def main():
# --- Set up all options --------------------------------------------------
# determine normalization factor for NormAn winding.
S = 20.0
Ly = 12.0
normFactor = 4.0 * (S + Ly) ** 2
# some plotting color and label options
colors = ["Salmon", "MediumSpringGreen", "DarkViolet", "Fuchsia", "Blue", "Maroon"]
RandomColors = False
if RandomColors:
random.shuffle(colors)
# get names of all subdirectories az_XXX.
direcs = glob.glob("az_*")
# build array of Lz values for plotting Lz vs winding.
azValues = pl.array([])
# 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([])
# --- loop over and collect/analyze all data from files -------------------
for nd, d in enumerate(sorted(direcs)):
os.chdir("./" + d)
print d[3:]
azValues = pl.append(azValues, float(d[3:]))
# --- angular winding ---
f = glob.glob("*AveragedNtwind*")[0]
aCol = 3
sCol = 4
bCol = 5
avg, stdErr = cT.crunchZfile(f, aCol, sCol, bCol, normFactor)
print avg
windingAverages = pl.append(windingAverages, avg)
windingErrors = pl.append(windingErrors, stdErr)
# --- film densities ---
f = glob.glob("*AveragedBipart*")[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, normFactor)
bulkAverages = pl.append(bulkAverages, avg)
bulkErrors = pl.append(bulkErrors, stdErr)
os.chdir("..")
invaz = 1.0 / azValues
figg = pl.figure(1)
ax = figg.add_subplot(111)
pl.errorbar(invaz, windingAverages, windingErrors, fmt="o", color="Indigo")
pl.xlabel(r"$1/a_z\ [\si{\angstrom}^{-1}]$", fontsize=20)
pl.ylabel(r"$\langle \Omega \rangle$", fontsize=26)
pl.grid(True)
pl.tick_params(axis="both", which="major", labelsize=16)
pl.tick_params(axis="both", which="minor", labelsize=16)
xticks = ax.xaxis.get_major_ticks()
xticks[0].set_visible(False)
pl.tight_layout()
pl.savefig("Omega_vs_inverseLZ_S20.pdf", format="pdf", bbox_inches="tight")
figg = pl.figure(2)
ax = figg.add_subplot(111)
pl.errorbar(invaz, filmAverages, filmErrors, fmt="o")
pl.xlabel(r"$1/L_z\ [\si{\angstrom}^{-1}]$", fontsize=20)
pl.ylabel(r"$ \rho_{\text{film}} $", fontsize=26)
pl.grid(True)
pl.tick_params(axis="both", which="major", labelsize=16)
pl.tick_params(axis="both", which="minor", labelsize=16)
xticks = ax.xaxis.get_major_ticks()
xticks[0].set_visible(False)
pl.tight_layout()
pl.show()
def main():
omega = True
energy = False
superFrac = True
# --- Set up all options --------------------------------------------------
Ly = 12.0
# parse command line, getting algorithmic and plotting options.
args = jk.parseCMD()
reduceType = args.reduceType
direc = args.fileNames[0]
nCol = args.nCol
nEst = args.nEst
# some plotting color and label options
xLab = jk.getXlabel(reduceType)
extent = args.bulkSeparation
colors = [
'Salmon', 'MediumSpringGreen', 'DarkViolet', 'Fuchsia', 'Blue',
'Maroon'
]
if args.RandomColors:
random.shuffle(colors)
# set up figure that displays all data
figg = pl.figure(1)
ax = figg.add_subplot(111)
pl.xlabel(r'$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():
# --- Set up all options --------------------------------------------------
# determine normalization factor for NormAn winding.
S = 20.0
Ly = 12.0
normFactor = 4.0 * (S + Ly)**2
# some plotting color and label options
colors = [
'Salmon', 'MediumSpringGreen', 'DarkViolet', 'Fuchsia', 'Blue',
'Maroon'
]
RandomColors = False
if RandomColors:
random.shuffle(colors)
# get names of all subdirectories az_XXX.
direcs = glob.glob('az_*')
# build array of Lz values for plotting Lz vs winding.
azValues = pl.array([])
# 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([])
# --- loop over and collect/analyze all data from files -------------------
for nd, d in enumerate(sorted(direcs)):
os.chdir('./' + d)
print d[3:]
azValues = pl.append(azValues, float(d[3:]))
# --- angular winding ---
f = glob.glob('*AveragedNtwind*')[0]
aCol = 3
sCol = 4
bCol = 5
avg, stdErr = cT.crunchZfile(f, aCol, sCol, bCol, normFactor)
print avg
windingAverages = pl.append(windingAverages, avg)
windingErrors = pl.append(windingErrors, stdErr)
# --- film densities ---
f = glob.glob('*AveragedBipart*')[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, normFactor)
bulkAverages = pl.append(bulkAverages, avg)
bulkErrors = pl.append(bulkErrors, stdErr)
os.chdir('..')
invaz = 1.0 / azValues
figg = pl.figure(1)
ax = figg.add_subplot(111)
pl.errorbar(invaz, windingAverages, windingErrors, fmt='o', color='Indigo')
pl.xlabel(r'$1/a_z\ [\si{\angstrom}^{-1}]$', fontsize=20)
pl.ylabel(r'$\langle \Omega \rangle$', fontsize=26)
pl.grid(True)
pl.tick_params(axis='both', which='major', labelsize=16)
pl.tick_params(axis='both', which='minor', labelsize=16)
xticks = ax.xaxis.get_major_ticks()
xticks[0].set_visible(False)
pl.tight_layout()
pl.savefig('Omega_vs_inverseLZ_S20.pdf', format='pdf', bbox_inches='tight')
figg = pl.figure(2)
ax = figg.add_subplot(111)
pl.errorbar(invaz, filmAverages, filmErrors, fmt='o')
pl.xlabel(r'$1/L_z\ [\si{\angstrom}^{-1}]$', fontsize=20)
pl.ylabel(r'$ \rho_{\text{film}} $', fontsize=26)
pl.grid(True)
pl.tick_params(axis='both', which='major', labelsize=16)
pl.tick_params(axis='both', which='minor', labelsize=16)
xticks = ax.xaxis.get_major_ticks()
xticks[0].set_visible(False)
pl.tight_layout()
pl.show()
def main():
omega = True
energy = False
superFrac = True
# --- Set up all options --------------------------------------------------
Ly = 12.0
# parse command line, getting algorithmic and plotting options.
args = jk.parseCMD()
reduceType = args.reduceType
direc = args.fileNames[0]
nCol = args.nCol
nEst = args.nEst
# some plotting color and label options
xLab = jk.getXlabel(reduceType)
extent = args.bulkSeparation
colors = ['Salmon','MediumSpringGreen','DarkViolet','Fuchsia','Blue',
'Maroon']
if args.RandomColors:
random.shuffle(colors)
# set up figure that displays all data
figg = pl.figure(1)
ax = figg.add_subplot(111)
pl.xlabel(r'$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()
