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():
# 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()