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#!/usr/bin/python3 |
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from trisurf import tsmgr |
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from trisurf import trisurf |
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from trisurf import statistics |
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from trisurf import analyses |
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print("Running trisurf version "+ tsmgr.getTrisurfVersion()) |
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Runs=[] |
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Nshell=25 |
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#--------- F = 0 ------------ |
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#kapa_list=[10,20,30,40,50] |
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#p=[5,10,15,20,25] |
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#N=5*Nshell**2+2 |
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#Nc_list=[int(N*pp/100) for pp in p] |
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#for kapa in kapa_list: |
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# for Nc in Nc_list: |
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# run=trisurf.Runner(tape='tape_Nc'+str(Nc)+'_k'+str(kapa)) |
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# run.setMaindir(("N", "k", "V", "_Nc", "_c","_w"), ("nshell","xk0","constvolswitch","number_of_vertices_with_c0","c0", "w")) |
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# run.setSubdir("run0") |
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# Runs.append(run) |
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#---------------------------- |
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#--------- F = 0 ------------ |
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kapa_list=[15,16,17,18,19,20,21,22] |
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#p=[5,7.5,10,12.5] |
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p=[8,8.5,9,9.5,10.5,11,11.5,12] |
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N=5*Nshell**2+2 |
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Nc_list=[int(N*pp/100) for pp in p] |
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#print(Nc_list) |
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#spremenil sem, ker nimam vseh podatkov!!! |
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kapa_list=[15,16,18,19,20,21,21,22] |
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Nc_list=[156,234,312,390] |
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for kapa in kapa_list: |
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for Nc in Nc_list: |
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#print('tape_Nc'+str(Nc)+'_k'+str(kapa)) |
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run=trisurf.Runner(tape='tape_Nc'+str(Nc)+'_k'+str(kapa)) |
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run.setMaindir(("N", "k", "V", "_Nc", "_c","_w"), ("nshell","xk0","constvolswitch","number_of_vertices_with_c0","c0", "w")) |
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run.setSubdir("run0") |
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Runs.append(run) |
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#Here is how we wrap functions |
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def plotvolume(run, **kwargs): |
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from trisurf import analyses |
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analyses.plotColumnFromPostProcess(run,column='Volume',**kwargs) |
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def plotbondrate(run, **kwargs): |
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from trisurf import analyses |
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analyses.plotColumnFromPostProcess(run,column='VertexMoveSucessRate',filename='statistics.csv',**kwargs) |
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def runningavgc0(run, **kwargs): |
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from trisurf import analyses |
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analyses.plotrunningavginteractive(run, scalar_field='spontaneous_curvature', **kwargs) |
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#start manager with configured runs |
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tsmgr.start(Runs, analyses={'demo':analyses.demo,'runningavg':analyses.plotrunningavginteractive, 'plothbar':analyses.plotColumnFromPostProcess, 'plotvol':plotvolume, 'plotbondrate':plotbondrate, 'runningavgC0':runningavgc0}) |
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#here is how we combine statistics of multiple runs |
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#statistics.combine([Runs[1],Runs[2]]) |