add a few handy python scripts for pre/post-processing purposes
This commit is contained in:
Rex Zhe Li
213
example/Python/LBPM_Postprocess_ID_SignDist_Recon.py
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213
example/Python/LBPM_Postprocess_ID_SignDist_Recon.py
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#!/usr/bin/env python
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import sys
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import numpy as np
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import matplotlib.pyplot as plt
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from glob import glob
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from netCDF4 import Dataset
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def write_NetCDF_file_py(file_name,data_name,data_IN,data_IN_dtype,lx,ly,lz):
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# Important !
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# Note the dataOUT should have the shape (lz,ly,lx)
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data_IN.shape=(lz,ly,lx) # make sure data_IN has the right shape
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# open a new netCDF file for writing.
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ncfile = Dataset(file_name,'w')
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# create the output data.
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# create the x, y and z dimensions.
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ncfile.createDimension('x',lx)
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ncfile.createDimension('y',ly)
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ncfile.createDimension('z',lz)
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# create the variable (4 byte integer in this case)
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# first argument is name of variable, second is datatype, third is
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# a tuple with the names of dimensions.
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data = ncfile.createVariable(data_name,data_IN_dtype,('z','y','x'))
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data[:] = data_IN
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#data.voxel_unit = 'micrometer'
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#data.voxel_size=5.7
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ncfile.close()
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print '**Info: The *.nc file is written successfully !'
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#end def
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# Check if there is a proper command line argument
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if len(sys.argv) !=2:
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sys.stderr.write('**Error: Usage: ' + sys.argv[0] + ' <Domain.in>\n')
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sys.exit()
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# end if
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# Read 'Domain.in' to obtain the size of 'ID.xxxxxx' and 'SignDist.xxxxx'
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f = open(sys.argv[1],'r')
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lines = f.readlines()
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# Read processors configuration
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nproc_x,nproc_y,nproc_z=np.fromstring(lines[0].splitlines()[0],dtype = np.int32,sep=' ')
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# Read subdomain dimensions
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lx,ly,lz = np.fromstring(lines[1].splitlines()[0],dtype = np.int32,sep=' ')
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# Read domain dimensions
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LX,LY,LZ = np.fromstring(lines[3].splitlines()[0],dtype = np.float32,sep=' ')
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LX=int(LX)
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LY=int(LY)
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LZ=int(LZ)
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f.close()
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## -------- Setup output data information -------------##
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# 1. For *.nc file
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# 1.1 for ID.* files
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id_data_output_file='ID_All.nc'
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id_data_output_variable='segmented'
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# 1.2 for SignDist.* files
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dist_data_output_file='SignDist_All.nc'
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dist_data_output_variable='signed_distance'
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# 1.3 for Phase field files
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phase_data_output_file='Phase_All.nc'
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phase_data_output_variable='phase_field'
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# 2. For *.bin files
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id_data_output_file_bin='ID_All.bin'
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dist_data_output_file_bin='SignDist_All.bin'
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phase_data_output_file_bin='Phase_All.bin'
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# The size of the subdomain
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lx+=2 # consider the halo layer
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ly+=2
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lz+=2
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id_group = glob('ID.*')
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id_group.sort()
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dist_group = glob('SignDist.*')
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dist_group.sort()
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# Reconstruct ID.xxxxx data
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for z_axis in np.arange(nproc_z):
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for y_axis in np.arange(nproc_y):
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for x_axis in np.arange(0,nproc_x,2):
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if nproc_x%2==0: # nproc_x is an even number
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temp_x1 = np.fromfile(id_group[z_axis*nproc_y*nproc_x+y_axis*nproc_x+x_axis],dtype=np.int8)
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temp_x2 = np.fromfile(id_group[z_axis*nproc_y*nproc_x+y_axis*nproc_x+x_axis+1],dtype=np.int8)
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temp_x1.shape = (lz,ly,lx)
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temp_x2.shape = (lz,ly,lx)
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temp_x1 = temp_x1[1:lz-1,1:ly-1,1:lx-1]
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temp_x2 = temp_x2[1:lz-1,1:ly-1,1:lx-1]
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temp_x1 = np.concatenate((temp_x1,temp_x2),axis=2)
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else: # nproc_x is an odd number
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if nproc_x==1:
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temp_x1 = np.fromfile(id_group[z_axis*nproc_y*nproc_x+y_axis*nproc_x+x_axis],dtype=np.int8)
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temp_x1.shape = (lz,ly,lx)
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temp_x1 = temp_x1[1:lz-1,1:ly-1,1:lx-1]
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elif x_axis < nproc_x-2: # i.e. nproc_x = 3 or 5 or 7 ...
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temp_x1 = np.fromfile(id_group[z_axis*nproc_y*nproc_x+y_axis*nproc_x+x_axis],dtype=np.int8)
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temp_x2 = np.fromfile(id_group[z_axis*nproc_y*nproc_x+y_axis*nproc_x+x_axis+1],dtype=np.int8)
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temp_x1.shape = (lz,ly,lx)
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temp_x2.shape = (lz,ly,lx)
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temp_x1 = temp_x1[1:lz-1,1:ly-1,1:lx-1]
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temp_x2 = temp_x2[1:lz-1,1:ly-1,1:lx-1]
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temp_x1 = np.concatenate((temp_x1,temp_x2),axis=2)
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#end if
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#end if
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if x_axis == 0:
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ID_x_temp = temp_x1.copy()
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else:
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ID_x_temp = np.concatenate((ID_x_temp,temp_x1),axis=2)
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#end if
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#end for
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if nproc_x !=1 and nproc_x%2 != 0:
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temp_x1 = np.fromfile(id_group[z_axis*nproc_y*nproc_x+y_axis*nproc_x+nproc_x-1],dtype=np.int8)
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ID_x_temp = np.concatenate((ID_x_temp,temp_x1),axis=2)
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#end if
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if y_axis == 0:
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ID_y_temp = ID_x_temp.copy()
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else:
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ID_y_temp = np.concatenate((ID_y_temp,ID_x_temp),axis=1)
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#end if
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#end for
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if z_axis == 0:
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ID_z_temp = ID_y_temp.copy()
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else:
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ID_z_temp = np.concatenate((ID_z_temp,ID_y_temp),axis=0)
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#end if
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#end for
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## ---------------- ID decoding ------------------------- ##
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# By convention, in LBPM-WIA, we have segmented image as:
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# 0 -> solid phase
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# 1 -> non-wetting phase
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# 2 -> wetting phase
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## ---------------- ID decoding ------------------------- ##
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# By default, the inlet layer is non-wetting phase, and the outlet layer is wetting phase
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# Also by default, the flow direction is along z-axis, and the inlet/outlet plane is in x-y plane
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ID_z_temp[0,:] =1
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ID_z_temp[-1,:]=2
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ID_z_temp.tofile(id_data_output_file_bin)
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write_NetCDF_file_py(id_data_output_file,id_data_output_variable,ID_z_temp,ID_z_temp.dtype.char,LX,LY,LZ)
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# Create phase field based on the ID profiles
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# By convention, in LBPM-WIA, we have segmented image as:
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# 0 -> solid phase
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# 1 -> non-wetting phase
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# 2 -> wetting phase
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phase = ID_z_temp.copy()
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phase = phase.astype(np.float32)
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#TODO: make this file read in Color.in file so phi_s can be assigned according to Color.in
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phase[phase==0.0] = -0.55 # solid phase
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phase[phase==1.0] = 1.0 # non-wetting phase
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phase[phase==2.0] = -1.0 # wetting pahse
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phase.tofile(phase_data_output_file_bin)
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write_NetCDF_file_py(phase_data_output_file,phase_data_output_variable,phase,phase.dtype.char,LX,LY,LZ)
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# Reconstruct SignDist.xxxxx data
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for z_axis in np.arange(nproc_z):
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for y_axis in np.arange(nproc_y):
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for x_axis in np.arange(0,nproc_x,2):
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if nproc_x%2==0: # nproc_x is an even number
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temp_x1 = np.fromfile(dist_group[z_axis*nproc_y*nproc_x+y_axis*nproc_x+x_axis],dtype=np.float64)
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temp_x2 = np.fromfile(dist_group[z_axis*nproc_y*nproc_x+y_axis*nproc_x+x_axis+1],dtype=np.float64)
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temp_x1.shape = (lz,ly,lx)
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temp_x2.shape = (lz,ly,lx)
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temp_x1 = temp_x1[1:lz-1,1:ly-1,1:lx-1]
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temp_x2 = temp_x2[1:lz-1,1:ly-1,1:lx-1]
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temp_x1 = np.concatenate((temp_x1,temp_x2),axis=2)
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else: # nproc_x is an odd number
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if nproc_x==1:
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temp_x1 = np.fromfile(id_group[z_axis*nproc_y*nproc_x+y_axis*nproc_x+x_axis],dtype=np.int8)
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temp_x1.shape = (lz,ly,lx)
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temp_x1 = temp_x1[1:lz-1,1:ly-1,1:lx-1]
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elif x_axis < nproc_x-2: # i.e. nproc_x = 3 or 5 or 7 ...
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temp_x1 = np.fromfile(dist_group[z_axis*nproc_y*nproc_x+y_axis*nproc_x+x_axis],dtype=np.float64)
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temp_x2 = np.fromfile(dist_group[z_axis*nproc_y*nproc_x+y_axis*nproc_x+x_axis+1],dtype=np.float64)
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temp_x1.shape = (lz,ly,lx)
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temp_x2.shape = (lz,ly,lx)
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temp_x1 = temp_x1[1:lz-1,1:ly-1,1:lx-1]
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temp_x2 = temp_x2[1:lz-1,1:ly-1,1:lx-1]
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temp_x1 = np.concatenate((temp_x1,temp_x2),axis=2)
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#end if
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#end if
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if x_axis == 0:
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ID_x_temp = temp_x1.copy()
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else:
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ID_x_temp = np.concatenate((ID_x_temp,temp_x1),axis=2)
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#end if
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#end for
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if nproc_x !=1 and nproc_x%2 !=0:
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temp_x1 = np.fromfile(dist_group[z_axis*nproc_y*nproc_x+y_axis*nproc_x+nproc_x-1],dtype=np.float64)
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ID_x_temp = np.concatenate((ID_x_temp,temp_x1),axis=2)
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#end if
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if y_axis == 0:
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ID_y_temp = ID_x_temp.copy()
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else:
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ID_y_temp = np.concatenate((ID_y_temp,ID_x_temp),axis=1)
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#end if
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#end for
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if z_axis == 0:
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ID_z_temp = ID_y_temp.copy()
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else:
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ID_z_temp = np.concatenate((ID_z_temp,ID_y_temp),axis=0)
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#end if
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#end for
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ID_z_temp.tofile(dist_data_output_file_bin)
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write_NetCDF_file_py(dist_data_output_file,dist_data_output_variable,ID_z_temp,ID_z_temp.dtype.char,LX,LY,LZ)
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