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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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| | | |
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| | | print("Running trisurf version "+ tsmgr.getTrisurfVersion()) |
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| | | Runs=[] |
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| | | run.setSubdir("run0") |
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| | | Runs.append(run) |
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| | | |
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| | | #---------------------------- |
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| | | |
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| | | #kapa_list=[20,30] |
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| | | #p=[10] |
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| | | # |
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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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| | | # |
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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(snapshot='is_from_N25k'+str(kapa)+'V0_Nc312_c1.0.vtu') |
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| | | # run.setMaindir(("N", "k", "V", "_Nc", "_c","_w","_F"), ("nshell","xk0","constvolswitch","number_of_vertices_with_c0","c0", "w","F")) |
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| | | # run.setSubdir("run0") |
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| | | # |
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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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| | | |
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| | | #---------------------------- |
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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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| | | |
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| | | |
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| | | #Nov format: |
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| | | #hosts=({'name':'Hestia','address':'127.0.0.1', 'runs':Runs, 'username':'samo'},) |
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| | | |
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| | | def analyze(run, **kwargs): |
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| | | host=kwargs.get('host', None) |
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| | | print("Demo analysis") |
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| | | print("Analysis on host "+host['name']+" for run "+run.Dir.fullpath()+" completed") |
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| | | print("here comes info on the run variable:") |
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| | | print(run) |
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| | | print("here comes info on the host variable:") |
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| | | print(host) |
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| | | print("here comes info on the args variable:") |
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| | | print(kwargs.get('args',None)) |
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| | | |
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| | | def plothbar(run, **kwargs): |
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| | | import matplotlib.pyplot as plt |
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| | | def smooth(y, box_pts): |
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| | | import numpy as np |
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| | | box = np.ones(box_pts)/box_pts |
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| | | y_smooth = np.convolve(y, box, mode='same') |
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| | | return y_smooth |
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| | | table=trisurf.Statistics(run.Dir.fullpath(),filename='data_tspoststat.csv').getTable() |
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| | | plt.plot(table['hbar'], '.') |
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| | | plt.title(run.Dir.fullpath()) |
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| | | plt.xlabel('Iteration') |
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| | | plt.ylabel('hbar') |
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| | | smooth_window=10 |
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| | | smoothed=smooth(table['hbar'],smooth_window) |
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| | | plt.plot(tuple(range(int(smooth_window/2),len(smoothed)-int(smooth_window/2))),smoothed[int(smooth_window/2):-int(smooth_window/2)]) |
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| | | plt.show() |
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| | | print |
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| | | #if return False or no return statement, the analysis will continue with next running instance in the list. if return True, the analysis will stop after this run. |
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| | | return False |
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| | | def plotrunningavg(run, **kwargs): |
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| | | import matplotlib.pyplot as plt |
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| | | table=trisurf.Statistics(run.Dir.fullpath(),filename='data_tspoststat.csv').getTable() |
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| | | def running_avg(col): |
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| | | import numpy as np |
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| | | avg=[] |
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| | | for i in range(0,len(col)): |
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| | | avg.append(np.average(col[:-i])) |
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| | | return avg |
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| | | ra=running_avg(table['hbar']) |
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| | | plt.plot(ra) |
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| | | plt.title('Running average') |
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| | | plt.ylabel('1/n sum_i=niter^n(hbar_i)') |
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| | | plt.xlabel('n') |
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| | | plt.show() |
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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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| | | |
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| | | #start manager with configured runs |
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| | | tsmgr.start(Runs, analyses={'analyze1':analyze, 'plotrunningavg':plotrunningavg, 'plothbar':plothbar}) |
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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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| | | |
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| | | #statistics.combine(Runs) |
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| | | #here is how we combine statistics of multiple runs |
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| | | #statistics.combine([Runs[1],Runs[2]]) |
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