45 lines
1.4 KiB
Python
45 lines
1.4 KiB
Python
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import numpy as np
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from glob import glob
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from lbpm_solid_coordinate_number_utils import *
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import matplotlib.pyplot as plt
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#NOTE: I could have read the 'Domain.in' files to read the information about the subdomain
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# but let's assume for now all the subdomains are strictly cubic
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# set the name of the full domain in *nc format
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domain_file_name = "domain1_256.nc"
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stats_OUT = solid_coord_fulldomain(domain_file_name)
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Aws = cal_Aws_fulldomain(domain_file_name)*1.0 # Convert Aws to a float number
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stats_OUT = np.vstack((stats_OUT,np.array([99,Aws]))) #The code 99 is dummy - I just want to attach the Aws to the stats_OUT data
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np.savetxt(domain_file_name[:-len(".nc")]+"_stats.txt",stats_OUT)
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# Trial plot
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plt.figure(1)
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plt.semilogy(stats_OUT[1:-1,0],stats_OUT[1:-1,1]/stats_OUT[-1,-1],'ro-')
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plt.ylabel('Partial Aws / Total Aws')
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plt.xlabel('Number of solid neighbours')
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plt.grid(True)
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plt.show()
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#TODO: make the routine that can analyse individual subdomains and agglomerate the itemfreq data from all subdomains
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## Load the ID field "ID.00*"
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#ID_prefix="ID."
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#halo_layer = 1 # the length of the halo layer
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#id_group = glob(ID_prefix+'*')
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#id_group.sort()
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#
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#
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#if not id_group:
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# print 'Error: No data files: '+id_group
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#else:
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# for ii in range(len(id_group)):
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# print '**Info: Read data file: '+id_group[ii]
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# print "**Info: Start analysing the solid coordinate number......"
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# # call function here
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