def getScalarStats(data, weights, mergedstatus, dim=0):
''' Get the average and error of all columns in the data matrix for a scalar
estimator.'''
if data.ndim > dim:
if mergedstatus:
# Columns alternate between actual data and their variances
dataArray = data[:, 0::2]
varArray = data[:, 1::2]
# Compute the standard error using the variances (requires unflattened
# weights array
dataErr = np.sqrt(
np.sum(varArray * weights**2, dim) / (np.sum(weights, 0))**2)
# Average all the data (requires flattened weights array)
weights = weights.flatten()
dataAve = np.average(dataArray, dim, weights=weights)
else:
weights = weights.flatten()
numBins = np.size(data, dim)
dataAve = np.average(data, dim, weights=weights)
dataAve2 = np.average(data * data, dim, weights=weights)
bins = MCstat.bin(data)
dataErr = np.amax(bins, axis=0)
dataErr2 = np.sqrt(
abs(dataAve2 - dataAve**2) / (1.0 * numBins - 1.0))
else:
dataAve = data
dataErr = 0.0 * data
return dataAve, dataErr
def getStats(data,dim=0):
''' Get the average and error of all columns in the data matrix. '''
if ndim(data) > dim:
numBins = size(data,dim)
dataAve = average(data,dim)
dataAve2 = average(data*data,dim)
bins = MCstat.bin(data)
dataErr = amax(bins,axis=0)
dataErr2 = sqrt( abs(dataAve2-dataAve**2)/(1.0*numBins-1.0) )
# for n,d in enumerate(dataErr):
# if d > 2.0*dataErr2[n]:
# dataErr[n] = 2.0*dataErr2[n]
# try:
# bins = MCstat.bin(data)
# dataErr = amax(bins,axis=0)
# except:
# dataErr = sqrt( abs(dataAve2-dataAve**2)/(1.0*numBins-1.0) )
else:
dataAve = data
dataErr = 0.0*data
return dataAve,dataErr
def getStats(data,SpanJobAverage=-1,waverage=False,dim=0):
''' Get the average and error of all columns in the data matrix. '''
#SpanJobAverage tells whether the estimator file contains the Bins
#column added by the merge scripts during merge of a spanned job
if ndim(data) > dim:
numBins = size(data,dim)
if SpanJobAverage != -1:
dataAve = sum(data[:,:-1]*data[:,-1][:,newaxis],dim)/(1.0*sum(data[:,-1]))
dataErr = std(data[:,:-1],0)/sqrt(len(data[:,0])-1.0)
elif waverage:
vals = data[:,0::2]
errs = data[:,1::2]
weights = 1.0/(errs*errs)
# http://en.wikipedia.org/wiki/Weighted_arithmetic_mean#Dealing_with_variance
dataErr = np.sqrt(1.0/np.sum(weights,axis=dim))
dataAve = np.average(vals,axis=0,weights=weights)
# Little hack of the average function to compute the weighted std
#dataErr = np.sqrt(np.average(1.0/weights,axis=dim, weights=weights)/weights.shape[0])
else:
dataAve = average(data,dim)
bins = MCstat.bin(data)
dataErr = amax(bins,axis=0)
return dataAve,dataErr
def getScalarStats(data,weights,mergedstatus,dim=0):
''' Get the average and error of all columns in the data matrix for a scalar
estimator.'''
if data.ndim > dim:
if mergedstatus:
# Columns alternate between actual data and their variances
dataArray = data[:,0::2]
varArray = data[:,1::2]
# Compute the standard error using the variances (requires unflattened
# weights array
dataErr = np.sqrt(np.sum(varArray*weights**2,dim)/(np.sum(weights,0))**2)
# Average all the data (requires flattened weights array)
weights=weights.flatten()
dataAve = np.average(dataArray,dim,weights=weights)
else:
weights=weights.flatten()
numBins = np.size(data,dim)
dataAve = np.average(data,dim,weights=weights)
dataAve2 = np.average(data*data,dim,weights=weights)
bins = MCstat.bin(data)
dataErr = np.amax(bins,axis=0)
dataErr2 = np.sqrt( abs(dataAve2-dataAve**2)/(1.0*numBins-1.0) )
else:
dataAve = data
dataErr = 0.0*data
return dataAve,dataErr
def addFromToSkipping(FromfileName,outFile,skip=0,average=False,waverage=False):
'''add SSEXY measurements from FromfileName to toFile, while skipping skip first measurements'''
#Read the measurements we want to keep
inList,numLines = ReadSkipping(FromfileName,skip)
if len(inList) == 0:
return 0
#Write them
if len(inList) != 0:
if average or waverage:
data = np.loadtxt(inList)
aves = np.average(data,0)
print "From: %s" %FromfileName
if average:
for ave in aves:
outFile.write('%16.8E' %ave)
outFile.write('%16d' %len(data[0:]))
if waverage:
errs = amax(MCstat.bin(data),axis=0)
# check if aves is a scalar
if not hasattr(aves, "__len__"): aves = np.array([aves])
for (ave,err) in zip(aves,errs):
outFile.write('%16.8E%16.8E' %(ave,err))
outFile.write('\n')
else:
outFile.writelines(inList)
return numLines
def CreateFileSkipping(FromfileName,oldSSEXYID,newSSEXYID,skip=0,average=False,waverage=False):
'''Create a new file whose oldSSEXYID is replaced with newSSEXYID and
skip ${skip} first measurements
Set average to True in order to write out just one average'''
#Read and adapt the header
inFile = open(FromfileName,'r');
#firstLine = inFile.readline().replace(str(oldSSEXYID),str(newSSEXYID));
secondLine = inFile.readline()
inFile.close();
#Read the measurements we want to keep
inList,numLines = ReadSkipping(FromfileName,skip)
if len(inList) == 0:
return '',0
# get the output file name and open the file for writing
outName = FromfileName.replace(str(oldSSEXYID),str(newSSEXYID))
outFile = open('MERGED/' + outName,'w');
print('To: MERGED/%-80s' % outName)
#write the header
#outFile.write(firstLine)
secondLine = secondLine.rstrip('\n')
if waverage:
headers = secondLine.split()
temp = '#%15s%16s' %(headers[1],'+/-')
for header in headers[2:]:
temp += '%16s%16s' %(header,'+/-')
secondLine = temp
if average:
secondLine += '%16s' %('Bins')
outFile.write(secondLine+'\n')
#if there is more measurements than we want to skip
if numLines > 0:
#write the content
if average or waverage:
data = np.loadtxt(inList)
aves = np.average(data,0)
print "From: %s" %FromfileName
if average:
for ave in aves:
outFile.write('%16.8E' %ave)
outFile.write('%16d' %len(data[0:]))
if waverage:
errs = amax(MCstat.bin(data),axis=0)
for (ave,err) in zip(aves,errs):
outFile.write('%16.8E%16.8E' %(ave,err))
outFile.write('\n')
else:
outFile.writelines(inList)
return outFile,numLines
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 Re_Bin(path ='', plot = True):
"""This function uses MCstat to rebin all data,
plots and returns the data"""
if path[-1] != '/':
path +='/'
bins_o = np.loadtxt(path + "01.data")
bin_err = MCstat.bin(bins_o)
if plot == True:
#get names
tags = open(path+ "01.data", "r").readline().lstrip("#").split()
#replace("_", "-").split() #issue with latex and using '_'
#begin plotting subroutine
fig = plt.figure()
ax = fig.add_subplot(111)
bin_level = range(len(bin_err))
#loop over all cut names
print "Plotting ReBining: %s ..." %(path)
#check for data to make sure data stored in each row
for i in range(len(tags)):
ax.plot(bin_level, bin_err[:,i], label = tags[i], linewidth = 2)
ax.legend(loc = 'upper left',fontsize = 'xx-small')
ax.set_title("Re-Bin of raw %s" %(path.strip('/').split('/')[-1]))
ax.set_xlabel("Bin Level")
ax.set_ylabel("Error")
plt.savefig(path + 'ReBin_Error.png')
plt.close() #removes figure/canvas
print "Done"
#returns the maximum error and its associated mean (without nasty loops)
index = bin_err.argmax(axis = 0)
return bin_err[index, range(len(index))]
def getVectorStats(data, weights, mergedstatus, dim=0):
''' Get the average and error of all columns in the data matrix for a vector
estimator.'''
if data.ndim > dim:
if mergedstatus:
numBins = np.size(data, dim)
# Average all the data (requires flattened weights array)
weights = weights.flatten()
dataAve = np.average(data, dim, weights=weights)
dataAve2 = np.average(data * data, dim, weights=weights)
# For a vector estimator get the error the usual way, ignoring the
# weights. This introduces a slight inaccurary in error bars but saves
# a lot of time and effort
# MC stat will not work unless you have more than 32 random seeds.
# The binning analysis done in MCstat is for a long run of correlated
# bins. However, for a merged file all "bins" (avrgs of random seeds)
# are completetely uncorrelated so we don't even need to do the binning
# analysis
#bins = MCstat.bin(data)
#dataErr = np.amax(bins,axis=0)
dataErr = np.sqrt(
abs(dataAve2 - dataAve**2) / (1.0 * numBins - 1.0))
else:
weights = weights.flatten()
numBins = np.size(data, dim)
dataAve = np.average(data, dim, weights=weights)
dataAve2 = np.average(data * data, dim, weights=weights)
bins = MCstat.bin(data)
dataErr = np.amax(bins, axis=0)
dataErr2 = np.sqrt(
abs(dataAve2 - dataAve**2) / (1.0 * numBins - 1.0))
else:
dataAve = data
dataErr = 0.0 * data
return dataAve, dataErr
def getVectorStats(data,weights,mergedstatus,dim=0):
''' Get the average and error of all columns in the data matrix for a vector
estimator.'''
if data.ndim > dim:
if mergedstatus:
numBins = np.size(data,dim)
# Average all the data (requires flattened weights array)
weights=weights.flatten()
dataAve = np.average(data,dim,weights=weights)
dataAve2 = np.average(data*data,dim,weights=weights)
# For a vector estimator get the error the usual way, ignoring the
# weights. This introduces a slight inaccurary in error bars but saves
# a lot of time and effort
# MC stat will not work unless you have more than 32 random seeds.
# The binning analysis done in MCstat is for a long run of correlated
# bins. However, for a merged file all "bins" (avrgs of random seeds)
# are completetely uncorrelated so we don't even need to do the binning
# analysis
#bins = MCstat.bin(data)
#dataErr = np.amax(bins,axis=0)
dataErr = np.sqrt( abs(dataAve2-dataAve**2)/(1.0*numBins-1.0) )
else:
weights = weights.flatten()
numBins = np.size(data,dim)
dataAve = np.average(data,dim,weights=weights)
dataAve2 = np.average(data*data,dim,weights=weights)
bins = MCstat.bin(data)
dataErr = np.amax(bins,axis=0)
dataErr2 = np.sqrt( abs(dataAve2-dataAve**2)/(1.0*numBins-1.0) )
else:
dataAve = data
dataErr = 0.0*data
return dataAve,dataErr
def main():
# setup the command line parser options
parser = argparse.ArgumentParser(description='Plot binning analysis for \
MC Data for Scalar Estimators.')
parser.add_argument('fileNames', help='Scalar estimator files', nargs='+')
parser.add_argument('--estimator',
'-e',
help='A list of estimator names \
that are to be plotted.',
type=str)
parser.add_argument('--skip',
'-s',
help='Number of measurements to be \
skipped in the binning analysis.',
type=int,
default=0)
parser.add_argument('--scale',
help='Option to compare binning results \
for different parameters',
action='store_true')
args = parser.parse_args()
fileNames = args.fileNames
scale = args.scale
if len(fileNames) < 1:
parser.error("Need to specify at least one scalar estimator file")
# We count the number of lines in the estimator file to make sure we have
# some data and grab the headers
headers = pimchelp.getHeadersDict(fileNames[0])
# If we don't choose an estimator, provide a list of possible ones
if not args.estimator or args.estimator not in headers:
errorString = "Need to specify one of:\n"
for head, index in headers.iteritems():
errorString += "\"%s\"" % head + " "
parser.error(errorString)
numFiles = len(fileNames)
col = list([headers[args.estimator]])
# Attempt to find a 'pretty name' for the label, otherwise just default to
# the column heading
label = pimchelp.Description()
try:
yLong = label.estimatorLongName[args.estimator]
except:
yLong = args.estimator
try:
yShort = label.estimatorShortName[args.estimator]
except:
yShort = args.estimator
# ============================================================================
# Figure 1 : Error vs. bin level
# ============================================================================
figure(1)
connect('key_press_event', kevent.press)
colors = loadgmt.getColorList('cw/1', 'cw1-029', max(numFiles, 2))
n = 0
for fileName in fileNames:
dataFile = open(fileName, 'r')
dataLines = dataFile.readlines()
dataFile.close()
if len(dataLines) > 2:
data = loadtxt(fileName, usecols=col)
if not pyutils.isList(data):
data = list([data])
delta = MCstat.bin(data[args.skip:])
if n == 0:
delta_ar = np.zeros((numFiles, delta.shape[0]))
delta_ar[n, :] = delta.T
#delta_ar[n,:len(delta)] = delta.T
n += 1
if n > 1:
if scale:
for m in range(n):
plot(np.arange(len(delta_ar)),delta_ar[m],marker='s',markersize=4,\
linestyle='-',linewidth=1.0,color=colors[m],\
markeredgecolor=colors[m])
else:
Delta = np.average(delta_ar, 0)
dDelta = np.std(delta_ar, 0) / np.sqrt(n)
errorbar(np.arange(len(Delta)),Delta,dDelta,marker='s',markersize=4,\
linestyle='-',linewidth=1.0,color=colors[0],\
markeredgecolor=colors[0])
bin_ac = MCstat.bin_ac(Delta, dDelta)
bin_conv = MCstat.bin_conv(Delta, dDelta)
print 'Convergence Ratio: %1.2f+/-%1.2f' % (bin_conv['CF'],
bin_conv['dCF'])
print 'autocorrlelation time: %2.1f+/-%2.1f' % \
(bin_ac['tau'],bin_ac['dtau'])
else:
plot(delta,marker='s',markersize=4,linestyle='-',linewidth=1.0,\
color=colors[0],markeredgecolor=colors[0])
print 'Convergence Ratio: %1.3f' % MCstat.bin_conv(delta)['CF']
print 'autocorrlelation time: %3.3f' % MCstat.bin_ac(delta)['tau']
ylabel(r"$\Delta_l$")
xlabel("$l$")
title("Bin scaling: " + yLong)
show()
def main():
# setup the command line parser options
parser = OptionParser()
parser.add_option("-s", "--skip", dest="skip", type="int",\
help="how many input lines should we skip?")
parser.set_defaults(skip=0)
# parse the command line options and get the file name
(options, args) = parser.parse_args()
if len(args) < 1:
parser.error("need a file name")
fileNames = args
for fileName in fileNames:
normalize = False;
# We check to see if we are dealing with the one body density matrix
if fileName.find('obdm') != -1:
normalize = True
# We count the number of lines in the estimator file to make sure we
# have some data and grab the headers
estFile = open(fileName,'r');
estLines = estFile.readlines();
numLines = len(estLines) - 2 # We expect two comment lines
pimcid = estLines[0]
headers = estLines[1].split()
estFile.close()
# If we have data, compute averages and error
if numLines-options.skip > 0:
estData = pyutils.loadFile(fileName)
# Now we skip data rows to test for convergence
for n in range(options.skip):
estData.pop(0)
estAve = pyutils.average(estData,1)
bins = MCstat.bin(np.array(estData))
estErr = bins[-1,:]
numData = len(estData)
print pimcid, '# Number Samples %6d' % numData
if not normalize:
for n,ave in enumerate(estAve):
if len(headers) - 1 == len(estAve):
label = headers[n+1]
else:
label = 'Col #%02d:' % n
print '%-16s%12.5f\t%12.5f' % (label,estAve[n],estErr[n])
else:
for n,ave in enumerate(estAve):
normAve = estAve[n]/estAve[0]
if abs(estAve[n]) > 1E-10:
normErr = (estErr[n] / estAve[n]) * normAve
else:
normErr = 0.0;
if len(headers) - 1 == len(estAve):
label = headers[n+1]
else:
label = 'Col #%02d:' % n
print '%-16s%12.5f\t%12.5f' % (label,normAve,normErr)
def main():
# setup the command line parser options
parser = argparse.ArgumentParser(description='Plot Raw MC Equilibration Data for Scalar Estimators.')
parser.add_argument('fileNames', help='Scalar estimator files', nargs='+')
parser.add_argument('--estimator','-e', help='A list of estimator names that \
are to be plotted.', type=str)
parser.add_argument('--skip','-s', help='Number of measurements to be skipped \
in the average plot.', type=int, default=0)
parser.add_argument('--period','-p', help='Period of the simple moving \
average. default=50', type=int, default=50)
args = parser.parse_args()
fileNames = args.fileNames
if len(fileNames) < 1:
parser.error("Need to specify at least one scalar estimator file")
toSort = True
for fileName in fileNames:
if fileName.find('/') != -1:
toSort = False
if toSort:
fileNames.sort()
ffile = open(fileNames[0],'r')
headers = ffile.readline().lstrip('#').split()
headers += ['srt']
print headers
# If we don't choose an estimator, provide a list of possible ones
if (not args.estimator) or (args.estimator not in headers):
errorString = "Need to specify one of:\n"
for head,index in headers.iteritems():
errorString += "\"%s\"" % head + " "
parser.error(errorString)
numFiles = len(fileNames)
#colors = loadgmt.getColorList('cw/1','cw1-029',max(numFiles,2))
colors = ["#66CAAE", "#CF6BDD", "#E27844", "#7ACF57", "#92A1D6", "#E17597", "#C1B546",'b']
fig = figure(1,figsize=(13,6))
ax = subplot(111)
connect('key_press_event',kevent.press)
#rcParams.update(mplrc.aps['params'])
n = 0
E0 = 0
Es = []
dEs = []
srt = False
if args.estimator=='srt': srt=True
for i,fileName in enumerate(fileNames):
dataFile = open(fileName,'r');
dataLines = dataFile.readlines();
ffile = open(fileName,'r')
headers = ffile.readline().lstrip('#').split()
headers += ['srt']
if len(dataLines) > 2:
#params = GetFileParams(fileName)
if srt and not('ALRatio' in headers):
col = GetHeaderNumber(fileName,'nAred')
dataR = loadtxt(fileName,usecols=col)
col = GetHeaderNumber(fileName,'nAext')
dataE = loadtxt(fileName,usecols=col)
daveR = amax(MCstat.bin(dataR[args.skip:]),axis=0)
aveR = average(dataR[args.skip:])
daveE = amax(MCstat.bin(dataE[args.skip:]),axis=0)
aveE = average(dataE[args.skip:])
#S2 = -umath.log(ufloat(aveE,daveE)/ufloat(aveR,daveR))
S2 = -1.0*umath.log(ufloat(aveE,daveE)/ufloat(aveR,daveR))
print 'Entropy = ', S2
Es += [S2.n]
dEs += [S2.s]
else:
if srt: col = GetHeaderNumber(fileName,'ALRatio')
else : col = GetHeaderNumber(fileName,args.estimator)
data = loadtxt(fileName,usecols=col)
ID = fileName[-14:-4]
if size(data) > 1:
sma = simpleMovingAverage(args.period,data[args.skip:])
ax.plot(sma,color=colors[i%len(colors)],linewidth=3,linestyle='-')
bins = MCstat.bin(data[args.skip:])
dataErr = amax(bins,axis=0)
dataAve = average(data[args.skip:])
if srt or (args.estimator=='ALRatio'):
S2 = -1.0*umath.log(ufloat(dataAve,dataErr))
print 'Entropy = ', S2
Es += [S2.n]
dEs += [S2.s]
#ax.plot(bins,color=colors[i%len(colors)],linewidth=1,marker='None',linestyle='-')
#if E0 == 0: E0 = dataAve
print '%0.6f +/- %0.6f ' %(dataAve-E0,dataErr)
#print dataAve-E0
else:
print '%s contains no measurements' %fileName
xlabel('MC bins (p=%s)' %args.period)
ylabel(args.estimator)
#tight_layout()
print Es
print dEs
#legend()
show()
def main():
# setup the command line parser options
parser = argparse.ArgumentParser(description='Plot binning analysis for \
MC Data for Scalar Estimators.')
parser.add_argument('fileNames', help='Scalar estimator files', nargs='+')
parser.add_argument('--estimator','-e', help='A list of estimator names \
that are to be plotted.', type=str)
parser.add_argument('--skip','-s', help='Number of measurements to be \
skipped in the binning analysis.', type=int, default=0)
parser.add_argument('--scale', help='Option to compare binning results \
for different parameters', action='store_true')
args = parser.parse_args()
fileNames = args.fileNames
scale = args.scale
if len(fileNames) < 1:
parser.error("Need to specify at least one scalar estimator file")
# We count the number of lines in the estimator file to make sure we have
# some data and grab the headers
headers = pimchelp.getHeadersDict(fileNames[0])
# If we don't choose an estimator, provide a list of possible ones
if not args.estimator or args.estimator not in headers:
errorString = "Need to specify one of:\n"
for head,index in headers.iteritems():
errorString += "\"%s\"" % head + " "
parser.error(errorString)
numFiles = len(fileNames)
col = list([headers[args.estimator]])
# Attempt to find a 'pretty name' for the label, otherwise just default to
# the column heading
label = pimchelp.Description()
try:
yLong = label.estimatorLongName[args.estimator]
except:
yLong = args.estimator
try:
yShort = label.estimatorShortName[args.estimator]
except:
yShort = args.estimator
# ============================================================================
# Figure 1 : Error vs. bin level
# ============================================================================
figure(1)
connect('key_press_event',kevent.press)
colors = loadgmt.getColorList('cw/1','cw1-029',max(numFiles,2))
n = 0
for fileName in fileNames:
dataFile = open(fileName,'r');
dataLines = dataFile.readlines();
dataFile.close()
if len(dataLines) > 2:
data = loadtxt(fileName,usecols=col)
if not pyutils.isList(data):
data = list([data])
delta = MCstat.bin(data[args.skip:])
if n == 0:
delta_ar = np.zeros((numFiles,delta.shape[0]))
delta_ar[n,:] = delta.T
#delta_ar[n,:len(delta)] = delta.T
n += 1
if n > 1:
if scale:
for m in range(n):
plot(np.arange(len(delta_ar)),delta_ar[m],marker='s',markersize=4,\
linestyle='-',linewidth=1.0,color=colors[m],\
markeredgecolor=colors[m])
else:
Delta = np.average(delta_ar,0)
dDelta = np.std(delta_ar,0)/np.sqrt(n)
errorbar(np.arange(len(Delta)),Delta,dDelta,marker='s',markersize=4,\
linestyle='-',linewidth=1.0,color=colors[0],\
markeredgecolor=colors[0])
bin_ac = MCstat.bin_ac(Delta,dDelta)
bin_conv = MCstat.bin_conv(Delta,dDelta)
print 'Convergence Ratio: %1.2f+/-%1.2f'%(bin_conv['CF'],bin_conv['dCF'])
print 'autocorrlelation time: %2.1f+/-%2.1f' % \
(bin_ac['tau'],bin_ac['dtau'])
else:
plot(delta,marker='s',markersize=4,linestyle='-',linewidth=1.0,\
color=colors[0],markeredgecolor=colors[0])
print 'Convergence Ratio: %1.3f' % MCstat.bin_conv(delta)['CF']
print 'autocorrlelation time: %3.3f' % MCstat.bin_ac(delta)['tau']
ylabel(r"$\Delta_l$")
xlabel("$l$")
title("Bin scaling: "+yLong)
show()
def main():
# setup the command line parser options
parser = OptionParser()
parser.add_option("-s", "--skip", dest="skip", type="int",\
help="how many input lines should we skip?")
parser.set_defaults(skip=0)
# parse the command line options and get the file name
(options, args) = parser.parse_args()
if len(args) < 1:
parser.error("need a file name")
fileNames = args
for fileName in fileNames:
normalize = False
# We check to see if we are dealing with the one body density matrix
if fileName.find('obdm') != -1:
normalize = True
# We count the number of lines in the estimator file to make sure we
# have some data and grab the headers
estFile = open(fileName, 'r')
estLines = estFile.readlines()
numLines = len(estLines) - 2 # We expect two comment lines
pimcid = estLines[0]
headers = estLines[1].split()
estFile.close()
# If we have data, compute averages and error
if numLines - options.skip > 0:
estData = pyutils.loadFile(fileName)
# Now we skip data rows to test for convergence
for n in range(options.skip):
estData.pop(0)
estAve = pyutils.average(estData, 1)
bins = MCstat.bin(np.array(estData))
estErr = bins[-1, :]
numData = len(estData)
print pimcid, '# Number Samples %6d' % numData
if not normalize:
for n, ave in enumerate(estAve):
if len(headers) - 1 == len(estAve):
label = headers[n + 1]
else:
label = 'Col #%02d:' % n
print '%-16s%12.5f\t%12.5f' % (label, estAve[n], estErr[n])
else:
for n, ave in enumerate(estAve):
normAve = estAve[n] / estAve[0]
if abs(estAve[n]) > 1E-10:
normErr = (estErr[n] / estAve[n]) * normAve
else:
normErr = 0.0
if len(headers) - 1 == len(estAve):
label = headers[n + 1]
else:
label = 'Col #%02d:' % n
print '%-16s%12.5f\t%12.5f' % (label, normAve, normErr)
def main():
# setup the command line parser options
parser = argparse.ArgumentParser(description=
'Plot PIMC estimator vs. parameter')
parser.add_argument('fileNames', help='Scalar estimator files', nargs='+')
args = parser.parse_args()
fileName = args.fileNames[0]
dataType = fileName.split('-')[1]
# Get first line of file
MCfile = open(fileName,'r')
MCfile.readline()
x = np.array(MCfile.readline().split()[1:]).astype(np.float)
MCfile.close()
# Get MC data
MCdata = np.loadtxt(fileName)
mean = np.average(MCdata,axis=0)
# error = np.std(MCdata,axis=0)/np.sqrt(MCdata.shape[0])
bins = MCstat.bin(np.array(MCdata))
error = bins[-1,:]
if dataType == 'occ':
N = np.sum(MCdata[0,:])
y = mean/N
dy = error/N
n0 = y[0]
dn0 = dy[0]
elif dataType == 'pn0':
N = 2*x[-1]
y = mean
dy = error
n0 = np.sum( mean*2*x)/N
ai = 1.0-(2.0*x)/N
dn0 = np.sqrt( np.sum((error[0:-1]*ai[0:-1])**2) )
nzInds = np.nonzero(y[:-1] > 2.0*dy[:-1])[0]
ynz = y[nzInds]
dynz = dy[nzInds]
y = ynz
dy = dynz
x = x[nzInds]
Sigma = np.sum(ynz)
Svn = -np.sum( ynz*np.log(ynz) ) - (1.0-Sigma)*np.log(1.0-Sigma)
dSdP = -(np.log(ynz) - np.log(1.0-Sigma))
dSvn = np.sqrt(np.sum( (dSdP*dynz)**2 ))
S2 = -np.log( np.sum( ynz**2 ) + (1.0-Sigma)**2)
dS2dP = (-1.0)*np.exp(S2)*(2.0*ynz-2.0*(1.0-Sigma))
dS2 = np.sqrt(np.sum( (dS2dP*dynz)**2 ))
print "\tvon Neumann entropy:\t" + str(Svn) + ' +/- ' + str(dSvn)
print "\t2nd Renyi entropy:\t" + str(S2) + ' +/- ' + str(dS2)
print "\tCondensate fraction:\t" + str(n0) + ' +/- ' + str(dn0)
f,ax = plt.subplots()
ax.errorbar(x,y,dy,linestyle='--',marker='o',markersize=4,capsize=4)
ax.set_yscale('log')
plt.xlim([x[0]-0.25,x[-1]+0.25])
plt.xlabel(r'$m$')
plt.ylabel(r'$n$')
plt.show()
