def main():
parser = argparse.ArgumentParser(description='')
parser.add_argument('--MC','-Q', help='Quantum training file', nargs='+')
parser.add_argument('--ED','-C', help='Classical training file', nargs='+')
parser.add_argument('--clamped', help='Clamped simulation', default=False, action='store_true')
args = vars(parser.parse_args())
if 'MC' in args.keys():
skip = 3 # skip estimators we don't care about
for filename in args['MC']:
fparams = ssexyhelp.getReduceParamMap(filename)
data = np.loadtxt(filename)
headers = Hfile.getHeaders(filename)
if not(args['clamped']): (first, last) = (headers.index('X0'), headers.index('sX0'))
else: (first, last) = (headers.index('sX0'), len(headers))
aves = np.zeros(last-first)
stds = np.zeros(last-first)
for i, c in enumerate(range(first, last)):
cdata = data[~np.isnan(data[:,c]),c]
aves[i] = np.mean(cdata)
stds[i] = np.amax(MCstat.bin(cdata[np.newaxis].T))
if 'ED' in args.keys():
for filename in args['ED']:
fparams = ssexyhelp.getReduceParamMap(filename)
ED = np.loadtxt(filename)
print ED
print aves
print stds
colors = ["#66CAAE", "#CF6BDD", "#E27844", "#7ACF57", "#92A1D6", "#E17597", "#C1B546"]
fig = pl.figure(1, figsize=(10,5))
pl.connect('key_press_event',kevent.press)
ax = pl.subplot(111)
ax.plot((ED-aves)/stds, color=colors[0])#, lw=2, m='o', ls='')#, label=r'$data$')
pl.ylabel(r'$(ED-MC)/\Delta_{MC}$')
pl.xlabel(r'$Averages$')
lgd = pl.legend(loc = 'best')
lheaders = []
for head in Hfile.getHeaders(args['ED'][0]):
lheaders += [r'$%s$' %head]
pl.xticks(range(len(lheaders)), lheaders, rotation='vertical')
#lgd.draggable(state=True)
#lgd.draw_frame(False)
pl.tight_layout()
pl.show()
def main():
parser = argparse.ArgumentParser(description='')
parser.add_argument('filenames', help='Training output files', nargs='+')
args = parser.parse_args()
filenames = args.filenames
specs = []
for i,filen in enumerate(filenames):
fparams = ssexyhelp.getReduceParamMap(filen)
fparams['filename'] = filen
fparams['index'] = i
specs += [fparams]
specs = getInfoSpecifiers(specs)
specs = augmentMissingSpecifiers(specs)
for spec in specs:
filename = spec.pop('filename')
# Read off the list of headers
# Load and check the data
fdata = np.loadtxt(filename)
fdata = cleanData(fdata.view())
headers = getHeaders(filename)
# Augment the data set with new quantities
QE, CE = computeEnergies(headers, fdata)
nheaders += ['E_Q', 'E_C']
fdata = np.hstack([fdata, QE, CE])
spec['data'] = fdata.view(dtype= zip(nheaders, [str(fdata.dtype)]*len(nheaders))).copy()
def main():
# setup the command line parser options
parser = argparse.ArgumentParser(description='')
parser.add_argument('filenames', help='Training output files', nargs='+')
args = parser.parse_args()
filenames = args.filenames
pl.connect('key_press_event',kevent.press)
ddata = {}
for filename in filenames:
rdata = np.loadtxt(filename)
if len(rdata.shape)> 1: pindex = (-1,0)
else: pindex = (0)
#(nr, nc) = rdata.shape
fparams = ssexyhelp.getReduceParamMap(filename)
(alpha, Ns, beta, delta) = (fparams['alpha'], fparams['N'] , fparams['b'], fparams['delta'])
seed = int(findKbV(fparams, ''))
if not(delta in ddata.keys()): ddata[delta] = np.zeros(30)
else: ddata[delta][seed] = rdata[pindex]
fig = pl.figure(1, figsize=(13,6))
ax = pl.subplot(111)
colors = ["#66CAAE", "#CF6BDD", "#E27844", "#7ACF57", "#92A1D6", "#E17597", "#C1B546",'b']
i = 0
for delta, data in ddata.iteritems():
ax.plot(range(len(data)), data,
color=colors[i], lw=3, ls='-',
label = r'$\Delta = %0.2f$' %delta)
i += 1
pl.xlabel('Instance')
pl.ylabel('LL')
pl.legend(loc = 'best')
pl.title(r'$\beta=%0.2f \, \alpha=%0.2f $' %(beta, alpha), fontsize = 20)
pl.tight_layout()
pl.show()
def main():
parser = argparse.ArgumentParser(description='')
parser.add_argument('--quant','-Q', help='Quantum training file', nargs='+')
parser.add_argument('--class','-C', help='Classical training file', nargs='+')
args = vars(parser.parse_args())
colors = ["#66CAAE", "#CF6BDD", "#E27844", "#7ACF57", "#92A1D6", "#E17597", "#C1B546"]
fig = pl.figure(1, figsize=(10,5))
pl.connect('key_press_event',kevent.press)
ax = pl.subplot(111)
# ----------------------------------------------------------------------
if 'class' in args.keys():
cdata = np.zeros((5, len(args['class'])//5))
ssizes = []
i = -1
for filename in args['class']:
data = Hfile.cleanData(np.loadtxt(filename))
cKL = np.amin(data[:,1])
fparams = ssexyhelp.getReduceParamMap(filename)
(bsize, seed) = (int(fparams['ssise']), int(filename[:-4].split('_')[-1]))
if not(bsize in ssizes):
i += 1
ssizes += [bsize]
cdata[seed, i] = cKL
cKLs = np.sum(cdata, axis = 0)
ax.plot(ssizes, cKLs, color=colors[1], lw=1, ls='-', label=r'$classical$')
# ----------------------------------------------------------------------
if 'quant' in args.keys():
qdata = np.zeros((5, len(args['quant'])//5))
ssizes = []
i = -1
for filename in args['quant']:
data = cleanData(np.loadtxt(filename))
qKL = np.amin(data[:,1])
fparams = ssexyhelp.getReduceParamMap(filename)
(bsize, seed) = (int(fparams['ssise']), int(filename[:-4].split('_')[-1]))
if not(bsize in ssizes):
i += 1
ssizes += [bsize]
qdata[seed, i] = qKL
qKLs = np.sum(qdata, axis = 0)
ax.plot(ssizes, qKLs, color=colors[2], lw=1, ls='-', label=r'$quantum$')
pl.xlabel(r'$Training \, sample \, size$')
pl.ylabel(r'$KL-divergence$')
lgd = pl.legend(loc = 'best')
lgd.draggable(state=True)
lgd.draw_frame(False)
pl.tight_layout()
pl.show()
def main():
parser = argparse.ArgumentParser(description='')
parser.add_argument('--quant', '-Q', help='Quantum training file', nargs='+')
parser.add_argument('--squant1', '-S', help='Fixed delta quantum training file', nargs='+')
parser.add_argument('--aquant1', help='Approximate quantum training file', nargs='+')
parser.add_argument('--squant2', help='Fixed delta quantum training file', nargs='+')
parser.add_argument('--squant3', help='Fixed delta quantum training file', nargs='+')
parser.add_argument('--aquant2', help='Approximate quantum training file', nargs='+')
parser.add_argument('--aquant3', help='Approximate quantum training file', nargs='+')
parser.add_argument('--class','-C', help='Classical training file', nargs='+')
args = vars(parser.parse_args())
colors = ["#66CAAE", "#CF6BDD", "#E27844", "#7ACF57", "#92A1D6", "#E17597", "#C1B546"]
fig = pl.figure(1, figsize=(10,5))
pl.connect('key_press_event',kevent.press)
ax = pl.subplot(111)
# ----------------------------------------------------------------------
if 'class' in args.keys():
cdata = {}
i = -1
for filename in args['class']:
data = Hfile.cleanData(np.loadtxt(filename))
LL = data[-1,0]
fparams = ssexyhelp.getReduceParamMap(filename)
N = int(fparams['N'])
if not(N in cdata.keys()): cdata[N] = []
cdata[N] += [LL]
(Ns, vLLs, eLLs) = (cdata.keys(), [], [])
Ns.sort()
for N in Ns:
vLLs += [np.mean(np.array(cdata[N]))]
eLLs += [np.std(np.array(cdata[N]))/float(np.sqrt(len(cdata[N])))]
cLLs = unumpy.uarray((np.array(vLLs), np.array(eLLs)))
#ax.errorbar(Ns, unumpy.nominal_values(cLLs), unumpy.std_devs(cLLs), color=colors[1], lw=1, marker='o', ms=4, ls='', label=r'$classical$')
# ----------------------------------------------------------------------
if 'quant' in args.keys():
qdata = {}
i = -1
for filename in args['quant']:
data = Hfile.cleanData(np.loadtxt(filename))
LL = data[-1,0]
fparams = ssexyhelp.getReduceParamMap(filename)
N = int(fparams['N'])
if not(N in qdata.keys()): qdata[N] = []
qdata[N] += [LL]
(Ns, vLLs, eLLs) = (qdata.keys(), [], [])
Ns.sort()
for N in Ns:
vLLs += [np.mean(np.array(qdata[N]))]
eLLs += [np.std(np.array(qdata[N]))/float(np.sqrt(len(qdata[N])))]
eqLLs = cLLs - unumpy.uarray((np.array(vLLs), np.array(eLLs)))
ax.errorbar(Ns, unumpy.nominal_values(eqLLs), unumpy.std_devs(eqLLs), color=colors[0], lw=1, ls=':', marker='s', ms=4, label=r'$exact \, quantum$')
#ax.plot(Ns, unumpy.nominal_values(eqLLs), color=colors[2], lw=1, ls='-', marker='s', ms=4, label=r'$exact \, quantum$')
# ----------------------------------------------------------------------
if 'aquant1' in args.keys():
aqdata = {}
i = -1
for filename in args['aquant1']:
data = Hfile.cleanData(np.loadtxt(filename))
LL = data[-1,0]
fparams = ssexyhelp.getReduceParamMap(filename)
N = int(fparams['N'])
if not(N in aqdata.keys()): aqdata[N] = []
aqdata[N] += [LL]
(Ns, vLLs, eLLs) = (aqdata.keys(), [], [])
Ns.sort()
for N in Ns:
vLLs += [np.mean(np.array(aqdata[N]))]
eLLs += [np.std(np.array(aqdata[N]))/float(np.sqrt(len(aqdata[N])))]
aqLLs = cLLs - unumpy.uarray((np.array(vLLs), np.array(eLLs)))
ax.errorbar(Ns, unumpy.nominal_values(aqLLs), unumpy.std_devs(aqLLs), color=colors[1], lw=1, ls='-', marker='p', ms=4, label=r'$appr. \,quantum \, \Delta=1$')
#ax.plot(Ns, unumpy.nominal_values(aqLLs), color=colors[3], lw=1, ls='-', marker='p', ms=4, label=r'$appr. \,quantum \, \Delta=2$')
# ----------------------------------------------------------------------
if 'squant1' in args.keys():
qdata = {}
i = -1
for filename in args['squant1']:
print filename
data = Hfile.cleanData(np.loadtxt(filename))
LL = data[-1,0]
fparams = ssexyhelp.getReduceParamMap(filename)
N = int(fparams['N'])
if not(N in qdata.keys()): qdata[N] = []
qdata[N] += [LL]
(Ns, vLLs, eLLs) = (qdata.keys(), [], [])
Ns.sort()
for N in Ns:
vLLs += [np.mean(np.array(qdata[N]))]
eLLs += [np.std(np.array(qdata[N]))/float(np.sqrt(len(qdata[N])))]
eqLLs = cLLs[:-1] - unumpy.uarray((np.array(vLLs), np.array(eLLs)))
ax.errorbar(Ns, unumpy.nominal_values(eqLLs), unumpy.std_devs(eqLLs), color=colors[1], lw=1, ls='--', marker='s', ms=4, label=r'$fixed \, quantum \, \Delta=1$')
#ax.plot(Ns, unumpy.nominal_values(eqLLs), color=colors[5], lw=1, ls='-', marker='s', ms=4, label=r'$fixed \, quantum \, \Delta =1$')
# ----------------------------------------------------------------------
if 'aquant2' in args.keys():
aqdata = {}
i = -1
for filename in args['aquant2']:
data = Hfile.cleanData(np.loadtxt(filename))
LL = data[-1,0]
fparams = ssexyhelp.getReduceParamMap(filename)
N = int(fparams['N'])
if not(N in aqdata.keys()): aqdata[N] = []
aqdata[N] += [LL]
(Ns, vLLs, eLLs) = (aqdata.keys(), [], [])
Ns.sort()
for N in Ns:
vLLs += [np.mean(np.array(aqdata[N]))]
eLLs += [np.std(np.array(aqdata[N]))/float(np.sqrt(len(aqdata[N])))]
aqLLs = cLLs - unumpy.uarray((np.array(vLLs), np.array(eLLs)))
ax.errorbar(Ns, unumpy.nominal_values(aqLLs), unumpy.std_devs(aqLLs), color=colors[2], lw=1, ls='-', marker='p', ms=4, label=r'$appr. \,quantum \, \Delta=2$')
#ax.plot(Ns, unumpy.nominal_values(aqLLs), color=colors[3], lw=1, ls='-', marker='p', ms=4, label=r'$appr. \,quantum \, \Delta=2$')
# ----------------------------------------------------------------------
if 'squant2' in args.keys():
qdata = {}
i = -1
for filename in args['squant2']:
data = Hfile.cleanData(np.loadtxt(filename))
LL = data[-1,0]
fparams = ssexyhelp.getReduceParamMap(filename)
N = int(fparams['N'])
if not(N in qdata.keys()): qdata[N] = []
qdata[N] += [LL]
(Ns, vLLs, eLLs) = (qdata.keys(), [], [])
Ns.sort()
for N in Ns:
vLLs += [np.mean(np.array(qdata[N]))]
eLLs += [np.std(np.array(qdata[N]))/float(np.sqrt(len(qdata[N])))]
eqLLs = cLLs[:-1] - unumpy.uarray((np.array(vLLs), np.array(eLLs)))
ax.errorbar(Ns, unumpy.nominal_values(eqLLs), unumpy.std_devs(eqLLs), color=colors[2], lw=1, ls='--', marker='s', ms=4, label=r'$fixed \, quantum \, \Delta=3$')
#ax.plot(Ns, unumpy.nominal_values(eqLLs), color=colors[5], lw=1, ls='-', marker='s', ms=4, label=r'$fixed \, quantum \, \Delta =1$')
# ----------------------------------------------------------------------
if 'aquant3' in args.keys():
aqdata = {}
i = -1
for filename in args['aquant3']:
data = Hfile.cleanData(np.loadtxt(filename))
LL = data[-1,0]
fparams = ssexyhelp.getReduceParamMap(filename)
N = int(fparams['N'])
if not(N in aqdata.keys()): aqdata[N] = []
aqdata[N] += [LL]
(Ns, vLLs, eLLs) = (aqdata.keys(), [], [])
Ns.sort()
for N in Ns:
vLLs += [np.mean(np.array(aqdata[N]))]
eLLs += [np.std(np.array(aqdata[N]))/float(np.sqrt(len(aqdata[N])))]
aqLLs = cLLs - unumpy.uarray((np.array(vLLs), np.array(eLLs)))
ax.errorbar(Ns, unumpy.nominal_values(aqLLs), unumpy.std_devs(aqLLs), color=colors[3], lw=1, ls='-', marker='p', ms=4, label=r'$appr. \,quantum \, \Delta=3$')
#ax.plot(Ns, unumpy.nominal_values(aqLLs), color=colors[3], lw=1, ls='-', marker='p', ms=4, label=r'$appr. \,quantum \, \Delta=2$')
# ----------------------------------------------------------------------
if 'squant3' in args.keys():
qdata = {}
i = -1
for filename in args['squant3']:
data = Hfile.cleanData(np.loadtxt(filename))
LL = data[-1,0]
fparams = ssexyhelp.getReduceParamMap(filename)
N = int(fparams['N'])
if not(N in qdata.keys()): qdata[N] = []
qdata[N] += [LL]
(Ns, vLLs, eLLs) = (qdata.keys(), [], [])
Ns.sort()
for N in Ns:
vLLs += [np.mean(np.array(qdata[N]))]
eLLs += [np.std(np.array(qdata[N]))/float(np.sqrt(len(qdata[N])))]
eqLLs = cLLs[:-1] - unumpy.uarray((np.array(vLLs), np.array(eLLs)))
ax.errorbar(Ns, unumpy.nominal_values(eqLLs), unumpy.std_devs(eqLLs), color=colors[3], lw=1, ls='--', marker='s', ms=4, label=r'$fixed \, quantum \, \Delta=3$',capthick=2.0, capsize=3.0, markersize='5')
#ax.plot(Ns, unumpy.nominal_values(eqLLs), color=colors[5], lw=1, ls='-', marker='s', ms=4, label=r'$fixed \, quantum \, \Delta =1$')
pl.xlabel(r'$System \, size$')
pl.ylabel(r'$\Delta LL$')
ax.set_xlim([3,11])
ax.set_yscale('log')
lgd = pl.legend(loc = 'best')
lgd.draggable(state=True)
lgd.draw_frame(False)
pl.tight_layout()
pl.show()
def main():
# setup the command line parser options
parser = argparse.ArgumentParser(description='')
parser.add_argument('filenames', help='Training output files', nargs='+')
args = parser.parse_args()
filenames = args.filenames
pl.connect('key_press_event',kevent.press)
ddata = {}
for filename in filenames:
rdata = np.loadtxt(filename)
if len(rdata.shape)> 1: pindex = (-1,0)
else: pindex = (0)
inFile = open(filename,'r');
inLine = inFile.readlines();
headers = inLine[0].split(' ')
headers.pop(0)
header = []
for head in headers:
if head!='': header += [head]
inFile.close()
split1 = header.index(' <Z0>')
split2 = header.index(' <X0>')
split3 = header.index(' <ZZ(0, 1)>')
fparams = ssexyhelp.getReduceParamMap(filename)
#(alpha, Ns, beta, mode) = (fparams['alpha'], fparams['N'] , fparams['b'], fparams['mode'])
(mode) = (fparams['mode']+str(fparams['modes'])+str(fparams['iw']))
pindex = len(rdata[:, 0])
if np.isnan(rdata[-1,0]) or rdata[-2,0]<rdata[-1,0]:
print "Weird LL: ", filename
pindex += -1
if not (mode in ddata.keys()):
ddata[mode] = {}
#seed = int(findKbV(fparams, ''))
seed = int(fparams['hseed'])
#seed = int(findKbV(fparams, ''))
#print rdata[:pindex, 0]
#print ddata[mode][seed, 0, :]
#print 'pindex', pindex,rdata[: pindex, 1:split1]
Es = rdata[: pindex, 1:split1]*rdata[:pindex, split1:]
split2 -= split1
split3 -= split1
split1 = 0
ddata[mode][seed] = {}
ddata[mode][seed][0] = rdata[:pindex,0]
ddata[mode][seed][1] = np.sum(Es[:, split1:split2], axis=1)\
+ np.sum(Es[:, split3:], axis=1)
ddata[mode][seed][2] = np.sum(Es[:, split2:split3], axis=1)
#if not(delta in ddata.keys()): ddata[delta] = np.zeros(30)
#else: ddata[delta][seed] = rdata[pindex]
fig = pl.figure(1, figsize=(13,6))
ax = pl.subplot(111)
colors = ["#66CAAE", "#CF6BDD", "#E27844", "#7ACF57", "#92A1D6", "#E17597", "#C1B546",'b']
i = 0
print ddata
for label, data in ddata.iteritems():
if label == 'quant': col = colors[0]
else: col = colors[1]
for seed,sdata in data.iteritems():
ax.plot(sdata[1][0], sdata[2][0], marker='.')
ax.plot(sdata[1], sdata[2],
ls = ['-','--',':'][(seed+2)%3])
#color=col, lw=3, ls='-')
#, label = r'$mode = %s$' %label)
pl.xlabel(r'$E_C$')
pl.ylabel(r'$E_Q$')
pl.legend(loc = 'best')
#pl.title(r'$\beta=%0.2f \, \alpha=%0.2f $' %(beta, alpha), fontsize = 20)
pl.tight_layout()
pl.show()
