def main():
# --- Set up all options --------------------------------------------------
# parse command line, getting algorithmic and plotting options.
args = jk.parseCMD()
reduceType = args.reduceType
direc = args.fileNames[0]
Lx = 12.0
# some plotting color and label options
colors = ['Salmon','MediumSpringGreen','DarkViolet','Fuchsia','Blue',
'Maroon']
if args.RandomColors:
random.shuffle(colors)
# set up figure that displays all data
figg = pl.figure(1)
ax = figg.add_subplot(111)
pl.xlabel(r'$T\ [K]$', fontsize=20)
pl.ylabel('Spatial Density '+r'$[\si{\angstrom}^{-d}]$', fontsize=20)
pl.grid(True)
pl.xlim([0.5,3.0])
pl.ylim([0,0.07])
pl.tick_params(axis='both', which='major', labelsize=16)
pl.tick_params(axis='both', which='minor', labelsize=16)
yticks = ax.yaxis.get_major_ticks()
yticks[0].set_visible(False)
# set up bulk SVP densities for plot
bulkVert = -30
minMus = 0.5
maxMus = 2.5
boxSubtract = 1.6
pl.plot([minMus, maxMus], [0.02198, 0.02198], 'k-', lw=3)
pl.annotate('3d SVP', xy=(maxMus - boxSubtract, 0.02195), #xycoords='data',
xytext=(-50, bulkVert), textcoords='offset points',
bbox=dict(boxstyle="round", fc="0.8"),
arrowprops=dict(arrowstyle="->",
connectionstyle="angle,angleA=0,angleB=90,rad=10"),
)
pl.plot([minMus, maxMus], [0.0432, 0.0432], 'k-', lw=3)
pl.annotate('2d SVP', xy=(maxMus - boxSubtract, 0.0432), #xycoords='data',
xytext=(-50, 30), textcoords='offset points',
bbox=dict(boxstyle="round", fc="0.8"),
arrowprops=dict(arrowstyle="->",
connectionstyle="angle,angleA=0,angleB=90,rad=10"),
)
pl.figure(2)
pl.xlabel(r'$T\ [K]$', fontsize=20)
pl.ylabel('Number of Particles', fontsize=20)
pl.grid(True)
pl.xlim([0.5,3.0])
pl.ylim([0,2])
pl.tick_params(axis='both', which='major', labelsize=16)
pl.tick_params(axis='both', which='minor', labelsize=16)
yticks = ax.yaxis.get_major_ticks()
yticks[0].set_visible(False)
# --- loop over all values of S -------------------------------------------
os.chdir(direc)
Svals = glob.glob('S*')
for nS, Sval in enumerate(Svals):
os.chdir(Sval)
# store bulk separation value
S = re.search(r'\d+',Sval).group(0)
# projected area of film region
projArea = float(S)*Lx
# set label for plot
if 'distinguishable' in Sval:
labell = 'S = '+str(S)+', Boltzmannons'
else:
labell = 'S = '+str(S)+', Bosons'
# get all temperature directory names
Tdirs = sorted(glob.glob('T*'))
Ts = pl.array([])
Films = pl.array([])
Bulks = pl.array([])
filmErrs = pl.array([])
bulkErrs = pl.array([])
# --- loop over all temperature values --------------------------------
for Tdir in Tdirs:
os.chdir(Tdir)
# build array of temperatures
Ts = pl.append(Ts, float(Tdir[1:]))
# get data file name
f = glob.glob('*Bipart*')[0]
#densData = pl.genfromtxt(f, delimiter=',')
filmavg,filmstd,filmbins,bulkavg,bulkstd,bulkbins = pl.genfromtxt(f,
unpack=True, usecols=(0,1,2,3,4,5), delimiter=',')
# get rid of any items which are not numbers..
# this is some beautiful Python juju.
filmbins = filmbins[pl.logical_not(pl.isnan(filmbins))]
filmstd = filmstd[pl.logical_not(pl.isnan(filmstd))]
filmavg = filmavg[pl.logical_not(pl.isnan(filmavg))]
bulkbins = bulkbins[pl.logical_not(pl.isnan(bulkbins))]
bulkstd = bulkstd[pl.logical_not(pl.isnan(bulkstd))]
bulkavg = bulkavg[pl.logical_not(pl.isnan(bulkavg))]
filmweights = filmbins/pl.sum(filmbins)
bulkweights = bulkbins/pl.sum(bulkbins)
filmavg *= filmweights
bulkavg *= bulkweights
filmstd *= filmweights
bulkstd *= bulkweights
film = pl.sum(filmavg)
bulk = pl.sum(bulkavg)
filmstdErr = pl.sum(filmstd)
bulkstdErr = pl.sum(bulkstd)
Films = pl.append(Films, film)
Bulks = pl.append(Bulks, bulk)
filmErrs = pl.append(filmErrs, filmstdErr)
bulkErrs = pl.append(bulkErrs, bulkstdErr)
os.chdir('..')
# add data to plot for given S value.
pl.figure(1)
pl.errorbar(Ts, Films, filmErrs, fmt='-o', color=colors[nS],
label=labell+', 2d')
pl.errorbar(Ts, Bulks, bulkErrs, fmt = '--d', color=colors[nS],
label=labell+', 3d')
pl.figure(2)
pl.errorbar(Ts, Films*projArea, fmt='-o', color=colors[nS],
label = labell+', 2d')
os.chdir('..')
pl.figure(1)
pl.legend()
#pl.savefig('Densities_vs_T_allS.pdf', format='pdf',
# bbox_inches='tight')
pl.figure(2)
pl.legend()
pl.show()
def main():
estimList = []
QHO = False
args = aTools.parseCMD()
fileNames = args.fileNames
skip = args.skip
# Check if any jackknife data files exist already.
check = glob.glob('*Jack*')
if check != []:
sys.exit('Check your directory for Jackknife data files')
reduceType = args.reduceType
# check which ensemble
canonical=True
if fileNames[0][0]=='g':
canonical=False
# create list of temperatures
temps = pl.array([])
tempList = aTools.getHeadersFromFile(fileNames[0])
for temp in tempList:
temps = pl.append(temps,float(temp))
# perform jackknife analysis of data, writing to disk
for fileName in fileNames:
print '\n\n---',fileName,'---\n'
# ========================================
# NON TRIVIAL WINDING ESTIMATOR
# ========================================
if 'Ntwind' in fileName:
estimList.append('Ntwind')
ntWinds, ntWindErrs = pl.array([]), pl.array([])
fout = open('JackKnifeData_ntWind.dat','w')
fout.write('#%15s\t%16s\n'%(reduceType, '<W^2>'))
# read in data and call jackknife method.
for n,temp in enumerate(tempList):
ntWind = pl.array([])
with open(fileName, 'rb') as f:
reader = csv.reader(f)
for row in reader:
if row[0][0] == '#':
continue
else:
try:
ntWind = pl.append(ntWind, float(row[n]))
except:
continue
ntWindAve, ntWindErr = aTools.jackknife(ntWind[skip:])
print reduceType,' = ', temp
print '<W^2> = ', ntWindAve,' +/- ',ntWindErr
print 'numBins: ',len(ntWind),'\n'
ntWinds = pl.append(ntWinds, ntWindAve)
ntWindErrs = pl.append(ntWindErrs, ntWindErr)
fout.write('%16.8E\t%16.8E\t%16.8E\n' %(
float(temp), ntWindAve, ntWindErr ))
fout.close()
# ========================================
# ENERGY AND SPECIFIC HEAT ESTIMATOR
# ========================================
if 'Estimator' in fileName:
estimList.append('Estimator')
Cvs,CvsErr = pl.array([]),pl.array([])
Es, EsErr = pl.array([]), pl.array([])
fout = open('JackKnifeData_Cv.dat', 'w')
fout.write('#%15s\t%16s\t%16s\t%16s\t%16s\n'% (
reduceType, 'E', 'Eerr', 'Cv', 'CvErr'))
# read in data and call jackknife method.
n = 0
for temp in tempList:
E = pl.array([])
Cv1 = pl.array([])
Cv2 = pl.array([])
Cv3 = pl.array([])
with open(fileName, 'rb') as f:
reader = csv.reader(f)
for row in reader:
if row[0][0] == '#':
continue
else:
try:
E = pl.append(E, float(row[n]))
Cv1 = pl.append(Cv1, float(row[n+1]))
Cv2 = pl.append(Cv2, float(row[n+2]))
Cv3 = pl.append(Cv3, float(row[n+3]))
except:
continue
n += 4
EAve, Eerr = aTools.jackknife(E[skip:])
jkAve, jkErr = aTools.jackknife(
Cv1[skip:],Cv2[skip:],Cv3[skip:])
print reduceType,' = ',float(temp)
print '<E> = ',EAve,' +/- ',Eerr
print '<Cv> = ',jkAve,' +/- ',jkErr
Es = pl.append(Es, EAve)
Cvs = pl.append(Cvs, jkAve)
EsErr = pl.append(EsErr, Eerr)
CvsErr = pl.append(CvsErr, jkErr)
fout.write('%16.8E\t%16.8E\t%16.8E\t%16.8E\t%16.8E\n' %(
float(temp), EAve, Eerr, jkAve, jkErr))
fout.close()
# ========================================
# SUPERFLUID ESTIMATORS
# ========================================
if 'Super' in fileName:
estimList.append('Super')
rhos_rhos, rhos_rhoErr = pl.array([]), pl.array([])
Wx2s, Wx2Err = pl.array([]), pl.array([])
Wy2s, Wy2Err = pl.array([]), pl.array([])
Wz2s, Wz2Err = pl.array([]), pl.array([])
fout = open('JackKnifeData_super.dat','w')
fout.write('#%15s,\t%16s,\t%16s,\t%16s,\t%16s,\t\
%16s,\t%16s,\t%16s,\t%16s\n'%(
reduceType, 'rho_s/rho', 'rho_s/rhoErr', 'Wx^2', 'Wx2_err',
'Wy^2', 'Wy2_err', 'Wz^2', 'Wz2_err'))
# read in data and call jackknife method.
n = 0
for temp in tempList:
rhos_rho = pl.array([])
wx2 = pl.array([])
wy2 = pl.array([])
wz2 = pl.array([])
with open(fileName, 'rb') as f:
reader = csv.reader(f)
for row in reader:
if row[0][0] == '#':
continue
else:
try:
rhos_rho = pl.append(rhos_rho,
float(row[n]))
wx2 = pl.append(wx2, float(row[n+1]))
wy2 = pl.append(wy2, float(row[n+2]))
wz2 = pl.append(wz2, float(row[n+3]))
except:
continue
n += 4
superAve, superErr = aTools.jackknife(rhos_rho[skip:])
wx2Ave, wx2Err = aTools.jackknife(wx2[skip:])
wy2Ave, wy2Err = aTools.jackknife(wy2[skip:])
wz2Ave, wz2Err = aTools.jackknife(wz2[skip:])
print 'rho_s/rho = ', superAve,' +/- ',superErr
rhos_rhos = pl.append(rhos_rhos, superAve)
rhos_rhoErr = pl.append(rhos_rhoErr, superErr)
Wx2s = pl.append(Wx2s, wx2Ave)
Wx2Err = pl.append(Wx2Err, wx2Err)
Wy2s = pl.append(Wy2s, wy2Ave)
Wy2Err = pl.append(Wy2Err, wy2Err)
Wz2s = pl.append(Wz2s, wz2Ave)
Wz2Err = pl.append(Wz2Err, wz2Err)
fout.write('%16.8E,\t%16.8E,\t%16.8E,\t%16.8E,\t\
%16.8E,\t%16.8E,\t%16.8E,\t%16.8E,\t%16.8E,\n' %(
float(temp), superAve, superErr, wx2Ave, wx2Err,
wy2Ave, wy2Err, wz2Ave, wz2Err))
# ========================================
# FILM AND BULK DENSITY ESTIMATORS
# ========================================
if 'Bipart' in fileName:
estimList.append('Bipart')
filmDenses, bulkDenses = pl.array([]), pl.array([])
filmDensErrs, bulkDensErrs = pl.array([]), pl.array([])
fout = open('JackKnifeData_bipart.dat','w')
fout.write('#%15s\t%16s\t%16s\t%16s\t%16s\n'%(reduceType,
'filmDens', 'filmDensErr', 'bulkDens', 'bulkDensErr'))
# read in data and call jackknife method.
n = 0
for temp in tempList:
filmDens = pl.array([])
bulkDens = pl.array([])
with open(fileName, 'rb') as f:
reader = csv.reader(f)
for row in reader:
if row[0][0] == '#':
continue
else:
try:
filmDens = pl.append(filmDens,
float(row[n+2]))
bulkDens = pl.append(bulkDens,
float(row[n+3]))
except:
continue
n += 2
filmDensAve, filmDensErr = aTools.jackknife(filmDens[skip:])
bulkDensAve, bulkDensErr = aTools.jackknife(bulkDens[skip:])
print 'filmDens = ', filmDensAve,' +/- ',filmDensErr
print 'bulkDens = ', bulkDensAve,' +/- ',bulkDensErr
filmDenses = pl.append(filmDenses, filmDensAve)
filmDensErrs = pl.append(filmDensErrs, filmDensErr)
bulkDenses = pl.append(bulkDenses, bulkDensAve)
bulkDensErrs = pl.append(bulkDensErrs, bulkDensErr)
fout.write('%16.8E\t%16.8E\t%16.8E\t%16.8E\t%16.8E\n' %(
float(temp), filmDensAve, filmDensErr,
bulkDensAve, bulkDensErr))
fout.close()
# =========================================================================
# PLOTTING SECTION
# =========================================================================
xLab = jk.getXlabel(reduceType)
plotNum = 1
# PLOT ntWinding number squared
if 'Ntwind' in estimList:
pl.figure(plotNum)
pl.errorbar(temps, ntWinds, ntWindErrs, fmt='o')
pl.xlabel(xLab, fontsize=20)
pl.ylabel(r'$\langle \Omega^2 \rangle$', fontsize=20)
pl.grid(True)
plotNum += 1
# PLOT specific heat vs. temperature
if 'Estimator' in estimList:
pl.figure(2)
ax1 = pl.subplot(211)
#if QHO: # plot analytic result
# pl.plot(tempRange,CvAnalytic, label='Exact')
pl.errorbar(temps,Cvs,CvsErr, label='PIMC',color='Violet',fmt='o')
pl.ylabel('Specific Heat', fontsize=20)
pl.grid(True)
pl.legend(loc=2)
# PLOT the energy vs. temperature
pl.setp(ax1.get_xticklabels(), visible=False)
ax2 = pl.subplot(212, sharex=ax1)
#if QHO: # plot analytic result
# pl.plot(tempRange,Eanalytic, label='Exact')
pl.errorbar(temps,Es,EsErr, label='PIMC virial',color='Lime',fmt='o')
pl.xlabel(xLab, fontsize=20)
pl.ylabel('Energy [K]', fontsize=20)
pl.grid(True)
pl.legend(loc=2)
plotNum += 1
# PLOT superfluid properties
if 'Super' in estimList:
pl.figure(plotNum)
pl.errorbar(temps, rhos_rhos, rhos_rhoErr, fmt='o')
pl.xlabel(xLab, fontsize=20)
pl.ylabel('Superfluid Stiffness', fontsize=20)
pl.grid(True)
plotNum += 1
pl.figure(plotNum)
pl.errorbar(temps, Wx2s, Wx2Err, fmt='o', label=r'$\langle W_x^2 \rangle$')
pl.errorbar(temps, Wy2s, Wy2Err, fmt='o', label=r'$\langle W_y^2 \rangle$')
pl.errorbar(temps, Wz2s, Wz2Err, fmt='o', label=r'$\langle W_z^2 \rangle$')
pl.xlabel(xLab, fontsize=20)
pl.ylabel(r'$\langle W_i^2 \rangle$', fontsize=20)
pl.legend()
pl.grid(True)
plotNum += 1
pl.figure(plotNum)
pl.errorbar(temps, Wz2s, Wz2Err, fmt='o', label=r'$\langle W_z^2 \rangle$')
pl.xlabel(xLab, fontsize=20)
pl.ylabel(r'$\langle W_z^2 \rangle$', fontsize=20)
pl.legend()
pl.grid(True)
plotNum += 1
# PLOT film and bulk densities
if 'Bipart' in estimList:
pl.figure(plotNum)
pl.errorbar(temps, filmDenses, filmDensErrs, label='film', fmt='o')
pl.errorbar(temps, bulkDenses, bulkDensErrs, label='bulk', fmt='o')
pl.xlabel(xLab, fontsize=20)
pl.ylabel(r'$\mathrm{Density}\ [\AA^{-d}]$', fontsize=20)
pl.legend()
pl.grid(True)
pl.show()
# BELOW IS RESIDUE FROM A PREVIOUS VERSION OF THIS SCRIPT.
# SOME OF IT MIGHT PROVE USEFUL.
'''else:
print 'Found existing data file in CWD.'
temps, Es, EsErr, Cvs, CvsErr = pl.loadtxt(
'JackKnifeData_Cv.dat',
unpack=True)
if not QHO:
temps, rhos_rhos, rhos_rhoErr, Wx2s, Wx2Err, Wy2s, Wy2Err, Wz2s, Wz2Err = pl.loadtxt(
'JackKnifeData_super.dat',
unpack=True)
if excVol:
temps, filmDenses, filmDensErrs, bulkDenses, bulkDensErrs = pl.loadtxt(
'JackKnifeData_bipart.dat',
unpack=True)'''
'''
def main():
omega = True
energy = False
superFrac = True
# --- Set up all options --------------------------------------------------
Ly = 12.0
# parse command line, getting algorithmic and plotting options.
args = jk.parseCMD()
reduceType = args.reduceType
direc = args.fileNames[0]
nCol = args.nCol
nEst = args.nEst
# some plotting color and label options
xLab = jk.getXlabel(reduceType)
extent = args.bulkSeparation
colors = [
'Salmon', 'MediumSpringGreen', 'DarkViolet', 'Fuchsia', 'Blue',
'Maroon'
]
if args.RandomColors:
random.shuffle(colors)
# set up figure that displays all data
figg = pl.figure(1)
ax = figg.add_subplot(111)
pl.xlabel(r'$T\ [K]$', fontsize=20)
pl.ylabel(r'$\langle \Omega \rangle$', fontsize=20)
pl.grid(True)
pl.xlim([0.4, 2.6])
pl.tick_params(axis='both', which='major', labelsize=16)
pl.tick_params(axis='both', which='minor', labelsize=16)
yticks = ax.yaxis.get_major_ticks()
yticks[0].set_visible(False)
figg2 = pl.figure(2)
ax = figg2.add_subplot(111)
pl.ylabel(r'$ \rho_s/\rho $', fontsize=20)
pl.xlabel(r'$T\ [K]$', fontsize=20)
pl.grid(True)
pl.xlim([0.4, 2.6])
pl.tick_params(axis='both', which='major', labelsize=16)
pl.tick_params(axis='both', which='minor', labelsize=16)
yticks = ax.yaxis.get_major_ticks()
yticks[0].set_visible(False)
figg3 = pl.figure(3)
ax = figg3.add_subplot(111)
pl.ylabel(r'$ \rho_{\text{film}}\ [\si{\angstrom}^{-2}] $', fontsize=20)
pl.xlabel(r'$T\ [K]$', fontsize=20)
pl.grid(True)
pl.xlim([0.4, 2.6])
pl.tick_params(axis='both', which='major', labelsize=16)
pl.tick_params(axis='both', which='minor', labelsize=16)
yticks = ax.yaxis.get_major_ticks()
yticks[0].set_visible(False)
figg4 = pl.figure(4)
ax = figg4.add_subplot(111)
pl.ylabel(r'$\langle \rho_{\text{bulk}} \rangle$', fontsize=20)
pl.xlabel(r'$T\ [K]$', fontsize=20)
pl.grid(True)
pl.xlim([0.4, 2.6])
pl.tick_params(axis='both', which='major', labelsize=16)
pl.tick_params(axis='both', which='minor', labelsize=16)
yticks = ax.yaxis.get_major_ticks()
yticks[0].set_visible(False)
# --- loop over all values of S -------------------------------------------
os.chdir(direc)
Svals = glob.glob('S*')
Svals = natsort.natsorted(Svals)
print Svals
for nS, Sval in enumerate(Svals):
os.chdir(Sval)
# store bulk separation value
S = re.search(r'\d+', Sval).group(0)
print S
# set normalization
if omega:
normFactor = 4.0 * (float(S) + Ly)**2
else:
normFactor = 1.0
print normFactor
# set label for plot
#if 'distinguishable' in Sval:
if 'noSwaps' in Sval:
labell = 'S = ' + str(
S) + ' ' + r'$\si{\angstrom}$' ', Boltzmannons'
else:
labell = 'S = ' + str(S) + ' ' + r'$\si{\angstrom}$' + ', Bosons'
# get all temperature directory names
Tdirs = sorted(glob.glob('T*'))
# build array of norman winding values along with bins, for Lz plotting.
windingAverages = pl.array([])
windingErrors = pl.array([])
filmAverages = pl.array([])
filmErrors = pl.array([])
bulkAverages = pl.array([])
bulkErrors = pl.array([])
superAverages = pl.array([])
superErrors = pl.array([])
Ts = pl.array([])
#Omegas = pl.array([])
#Errs = pl.array([])
#Films = pl.array([])
#Ferrs = pl.array([])
#Bulks = pl.array([])
#Berrs = pl.array([])
#Supers = pl.array([])
#Serrs = pl.array([])
# --- loop over all temperature values --------------------------------
for Tdir in Tdirs:
os.chdir(Tdir)
# build array of temperatures
Ts = pl.append(Ts, float(Tdir[1:]))
# --- angular winding ---
f = glob.glob('*zAveragedNtwind*')[0]
aCol = 3
sCol = 4
bCol = 5
avg, stdErr = cT.crunchZfile(f, aCol, sCol, bCol, normFactor)
windingAverages = pl.append(windingAverages, avg)
windingErrors = pl.append(windingErrors, stdErr)
# --- film densities ---
f = glob.glob('*zAveragedBipart*')[0]
aCol = 0
sCol = 1
bCol = 2
avg, stdErr = cT.crunchZfile(f, aCol, sCol, bCol, 1.0)
filmAverages = pl.append(filmAverages, avg)
filmErrors = pl.append(filmErrors, stdErr)
# --- bulk densities ---
aCol = 3
sCol = 4
bCol = 5
avg, stdErr = cT.crunchZfile(f, aCol, sCol, bCol, 1.0)
bulkAverages = pl.append(bulkAverages, avg)
bulkErrors = pl.append(bulkErrors, stdErr)
# --- superfluid fractions ---
f = glob.glob('*zAveragedSuper*')[0]
aCol = 0
sCol = 1
bCol = 2
avg, stdErr = cT.crunchZfile(f, aCol, sCol, bCol, 1.0)
superAverages = pl.append(superAverages, avg)
superErrors = pl.append(superErrors, stdErr)
# ----------------------
os.chdir('..')
# add data to plot for given S value.
pl.figure(1)
pl.errorbar(Ts,
windingAverages,
windingErrors,
fmt='-o',
color=colors[nS],
label=labell)
pl.figure(2)
pl.errorbar(Ts,
superAverages,
superErrors,
fmt='-o',
color=colors[nS],
label=labell)
pl.figure(3)
pl.errorbar(Ts,
filmAverages,
filmErrors,
fmt='-o',
color=colors[nS],
label=labell)
pl.figure(4)
pl.errorbar(Ts,
bulkAverages,
bulkErrors,
fmt='-o',
color=colors[nS],
label=labell)
os.chdir('..')
pl.figure(1)
pl.legend()
pl.savefig('Omega_vs_T_allS.pdf', format='pdf', bbox_inches='tight')
pl.figure(2)
pl.legend()
#pl.savefig('SuperFrac_vs_T_allS.pdf', format='pdf',
# bbox_inches='tight')
pl.figure(3)
pl.legend()
pl.savefig('FilmDensities_vs_T_allS.pdf',
format='pdf',
bbox_inches='tight')
pl.figure(4)
pl.legend()
pl.show()
def main():
estimList = []
QHO = False
args = aTools.parseCMD()
fileNames = args.fileNames
skip = args.skip
# Check if any jackknife data files exist already.
check = glob.glob('*Jack*')
if check != []:
sys.exit('Check your directory for Jackknife data files')
reduceType = args.reduceType
# check which ensemble
canonical = True
if fileNames[0][0] == 'g':
canonical = False
# create list of temperatures
temps = pl.array([])
tempList = aTools.getHeadersFromFile(fileNames[0])
for temp in tempList:
temps = pl.append(temps, float(temp))
# perform jackknife analysis of data, writing to disk
for fileName in fileNames:
print '\n\n---', fileName, '---\n'
# ========================================
# NON TRIVIAL WINDING ESTIMATOR
# ========================================
if 'Ntwind' in fileName:
estimList.append('Ntwind')
ntWinds, ntWindErrs = pl.array([]), pl.array([])
fout = open('JackKnifeData_ntWind.dat', 'w')
fout.write('#%15s\t%16s\n' % (reduceType, '<W^2>'))
# read in data and call jackknife method.
for n, temp in enumerate(tempList):
ntWind = pl.array([])
with open(fileName, 'rb') as f:
reader = csv.reader(f)
for row in reader:
if row[0][0] == '#':
continue
else:
try:
ntWind = pl.append(ntWind, float(row[n]))
except:
continue
ntWindAve, ntWindErr = aTools.jackknife(ntWind[skip:])
print reduceType, ' = ', temp
print '<W^2> = ', ntWindAve, ' +/- ', ntWindErr
print 'numBins: ', len(ntWind), '\n'
ntWinds = pl.append(ntWinds, ntWindAve)
ntWindErrs = pl.append(ntWindErrs, ntWindErr)
fout.write('%16.8E\t%16.8E\t%16.8E\n' %
(float(temp), ntWindAve, ntWindErr))
fout.close()
# ========================================
# ENERGY AND SPECIFIC HEAT ESTIMATOR
# ========================================
if 'Estimator' in fileName:
estimList.append('Estimator')
Cvs, CvsErr = pl.array([]), pl.array([])
Es, EsErr = pl.array([]), pl.array([])
fout = open('JackKnifeData_Cv.dat', 'w')
fout.write('#%15s\t%16s\t%16s\t%16s\t%16s\n' %
(reduceType, 'E', 'Eerr', 'Cv', 'CvErr'))
# read in data and call jackknife method.
n = 0
for temp in tempList:
E = pl.array([])
Cv1 = pl.array([])
Cv2 = pl.array([])
Cv3 = pl.array([])
with open(fileName, 'rb') as f:
reader = csv.reader(f)
for row in reader:
if row[0][0] == '#':
continue
else:
try:
E = pl.append(E, float(row[n]))
Cv1 = pl.append(Cv1, float(row[n + 1]))
Cv2 = pl.append(Cv2, float(row[n + 2]))
Cv3 = pl.append(Cv3, float(row[n + 3]))
except:
continue
n += 4
EAve, Eerr = aTools.jackknife(E[skip:])
jkAve, jkErr = aTools.jackknife(Cv1[skip:], Cv2[skip:],
Cv3[skip:])
print reduceType, ' = ', float(temp)
print '<E> = ', EAve, ' +/- ', Eerr
print '<Cv> = ', jkAve, ' +/- ', jkErr
Es = pl.append(Es, EAve)
Cvs = pl.append(Cvs, jkAve)
EsErr = pl.append(EsErr, Eerr)
CvsErr = pl.append(CvsErr, jkErr)
fout.write('%16.8E\t%16.8E\t%16.8E\t%16.8E\t%16.8E\n' %
(float(temp), EAve, Eerr, jkAve, jkErr))
fout.close()
# ========================================
# SUPERFLUID ESTIMATORS
# ========================================
if 'Super' in fileName:
estimList.append('Super')
rhos_rhos, rhos_rhoErr = pl.array([]), pl.array([])
Wx2s, Wx2Err = pl.array([]), pl.array([])
Wy2s, Wy2Err = pl.array([]), pl.array([])
Wz2s, Wz2Err = pl.array([]), pl.array([])
fout = open('JackKnifeData_super.dat', 'w')
fout.write('#%15s,\t%16s,\t%16s,\t%16s,\t%16s,\t\
%16s,\t%16s,\t%16s,\t%16s\n' %
(reduceType, 'rho_s/rho', 'rho_s/rhoErr', 'Wx^2',
'Wx2_err', 'Wy^2', 'Wy2_err', 'Wz^2', 'Wz2_err'))
# read in data and call jackknife method.
n = 0
for temp in tempList:
rhos_rho = pl.array([])
wx2 = pl.array([])
wy2 = pl.array([])
wz2 = pl.array([])
with open(fileName, 'rb') as f:
reader = csv.reader(f)
for row in reader:
if row[0][0] == '#':
continue
else:
try:
rhos_rho = pl.append(rhos_rho, float(row[n]))
wx2 = pl.append(wx2, float(row[n + 1]))
wy2 = pl.append(wy2, float(row[n + 2]))
wz2 = pl.append(wz2, float(row[n + 3]))
except:
continue
n += 4
superAve, superErr = aTools.jackknife(rhos_rho[skip:])
wx2Ave, wx2Err = aTools.jackknife(wx2[skip:])
wy2Ave, wy2Err = aTools.jackknife(wy2[skip:])
wz2Ave, wz2Err = aTools.jackknife(wz2[skip:])
print 'rho_s/rho = ', superAve, ' +/- ', superErr
rhos_rhos = pl.append(rhos_rhos, superAve)
rhos_rhoErr = pl.append(rhos_rhoErr, superErr)
Wx2s = pl.append(Wx2s, wx2Ave)
Wx2Err = pl.append(Wx2Err, wx2Err)
Wy2s = pl.append(Wy2s, wy2Ave)
Wy2Err = pl.append(Wy2Err, wy2Err)
Wz2s = pl.append(Wz2s, wz2Ave)
Wz2Err = pl.append(Wz2Err, wz2Err)
fout.write('%16.8E,\t%16.8E,\t%16.8E,\t%16.8E,\t\
%16.8E,\t%16.8E,\t%16.8E,\t%16.8E,\t%16.8E,\n' %
(float(temp), superAve, superErr, wx2Ave, wx2Err,
wy2Ave, wy2Err, wz2Ave, wz2Err))
# ========================================
# FILM AND BULK DENSITY ESTIMATORS
# ========================================
if 'Bipart' in fileName:
estimList.append('Bipart')
filmDenses, bulkDenses = pl.array([]), pl.array([])
filmDensErrs, bulkDensErrs = pl.array([]), pl.array([])
fout = open('JackKnifeData_bipart.dat', 'w')
fout.write('#%15s\t%16s\t%16s\t%16s\t%16s\n' %
(reduceType, 'filmDens', 'filmDensErr', 'bulkDens',
'bulkDensErr'))
# read in data and call jackknife method.
n = 0
for temp in tempList:
filmDens = pl.array([])
bulkDens = pl.array([])
with open(fileName, 'rb') as f:
reader = csv.reader(f)
for row in reader:
if row[0][0] == '#':
continue
else:
try:
filmDens = pl.append(filmDens,
float(row[n + 2]))
bulkDens = pl.append(bulkDens,
float(row[n + 3]))
except:
continue
n += 2
filmDensAve, filmDensErr = aTools.jackknife(filmDens[skip:])
bulkDensAve, bulkDensErr = aTools.jackknife(bulkDens[skip:])
print 'filmDens = ', filmDensAve, ' +/- ', filmDensErr
print 'bulkDens = ', bulkDensAve, ' +/- ', bulkDensErr
filmDenses = pl.append(filmDenses, filmDensAve)
filmDensErrs = pl.append(filmDensErrs, filmDensErr)
bulkDenses = pl.append(bulkDenses, bulkDensAve)
bulkDensErrs = pl.append(bulkDensErrs, bulkDensErr)
fout.write('%16.8E\t%16.8E\t%16.8E\t%16.8E\t%16.8E\n' %
(float(temp), filmDensAve, filmDensErr, bulkDensAve,
bulkDensErr))
fout.close()
# =========================================================================
# PLOTTING SECTION
# =========================================================================
xLab = jk.getXlabel(reduceType)
plotNum = 1
# PLOT ntWinding number squared
if 'Ntwind' in estimList:
pl.figure(plotNum)
pl.errorbar(temps, ntWinds, ntWindErrs, fmt='o')
pl.xlabel(xLab, fontsize=20)
pl.ylabel(r'$\langle \Omega^2 \rangle$', fontsize=20)
pl.grid(True)
plotNum += 1
# PLOT specific heat vs. temperature
if 'Estimator' in estimList:
pl.figure(2)
ax1 = pl.subplot(211)
#if QHO: # plot analytic result
# pl.plot(tempRange,CvAnalytic, label='Exact')
pl.errorbar(temps, Cvs, CvsErr, label='PIMC', color='Violet', fmt='o')
pl.ylabel('Specific Heat', fontsize=20)
pl.grid(True)
pl.legend(loc=2)
# PLOT the energy vs. temperature
pl.setp(ax1.get_xticklabels(), visible=False)
ax2 = pl.subplot(212, sharex=ax1)
#if QHO: # plot analytic result
# pl.plot(tempRange,Eanalytic, label='Exact')
pl.errorbar(temps,
Es,
EsErr,
label='PIMC virial',
color='Lime',
fmt='o')
pl.xlabel(xLab, fontsize=20)
pl.ylabel('Energy [K]', fontsize=20)
pl.grid(True)
pl.legend(loc=2)
plotNum += 1
# PLOT superfluid properties
if 'Super' in estimList:
pl.figure(plotNum)
pl.errorbar(temps, rhos_rhos, rhos_rhoErr, fmt='o')
pl.xlabel(xLab, fontsize=20)
pl.ylabel('Superfluid Stiffness', fontsize=20)
pl.grid(True)
plotNum += 1
pl.figure(plotNum)
pl.errorbar(temps,
Wx2s,
Wx2Err,
fmt='o',
label=r'$\langle W_x^2 \rangle$')
pl.errorbar(temps,
Wy2s,
Wy2Err,
fmt='o',
label=r'$\langle W_y^2 \rangle$')
pl.errorbar(temps,
Wz2s,
Wz2Err,
fmt='o',
label=r'$\langle W_z^2 \rangle$')
pl.xlabel(xLab, fontsize=20)
pl.ylabel(r'$\langle W_i^2 \rangle$', fontsize=20)
pl.legend()
pl.grid(True)
plotNum += 1
pl.figure(plotNum)
pl.errorbar(temps,
Wz2s,
Wz2Err,
fmt='o',
label=r'$\langle W_z^2 \rangle$')
pl.xlabel(xLab, fontsize=20)
pl.ylabel(r'$\langle W_z^2 \rangle$', fontsize=20)
pl.legend()
pl.grid(True)
plotNum += 1
# PLOT film and bulk densities
if 'Bipart' in estimList:
pl.figure(plotNum)
pl.errorbar(temps, filmDenses, filmDensErrs, label='film', fmt='o')
pl.errorbar(temps, bulkDenses, bulkDensErrs, label='bulk', fmt='o')
pl.xlabel(xLab, fontsize=20)
pl.ylabel(r'$\mathrm{Density}\ [\AA^{-d}]$', fontsize=20)
pl.legend()
pl.grid(True)
pl.show()
# BELOW IS RESIDUE FROM A PREVIOUS VERSION OF THIS SCRIPT.
# SOME OF IT MIGHT PROVE USEFUL.
'''else:
print 'Found existing data file in CWD.'
temps, Es, EsErr, Cvs, CvsErr = pl.loadtxt(
'JackKnifeData_Cv.dat',
unpack=True)
if not QHO:
temps, rhos_rhos, rhos_rhoErr, Wx2s, Wx2Err, Wy2s, Wy2Err, Wz2s, Wz2Err = pl.loadtxt(
'JackKnifeData_super.dat',
unpack=True)
if excVol:
temps, filmDenses, filmDensErrs, bulkDenses, bulkDensErrs = pl.loadtxt(
'JackKnifeData_bipart.dat',
unpack=True)'''
'''
def main():
omega = True
energy = False
superFrac = True
# --- Set up all options --------------------------------------------------
Ly = 12.0
# parse command line, getting algorithmic and plotting options.
args = jk.parseCMD()
reduceType = args.reduceType
direc = args.fileNames[0]
nCol = args.nCol
nEst = args.nEst
# some plotting color and label options
xLab = jk.getXlabel(reduceType)
extent = args.bulkSeparation
colors = ['Salmon','MediumSpringGreen','DarkViolet','Fuchsia','Blue',
'Maroon']
if args.RandomColors:
random.shuffle(colors)
# set up figure that displays all data
figg = pl.figure(1)
ax = figg.add_subplot(111)
pl.xlabel(r'$T\ [K]$', fontsize=20)
pl.ylabel(r'$\langle \Omega \rangle$', fontsize=20)
pl.grid(True)
pl.xlim([0.4,2.6])
pl.tick_params(axis='both', which='major', labelsize=16)
pl.tick_params(axis='both', which='minor', labelsize=16)
yticks = ax.yaxis.get_major_ticks()
yticks[0].set_visible(False)
figg2 = pl.figure(2)
ax = figg2.add_subplot(111)
pl.ylabel(r'$ \rho_s/\rho $', fontsize=20)
pl.xlabel(r'$T\ [K]$', fontsize=20)
pl.grid(True)
pl.xlim([0.4,2.6])
pl.tick_params(axis='both', which='major', labelsize=16)
pl.tick_params(axis='both', which='minor', labelsize=16)
yticks = ax.yaxis.get_major_ticks()
yticks[0].set_visible(False)
figg3 = pl.figure(3)
ax = figg3.add_subplot(111)
pl.ylabel(r'$ \rho_{\text{film}}\ [\si{\angstrom}^{-2}] $', fontsize=20)
pl.xlabel(r'$T\ [K]$', fontsize=20)
pl.grid(True)
pl.xlim([0.4,2.6])
pl.tick_params(axis='both', which='major', labelsize=16)
pl.tick_params(axis='both', which='minor', labelsize=16)
yticks = ax.yaxis.get_major_ticks()
yticks[0].set_visible(False)
figg4 = pl.figure(4)
ax = figg4.add_subplot(111)
pl.ylabel(r'$\langle \rho_{\text{bulk}} \rangle$', fontsize=20)
pl.xlabel(r'$T\ [K]$', fontsize=20)
pl.grid(True)
pl.xlim([0.4,2.6])
pl.tick_params(axis='both', which='major', labelsize=16)
pl.tick_params(axis='both', which='minor', labelsize=16)
yticks = ax.yaxis.get_major_ticks()
yticks[0].set_visible(False)
# --- loop over all values of S -------------------------------------------
os.chdir(direc)
Svals = glob.glob('S*')
Svals = natsort.natsorted(Svals)
print Svals
for nS, Sval in enumerate(Svals):
os.chdir(Sval)
# store bulk separation value
S = re.search(r'\d+',Sval).group(0)
print S
# set normalization
if omega:
normFactor = 4.0*(float(S)+Ly)**2
else:
normFactor = 1.0
print normFactor
# set label for plot
#if 'distinguishable' in Sval:
if 'noSwaps' in Sval:
labell = 'S = '+str(S)+' '+r'$\si{\angstrom}$'', Boltzmannons'
else:
labell = 'S = '+str(S)+' '+r'$\si{\angstrom}$'+', Bosons'
# get all temperature directory names
Tdirs = sorted(glob.glob('T*'))
# build array of norman winding values along with bins, for Lz plotting.
windingAverages = pl.array([])
windingErrors = pl.array([])
filmAverages = pl.array([])
filmErrors = pl.array([])
bulkAverages = pl.array([])
bulkErrors = pl.array([])
superAverages = pl.array([])
superErrors = pl.array([])
Ts = pl.array([])
#Omegas = pl.array([])
#Errs = pl.array([])
#Films = pl.array([])
#Ferrs = pl.array([])
#Bulks = pl.array([])
#Berrs = pl.array([])
#Supers = pl.array([])
#Serrs = pl.array([])
# --- loop over all temperature values --------------------------------
for Tdir in Tdirs:
os.chdir(Tdir)
# build array of temperatures
Ts = pl.append(Ts, float(Tdir[1:]))
# --- angular winding ---
f = glob.glob('*zAveragedNtwind*')[0]
aCol = 3
sCol = 4
bCol = 5
avg,stdErr = cT.crunchZfile(f,aCol,sCol,bCol,normFactor)
windingAverages = pl.append(windingAverages, avg)
windingErrors = pl.append(windingErrors, stdErr)
# --- film densities ---
f = glob.glob('*zAveragedBipart*')[0]
aCol = 0
sCol = 1
bCol = 2
avg,stdErr = cT.crunchZfile(f,aCol,sCol,bCol,1.0)
filmAverages = pl.append(filmAverages, avg)
filmErrors = pl.append(filmErrors,stdErr)
# --- bulk densities ---
aCol = 3
sCol = 4
bCol = 5
avg,stdErr = cT.crunchZfile(f,aCol,sCol,bCol,1.0)
bulkAverages = pl.append(bulkAverages,avg)
bulkErrors = pl.append(bulkErrors, stdErr)
# --- superfluid fractions ---
f = glob.glob('*zAveragedSuper*')[0]
aCol = 0
sCol = 1
bCol = 2
avg,stdErr = cT.crunchZfile(f,aCol,sCol,bCol,1.0)
superAverages = pl.append(superAverages,avg)
superErrors = pl.append(superErrors, stdErr)
# ----------------------
os.chdir('..')
# add data to plot for given S value.
pl.figure(1)
pl.errorbar(Ts, windingAverages, windingErrors, fmt='-o', color=colors[nS],
label=labell)
pl.figure(2)
pl.errorbar(Ts, superAverages, superErrors, fmt='-o', color=colors[nS],
label=labell)
pl.figure(3)
pl.errorbar(Ts, filmAverages, filmErrors, fmt='-o', color=colors[nS],
label=labell)
pl.figure(4)
pl.errorbar(Ts, bulkAverages, bulkErrors, fmt='-o', color=colors[nS],
label=labell)
os.chdir('..')
pl.figure(1)
pl.legend()
pl.savefig('Omega_vs_T_allS.pdf', format='pdf',
bbox_inches='tight')
pl.figure(2)
pl.legend()
#pl.savefig('SuperFrac_vs_T_allS.pdf', format='pdf',
# bbox_inches='tight')
pl.figure(3)
pl.legend()
pl.savefig('FilmDensities_vs_T_allS.pdf', format='pdf',
bbox_inches='tight')
pl.figure(4)
pl.legend()
pl.show()
