Add preprocess scripts to automate the process of writing all sorts of
input files
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zherexli
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123
workflows/Preprocess/preprocess.py
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123
workflows/Preprocess/preprocess.py
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#!/usr/bin/env python
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import numpy as np
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import sys
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import os
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import shutil
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from preprocess_utils import *
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# 1. this script take one input argument: experiment.csv
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# 2. Users need to put two things into this script
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# 2.1 surface tension in physical units
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# 2.2 physical depth
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# 3. Read csv + convert to LBM information
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# 4. write Color.in,-> this needs info from LBM pressure
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# 5. write Segmented.in -> this needs names of segmented image data
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# 6. write Domain.in -> this needs info on how users want to decompose the domain
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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('Usage: ' + sys.argv[0] + ' <Input experiment file>\n')
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sys.exit()
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# end if
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#experiment_file = 'experiment.csv' # Give the name of the experiment file (e.g. *.csv)
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experiment_file = sys.argv[1] # Give the name of the experiment file (e.g. *.csv)
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seg_image_data_suffix = '_segmented.raw'# suffix of the segmented image data
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# (should be consistent with what's in the segmenting script)
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# TODO: It'd be good to find a better way to do this
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image_format = '.tiff'
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process_name = 'drainage'
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#### Users need to put information here ####
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ift = 24.0 # dyne/cm
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Depth = 8.8 # micron
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# A list of all default values in 'Color.in'
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# Para['tau'] = 0.7
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# Para['alpha'] = 0.005
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# Para['beta'] = 0.95
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# Para['phi_solid'] = -1.0
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# Para['saturation'] = 0.0
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# Para['Fx'] = 0.0
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# Para['Fy'] = 0.0
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# Para['Fz'] = 0.0
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# Para['Restart'] = 0
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# Para['pBC'] = 1
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# Para['din'] = 1.001
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# Para['dout'] = 0.999
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# Para['maxtime'] = 100005
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# Para['interval'] = 2000
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# Para['tolerance'] = 1e-5
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# ***** Update any variables in 'Color.in', using names given in the key ***** #
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alpha = 0.01
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# **************************************************************************** #
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# A list of all default values in 'Domain.in'
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# Para['nprocx'] = 1
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# Para['nprocy'] = 2
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# Para['nprocz'] = 2
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# Para['nx'] = 1
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# Para['ny'] = 2
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# Para['nz'] = 2
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# Para['nspheres'] = 0 # deprecated
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# Para['Lx'] = 10
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# Para['Ly'] = 500
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# Para['Lz'] = 500
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# ***** Update any variables in 'Domain.in', using names given in the key ***** #
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# ***************************************************************************** #
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# A list of all default values in 'Segmented.in'
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# Para['file_name'] = 'Micromodel_1_segmented.raw'
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# Para['Nx'] = 10
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# Para['Ny'] = 500
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# Para['Nz'] = 500
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# Para['xStart'] = 0
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# Para['yStart'] = 0
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# Para['zStart'] = 0
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# ***** Update any variables in 'Segmented.in', using names given in the key ***** #
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# ******************************************************************************** #
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# Extract key parameters for LBM simulation from the experimental input *.csv file
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(Seg_data_name,din,dout)=get_LBM_parameters(experiment_file,seg_image_data_suffix,image_format,\
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ift=ift,Depth=Depth)
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# Now 'name_for_Segmented_in' should match the name of segmented data files that are already generated
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# Write out 'Color.in', 'Domain.in' and 'Segmented.in' files
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cwd = os.getcwd()
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for k in range(Seg_data_name.size):
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tag = k+1 # tag number for different folders to be created
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dir_name = process_name+'_'+str(tag)
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print "Creating folder : "+dir_name
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if not os.path.exists(dir_name):
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os.mkdir(dir_name)
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#end if
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# Either move the corresponding '*.raw' data file to the folder 'dir_name'
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#os.rename('./'+Seg_data_name[k],'./'+dir_name+'/'+Seg_data_name[k])
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# Or copy the corresponding '*.raw' data file to the folder 'dir_name'
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shutil.copy('./'+Seg_data_name[k],'./'+dir_name)
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# Change to the corresponding folder and write all input files
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os.chdir(dir_name)
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write_Color_in_file(din=din[k],dout=dout[k],alpha=alpha)
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write_Segment_in_file(Seg_data_name=Seg_data_name[k])
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write_Domain_in_file()
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os.chdir(cwd)
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#end for
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272
workflows/Preprocess/preprocess_utils.py
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272
workflows/Preprocess/preprocess_utils.py
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#!/usr/bin/env python
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import os
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import numpy as np
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import csv
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def get_LBM_parameters(csv_file_name,base_name_suffix,image_format,ift=24,Depth=8.8,**kwargs):
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# 'ift': dyne/cm
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# 'Depth': micron
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# Users need to provide the following information
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# 1. surface tension in physical unit
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# 2. physical depth in physical unit
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# 3. experimental file e.g. *.csv
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# 4. Other optional information, which is summarised in a dictionary
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# 'Para', including:
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# Para['D']: characteristic length
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# Para['alpha']: LBM parameters, controlling the surface tension
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# Para['fitting']: LBM parameters, extracted from the bubble test
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# Experimental definitions - units are converted in terms of N, m
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micron=1e-6
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cm=1e-2
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dyne=1e-5
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kPa=1e3
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Para={'D':30.0} # characteristic length
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#length=500*micron
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# LBM related parameters
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# TODO: for parameters like 'alpha', you should find a way to
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# communicate with other functions
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# It cannot be hard-coded here
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Para['alpha'] = 0.01
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Para['fitting'] = 5.796
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# Check users' input arguments
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for key in kwargs:
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if key in Para.keys():
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Para[key]=kwargs[key]
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else:
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print "Error: "+key+" is not a valid input !\n"
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print "Error: LBM pressure boundaries are not set successfully !"
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return
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#end if
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#end for
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IFT = Para['alpha'] * Para['fitting']
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# 'ift' is the surface tension from the experiment in physical unit
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ift=ift*dyne/cm # This converts the unit of 'ift' to SI unit
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# Process the experimental data
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# 'EXP_data' : original experimental data
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# It is a recorded numpy array, which means that its component
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# can be accessed by 'EXP_data.sw' or 'EXP_data['sw']'
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# About 'EXP_data' see more information from the function 'read_csv'
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EXP_data = read_csv(csv_file_name)
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# A few notes for the experimental data
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# 'Jwn': mean curvature in physical unit (e.g. 1/[micron])
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# 'pwn': pressure difference in physical (e.g. kPa)
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# Overall we need to map the measured physical pressures to get the principle radius of curvature R1
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# and associated curvature J1, and similarly R2 and J2 in the model's depth direction
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# J1: principal curvature 1
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# J2: principal curvature 2 along the model's depth direction
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# 'pc' is the dimensionless mean curvature scaled by the length scale 'D32'
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# 'pc' is extracted from experimentally measured mean curvature 'Jwn'
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pc = Para['D']*micron*EXP_data.Jwn*(1.0/micron)
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# Alternatively, the dimensionless mean curvature can also be extracted from the
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# experimentally measured pressure difference (i.e. capillary pressure)
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# 'pwn' is the dimensionless mean curvature scaled by the length scale 'D32'
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# pwn = Para['D']*micron*EXP_data.pwn*kPa/ift
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# Curvature is fixed in the micromodel "depth" direction
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J2 = Para['D']*micron/(Depth*micron/2.0)
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# infer the curvature in the other direction
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J1 = pc-J2
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# determine the LBM pressure difference
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dp=(J1+J2)*IFT/Para['D']
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# determine the boundary pressure values for Color.in
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din = 1.0+0.5*dp
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dout = 1.0-0.5*dp
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# Generate names of segmented image data for Segmented.in file
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# Need the input 'base_name_suffix' (e.g. '_segmented.raw')
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data_name_for_Segmented_in = EXP_data.image_name.copy()
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data_name_for_Segmented_in = data_name_for_Segmented_in.replace(image_format,base_name_suffix)
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return (data_name_for_Segmented_in,din,dout)
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#end def
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def write_Domain_in_file(**kwargs):
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# For most of the parameters in the Color.in file
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# you wouldn't need to worry about them as by default
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# they are all hard-coded here
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Para={'nprocx':1}
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Para['nprocy']=2
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Para['nprocz']=2
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Para['nx']=1
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Para['ny']=2
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Para['nz']=2
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Para['nspheres']=0 # deprecated
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Para['Lx']=10
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Para['Ly']=500
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Para['Lz']=500
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for key in kwargs:
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if key in Para.keys():
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Para[key]=kwargs[key]
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else:
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print "Error: "+key+" is not a valid input !\n"
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print "Error: Domain.in file is not writen."
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return
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#end if
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#end for
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# Check if the dompositoin requirement is satisfied
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if (Para['nx']*Para['nprocx']>Para['Lx']) or \
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(Para['ny']*Para['nprocy']>Para['Ly']) or \
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(Para['nz']*Para['nprocz']>Para['Lz']):
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print "Error: The decomposed size does not match the total domain size!"
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return
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#end if
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# write the Domain.in file
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ParamFile = open("Domain.in","w")
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ParamFile.write("%i " % Para['nprocx'])
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ParamFile.write("%i " % Para['nprocy'])
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ParamFile.write("%i\n" % Para['nprocz'])
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ParamFile.write("%i " % Para['nx'])
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ParamFile.write("%i " % Para['ny'])
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ParamFile.write("%i\n" % Para['nz'])
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ParamFile.write("%i\n" % Para['nspheres'])
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ParamFile.write("%.1f " % Para['Lx'])
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ParamFile.write("%.1f " % Para['Ly'])
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ParamFile.write("%.1f\n" % Para['Lz'])
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ParamFile.close()
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print "A Domain.in file is created."
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#end def
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def write_Segment_in_file(**kwargs):
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# For most of the parameters in the Color.in file
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# you wouldn't need to worry about them as by default
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# they are all hard-coded here
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Para={'Seg_data_name':'Micromodel_1_segmented.raw'}
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Para['Nx']=10
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Para['Ny']=500
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Para['Nz']=500
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Para['xStart']=0
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Para['yStart']=0
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Para['zStart']=0
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for key in kwargs:
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if key in Para.keys():
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Para[key]=kwargs[key]
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else:
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print "Error: "+key+" is not a valid input !\n"
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print "Error: Segmented.in file is not writen."
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return
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#end if
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#end for
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ParamFile = open("Segmented.in","w")
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ParamFile.write("%s\n" % Para['Seg_data_name'])
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ParamFile.write("%i " % Para['Nx'])
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ParamFile.write("%i " % Para['Ny'])
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ParamFile.write("%i\n" % Para['Nz'])
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ParamFile.write("%i " % Para['xStart'])
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ParamFile.write("%i " % Para['yStart'])
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ParamFile.write("%i\n" % Para['zStart'])
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ParamFile.close()
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print "A Segmented.in file is created."
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#end def
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def write_Color_in_file(**kwargs):
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# For most of the parameters in the Color.in file
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# you wouldn't need to worry about them as by default
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# they are all hard-coded here
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Para={'tau':0.7}
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Para['alpha']=0.01
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Para['beta']=0.95
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Para['phi_solid']=-1.0
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Para['saturation']=0.0
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Para['Fx']=0.0
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Para['Fy']=0.0
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Para['Fz']=0.0
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Para['Restart']=0
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Para['pBC']=1
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Para['din']=1.001
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Para['dout']=0.999
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Para['maxtime']=100005
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Para['interval']=2000
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Para['tolerance']=1e-5
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for key in kwargs:
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if key in Para.keys():
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Para[key]=kwargs[key]
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else:
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print "Error: "+key+" is not a valid input !\n"
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print "Error: Color.in file is not writen."
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return
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#end if
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#end for
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# write the color.in file
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ParamFile = open("Color.in","w")
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ParamFile.write("%f\n" % Para['tau'])
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ParamFile.write("%f " % Para['alpha'])
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ParamFile.write("%f " % Para['beta'])
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ParamFile.write("%f\n" % Para['phi_solid'])
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ParamFile.write("%f\n" % Para['saturation'])
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ParamFile.write("%f " % Para['Fx'])
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ParamFile.write("%f " % Para['Fy'])
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ParamFile.write("%f\n" % Para['Fz'])
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ParamFile.write("%i " % Para['Restart'])
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ParamFile.write("%i " % Para['pBC'])
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ParamFile.write("%f " % Para['din'])
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ParamFile.write("%f\n" % Para['dout'])
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ParamFile.write("%i " % Para['maxtime'])
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ParamFile.write("%i " % Para['interval'])
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ParamFile.write("%e\n" % Para['tolerance'])
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ParamFile.close()
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print "A Color.in file is created."
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#end def
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def read_csv(csv_file_name):
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#TODO Haven't thought about a better way of doing this
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# Right now I just hard-code the possible variables from the experiment
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# which means users are forced to prepare their *.csv file as listed here
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image_name = []
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sw = []
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Jwn = []
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pwn = []
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with open(csv_file_name,"r") as f:
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for line in f:
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reader=csv.reader(f,delimiter=' ') #Note the header is skipped by default
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for row in reader:
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image_name.append(row[0])
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sw.append(float(row[1]))
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Jwn.append(float(row[2]))
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pwn.append(float(row[3]))
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#end for
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#end for
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#end with
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# Convter the list to numpy array
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image_name_array = np.asarray(image_name,dtype=str)
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sw_array = np.asarray(sw,dtype=np.float32)
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Jwn_array = np.asarray(Jwn,dtype=np.float32)
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pwn_array = np.asarray(pwn,dtype=np.float32)
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# Restore the original shape of the experimental data
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experiment_data = np.column_stack((image_name_array,sw_array,Jwn_array,pwn_array))
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# Unfortunately the column stack will cast all float data to strings
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experiment_data = np.core.records.fromrecords(experiment_data,names='image_name,sw,Jwn,pwn')
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dt=experiment_data.dtype.descr
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dt[1] = (dt[1][0],'float32')
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dt[2] = (dt[2][0],'float32')
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dt[3] = (dt[3][0],'float32')
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experiment_data = experiment_data.astype(dt)
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return experiment_data
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#end def
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