#!/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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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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#----------------------------
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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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#----------------------------
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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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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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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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#start manager with configured runs
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tsmgr.start(Runs, analyses={'analyze1':analyze, 'plotrunningavg':plotrunningavg, 'plothbar':plothbar})
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#statistics.combine(Runs)
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#statistics.combine([Runs[1],Runs[2]])
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