Exemplo n.º 1
0
def main(**kwargs):
    v = {-1, +1, +2}  # Set of votes.
    n = 5000      # Number of iterations.

    g = three_leaders(v)

    measures = {j: [] for j in v.union({0})}
    
    # Main cycle.
    for i in range(n):
        simulation.iteration(g, v)

        for j in v.union({0}):
            measures[j].append(formulae.occupation_measure(g, j))

        # Log.
        if 'log' in kwargs:
            kwargs['log'](i, g, v)

    # Plot.
    if kwargs.get('plot', False):
        colours = {-1: 'r', 0: 'g', +1: 'b', +2: 'y'}
        cesaro = {x: formulae.cesaro(y) for x, y in measures.items()}

        plot.lines(measures, colours)
        plot.lines(cesaro, colours)

        plot.show()
Exemplo n.º 2
0
def plot_boot(data, step=10, tag=''):
  N = min([len(boot[k][st]) for k in boot.keys()])
  data = {k: {} for k in labels[5:]}  
  for j,k in enumerate(labels[5:]):
    for ex, b in boot.items():
      # data[k][ex] = [min(np.abs(b[i][j][2]-b[i][j][1]),1.) for i in range(N)]
      data[k][ex] = [min(b[st][j],1.) for i in range(N)]
  for k, v in data.items():
    P.lines(v, 'Conv-%d-%s-%s'%(st,tag,k), step=step, xlabel='Simulation Time (in ns)')
Exemplo n.º 3
0
def plotsep(boot, step=10, tag=''):
  N = min([len(v) for v in boot.values()])
  data = {k: {} for k in labels}  
  for j,k in enumerate(labels):
    for ex, b in boot.items():
      # data[k][ex] = [min(np.abs(b[i][j][2]-b[i][j][1]),1.) for i in range(N)]
      data[k][ex] = [min(b[i][1][j],1.) for i in range(N)]
  for k, v in data.items():
    P.lines(v, 'Conv-%s-%s'%(tag,k), step=step, xlabel='Simulation Time (in ns)')
Exemplo n.º 4
0
def graphexprB(elist, step=5, tw=True):
  obsbin = {name: getobs(name) for name in elist}
  if tw:
    obslist = {k: obs2tw(v) for k, v in obsbin.items()}
  else:
    obslist = obsbin
  convlist = {k: calc_conv(v, step=step*1000) for k,v in obslist.items()}
  series = {}
  for k, v in convlist.items():
    for tw, data in v.items():
      if tw not in series:
        series[tw] = {}
      series[tw][k] = data
  for k, v in series.items():
    P.lines(v, 'convB-'+k, step=step)
Exemplo n.º 5
0
def plotm(boot, step=10, tag=''):
  N = min([len(v) for v in boot.values()])
  merge = {k: [np.mean([min(1., boot[k][i][1][f]) for f in range(10, 20)]) for i in range(N)] for k in boot.keys()}
  P.lines(merge, 'Conv-%s-CI-MERGE'%tag, step=step, xlabel='Simulation Time (in ns)')
  merge = {k: [np.mean([min(1., boot[k][i][2][f]) for f in range(10, 20)]) for i in range(N)] for k in boot.keys()}
  P.lines(merge, 'Conv-%s-STD-MERGE'%tag, step=step, xlabel='Simulation Time (in ns)')
  merge = {k: [np.mean([min(1., boot[k][i][3][f]) for f in range(10, 20)]) for i in range(N)] for k in boot.keys()}
  P.lines(merge, 'Conv-%s-Err-MERGE'%tag, step=step, xlabel='Simulation Time (in ns)')
  merge = {k: [np.mean([min(1., boot[k][i][4][f]) for f in range(10, 20)]) for i in range(N)] for k in boot.keys()}
  P.lines(merge, 'Conv-%s-CiMu-MERGE'%tag, step=step, xlabel='Simulation Time (in ns)')
Exemplo n.º 6
0
def graphexprC(elist, step=5, tw=True):
  obsbin = {name: getobs(name) for name in elist}
  if tw:
    obslist = {k: obs2tw(v) for k, v in obsbin.items()}
  else:
    obslist = obsbin
  seriesList = getdistinct(obslist[elist[0]])
  series = {k: {} for k in seriesList}
  for name in elist:
    print('Bootstrapping on ', name)
    for k in seriesList:
      series[k][name] = []
    N = len(obslist[name])
    for n in range(0, N, step*1000):
      c = bootstrap_iter(obslist[name][:min(n,N)], size=step)
      for k in seriesList:
        if k in c.keys():
          series[k][name].append(min(c[k][1]/c[k][0], 1.))
        else:
          series[k][name].append(1.)
  for k, v in series.items():
    P.lines(v, 'convC-'+k, step=step)
Exemplo n.º 7
0
LB={}
LB['Serial'] = np.load(home+'/work/results/serial_labels.npy').reshape(1, 270402)
LB['Parallel'] = np.array([i[:15000] for i in np.load(home+'/work/results/denparallel_labels.npy')])
LB['Biased'] = np.load(home+'/work/results/denbias_labels.npy')
LB['Uniform'] = np.load(home+'/work/results/denuniform_labels.npy')
LB['Lattice'] = np.load(home+'/work/results/denlattice_labels.npy')

# Process and make graph
fsize = (TEXT_WIDTH, STD_HGT)
LBA = {k: [activity1(t, y+2, 50) for t in LB[k]] for y, k in enumerate(EXP_NAMES)}
idxlist = {'Serial': [0], 'Parallel': [2,3,4], 'Biased': [0,3,11,17,23,28,31,32,33,35], 'Uniform':list(range(10)), 'Lattice': [12,16,27,34,41,42,45,46,52,57]}
X = {k: np.array(list(it.chain(*[LBA[k][i] for i in v]))) for k,v in idxlist.items()}; xmax = min([len(v) for v in X.values()])

imp.reload(P)
P.lines({k: v[:xmax] for k,v in X.items()}, showlegend=False, labels=EXP_NAMES[::-1], lw=1, yticks=EXP_NAMES + [' '],  \
  fname='denovo_activity', ylim=(.85, 6.25), figsize=fsize, no_ytick=True, colors=EXP_COLORS[::-1], \
  xlabel='Simulation Time (normalized)', xticks=['%d'%i for i in range(0, 101, 10)], latex=True)

# idxlist = {'Serial': [0], 'Parallel': [1], 'Biased': [0,3,11,17,23,28,31,32,33,35], 'Uniform':[10,16,18,21,23,24,25,28,33,35], 'Lattice': [1,15,18,29,31,32,33,40,41,42]}



 

################
# TRADEOFF

# data,totc = [],[]; expname='biased6'
# with open(home+'/work/results/{0}_prov.log'.format(expname)) as provfile:
#   for line in provfile.read().strip().split('\n'):
#     if line.startswith('BASIN'):
Exemplo n.º 8
0
	elif plot_type == 'lp':
		i=1
		while True:
			aux = [point for point in input if point.l==i]
			if not len(aux):
				break
			aux.sort(key=lambda x:x.p)
			aux = [point.file for point in aux]
			flist.append(aux)
			i=i+1
	for l in flist:
		av,e = generate_data(l, field, conf)
		ylist.append(av)
		error.append(e)
		
		#	ylist,error=generate_data(input, field, conf)
		#	plot.line(ylist, error, output, title, xlabel, ylabel)
	#elif plot_type == 'lp':
	if not legend:
		warning("labels for lines not entered. Default labels will be used.")
	
	if len(ylist) == 1:
		plot.line(ylist[0], error[0], output, title, xlabel, ylabel)
	elif len(ylist) > 1:
		plot.lines(ylist, error, output, title, ylabel, legend)

else:
	fail(plot_type+"is not a valid plot type", 1)

#verificar se grafico foi gerado