def markersColors(numColors):
'''return the markers and colors used for plotting.'''
# He4 Pore PRL Colors
# colors = ['#556270','#1C3249','#4ECDC4','#19857D','#C7F464','#FF6B6B','#A62323'] #556270
# http://www.graphviz.org/content/color-names
if numColors == 1:
numColors+=1;
#colors = loadgmt.getColorList('cb/div','Spectral_08',numColors)
#colors = loadgmt.getColorList('cb/div','PiYG_07',numColors)
colors = loadgmt.getColorList('cb/qual','Set1_09',numColors)
# colors.reverse()
markers = loadgmt.getMarkerList()
return markers,colors
def markersColors(numColors):
'''return the markers and colors used for plotting.'''
# He4 Pore PRL Colors
# colors = ['#556270','#1C3249','#4ECDC4','#19857D','#C7F464','#FF6B6B','#A62323'] #556270
# http://www.graphviz.org/content/color-names
if numColors == 1:
numColors += 1
#colors = loadgmt.getColorList('cb/div','Spectral_08',numColors)
#colors = loadgmt.getColorList('cb/div','PiYG_07',numColors)
colors = loadgmt.getColorList('cb/qual', 'Set1_09', numColors)
# colors.reverse()
markers = loadgmt.getMarkerList()
return markers, colors
def main():
# parse the command line options
args = docopt(__doc__)
fileNames = args['<file>']
skip = int(args['--skip'])
period = int(args['--period'])
estimator = args['--estimator']
leglabel = args['--legend'] and args['--legend']
error = args['--error'] and float(args['--error'])
val = args['--hline'] and float(args['--hline'])
# if labels are not assigned, we default to the PIMCID
if not leglabel:
leglabel = []
for n,fileName in enumerate(fileNames):
leglabel.append(fileName[-13:-4])
# 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 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[estimator]])
# Attempt to find a 'pretty name' for the label, otherwise just default to
# the column heading
label = pimchelp.Description()
try:
yLong = label.estimatorLongName[estimator]
except:
yLong = estimator
try:
yShort = label.estimatorShortName[estimator]
except:
yShort = estimator
# get a label for a possible horizontal line
if args['--hline']:
if args['--hlabel']:
hlabel = args['--hlabel']
else:
hlabel = yShort.split()[0] + ' = ' + args['--hline']
# First we load and store all the data
data = []
for fileName in fileNames:
dataFile = open(fileName,'r');
dataLines = dataFile.readlines();
dataFile.close()
if len(dataLines) > 2:
data.append(loadtxt(fileName,usecols=col,unpack=True))
# ============================================================================
# Figure 1 : column vs. MC Steps
# ============================================================================
figure(1)
connect('key_press_event',kevent.press)
colors = loadgmt.getColorList('cw/1','cw1-029',max(numFiles,2))
#colors = loadgmt.getColorList('cw/1','cw1-013',max(numFiles,2))
#colors = loadgmt.getColorList('oc','rainbow',max(numFiles,2))
#colors = loadgmt.getColorList('grass','bgyr',max(numFiles,2))
colors = ["#70D44A", "#BE5AD4", "#D04537", "#81D0D5", "#393A2E", "#C49ECA",
"#C5CB7A", "#523767", "#D39139", "#C8488C", "#CB817A", "#73D999",
"#6F2836", "#6978CF", "#588569", "#CDC3AD", "#5C7890", "#7F5327",
"#D0D33D", "#5B7D2E"]
colors = ["#53B0AD", "#D74C20", "#CD53DA", "#58C038", "#5F4B7A", "#49622A",
"#CE4379", "#D2912E", "#7970D2", "#749AC9", "#7D5121", "#5EAC72",
"#CB85AA", "#853B46", "#396465", "#A5A33E", "#D47F5B", "#BA4EA5",
"#C93F44", "#5D9937"]
colors =["#CAC5E8", "#E1D273", "#82DFCE", "#F1AF92", "#E1E7CF", "#92C798",
"#CDF197", "#DDD199", "#ECB1D1", "#91C7DE", "#E3AF6E", "#ECAAAC",
"#CEB29C", "#B2BCA9", "#B0C778", "#DBCDD7", "#B5DEE0", "#A5ECB4",
"#C5E6C0", "#E5CCC0"]
colors = ["#688EAF", "#FC991D", "#7DEB74", "#FA6781", "#8B981D", "#BB7548",
"#AD8FE4", "#96E4AA", "#D669B0", "#E1C947", "#A78200", "#7C9FE4",
"#957DA6", "#75BF38", "#C3B059", "#51C17A", "#79AEBB", "#2790AC",
"#688ECE", "#749DB7"]
colors += colors
colors += colors
colors += colors
print len(colors)
for n,cdata in enumerate(data):
plot(cdata[skip:],marker='s',color=colors[n],markeredgecolor=colors[n],\
markersize=4,linestyle='-',linewidth=1.0)
ylabel(yLong)
xlabel("MC Bin Number")
# ============================================================================
# Figure 2 : running average of column vs. MC Bins
# ============================================================================
figure(2)
connect('key_press_event',kevent.press)
n = 0
for n,cdata in enumerate(data):
if size(cdata) > 1:
# Get the cumulative moving average
if args['--error']:
cma = cumulativeMovingAverage(cdata[skip:])
sem = error*ones_like(cma)
elif args['--nobin']:
cma,sem = cumulativeMovingAverageWithError(cdata[skip:])
else:
cma = cumulativeMovingAverage(cdata[skip:])
ave,err = getStats(cdata[skip:])
sem = err*ones_like(cma)
print '%s: %s = %8.4E +- %8.4E' % (leglabel[n],yShort, ave,err)
sma = simpleMovingAverage(50,cdata[skip:])
x = range(int(0.10*len(cma)),len(cma))
plot(x,cma[x],color=colors[n],linewidth=1.0,marker='None',linestyle='-',
label=leglabel[n])
fill_between(x, cma[x]-sem[x], cma[x]+sem[x],color=colors[n], alpha=0.1)
n += 1
# Add a possible horizontal line indicating some value
if args['--hline']:
axhline(y=val,color='gray',linewidth=2.5, label=hlabel)
ylabel(yLong)
xlabel("MC Bin Number")
tight_layout()
leg = legend(loc='best', frameon=False, prop={'size':16},markerscale=2, ncol=2)
for l in leg.get_lines():
l.set_linewidth(4.0)
# Perform a Welch's t-test
if args['--ttest']:
# We only perform the Welch's t test if we have multiple samples we are
# comparing
N = len(data)
if N > 1:
tval = zeros([N,N])
p = zeros([N,N])
for i in range(N):
for j in range(i+1,N):
tval[i,j],p[i,j] = stats.ttest_ind(data[i][skip:], data[j][skip:],
equal_var=False)
# ============================================================================
# Figure 3 : plot the estimator histogram along with t-test values
# ============================================================================
fig = figure(3)
connect('key_press_event',kevent.press)
for i in range(N):
n, bins, patches = hist(data[i], 100, normed=True, facecolor=colors[i],
alpha=0.75, label=leglabel[i],
edgecolor='w')
# Add the p-values from the t-test
y = 0.92
if N > 1:
figtext(0.78, y, 't-test p values', horizontalalignment='center',
verticalalignment='top', fontsize=15, backgroundcolor='white')
for i in range(N):
for j in range(i+1,N):
y -= 0.03
lab = 'p(' + leglabel[i] + ' - ' + leglabel[j] + ') = ' + '%4.2f'%p[i,j]
figtext(0.78, y, lab, horizontalalignment='center',
verticalalignment='top', fontsize=12,
backgroundcolor='white')
legend(loc='upper left', fontsize=15, frameon=False)
xlabel(yLong)
ylabel(r'$P($' + estimator + r'$)$')
show()
def main():
# define the mapping between short names and label names
shortFlags = ['n','T','N','t','u','V','L','W','D']
parMap = {'n':'Initial Density', 'T':'Temperature', 'N':'Initial Number Particles',
't':'Imaginary Time Step', 'u':'Chemical Potential', 'V':'Container Volume',
'L':'Container Length', 'W':'Virial Window', 'M':'Update Length'} #'M':'Update Slices (Mbar)'}
# setup the command line parser options
parser = OptionParser()
parser.add_option("-T", "--temperature", dest="T", type="float",
help="simulation temperature in Kelvin")
parser.add_option("-N", "--number-particles", dest="N", type="int",
help="number of particles")
parser.add_option("-n", "--density", dest="n", type="float",
help="number density in Angstroms^{-d}")
parser.add_option("-t", "--imag-time-step", dest="tau", type="float",
help="imaginary time step")
parser.add_option("-u", "--chemical-potential", dest="mu", type="float",
help="chemical potential in Kelvin")
parser.add_option("-L", "--Lz", dest="L", type="float",
help="Length in Angstroms")
parser.add_option("-V", "--volume", dest="V", type="float",
help="volume in Angstroms^d")
parser.add_option("-r", "--reduce", dest="reduce",
choices=['T','N','n','u','t','L','V','W','M'],
help="variable name for reduction [T,N,n,u,t,L,V,W,M]")
parser.add_option("--canonical", action="store_true", dest="canonical",
help="are we in the canonical ensemble?")
parser.add_option("-p", "--plot", action="store_true", dest="plot",
help="do we want to produce data plots?")
parser.add_option("-R", "--radius", dest="R", type="float",
help="radius in Angstroms")
parser.add_option("-s", "--skip", dest="skip", type="int",
help="number of measurements to skip")
parser.add_option("-e", "--estimator", dest="estimator", type="str",
help="specify a single estimator to reduce")
parser.add_option("-i", "--pimcid", dest="pimcid", type="str",
help="specify a single pimcid")
parser.set_defaults(canonical=False)
parser.set_defaults(plot=False)
parser.set_defaults(skip=0)
# parse the command line options and get the reduce flag
(options, args) = parser.parse_args()
# Determine the working directory
if args:
baseDir = args[0]
if baseDir == '.':
baseDir = ''
else:
baseDir = ''
skip = options.skip
if (not options.reduce):
parser.error("need a correct reduce flag (-r,--reduce): [T,N,n,u,t,L,V,W,D]")
# Check that we are in the correct ensemble
pimchelp.checkEnsemble(options.canonical)
dataName,outName = pimchelp.getFileString(options)
reduceFlag = []
reduceFlag.append(options.reduce)
reduceFlag.append(parMap[options.reduce])
# Create the PIMC analysis helper and fill up the simulation parameters maps
pimc = pimchelp.PimcHelp(dataName,options.canonical,baseDir=baseDir)
pimc.getSimulationParameters()
# Form the full output file name
if options.R == None:
outName += '.dat'
else:
outName += '-R-%04.1f.dat' % options.R
# possible types of estimators we may want to reduce
estList = ['estimator', 'super', 'obdm', 'pair', 'radial', 'number',
'radwind', 'radarea', 'planedensity', 'planearea',
'planewind','virial','linedensity','linepotential']
estDo = {e:False for e in estList}
# if we specify a single estimator, only do that one
if options.estimator:
estDo[options.estimator] = True
# otherwise test to see if the file exists
else:
for e in estList:
if pimc.getFileList(e):
estDo[e] = True
else:
estDo[e] = False
# We first reduce the scalar estimators and output them to disk
if estDo['estimator']:
head1,scAve1,scErr1 = getScalarEst('estimator',pimc,outName,reduceFlag,skip=skip)
if estDo['virial']:
head1,scAve1,scErr1 = getScalarEst('virial',pimc,outName,reduceFlag,skip=skip)
if estDo['super']:
head2,scAve2,scErr2 = getScalarEst('super',pimc,outName,reduceFlag,skip=skip)
# Now we do the normalized one body density matrix
if estDo['obdm']:
x1,ave1,err1 = getVectorEst('obdm',pimc,outName,reduceFlag,'r [A]','n(r)',skip=skip)
# Now we do the pair correlation function
if estDo['pair']:
x2,ave2,err2 = getVectorEst('pair',pimc,outName,reduceFlag,'r [A]','g(r)',skip=skip)
# The radial Density
if estDo['radial']:
x3,ave3,err3 = getVectorEst('radial',pimc,outName,reduceFlag,'r [A]','rho(r)',skip=skip)
# Compute the number distribution function and compressibility if we are in
# the grand canonical ensemble
if estDo['number']:
x4,ave4,err4 = getVectorEst('number',pimc,outName,reduceFlag,'N','P(N)',skip=skip)
# I don't know why this isn't working, MCStat is giving me an error, will
# return to this later. AGD
#kappa,kappaErr = getKappa(pimc,outName,reduceFlag)
# The radially averaged Winding superfluid density
if estDo['radwind']:
x5,ave5,err5 = getVectorEst('radwind',pimc,outName,reduceFlag,'r [A]','rho_s(r)',skip=skip)
# The radially averaged area superfliud density
if estDo['radarea']:
x6,ave6,err6 = getVectorEst('radarea',pimc,outName,reduceFlag,'r [A]','rho_s(r)',skip=skip)
if estDo['planewind']:
x7,ave7,err7 = getVectorEst('planewind',pimc,outName,reduceFlag,'n','rho_s(r)',skip=skip)
if estDo['planearea']:
x8,ave8,err8 = getVectorEst('planearea',pimc,outName,reduceFlag,'n','rho_s(r)',skip=skip)
if estDo['planedensity']:
x9,ave9,err9 = getVectorEst('planedensity',pimc,outName,reduceFlag,'n','rho(r)',skip=skip)
if estDo['linedensity']:
x10,ave10,err10 = getVectorEst('linedensity',pimc,outName,reduceFlag,\
'r [A]','rho1d(r)',skip=skip)
if estDo['linepotential']:
x11,ave11,err11 = getVectorEst('linepotential',pimc,outName,reduceFlag,\
'r [A]','V1d(r)',skip=skip)
# Do we show plots?
if options.plot:
figNum = 1
# Get the changing parameter that we are plotting against
param = []
for ID in pimc.id:
param.append(float(pimc.params[ID][reduceFlag[1]]))
numParams = len(param)
markers = loadgmt.getMarkerList()
colors = loadgmt.getColorList('cw/1','cw1-029',10)
# -----------------------------------------------------------------------------
# Plot the averaged data
# -----------------------------------------------------------------------------
if estDo['estimator']:
headLab = ['E/N','K/N','V/N','N', 'diagonal']
dataCol = []
for head in headLab:
n = 0
for h in head1:
if head == h:
dataCol.append(n)
break
n += 1
yLabelCol = ['Energy / N', 'Kinetic Energy / N', 'Potential Energy / N',\
'Number Particles', 'Diagonal Fraction']
# ============================================================================
# Figure -- Various thermodynamic quantities
# ============================================================================
for n in range(len(dataCol)):
figure(figNum)
connect('key_press_event',kevent.press)
errorbar(param, scAve1[:,dataCol[n]], yerr=scErr1[:,dataCol[n]],\
color=colors[n],marker=markers[n],markeredgecolor=colors[n],\
markersize=8,linestyle='None',capsize=4)
xlabel('%s'%options.reduce)
ylabel(yLabelCol[n])
tight_layout()
figNum += 1
# ============================================================================
# Figure -- The superfluid density
# ============================================================================
if estDo['super']:
figure(figNum)
connect('key_press_event',kevent.press)
errorbar(param, scAve2[:,0], yerr=scErr2[:,0],\
color=colors[0],marker=markers[0],markeredgecolor=colors[0],\
markersize=8,linestyle='None',capsize=4)
tight_layout()
xlabel('%s'%options.reduce)
ylabel('Superfluid Density')
# ============================================================================
# Figure -- The one body density matrix
# ============================================================================
if estDo['obdm']:
figNum += 1
figure(figNum)
connect('key_press_event',kevent.press)
ax = subplot(111)
for n in range(numParams):
lab = '%s = %s' % (options.reduce,param[n])
errorbar(x1[n,:], (ave1[n,:]+1.0E-15), err1[n,:],color=colors[n],marker=markers[0],\
markeredgecolor=colors[n], markersize=8,linestyle='None',label=lab)
#axis([0,21,1.0E-5,1.1])
xlabel('r [Angstroms]')
ylabel('One Body Density Matrix')
tight_layout()
legend(loc='best', frameon=False, prop={'size':16},ncol=2)
# ============================================================================
# Figure -- The pair correlation function
# ============================================================================
if estDo['pair']:
figNum += 1
figure(figNum)
connect('key_press_event',kevent.press)
for n in range(numParams):
lab = '%s = %s' % (options.reduce,param[n])
errorbar(x2[n,:], ave2[n,:], yerr=err2[n,:],color=colors[n],marker=markers[0],\
markeredgecolor=colors[n], markersize=8,linestyle='None',label=lab,capsize=6)
# axis([0,256,1.0E-5,1.2])
xlabel('r [Angstroms]')
ylabel('Pair Correlation Function')
legend(loc='best', frameon=False, prop={'size':16},ncol=2)
tight_layout()
# We only plot the compressibility if we are in the grand-canonical ensemble
if not options.canonical:
# ============================================================================
# Figure -- The Number distribution
# ============================================================================
if estDo['number']:
figNum += 1
figure(figNum)
connect('key_press_event',kevent.press)
# Find which column contains the average number of particles
for hn,h in enumerate(head1):
if h == 'N':
break
for n in range(numParams):
lab = '%s = %s' % (options.reduce,param[n])
aN = scAve1[n,hn]
errorbar(x4[n,:]-aN, ave4[n,:], err4[n,:],color=colors[n],marker=markers[0],\
markeredgecolor=colors[n],\
markersize=8,linestyle='None',label=lab,capsize=6)
axis([-30,30,0.0,1.2])
xlabel(r'$N-\langle N \rangle$')
ylabel('P(N)')
tight_layout()
legend(loc='best', frameon=False, prop={'size':16},ncol=2)
# ============================================================================
# Figure -- The Compressibility
# ============================================================================
#figNum += 1
#figure(figNum)
#connect('key_press_event',kevent.press)
#errorbar(param, kappa, yerr=kappaErr, color=colors[0],marker=markers[0],\
# markeredgecolor=colors[0], markersize=8,linestyle='None',capsize=6)
#tight_layout()
#xlabel('%s'%options.reduce)
#ylabel(r'$\rho^2 \kappa$')
# ============================================================================
# Figure -- The radial density
# ============================================================================
if len(glob.glob('CYLINDER')) > 0:
figNum += 1
figure(figNum)
connect('key_press_event',kevent.press)
ax = subplot(111)
for n in range(numParams):
lab = '%s = %s' % (options.reduce,param[n])
errorbar(x3[n,:], (ave3[n,:]+1.0E-15), err3[n,:],color=colors[n],marker=markers[0],\
markeredgecolor=colors[n], markersize=8,linestyle='None',label=lab)
#axis([0,21,1.0E-5,1.1])
tight_layout()
xlabel('r [Angstroms]')
ylabel('Radial Density')
legend(loc='best', frameon=False, prop={'size':16},ncol=2)
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():
# parse the command line options
args = docopt(__doc__)
fileNames = args['<file>']
skip = int(args['--skip'])
period = int(args['--period'])
estimator = args['--estimator']
leglabel = args['--legend'] and args['--legend']
error = args['--error'] and float(args['--error'])
val = args['--hline'] and float(args['--hline'])
# if labels are not assigned, we default to the PIMCID
if not leglabel:
leglabel = []
for n, fileName in enumerate(fileNames):
leglabel.append(fileName[-13:-4])
# 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 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[estimator]])
# Attempt to find a 'pretty name' for the label, otherwise just default to
# the column heading
label = pimchelp.Description()
try:
yLong = label.estimatorLongName[estimator]
except:
yLong = estimator
try:
yShort = label.estimatorShortName[estimator]
except:
yShort = estimator
# get a label for a possible horizontal line
if args['--hline']:
if args['--hlabel']:
hlabel = args['--hlabel']
else:
hlabel = yShort.split()[0] + ' = ' + args['--hline']
# First we load and store all the data
data = []
for fileName in fileNames:
dataFile = open(fileName, 'r')
dataLines = dataFile.readlines()
dataFile.close()
if len(dataLines) > 2:
data.append(loadtxt(fileName, usecols=col, unpack=True))
# ============================================================================
# Figure 1 : column vs. MC Steps
# ============================================================================
figure(1)
connect('key_press_event', kevent.press)
colors = loadgmt.getColorList('cw/1', 'cw1-029', max(numFiles, 2))
#colors = loadgmt.getColorList('cw/1','cw1-013',max(numFiles,2))
#colors = loadgmt.getColorList('oc','rainbow',max(numFiles,2))
#colors = loadgmt.getColorList('grass','bgyr',max(numFiles,2))
colors = [
"#70D44A", "#BE5AD4", "#D04537", "#81D0D5", "#393A2E", "#C49ECA",
"#C5CB7A", "#523767", "#D39139", "#C8488C", "#CB817A", "#73D999",
"#6F2836", "#6978CF", "#588569", "#CDC3AD", "#5C7890", "#7F5327",
"#D0D33D", "#5B7D2E"
]
colors = [
"#53B0AD", "#D74C20", "#CD53DA", "#58C038", "#5F4B7A", "#49622A",
"#CE4379", "#D2912E", "#7970D2", "#749AC9", "#7D5121", "#5EAC72",
"#CB85AA", "#853B46", "#396465", "#A5A33E", "#D47F5B", "#BA4EA5",
"#C93F44", "#5D9937"
]
colors = [
"#CAC5E8", "#E1D273", "#82DFCE", "#F1AF92", "#E1E7CF", "#92C798",
"#CDF197", "#DDD199", "#ECB1D1", "#91C7DE", "#E3AF6E", "#ECAAAC",
"#CEB29C", "#B2BCA9", "#B0C778", "#DBCDD7", "#B5DEE0", "#A5ECB4",
"#C5E6C0", "#E5CCC0"
]
colors = [
"#688EAF", "#FC991D", "#7DEB74", "#FA6781", "#8B981D", "#BB7548",
"#AD8FE4", "#96E4AA", "#D669B0", "#E1C947", "#A78200", "#7C9FE4",
"#957DA6", "#75BF38", "#C3B059", "#51C17A", "#79AEBB", "#2790AC",
"#688ECE", "#749DB7"
]
colors += colors
colors += colors
colors += colors
print len(colors)
for n, cdata in enumerate(data):
plot(cdata[skip:],marker='s',color=colors[n],markeredgecolor=colors[n],\
markersize=4,linestyle='-',linewidth=1.0)
ylabel(yLong)
xlabel("MC Bin Number")
# ============================================================================
# Figure 2 : running average of column vs. MC Bins
# ============================================================================
figure(2)
connect('key_press_event', kevent.press)
n = 0
for n, cdata in enumerate(data):
if size(cdata) > 1:
# Get the cumulative moving average
if args['--error']:
cma = cumulativeMovingAverage(cdata[skip:])
sem = error * ones_like(cma)
elif args['--nobin']:
cma, sem = cumulativeMovingAverageWithError(cdata[skip:])
else:
cma = cumulativeMovingAverage(cdata[skip:])
ave, err = getStats(cdata[skip:])
sem = err * ones_like(cma)
print '%s: %s = %8.4E +- %8.4E' % (leglabel[n], yShort, ave,
err)
sma = simpleMovingAverage(50, cdata[skip:])
x = range(int(0.10 * len(cma)), len(cma))
plot(x,
cma[x],
color=colors[n],
linewidth=1.0,
marker='None',
linestyle='-',
label=leglabel[n])
fill_between(x,
cma[x] - sem[x],
cma[x] + sem[x],
color=colors[n],
alpha=0.1)
n += 1
# Add a possible horizontal line indicating some value
if args['--hline']:
axhline(y=val, color='gray', linewidth=2.5, label=hlabel)
ylabel(yLong)
xlabel("MC Bin Number")
tight_layout()
leg = legend(loc='best',
frameon=False,
prop={'size': 16},
markerscale=2,
ncol=2)
for l in leg.get_lines():
l.set_linewidth(4.0)
# Perform a Welch's t-test
if args['--ttest']:
# We only perform the Welch's t test if we have multiple samples we are
# comparing
N = len(data)
if N > 1:
tval = zeros([N, N])
p = zeros([N, N])
for i in range(N):
for j in range(i + 1, N):
tval[i, j], p[i, j] = stats.ttest_ind(data[i][skip:],
data[j][skip:],
equal_var=False)
# ============================================================================
# Figure 3 : plot the estimator histogram along with t-test values
# ============================================================================
fig = figure(3)
connect('key_press_event', kevent.press)
for i in range(N):
n, bins, patches = hist(data[i],
100,
normed=True,
facecolor=colors[i],
alpha=0.75,
label=leglabel[i],
edgecolor='w')
# Add the p-values from the t-test
y = 0.92
if N > 1:
figtext(0.78,
y,
't-test p values',
horizontalalignment='center',
verticalalignment='top',
fontsize=15,
backgroundcolor='white')
for i in range(N):
for j in range(i + 1, N):
y -= 0.03
lab = 'p(' + leglabel[i] + ' - ' + leglabel[
j] + ') = ' + '%4.2f' % p[i, j]
figtext(0.78,
y,
lab,
horizontalalignment='center',
verticalalignment='top',
fontsize=12,
backgroundcolor='white')
legend(loc='upper left', fontsize=15, frameon=False)
xlabel(yLong)
ylabel(r'$P($' + estimator + r'$)$')
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():
# parse the command line options
args = docopt(__doc__)
fileNames = args['<file>']
skip = int(args['--skip'])
period = int(args['--period'])
estimators = args['--estimator']
leglabel = args['--legend'] and args['--legend']
error = args['--error'] and float(args['--error'])
# number of estimators must equal number of filenames given
if len(fileNames) != len(estimators):
print "\nNumber of estimators must equal number of filenames supplied."
print "Note that these must be in the same order!\n"
sys.exit()
# if labels are not assigned, we default to the PIMCID
if not leglabel:
leglabel = []
for n,fileName in enumerate(fileNames):
leglabel.append(fileName[-13:-4])
numFiles = len(fileNames)
# first load all of data from all files into a list
col = []
data = []
for numEst in range(len(estimators)):
headers = pimchelp.getHeadersDict(fileNames[numEst])
col = list([headers[estimators[numEst]]])
dataFile = open(fileNames[numEst],'r');
dataLines = dataFile.readlines();
dataFile.close()
if len(dataLines) > 2:
data.append(loadtxt(fileNames[numEst],usecols=col,unpack=True))
# loop over files, loading them and plotting all data wanted.
for numEst in range(len(estimators)):
# 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[numEst])
# If we don't choose an estimator, provide a list of possible ones
if estimators[numEst] not in headers:
errorString = "Need to specify one of:\n"
for head,index in headers.iteritems():
errorString += "\"%s\"" % head + " "
parser.error(errorString)
# Attempt to find a 'pretty name' for the label, otherwise just default to
# the column heading
label = pimchelp.Description()
try:
yLong = label.estimatorLongName[estimators[numEst]]
except:
yLong = estimators[numEst]
try:
yShort = label.estimatorShortName[estimators[numEst]]
except:
yShort = estimators[numEst]
# ============================================================================
# Figure 1 : column vs. MC Steps
# ============================================================================
figNum = 1
if args['--pdf']:
figure(figNum, dpi=40, figsize=(6,3.8))
else:
figure(figNum)
connect('key_press_event',kevent.press)
colors = loadgmt.getColorList('oc','rainbow',max(numFiles,2))
for n,cdata in enumerate(data):
plot(cdata[skip:],marker='s',color=colors[n],markeredgecolor=colors[n],\
markersize=4,linestyle='-',linewidth=1.0, label=leglabel[n])
ylabel(yLong)
xlabel("MC Bin Number")
if numEst == 0:
legend(loc=3, frameon=False, ncol=2)
if args['--pdf']:
savefig('col_vs_MCSteps.pdf', format='pdf',
bbox_inches='tight')
savefig('col_vs_MCSteps_trans.pdf', format='pdf',
bbox_inches='tight', transparent=True, dpi=40)
else:
savefig('col_vs_MCSteps_trans.png', format='png',
bbox_inches='tight', transparent=True)
# ============================================================================
# Figure 2 : running average of column vs. MC Bins
# ============================================================================
figNum += 1
if args['--pdf']:
figure(figNum, dpi=40, figsize=(6,3.8))
else:
figure(figNum, figsize=(6,3.8))
connect('key_press_event',kevent.press)
n = 0
for n,cdata in enumerate(data):
if size(cdata) > 1:
# Get the cumulative moving average
if args['--error']:
cma = cumulativeMovingAverage(cdata[skip:])
sem = error*ones_like(cma)
elif args['--bin']:
cma = cumulativeMovingAverage(cdata[skip:])
ave,err = getStats(cdata[skip:])
sem = err*ones_like(cma)
print '%s: %s = %8.4E +- %8.4E' % (leglabel[n],yShort, ave,err)
else:
cma,sem = cumulativeMovingAverageWithError(cdata[skip:])
sma = simpleMovingAverage(50,cdata[skip:])
x = range(int(0.10*len(cma)),len(cma))
plot(x,cma[x],color=colors[n],linewidth=1.0,marker='None',linestyle='-',
label=leglabel[n])
fill_between(x, cma[x]-sem[x], cma[x]+sem[x],color=colors[n], alpha=0.1)
#n += 1
ylabel(yLong)
xlabel("MC Bin Number")
if numEst == 0:
leg = legend(loc='best', frameon=False, prop={'size':12},markerscale=2, ncol=2)
for l in leg.get_lines():
l.set_linewidth(4.0)
if args['--pdf']:
savefig('runAve_vs_MCSteps.pdf', format='pdf',
bbox_inches='tight')
savefig('runAve_vs_MCSteps_trans.pdf', format='pdf',
bbox_inches='tight',transparent=True, dpi=40)
else:
savefig('runAve_vs_MCSteps_trans.png', format='png',
bbox_inches='tight',transparent=True)
# Perform a Welch's t-test
if args['--ttest']:
# We only perform the Welch's t test if we have multiple samples we are
# comparing
N = len(data)
if N > 1:
tval = zeros([N,N])
p = zeros([N,N])
for i in range(N):
for j in range(i+1,N):
tval[i,j],p[i,j] = stats.ttest_ind(data[i][skip:], data[j][skip:],
equal_var=False)
figNum += 1
# ============================================================================
# Figure 3 : plot the estimator histogram along with t-test values
# ============================================================================
if args['--pdf']:
fig = figure(figNum, dpi=40, figsize=(6,3.8))
else:
fig = figure(figNum)
connect('key_press_event',kevent.press)
for i in range(N):
n, bins, patches = hist(data[i], 100, normed=True, facecolor=colors[i],
alpha=0.75, label=leglabel[i],
edgecolor='w')
'''# Add the p-values from the t-test
y = 0.92
if N > 1:
figtext(0.78, y, 't-test p values', horizontalalignment='center',
verticalalignment='top', fontsize=15, backgroundcolor='white')
for i in range(N):
for j in range(i+1,N):
y -= 0.03
lab = 'p(' + leglabel[i] + ' - ' + leglabel[j] + ') = ' + '%4.2f'%p[i,j]
figtext(0.78, y, lab, horizontalalignment='center',
verticalalignment='top', fontsize=12,
backgroundcolor='white')'''
#legend(loc='upper left', fontsize=15, frameon=False)
if numEst == 0:
legend(loc='upper left', frameon=False)
xlabel(yLong)
yLabb = estimators[numEst]
if yLabb == 'Ecv/N':
yLabb = 'E/N'
ylabel(r'$P($' + yLabb + r'$)$')
if args['--pdf']:
savefig('ttest_histogram.pdf', format='pdf',
bbox_inches='tight')
savefig('ttest_histogram_trans.pdf', format='pdf',
bbox_inches='tight', transparent=True, dpi=40)
else:
savefig('ttest_histogram_trans.png', format='png',
bbox_inches='tight', transparent=True)
# ============================================================================
# Figure 4 : autocorrelation
# ============================================================================
figNum += 1
if args['--pdf']:
figure(figNum, dpi=40, figsize=(6,3.8))
else:
figure(figNum)
connect('key_press_event',kevent.press)
colors = loadgmt.getColorList('oc','rainbow',max(numFiles,2))
mcTime = arange(0,len(cdata[skip:]),1)
for n,cdata in enumerate(data):
plot(mcTime,estimated_autocorrelation(cdata[skip:]),
marker='s',color=colors[n],markeredgecolor=colors[n],
markersize=4,linestyle='-',linewidth=1.0, label=leglabel[n])
ylabel("Autocorrelation")
xlabel("Data")
if numEst == 0:
legend(loc=3, frameon=False, ncol=2)
'''if args['--pdf']:
savefig('autocorrelation.pdf', format='pdf',
bbox_inches='tight')
savefig('autocorrelation_trans.pdf', format='pdf',
bbox_inches='tight', transparent=True, dpi=40)
else:
savefig('autocorrelation_trans.png', format='png',
bbox_inches='tight', transparent=True)
'''
show()