#!/usr/bin/python3
|
from trisurf import tsmgr
|
from trisurf import trisurf
|
from trisurf import statistics
|
|
print("Running trisurf version "+ tsmgr.getTrisurfVersion())
|
Runs=[]
|
Nshell=25
|
|
#--------- F = 0 ------------
|
#kapa_list=[10,20,30,40,50]
|
#p=[5,10,15,20,25]
|
|
#N=5*Nshell**2+2
|
#Nc_list=[int(N*pp/100) for pp in p]
|
|
#for kapa in kapa_list:
|
# for Nc in Nc_list:
|
# run=trisurf.Runner(tape='tape_Nc'+str(Nc)+'_k'+str(kapa))
|
# run.setMaindir(("N", "k", "V", "_Nc", "_c","_w"), ("nshell","xk0","constvolswitch","number_of_vertices_with_c0","c0", "w"))
|
# run.setSubdir("run0")
|
# Runs.append(run)
|
|
#----------------------------
|
#--------- F = 0 ------------
|
kapa_list=[15,16,17,18,19,20,21,22]
|
#p=[5,7.5,10,12.5]
|
p=[8,8.5,9,9.5,10.5,11,11.5,12]
|
|
N=5*Nshell**2+2
|
Nc_list=[int(N*pp/100) for pp in p]
|
#print(Nc_list)
|
|
#spremenil sem, ker nimam vseh podatkov!!!
|
kapa_list=[15,16,18,19,20,21,21,22]
|
Nc_list=[156,234,312,390]
|
|
for kapa in kapa_list:
|
for Nc in Nc_list:
|
#print('tape_Nc'+str(Nc)+'_k'+str(kapa))
|
run=trisurf.Runner(tape='tape_Nc'+str(Nc)+'_k'+str(kapa))
|
run.setMaindir(("N", "k", "V", "_Nc", "_c","_w"), ("nshell","xk0","constvolswitch","number_of_vertices_with_c0","c0", "w"))
|
run.setSubdir("run0")
|
Runs.append(run)
|
|
#----------------------------
|
|
#kapa_list=[20,30]
|
#p=[10]
|
#
|
#N=5*Nshell**2+2
|
#Nc_list=[int(N*pp/100) for pp in p]
|
#
|
#for kapa in kapa_list:
|
# for Nc in Nc_list:
|
# run=trisurf.Runner(snapshot='is_from_N25k'+str(kapa)+'V0_Nc312_c1.0.vtu')
|
# run.setMaindir(("N", "k", "V", "_Nc", "_c","_w","_F"), ("nshell","xk0","constvolswitch","number_of_vertices_with_c0","c0", "w","F"))
|
# run.setSubdir("run0")
|
#
|
# Runs.append(run)
|
|
#----------------------------
|
|
|
|
#Nov format:
|
#hosts=({'name':'Hestia','address':'127.0.0.1', 'runs':Runs, 'username':'samo'},)
|
|
def analyze(run, **kwargs):
|
host=kwargs.get('host', None)
|
print("Demo analysis")
|
print("Analysis on host "+host['name']+" for run "+run.Dir.fullpath()+" completed")
|
print("here comes info on the run variable:")
|
print(run)
|
print("here comes info on the host variable:")
|
print(host)
|
print("here comes info on the args variable:")
|
print(kwargs.get('args',None))
|
|
def plothbar(run, **kwargs):
|
import matplotlib.pyplot as plt
|
|
def smooth(y, box_pts):
|
import numpy as np
|
box = np.ones(box_pts)/box_pts
|
y_smooth = np.convolve(y, box, mode='same')
|
return y_smooth
|
table=trisurf.Statistics(run.Dir.fullpath(),filename='data_tspoststat.csv').getTable()
|
plt.plot(table['hbar'], '.')
|
plt.title(run.Dir.fullpath())
|
plt.xlabel('Iteration')
|
plt.ylabel('hbar')
|
smooth_window=10
|
smoothed=smooth(table['hbar'],smooth_window)
|
plt.plot(tuple(range(int(smooth_window/2),len(smoothed)-int(smooth_window/2))),smoothed[int(smooth_window/2):-int(smooth_window/2)])
|
plt.show()
|
print
|
#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.
|
return False
|
|
def plotrunningavginteractive(run, **kwargs):
|
import matplotlib.pyplot as plt
|
from trisurf import VTKRendering as vtk
|
import math
|
from multiprocessing import Process
|
table=trisurf.Statistics(run.Dir.fullpath(),filename='data_tspoststat.csv').getTable()
|
def running_avg(col):
|
import numpy as np
|
avg=[]
|
for i in range(0,len(col)):
|
avg.append(np.average(col[:-i]))
|
return avg
|
def spawned_viewer(n):
|
vtk.Renderer(kwargs.get('args', None),kwargs.get('host',None),run, n)
|
|
fig=plt.figure(1)
|
ra=running_avg(table['hbar'])
|
l=len(table['hbar'])
|
plt.plot(ra)
|
plt.title('Running average')
|
plt.ylabel('1/n sum_i=niter^n(hbar_i)')
|
plt.xlabel('n')
|
def onclick(event):
|
#print('button=%d, x=%d, y=%d, xdata=%f, ydata=%f' % (event.button, event.x, event.y, event.xdata, event.ydata))
|
p=Process(target=spawned_viewer, args=(l-math.floor(event.xdata)-1,))
|
p.start()
|
cid = fig.canvas.mpl_connect('button_press_event', onclick)
|
plt.show()
|
plt.close(1)
|
#start manager with configured runs
|
tsmgr.start(Runs, analyses={'analyze1':analyze, 'plotrunningavg':plotrunningavginteractive, 'plothbar':plothbar})
|
|
#statistics.combine(Runs)
|
#statistics.combine([Runs[1],Runs[2]])
|
