(','.join(map(str,self.resolution))),
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'dimension: %s'%(','.join(map(str,self.dimension))),
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'elements: %i'%self.N_elements,
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'nodal_scalars: %i'%self.N_node_scalars,
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'elemental scalars: %i'%self.N_element_scalars,
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'end of header: %i'%self.dataOffset,
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]
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)
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def _keyedPackedArray(self,identifier,length = 3,type = 'd'):
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match = {'d': 8,'i': 4}
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self.file.seek(0)
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m = re.search('%s%s'%(identifier,'(.{%i})'%(match[type])*length),self.file.read(2048),re.DOTALL)
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values = []
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if m:
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for i in m.groups():
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values.append(struct.unpack(type,i)[0])
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return values
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def _keyedInt(self,identifier):
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value = None
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self.file.seek(0)
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m = re.search('%s%s'%(identifier,'(.{4})'),self.file.read(2048),re.DOTALL)
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if m:
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value = struct.unpack('i',m.group(1))[0]
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return value
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def _keyedString(self,identifier):
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value = None
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self.file.seek(0)
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m = re.search(r'(.{4})%s(.*?)\1'%identifier,self.file.read(2048),re.DOTALL)
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if m:
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value = m.group(2)
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return value
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def extrapolation(self,value):
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self.extrapolate = value
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def element_scalar(self,elem,idx):
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self.file.seek(self.dataOffset+(self.increment*(4+self.N_elements*self.N_element_scalars*8+4) + 4+(elem*self.N_element_scalars + idx)*8))
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value = struct.unpack('d',self.file.read(8))[0]
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return [elemental_scalar(node,value) for node in self.theElements[elem].items]
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def readScalar(resolution,file,distance,startingPosition,offset):
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currentPosition = startingPosition+offset*8+4 - distance*8 # we add distance later on
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field = numpy.zeros([resolution[0],resolution[1],resolution[2]], 'd')
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for z in range(0,resolution[2]):
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for y in range(0,resolution[1]):
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for x in range(0,resolution[0]):
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currentPosition = currentPosition + distance*8
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p.file.seek(currentPosition)
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field[x][y][z]=struct.unpack('d',p.file.read(8))[0]
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return field
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def readTensor(resolution,file,distance,startingPosition,offset):
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currentPosition = startingPosition+offset*8+4 - distance*8 # we add distance later on
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field = numpy.zeros([resolution[0],resolution[1],resolution[2],3,3], 'd')
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for z in range(0,resolution[2]):
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for y in range(0,resolution[1]):
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for x in range(0,resolution[0]):
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currentPosition = currentPosition + distance*8
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p.file.seek(currentPosition)
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for i in range(0,3):
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for j in range(0,3):
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field[x][y][z][i][j]=struct.unpack('d',p.file.read(8))[0]
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return field
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#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
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def calculateCauchyStress(p_stress,defgrad,res):
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#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
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c_stress = numpy.zeros([res[0],res[1],res[2],3,3],'d')
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for z in range(res[2]):
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for y in range(res[1]):
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for x in range(res[0]):
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jacobi = numpy.linalg.det(defgrad[x,y,z])
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c_stress[x,y,z] = numpy.dot(p_stress[x,y,z],numpy.transpose(defgrad[x,y,z]))/jacobi
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return c_stress
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# function writes scalar values to a mesh (geometry)
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def writeVtkAscii(filename,geometry,scalar,resolution):
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prodnn=(p.resolution[0]+1)*(p.resolution[1]+1)*(p.resolution[2]+1)
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vtk = open(filename, 'w')
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vtk.write('# vtk DataFile Version 3.1\n') # header
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vtk.write('just a test\n') # header
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vtk.write('ASCII\n') # header
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vtk.write('DATASET UNSTRUCTURED_GRID\n') # header
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vtk.write('POINTS ') # header
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vtk.write(str(prodnn)) # header
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vtk.write(' FLOAT\n') # header
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# nodes
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for k in range (resolution[2]+1):
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for j in range (resolution[1]+1):
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for i in range (resolution[0]+1):
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vtk.write('\t'.join(map(str,geometry[i,j,k]))+'\n')
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vtk.write('\n')
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vtk.write('CELLS ')
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vtk.write(str(resolution[0]*resolution[1]*resolution[2]))
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vtk.write('\t')
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vtk.write(str(resolution[0]*resolution[1]*resolution[2]*9))
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vtk.write('\n')
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# elements
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for i in range (resolution[2]):
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for j in range (resolution[1]):
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for k in range (resolution[0]):
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vtk.write('8')
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vtk.write('\t')
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base = i*(resolution[1]+1)*(resolution[2]+1)+j*(resolution[1]+1)+k
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vtk.write(str(base))
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vtk.write('\t')
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vtk.write(str(base+1))
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vtk.write('\t')
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vtk.write(str(base+resolution[1]+2))
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vtk.write('\t')
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vtk.write(str(base+resolution[1]+1))
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vtk.write('\t')
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base = base + (resolution[1]+1)*(resolution[2]+1)
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vtk.write(str(base))
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vtk.write('\t')
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vtk.write(str(base+1))
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vtk.write('\t')
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vtk.write(str(base+resolution[1]+2))
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vtk.write('\t')
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vtk.write(str(base+resolution[1]+1))
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vtk.write('\n')
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vtk.write('\n')
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vtk.write('CELL_TYPES ')
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vtk.write('\t')
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vtk.write(str(resolution[0]*resolution[1]*resolution[2]))
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vtk.write('\n')
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for i in range (resolution[0]*resolution[1]*resolution[2]):
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vtk.write('12\n')
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vtk.write('\nCELL_DATA ') # header
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vtk.write(str(resolution[0]*resolution[1]*resolution[2])) # header
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vtk.write('\n') # header
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vtk.write('SCALARS HorizontalSpeed float\n') # header
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vtk.write('LOOKUP_TABLE default\n') # header
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for k in range (resolution[2]):
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for j in range (resolution[1]):
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for i in range (resolution[0]):
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vtk.write(str(scalar[i,j,k]))
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vtk.write('\n')
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return
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# function writes scalar values to a point field
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def writeVtkAsciiDots(filename,coordinates,scalar,resolution):
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prodnn=(p.resolution[0])*(p.resolution[1])*(p.resolution[2])
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vtk = open(filename, 'w')
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vtk.write('# vtk DataFile Version 3.1\n') # header
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vtk.write('just a test\n') # header
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vtk.write('ASCII\n') # header
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vtk.write('DATASET UNSTRUCTURED_GRID\n') # header
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vtk.write('POINTS ') # header
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vtk.write(str(prodnn)) # header
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vtk.write(' FLOAT\n') # header
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# points
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for k in range (resolution[2]):
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for j in range (resolution[1]):
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for i in range (resolution[0]):
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vtk.write('\t'.join(map(str,coordinates[i,j,k]))+'\n')
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vtk.write('\n')
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vtk.write('CELLS ')
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vtk.write(str(prodnn))
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vtk.write('\t')
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vtk.write(str(prodnn*2))
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vtk.write('\n')
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for i in range(prodnn):
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vtk.write('1\t' + str(i) + '\n')
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vtk.write('CELL_TYPES ')
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vtk.write('\t')
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vtk.write(str(prodnn))
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vtk.write('\n')
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for i in range (prodnn):
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vtk.write('1\n')
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vtk.write('\nPOINT_DATA ') # header
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vtk.write(str(prodnn)) # header
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vtk.write('\n') # header
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vtk.write('SCALARS HorizontalSpeed float\n') # header
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vtk.write('LOOKUP_TABLE default\n') # header
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for k in range (resolution[2]):
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for j in range (resolution[1]):
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for i in range (resolution[0]):
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vtk.write(str(scalar[i,j,k]))
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vtk.write('\n')
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return
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# functiongives the corner box for the average defgrad
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def writeVtkAsciidefgrad_av(filename,diag,defgrad):
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points = numpy.array([\
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[0.0,0.0,0.0,],\
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[diag[0],0.0,0.0,],\
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[diag[0],diag[1],0.0,],\
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[0.0,diag[1],0.0,],\
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[0.0,0.0,diag[2],],\
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[diag[0],0.0,diag[2],],\
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[diag[0],diag[1],diag[2],],\
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[0.0,diag[1],diag[2],]]\
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)
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vtk = open(filename, 'w')
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vtk.write('# vtk DataFile Version 3.1\n') # header
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vtk.write('just a test\n') # header
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vtk.write('ASCII\n') # header
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vtk.write('DATASET UNSTRUCTURED_GRID\n') # header
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vtk.write('POINTS 8') # header
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vtk.write(' FLOAT\n') # header
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# points
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for p in range (8):
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vtk.write('\t'.join(map(str,numpy.dot(defgrad_av,points[p])))+'\n')
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vtk.write('\n')
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vtk.write('CELLS 8 16\n')
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vtk.write('\n'.join(['1\t%i'%i for i in range(8)])+'\n')
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vtk.write('CELL_TYPES 8\n')
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vtk.write('\n'.join(['1']*8)+'\n')
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return
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print '*********************************************************************************'
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print 'Post Processing for Material subroutine for BVP solution using spectral method'
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print '*********************************************************************************\n'
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#reading in the header of the results file
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name = 'dipl10'
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p = MPIEspectral_result(name+'.spectralOut')
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p.extrapolation('')
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print p
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# Ended reading of header
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res_x=p.resolution[0]
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res_y=p.resolution[1]
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res_z=p.resolution[2]
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# for i in range(1,3):
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# print('new step')
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# c_pos = p.dataOffset + i*(p.N_element_scalars*8*p.N_elements + 8) #8 accounts for header&footer
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# for j in range(p.N_element_scalars):
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# #def readScalar(resolution,file,distance,startingPosition,offset):
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# #currentPosition = startingPosition+offset*8+4 - distance*8 # we add distance later on
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# #field = numpy.zeros([resolution[0],resolution[1],resolution[2]], 'd')
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# #for z in range(0,resolution[2]):
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# #for y in range(0,resolution[1]):
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# #for x in range(0,resolution[0]):
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# currentPosition = c_pos + j*8 +4
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# p.file.seek(currentPosition)
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# print(struct.unpack('d',p.file.read(8)))
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ms=numpy.zeros([res_x,res_y,res_z,3], 'd')
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for i in range(249,250):
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c_pos = p.dataOffset + i*(p.N_element_scalars*8*p.N_elements + 8) #8 accounts for header&footer
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defgrad = readTensor(p.resolution,p.file,p.N_element_scalars,c_pos,16)
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logstrain = postprocessingMath.logstrain_mat(res_x,res_y,res_z,defgrad)
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logstrain2 = postprocessingMath.logstrain_spat(res_x,res_y,res_z,defgrad)
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p_stress = readTensor(p.resolution,p.file,p.N_element_scalars,c_pos,58)
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c_stress = postprocessingMath.calculate_cauchy(res_x,res_y,res_z,defgrad,p_stress)
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vm = postprocessingMath.calculate_mises(res_x,res_y,res_z,c_stress)
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defgrad_av=numpy.average(numpy.average(numpy.average(defgrad,0),0),0)
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centroids_coord = postprocessingMath.deformed(res_x,res_y,res_z,p.dimension,defgrad,defgrad_av)
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ms = postprocessingMath.mesh(p.resolution[0],p.resolution[1],p.resolution[2],p.dimension,defgrad_av,centroids_coord)
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writeVtkAscii(name+'-mesh-1-%s.vtk'%i,ms,logstrain[:,:,:,1,2],p.resolution)
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writeVtkAscii(name+'-mesh-2-%s.vtk'%i,ms,logstrain2[:,:,:,1,2],p.resolution)
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#writeVtkAsciidefgrad_av(name+'-box-%i.vtk'%i,p.dimension,defgrad_av)
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#writeVtkAsciiDots(name+'-points-%i.vtk'%i,centroids_coord,grain,p.resolution)
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sys.stdout.flush()
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