def estimate_haversine(self):
if self.verbose:
print("Estimating Spatial locality with Haversine distance .")
self.haversine = DistanceMetric.get_metric("haversine")
# prepare features Coordinates
longi, lat = pl.deg2rad(
hp.pix2ang(
nside=self._nside,
ipix=pl.arange(hp.nside2npix(self._nside)),
lonlat=True,
))
mask = pl.ma.masked_inside(longi, 0, pl.pi).mask
longi[mask] = -longi[mask]
longi[pl.logical_not(mask)] = 2 * pl.pi - longi[pl.logical_not(mask)]
Theta = pl.array([lat[self.galactic_mask],
longi[self.galactic_mask]]).T
angdist_matr = self.haversine.pairwise(Theta)
angdist_matr = pl.ma.fix_invalid(angdist_matr, fill_value=pl.pi).data
# all the distances are equally weighted and so far range in 0,1
weight_eucl = 1.0 / self._distance_matr.max()
weight_hav = 1.0 / angdist_matr.max()
self._distance_matr = pl.multiply(weight_eucl * self._distance_matr,
weight_hav * angdist_matr)
self._X = pl.concatenate([self._X, Theta], axis=1)
pass
def xyamb(xytab,qu,xyout=''):
mytb=taskinit.tbtool()
if not isinstance(qu,tuple):
raise Exception,'qu must be a tuple: (Q,U)'
if xyout=='':
xyout=xytab
if xyout!=xytab:
os.system('cp -r '+xytab+' '+xyout)
QUexp=complex(qu[0],qu[1])
print 'Expected QU = ',qu # , ' (',pl.angle(QUexp)*180/pi,')'
mytb.open(xyout,nomodify=False)
QU=mytb.getkeyword('QU')['QU']
P=pl.sqrt(QU[0,:]**2+QU[1,:]**2)
nspw=P.shape[0]
for ispw in range(nspw):
st=mytb.query('SPECTRAL_WINDOW_ID=='+str(ispw))
if (st.nrows()>0):
q=QU[0,ispw]
u=QU[1,ispw]
qufound=complex(q,u)
c=st.getcol('CPARAM')
fl=st.getcol('FLAG')
xyph0=pl.angle(pl.mean(c[0,:,:][pl.logical_not(fl[0,:,:])]),True)
print 'Spw = '+str(ispw)+': Found QU = '+str(QU[:,ispw]) # +' ('+str(pl.angle(qufound)*180/pi)+')'
#if ( (abs(q)>0.0 and abs(qu[0])>0.0 and (q/qu[0])<0.0) or
# (abs(u)>0.0 and abs(qu[1])>0.0 and (u/qu[1])<0.0) ):
if ( pl.absolute(pl.angle(qufound/QUexp)*180/pi)>90.0 ):
c[0,:,:]*=-1.0
xyph1=pl.angle(pl.mean(c[0,:,:][pl.logical_not(fl[0,:,:])]),True)
st.putcol('CPARAM',c)
QU[:,ispw]*=-1
print ' ...CONVERTING X-Y phase from '+str(xyph0)+' to '+str(xyph1)+' deg'
else:
print ' ...KEEPING X-Y phase '+str(xyph0)+' deg'
st.close()
QUr={}
QUr['QU']=QU
mytb.putkeyword('QU',QUr)
mytb.close()
QUm=pl.mean(QU[:,P>0],1)
QUe=pl.std(QU[:,P>0],1)
Pm=pl.sqrt(QUm[0]**2+QUm[1]**2)
Xm=0.5*atan2(QUm[1],QUm[0])*180/pi
print 'Ambiguity resolved (spw mean): Q=',QUm[0],'U=',QUm[1],'(rms=',QUe[0],QUe[1],')','P=',Pm,'X=',Xm
stokes=[1.0,QUm[0],QUm[1],0.0]
print 'Returning the following Stokes vector: '+str(stokes)
return stokes
def fixfeedpa(vis, defband='', forceband=''):
mytb = taskinit.tbtool()
mytb.open(vis + '/SPECTRAL_WINDOW')
spwnames = mytb.getcol('NAME')
mytb.close()
if len(forceband) > 0:
print 'Forcing band = ', forceband
spwnames[:] = forceband
defband = forceband
mytb.open(vis + '/FEED', nomodify=False)
spwids = mytb.getcol('SPECTRAL_WINDOW_ID')
ra = mytb.getcol('RECEPTOR_ANGLE')
ra[:, :] = 0.0
spwmask = (spwids > -1)
ra[0, spwmask] = [bandpa(spwnames[ispw]) for ispw in spwids[spwmask]]
spwmask = pl.logical_not(spwmask)
if (sum(spwmask) > 0):
if (len(defband) > 0):
print 'NB: Setting spwid=-1 rows in FEED table to RECEPTOR_ANGLE for band=' + str(
defband)
ra[0, spwmask] = bandpa(defband)
else:
print 'NB: Setting spwid=-1 rows in FEED table to RECEPTOR_ANGLE=(0,pi/2)'
ra[1, :] = ra[0, :] + (pi / 2.)
mytb.putcol('RECEPTOR_ANGLE', ra)
mytb.close()
def fixfeedpa(vis,defband='',forceband=''):
mytb=taskinit.tbtool()
mytb.open(vis+'/SPECTRAL_WINDOW')
spwnames=mytb.getcol('NAME')
mytb.close()
if len(forceband)>0:
print 'Forcing band = ',forceband
spwnames[:]=forceband
defband=forceband
mytb.open(vis+'/FEED',nomodify=False)
spwids=mytb.getcol('SPECTRAL_WINDOW_ID')
ra=mytb.getcol('RECEPTOR_ANGLE')
ra[:,:]=0.0
spwmask=(spwids>-1)
ra[0,spwmask]=[bandpa(spwnames[ispw]) for ispw in spwids[spwmask]]
spwmask=pl.logical_not(spwmask)
if (sum(spwmask)>0):
if (len(defband)>0):
print 'NB: Setting spwid=-1 rows in FEED table to RECEPTOR_ANGLE for band='+str(defband)
ra[0,spwmask]=bandpa(defband)
else:
print 'NB: Setting spwid=-1 rows in FEED table to RECEPTOR_ANGLE=(0,pi/2)'
ra[1,:]=ra[0,:]+(pi/2.)
mytb.putcol('RECEPTOR_ANGLE',ra)
mytb.close()
def crunchZfile(f, aCol, sCol, bCol, normFactor):
'''
Takes a zAveraged... data file generated from the crunchData
function of this library and produces the arithmetic mean
as well as the standard error from all seeds. The error
is done through the propagation of errors as:
e = sqrt{ \sum_k (c_k e_k)^2 } where e_k are the individual
seed's standard errors and c_k are the weighting coefficients
obeying \sum_k c_k = 1.
'''
avgs, stds, bins = pl.genfromtxt(f,
usecols=(aCol, sCol, bCol),
unpack=True,
delimiter=',')
# get rid of any items which are not numbers..
# this is some beautiful Python juju.
bins = bins[pl.logical_not(pl.isnan(bins))]
stds = stds[pl.logical_not(pl.isnan(stds))]
avgs = avgs[pl.logical_not(pl.isnan(avgs))]
# normalize data.
stds *= normFactor
avgs *= normFactor
weights = bins / pl.sum(bins)
avgs *= weights
stds *= weights # over-estimates error bars
stds *= stds
avg = pl.sum(avgs)
stdErr = pl.sum(stds)
stdErr = stdErr**0.5
return avg, stdErr
def get_not_annotation_indexes(_annotation, _excluded_annotations):
"""
method returns indexes of not annotation's values in _annotation parameter,
annotation values are defined in _excluded_annotations parameter
"""
if len(_annotation) == 0:
return EMPTY_ARRAY
elif _excluded_annotations == ALL_ANNOTATIONS:
return pl.array(pl.find(_annotation == 0))
else:
#find indexes of an annotation array which are NOT included
#in _excluded_annotations list
return pl.array(pl.where(pl.logical_not(
pl.in1d(_annotation, _excluded_annotations)))[0], dtype=int)
def crunchZfile(f,aCol,sCol,bCol,normFactor):
'''
Takes a zAveraged... data file generated from the crunchData
function of this library and produces the arithmetic mean
as well as the standard error from all seeds. The error
is done through the propagation of errors as:
e = sqrt{ \sum_k (c_k e_k)^2 } where e_k are the individual
seed's standard errors and c_k are the weighting coefficients
obeying \sum_k c_k = 1.
'''
avgs,stds,bins = pl.genfromtxt(f, usecols=(aCol, sCol, bCol),
unpack=True, delimiter=',')
# get rid of any items which are not numbers..
# this is some beautiful Python juju.
bins = bins[pl.logical_not(pl.isnan(bins))]
stds = stds[pl.logical_not(pl.isnan(stds))]
avgs = avgs[pl.logical_not(pl.isnan(avgs))]
# normalize data.
stds *= normFactor
avgs *= normFactor
weights = bins/pl.sum(bins)
avgs *= weights
stds *= weights # over-estimates error bars
stds *= stds
avg = pl.sum(avgs)
stdErr = pl.sum(stds)
stdErr = stdErr**0.5
return avg, stdErr
def xyamb(xytab, qu, xyout=''):
mytb = taskinit.tbtool()
if not isinstance(qu, tuple):
raise Exception, 'qu must be a tuple: (Q,U)'
if xyout == '':
xyout = xytab
if xyout != xytab:
os.system('cp -r ' + xytab + ' ' + xyout)
QUexp = complex(qu[0], qu[1])
print 'Expected QU = ', qu # , ' (',pl.angle(QUexp)*180/pi,')'
mytb.open(xyout, nomodify=False)
QU = mytb.getkeyword('QU')['QU']
P = pl.sqrt(QU[0, :]**2 + QU[1, :]**2)
nspw = P.shape[0]
for ispw in range(nspw):
st = mytb.query('SPECTRAL_WINDOW_ID==' + str(ispw))
if (st.nrows() > 0):
q = QU[0, ispw]
u = QU[1, ispw]
qufound = complex(q, u)
c = st.getcol('CPARAM')
fl = st.getcol('FLAG')
xyph0 = pl.angle(pl.mean(c[0, :, :][pl.logical_not(fl[0, :, :])]),
True)
print 'Spw = ' + str(ispw) + ': Found QU = ' + str(
QU[:, ispw]) # +' ('+str(pl.angle(qufound)*180/pi)+')'
#if ( (abs(q)>0.0 and abs(qu[0])>0.0 and (q/qu[0])<0.0) or
# (abs(u)>0.0 and abs(qu[1])>0.0 and (u/qu[1])<0.0) ):
if (pl.absolute(pl.angle(qufound / QUexp) * 180 / pi) > 90.0):
c[0, :, :] *= -1.0
xyph1 = pl.angle(
pl.mean(c[0, :, :][pl.logical_not(fl[0, :, :])]), True)
st.putcol('CPARAM', c)
QU[:, ispw] *= -1
print ' ...CONVERTING X-Y phase from ' + str(
xyph0) + ' to ' + str(xyph1) + ' deg'
else:
print ' ...KEEPING X-Y phase ' + str(xyph0) + ' deg'
st.close()
QUr = {}
QUr['QU'] = QU
mytb.putkeyword('QU', QUr)
mytb.close()
QUm = pl.mean(QU[:, P > 0], 1)
QUe = pl.std(QU[:, P > 0], 1)
Pm = pl.sqrt(QUm[0]**2 + QUm[1]**2)
Xm = 0.5 * atan2(QUm[1], QUm[0]) * 180 / pi
print 'Ambiguity resolved (spw mean): Q=', QUm[0], 'U=', QUm[
1], '(rms=', QUe[0], QUe[1], ')', 'P=', Pm, 'X=', Xm
stokes = [1.0, QUm[0], QUm[1], 0.0]
print 'Returning the following Stokes vector: ' + str(stokes)
return stokes
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():
args = parseCMD()
Temp = args.Temperature
Lx = 12.0
Ly = 12.0
az = 47.5
ay = 2.0
Svals = glob.glob('S*')
# define some plotting colors
colors = [
'Salmon', 'Blue', 'DarkViolet', 'MediumSpringGreen', 'Fuchsia',
'Yellow', 'Maroon'
]
# set up figure that displays all data
figg = pl.figure(1)
ax = figg.add_subplot(111)
pl.xlabel(r'$\mu\ [K]$', 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 = -3.0
maxMus = 5.0
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"),
)
solidDens = True
if solidDens:
pl.plot([minMus, maxMus], [0.0248, 0.0248], 'k-', lw=3)
pl.annotate(
'HCP solid SVP',
xy=(maxMus - boxSubtract, 0.0248), #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"),
)
# plot just the bulk density as a function of chemical potential
figg2 = pl.figure(2)
ax2 = figg2.add_subplot(111)
pl.xlabel(r'$\mu\ [K]$', 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 = -3.0
maxMus = 5.0
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"),
)
# number of particles in film region vs chemical potential
pl.figure(3)
pl.xlabel(r'$\mu\ [K]$', 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)
# normalized angular winding vs chemical potential
pl.figure(4)
pl.xlabel(r'$\mu\ [K]$', fontsize=20)
pl.ylabel(r'$\Omega$', fontsize=20)
pl.grid(True)
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)
# actual density in cell
pl.figure(5)
pl.xlabel(r'$\mu\ [K]$', fontsize=20)
pl.ylabel(r'$\rho\ [\si{\angstrom}^{-3}]$', fontsize=20)
pl.grid(True)
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 through known directory structure ------------------------------
for nS, Sval in enumerate(sorted(Svals)):
os.chdir(Sval)
# get bipartition file name
f = glob.glob('*Bipart*')[0]
# get angular winding file name
fw = glob.glob('*Ntwind*')[0]
# get estimator file name (includes total number)
fe = glob.glob('*Estimator*')[0]
# store bulk separation value
S = re.search(r'\d+', Sval).group(0)
# get label for plot
if 'distinguishable' in Sval:
labell = 'S = ' + str(S) + ', Boltzmannons'
else:
labell = 'S = ' + str(S) + ', Bosons'
# projected area of film region
projArea = float(S) * Lx
# accessible volume in cell
accessibleVol = Lx * Ly * (2.0 * az +
float(S)) - Lx * float(S) * (Ly - 2.0 * ay)
# multiply angular winding by normalization to be fixed in code later
omegaNorm = 4.0 * (float(S) + 1.0 * Ly)**2
# get chemical potential
headers = getHeadersFromFile(f)
mus = pl.array([])
Films = pl.array([])
Bulks = pl.array([])
filmErrs = pl.array([])
bulkErrs = pl.array([])
Omegas = pl.array([])
omegaErrs = pl.array([])
NumParts = pl.array([])
NumPartErrs = pl.array([])
n = 0
nw = 3
nn = 12
for head in headers:
# get values of chemical potential from file header
mus = pl.append(mus, float(head))
# --- Densities ---------------------------------------------------
filmavg, filmstd, filmbins, bulkavg, bulkstd, bulkbins = pl.genfromtxt(
f,
unpack=True,
usecols=(n, n + 1, n + 2, n + 3, n + 4, n + 5),
delimiter=',')
n += 6
# 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)
# ---- angular winding --------------------------------------------
omegaAvg, omegaStd, omegaBins = pl.genfromtxt(fw,
unpack=True,
usecols=(nw, nw + 1,
nw + 2),
delimiter=',')
nw += 9
# get rid of any items which are not numbers..
omegaBins = omegaBins[pl.logical_not(pl.isnan(omegaBins))]
omegaStd = omegaStd[pl.logical_not(pl.isnan(omegaStd))]
omegaAvg = omegaAvg[pl.logical_not(pl.isnan(omegaAvg))]
# normalize data.
omegaStd *= omegaNorm
omegaAvg *= omegaNorm
weights = omegaBins / pl.sum(omegaBins)
omegaAvg *= weights
omegaStd *= weights
Omega = pl.sum(omegaAvg)
omegaErr = pl.sum(omegaStd)
Omegas = pl.append(Omegas, Omega)
omegaErrs = pl.append(omegaErrs, omegaErr)
# ---- total number -----------------------------------------------
numAvg, numStd, numBins = pl.genfromtxt(fe,
unpack=True,
usecols=(nn, nn + 1,
nn + 2),
delimiter=',')
nn += 15
# get rid of any items which are not numbers..
numBins = numBins[pl.logical_not(pl.isnan(numBins))]
numStd = numStd[pl.logical_not(pl.isnan(numStd))]
numAvg = numAvg[pl.logical_not(pl.isnan(numAvg))]
# normalize data.
numStd /= (1.0 * accessibleVol)
numAvg /= (1.0 * accessibleVol)
weights = numBins / pl.sum(numBins)
numAvg *= weights
numStd *= weights
numPart = pl.sum(numAvg)
numPartErr = pl.sum(numStd)
NumParts = pl.append(NumParts, numPart)
NumPartErrs = pl.append(NumPartErrs, numPartErr)
# Sort data in order of increasing chemical potential. I love Python.
# This is another bit of magical Python juju.
mus, Films, Bulks, filmErrs, bulkErrs, Omegas, omegaErrs, NumParts, NumPartErrs = pl.asarray(
zip(*sorted(
zip(mus, Films, Bulks, filmErrs, bulkErrs, Omegas, omegaErrs,
NumParts, NumPartErrs))))
pl.figure(1)
pl.errorbar(mus,
Films,
filmErrs,
fmt='--o',
color=colors[nS],
label=labell + ', 2d',
markersize=8)
pl.errorbar(mus,
Bulks,
bulkErrs,
fmt='-d',
color=colors[nS],
label=labell + ', 3d',
markersize=8)
pl.figure(2)
pl.errorbar(mus,
Bulks,
bulkErrs,
fmt='-d',
color=colors[nS],
label=labell + ', 3d',
markersize=8)
pl.figure(3)
pl.errorbar(mus,
Films * projArea,
fmt='-o',
color=colors[nS],
label=labell + ', 2d',
markersize=8)
pl.figure(4)
pl.errorbar(mus,
Omegas,
omegaErrs,
fmt='-o',
color=colors[nS],
label=labell,
markersize=8)
pl.figure(5)
pl.errorbar(mus,
NumParts,
NumPartErrs,
fmt='-o',
color=colors[nS],
label=labell,
markersize=8)
os.chdir('..')
pl.figure(1)
pl.legend(loc=2)
pl.savefig('densities_vs_chemicalPotential_allS_1APR.pdf',
format='pdf',
bbox_inches='tight')
pl.figure(2)
pl.legend(loc=2)
pl.figure(3)
pl.legend(loc=2)
pl.figure(4)
pl.legend(loc=2)
pl.figure(5)
pl.legend(loc=2)
pl.show()
def main():
savePDF = True
solidDens = False
Lx = 12.0
Ly = 12.0
az = 47.5
ay = 2.0
bulkVol = 2.0 * Lx * Ly * az
lowX7 = -6.0
#lowX7 = -4.0 # boltzmannons
highX7 = 2.9
#highX7 = 2.5 # boltzmannons
# define some plotting colors
colors = [
'Navy', 'DarkViolet', 'MediumSpringGreen', 'Salmon', 'Fuchsia',
'Yellow', 'Maroon', 'Salmon', 'Blue'
]
# -------------------------------------------------------------------------
# bulk and film density on same plot
figg = pl.figure(1)
ax = figg.add_subplot(111)
pl.xlabel(r'$\text{Potential Shift}\ [K]$', fontsize=20)
pl.ylabel('Spatial Density ' + r'$[\si{\angstrom}^{-d}]$', fontsize=20)
pl.grid(True)
pl.xlim([-5.5, 3.5])
#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)
# bulk SVP density line
bulkVert = -30
minMus = -5.5
maxMus = 2.0
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=(-10, bulkVert),
textcoords='offset points',
bbox=dict(boxstyle="round", fc="0.8"),
arrowprops=dict(arrowstyle="->",
connectionstyle="angle,angleA=0,angleB=90,rad=10"),
)
# film SVP density line
pl.plot([minMus, maxMus], [0.0432, 0.0432], 'k-', lw=3)
pl.annotate(
'2d SVP',
xy=(maxMus - boxSubtract, 0.0432), #xycoords='data',
xytext=(30, -30),
textcoords='offset points',
bbox=dict(boxstyle="round", fc="0.8"),
arrowprops=dict(arrowstyle="->",
connectionstyle="angle,angleA=0,angleB=90,rad=10"),
)
if solidDens:
pl.plot([minMus, maxMus], [0.0248, 0.0248], 'k-', lw=3)
pl.annotate(
'HCP solid SVP',
xy=(maxMus - boxSubtract, 0.0248), #xycoords='data',
xytext=(-10, 30),
textcoords='offset points',
bbox=dict(boxstyle="round", fc="0.8"),
arrowprops=dict(arrowstyle="->",
connectionstyle="angle,angleA=0,angleB=90,rad=10"),
)
# -------------------------------------------------------------------------
# bulk density
figg2 = pl.figure(2)
ax2 = figg2.add_subplot(111)
pl.xlabel(r'$\text{Potential Shift}\ [K]$', fontsize=20)
pl.ylabel('Bulk Density ' + r'$[\si{\angstrom}^{-3}]$', fontsize=20)
pl.grid(True)
pl.xlim([-5.5, 0.5])
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 # changes text box above (positive) or below (negative) line
boxSubtract = 1.6
pl.plot([minMus, maxMus], [0.02198, 0.02198], 'k-', lw=3)
pl.annotate(
'3d SVP',
xy=(maxMus - boxSubtract, 0.02198), #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"),
)
# -------------------------------------------------------------------------
# number of particles in film region
pl.figure(3)
pl.xlabel(r'$\text{Potential Shift}\ [K]$', fontsize=20)
pl.ylabel(r'$N_{\text{film}}$', fontsize=20)
pl.grid(True)
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)
# -------------------------------------------------------------------------
# normalized angular winding
pl.figure(4)
pl.xlabel(r'$\text{Potential Shift}\ [K]$', fontsize=20)
pl.ylabel(r'$\Omega$', fontsize=20)
pl.grid(True)
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)
# -------------------------------------------------------------------------
# film density
figg5 = pl.figure(5)
ax5 = figg5.add_subplot(111)
pl.xlabel(r'$\text{Potential Shift}\ [K]$', fontsize=20)
pl.ylabel(r'$\text{Film Density}\ [\si{\angstrom}^{-2}]$', fontsize=20)
pl.ylim([0.03, 0.05])
pl.xlim([-5.5, 0.5])
pl.grid(True)
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)
# film SVP density line
pl.plot([lowX7, highX7], [0.0432, 0.0432], 'k-', lw=3)
pl.annotate(
'2d SVP',
xy=(-4, 0.0432), #xycoords='data',
xytext=(30, -30),
textcoords='offset points',
bbox=dict(boxstyle="round", fc="0.8"),
arrowprops=dict(arrowstyle="->",
connectionstyle="angle,angleA=0,angleB=90,rad=10"),
)
# -------------------------------------------------------------------------
# superfluid fraction
pl.figure(6)
pl.xlabel(r'$\text{Potential Shift}\ [K]$', fontsize=20)
pl.ylabel(r'$\rho_S/\rho$', fontsize=20)
pl.grid(True)
pl.xlim([-5.5, 0.5])
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)
# -------------------------------------------------------------------------
# film/bulk densities subplot
pl.figure(7)
ax7a = pl.subplot(211)
pl.ylabel(r'$\text{Film Density}\ [\si{\angstrom}^{-2}]$', fontsize=20)
pl.ylim([0.035, 0.05])
pl.grid(True)
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)
# film SVP density line
pl.plot([lowX7, highX7], [0.0432, 0.0432], 'k-', lw=3)
pl.annotate(
'2d SVP',
xy=(-2, 0.0432), #xycoords='data',
xytext=(30, -30),
textcoords='offset points',
bbox=dict(boxstyle="round", fc="0.8"),
arrowprops=dict(arrowstyle="->",
connectionstyle="angle,angleA=0,angleB=90,rad=10"),
)
pl.setp(ax7a.get_xticklabels(), visible=False)
ax7b = pl.subplot(212, sharex=ax7a)
pl.xlabel(r'$\text{Potential Shift}\ [K]$', fontsize=20)
pl.ylabel('Bulk Density ' + r'$[\si{\angstrom}^{-3}]$', fontsize=20)
pl.grid(True)
pl.xlim([lowX7, highX7])
pl.tick_params(axis='both', which='major', labelsize=16)
pl.tick_params(axis='both', which='minor', labelsize=16)
yticks = ax7b.yaxis.get_major_ticks()
yticks[0].set_visible(False)
# set up bulk SVP densities for plot
bulkVert = 15 # changes text box above (positive) or below (negative) line
boxSubtract = 1.6
pl.plot([lowX7, highX7], [0.02198, 0.02198], 'k-', lw=3)
pl.annotate(
'3d SVP',
xy=(1.2, 0.02198), #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"),
)
# -------------------------------------------------------------------------
# number of particles in bulk
pl.figure(8)
pl.xlabel(r'$\text{Potential Shift}\ [K]$', fontsize=20)
pl.ylabel(r'$N_{\text{bulk}}$', fontsize=20)
pl.grid(True)
pl.xlim([-5.5, 0.5])
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)
# -------------------------------------------------------------------------
Tvals = glob.glob('*T*')
Markers = ['-o', '-d', '-*']
for nT, Tval in enumerate(sorted(Tvals)):
os.chdir(Tval)
T = Tval[1:]
Svals = glob.glob('S*')
# --- loop through known directory structure --------------------------
for nS, Sval in enumerate(sorted(Svals)):
os.chdir(Sval)
Vdirs = glob.glob('*V*')
print os.getcwd()
# store bulk separation value
S = re.search(r'\d+', Sval).group(0)
# get label for plot
if 'distinguishable' in Sval:
labell = 'S = ' + str(S) + ', Boltzmannons, T=' + str(T)
else:
labell = 'S = ' + str(S) + ', Bosons, T=' + str(T)
shortlabell = 'S = ' + str(S) + ', T=' + str(T)
# projected area of film region
projArea = float(S) * Lx
# accessible volume in cell
#accessibleVol = Lx*(Ly*(float(S)+2.0*az) - float(S)*(Ly-2.0*ay))
accessibleVol = 2.0 * Lx * Ly * az + 2.0 * ay * Lx * float(S)
# multiply angular winding by norm. to be fixed in code later
omegaNorm = 4.0 * (float(S) + 1.0 * Ly)**2
# Arrays to hold all data
Vs = pl.array([])
Films = pl.array([])
Bulks = pl.array([])
filmErrs = pl.array([])
bulkErrs = pl.array([])
Omegas = pl.array([])
omegaErrs = pl.array([])
NumParts = pl.array([])
NumPartErrs = pl.array([])
Supers = pl.array([])
SuperErrs = pl.array([])
# pass potential shift directories for current S value
for Vdir in Vdirs:
os.chdir(Vdir)
# get bipartition file name
f = glob.glob('*Bipart*')[0]
# get angular winding file name
fw = glob.glob('*Ntwind*')[0]
# get estimator file name (includes total number)
fe = glob.glob('*Estimator*')[0]
# get superfrac file name
fs = glob.glob('*Super*')[0]
# build array of film potential shifts from directory names
Vs = pl.append(Vs, float(Vdir[1:]))
# --- Densities -----------------------------------------------
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)
# ---- angular winding ----------------------------------------
omegaAvg, omegaStd, omegaBins = pl.genfromtxt(fw,
unpack=True,
usecols=(3, 4,
5),
delimiter=',')
# get rid of any items which are not numbers..
omegaBins = omegaBins[pl.logical_not(pl.isnan(omegaBins))]
omegaStd = omegaStd[pl.logical_not(pl.isnan(omegaStd))]
omegaAvg = omegaAvg[pl.logical_not(pl.isnan(omegaAvg))]
# normalize data.
omegaStd *= omegaNorm
omegaAvg *= omegaNorm
weights = omegaBins / pl.sum(omegaBins)
omegaAvg *= weights
omegaStd *= weights
Omega = pl.sum(omegaAvg)
omegaErr = pl.sum(omegaStd)
Omegas = pl.append(Omegas, Omega)
omegaErrs = pl.append(omegaErrs, omegaErr)
# ---- total number -------------------------------------------
numAvg, numStd, numBins = pl.genfromtxt(fe,
unpack=True,
usecols=(12, 13, 14),
delimiter=',')
# get rid of any items which are not numbers..
numBins = numBins[pl.logical_not(pl.isnan(numBins))]
numStd = numStd[pl.logical_not(pl.isnan(numStd))]
numAvg = numAvg[pl.logical_not(pl.isnan(numAvg))]
weights = numBins / pl.sum(numBins)
numAvg *= weights
numStd *= weights
numPart = pl.sum(numAvg)
numPartErr = pl.sum(numStd)
NumParts = pl.append(NumParts, numPart)
NumPartErrs = pl.append(NumPartErrs, numPartErr)
# ---- superfluid fraction ------------------------------------
supAvg, supStd, supBins = pl.genfromtxt(fs,
unpack=True,
usecols=(0, 1, 2),
delimiter=',')
# get rid of any items which are not numbers..
supBins = supBins[pl.logical_not(pl.isnan(supBins))]
supStd = supStd[pl.logical_not(pl.isnan(supStd))]
supAvg = supAvg[pl.logical_not(pl.isnan(supAvg))]
# normalize data.
#supStd /= (1.0*accessibleVol)
#supAvg /= (1.0*accessibleVol)
weights = supBins / pl.sum(supBins)
supAvg *= weights
supStd *= weights
supPart = pl.sum(supAvg)
supPartErr = pl.sum(supStd)
Supers = pl.append(Supers, supPart)
SuperErrs = pl.append(SuperErrs, supPartErr)
os.chdir('..')
# Sort data in order of increasing chemical potential.
# This is another bit of magical Python juju.
Vs, Films, Bulks, filmErrs, bulkErrs, Omegas, omegaErrs,\
NumParts, NumPartErrs, Supers, SuperErrs = pl.asarray(
zip(*sorted(zip(Vs, Films, Bulks, filmErrs, bulkErrs,
Omegas, omegaErrs, NumParts, NumPartErrs, Supers,
SuperErrs))))
pl.figure(1)
pl.errorbar(Vs,
Films,
filmErrs,
fmt=Markers[nT],
color=colors[nS],
label=labell + ', 2d',
markersize=8)
pl.errorbar(Vs,
Bulks,
bulkErrs,
fmt=Markers[nT],
mec=colors[nS],
label=labell + ', 3d',
mfc='None',
markersize=8)
pl.figure(2)
pl.errorbar(Vs,
Bulks,
bulkErrs,
fmt=Markers[nT],
color=colors[nS],
label=labell,
markersize=8)
pl.figure(3)
pl.errorbar(Vs,
Films * projArea,
fmt=Markers[nT],
color=colors[nS],
label=labell,
markersize=8)
pl.figure(4)
pl.errorbar(Vs,
Omegas,
omegaErrs,
fmt=Markers[nT],
color=colors[nS],
label=labell,
markersize=8)
pl.figure(5)
pl.errorbar(Vs,
Films,
filmErrs,
fmt=Markers[nT],
color=colors[nS],
label=labell,
markersize=8)
pl.figure(6)
pl.errorbar(Vs,
Supers,
SuperErrs,
fmt=Markers[nT],
color=colors[nS],
label=labell,
markersize=8)
pl.figure(7)
ax7a.errorbar(Vs,
Films,
filmErrs,
fmt=Markers[nT],
color=colors[nS],
label=shortlabell,
markersize=8)
ax7b.errorbar(Vs,
Bulks,
bulkErrs,
fmt=Markers[nT],
color=colors[nS],
label=shortlabell,
markersize=8)
pl.figure(8)
pl.errorbar(Vs,
Bulks * bulkVol,
bulkErrs * bulkVol,
fmt=Markers[nT],
color=colors[nS],
label=labell,
markersize=8)
os.chdir('..')
os.chdir('..')
pl.figure(1)
pl.legend(loc=1)
pl.tight_layout()
pl.figure(2)
pl.legend(loc=1)
pl.tight_layout()
pl.figure(3)
pl.legend(loc=1)
pl.tight_layout()
pl.figure(4)
pl.legend(loc=1)
pl.tight_layout()
pl.figure(5)
pl.legend(loc=1)
pl.tight_layout()
pl.figure(6)
pl.legend(loc=1)
pl.tight_layout()
pl.figure(7)
pl.legend(loc=1, bbox_to_anchor=(1., 2.1))
major_formatter = FuncFormatter(my_formatter)
ax7a.yaxis.set_major_formatter(major_formatter)
pl.tight_layout()
if savePDF:
pl.savefig('Bulk_Film_Dens_vs_Vshift_allS_allT_18APR.pdf',
format='pdf',
bbox_inches='tight')
pl.figure(8)
pl.legend(loc=1)
pl.tight_layout()
pl.show()
def main():
args = parseCMD()
Temp = args.Temperature
Lx = 12.0
Ly = 12.0
az = 47.5
ay = 2.0
Svals = glob.glob('S*')
# define some plotting colors
colors = ['Salmon','Blue','DarkViolet','MediumSpringGreen','Fuchsia',
'Yellow','Maroon']
# set up figure that displays all data
figg = pl.figure(1)
ax = figg.add_subplot(111)
pl.xlabel(r'$\mu\ [K]$', 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 = -3.0
maxMus = 5.0
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"),
)
solidDens = True
if solidDens:
pl.plot([minMus, maxMus], [0.0248, 0.0248], 'k-', lw=3)
pl.annotate('HCP solid SVP', xy=(maxMus - boxSubtract, 0.0248), #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"),
)
# plot just the bulk density as a function of chemical potential
figg2 = pl.figure(2)
ax2 = figg2.add_subplot(111)
pl.xlabel(r'$\mu\ [K]$', 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 = -3.0
maxMus = 5.0
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"),
)
# number of particles in film region vs chemical potential
pl.figure(3)
pl.xlabel(r'$\mu\ [K]$', 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)
# normalized angular winding vs chemical potential
pl.figure(4)
pl.xlabel(r'$\mu\ [K]$', fontsize=20)
pl.ylabel(r'$\Omega$', fontsize=20)
pl.grid(True)
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)
# actual density in cell
pl.figure(5)
pl.xlabel(r'$\mu\ [K]$', fontsize=20)
pl.ylabel(r'$\rho\ [\si{\angstrom}^{-3}]$', fontsize=20)
pl.grid(True)
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 through known directory structure ------------------------------
for nS, Sval in enumerate(sorted(Svals)):
os.chdir(Sval)
# get bipartition file name
f = glob.glob('*Bipart*')[0]
# get angular winding file name
fw = glob.glob('*Ntwind*')[0]
# get estimator file name (includes total number)
fe = glob.glob('*Estimator*')[0]
# store bulk separation value
S = re.search(r'\d+',Sval).group(0)
# get label for plot
if 'distinguishable' in Sval:
labell = 'S = '+str(S)+', Boltzmannons'
else:
labell = 'S = '+str(S)+', Bosons'
# projected area of film region
projArea = float(S)*Lx
# accessible volume in cell
accessibleVol = Lx*Ly*(2.0*az+float(S)) - Lx*float(S)*(Ly-2.0*ay)
# multiply angular winding by normalization to be fixed in code later
omegaNorm = 4.0*(float(S)+1.0*Ly)**2
# get chemical potential
headers = getHeadersFromFile(f)
mus = pl.array([])
Films = pl.array([])
Bulks = pl.array([])
filmErrs = pl.array([])
bulkErrs = pl.array([])
Omegas = pl.array([])
omegaErrs = pl.array([])
NumParts = pl.array([])
NumPartErrs = pl.array([])
n = 0
nw = 3
nn = 12
for head in headers:
# get values of chemical potential from file header
mus = pl.append(mus, float(head))
# --- Densities ---------------------------------------------------
filmavg,filmstd,filmbins,bulkavg,bulkstd,bulkbins = pl.genfromtxt(f,
unpack=True, usecols=(n,n+1,n+2,n+3,n+4,n+5), delimiter=',')
n += 6
# 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)
# ---- angular winding --------------------------------------------
omegaAvg,omegaStd,omegaBins = pl.genfromtxt(fw,
unpack=True, usecols=(nw,nw+1,nw+2), delimiter=',')
nw += 9
# get rid of any items which are not numbers..
omegaBins = omegaBins[pl.logical_not(pl.isnan(omegaBins))]
omegaStd = omegaStd[pl.logical_not(pl.isnan(omegaStd))]
omegaAvg = omegaAvg[pl.logical_not(pl.isnan(omegaAvg))]
# normalize data.
omegaStd *= omegaNorm
omegaAvg *= omegaNorm
weights = omegaBins/pl.sum(omegaBins)
omegaAvg *= weights
omegaStd *= weights
Omega = pl.sum(omegaAvg)
omegaErr = pl.sum(omegaStd)
Omegas = pl.append(Omegas, Omega)
omegaErrs = pl.append(omegaErrs, omegaErr)
# ---- total number -----------------------------------------------
numAvg,numStd,numBins = pl.genfromtxt(fe,
unpack=True, usecols=(nn,nn+1,nn+2), delimiter=',')
nn += 15
# get rid of any items which are not numbers..
numBins = numBins[pl.logical_not(pl.isnan(numBins))]
numStd = numStd[pl.logical_not(pl.isnan(numStd))]
numAvg = numAvg[pl.logical_not(pl.isnan(numAvg))]
# normalize data.
numStd /= (1.0*accessibleVol)
numAvg /= (1.0*accessibleVol)
weights = numBins/pl.sum(numBins)
numAvg *= weights
numStd *= weights
numPart = pl.sum(numAvg)
numPartErr = pl.sum(numStd)
NumParts = pl.append(NumParts, numPart)
NumPartErrs = pl.append(NumPartErrs, numPartErr)
# Sort data in order of increasing chemical potential. I love Python.
# This is another bit of magical Python juju.
mus, Films, Bulks, filmErrs, bulkErrs, Omegas, omegaErrs, NumParts, NumPartErrs = pl.asarray(
zip(*sorted(zip(mus, Films, Bulks, filmErrs, bulkErrs,
Omegas, omegaErrs, NumParts, NumPartErrs))))
pl.figure(1)
pl.errorbar(mus, Films, filmErrs, fmt='--o', color=colors[nS],
label=labell+', 2d',
markersize=8)
pl.errorbar(mus, Bulks, bulkErrs, fmt = '-d', color=colors[nS],
label=labell+', 3d',
markersize=8)
pl.figure(2)
pl.errorbar(mus, Bulks, bulkErrs, fmt = '-d', color=colors[nS],
label=labell+', 3d',
markersize=8)
pl.figure(3)
pl.errorbar(mus, Films*projArea, fmt='-o', color=colors[nS],
label = labell+', 2d',
markersize=8)
pl.figure(4)
pl.errorbar(mus, Omegas, omegaErrs, fmt='-o', color=colors[nS],
label = labell, markersize=8)
pl.figure(5)
pl.errorbar(mus, NumParts, NumPartErrs, fmt='-o', color=colors[nS],
label = labell, markersize=8)
os.chdir('..')
pl.figure(1)
pl.legend(loc=2)
pl.savefig('densities_vs_chemicalPotential_allS_1APR.pdf', format='pdf',
bbox_inches='tight')
pl.figure(2)
pl.legend(loc=2)
pl.figure(3)
pl.legend(loc=2)
pl.figure(4)
pl.legend(loc=2)
pl.figure(5)
pl.legend(loc=2)
pl.show()
def main():
savePDF = True
solidDens = False
Lx = 12.0
Ly = 12.0
az = 47.5
ay = 2.0
bulkVol = 2.0*Lx*Ly*az
lowX7 = -6.0
#lowX7 = -4.0 # boltzmannons
highX7 = 2.9
#highX7 = 2.5 # boltzmannons
# define some plotting colors
colors = ['Navy','DarkViolet','MediumSpringGreen','Salmon','Fuchsia',
'Yellow','Maroon','Salmon','Blue']
# -------------------------------------------------------------------------
# bulk and film density on same plot
figg = pl.figure(1)
ax = figg.add_subplot(111)
pl.xlabel(r'$\text{Potential Shift}\ [K]$', fontsize=20)
pl.ylabel('Spatial Density '+r'$[\si{\angstrom}^{-d}]$', fontsize=20)
pl.grid(True)
pl.xlim([-5.5,3.5])
#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)
# bulk SVP density line
bulkVert = -30
minMus = -5.5
maxMus = 2.0
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=(-10, bulkVert), textcoords='offset points',
bbox=dict(boxstyle="round", fc="0.8"),
arrowprops=dict(arrowstyle="->",
connectionstyle="angle,angleA=0,angleB=90,rad=10"),
)
# film SVP density line
pl.plot([minMus, maxMus], [0.0432, 0.0432], 'k-', lw=3)
pl.annotate('2d SVP', xy=(maxMus - boxSubtract, 0.0432), #xycoords='data',
xytext=(30, -30), textcoords='offset points',
bbox=dict(boxstyle="round", fc="0.8"),
arrowprops=dict(arrowstyle="->",
connectionstyle="angle,angleA=0,angleB=90,rad=10"),
)
if solidDens:
pl.plot([minMus, maxMus], [0.0248, 0.0248], 'k-', lw=3)
pl.annotate('HCP solid SVP', xy=(maxMus - boxSubtract, 0.0248), #xycoords='data',
xytext=(-10, 30), textcoords='offset points',
bbox=dict(boxstyle="round", fc="0.8"),
arrowprops=dict(arrowstyle="->",
connectionstyle="angle,angleA=0,angleB=90,rad=10"),
)
# -------------------------------------------------------------------------
# bulk density
figg2 = pl.figure(2)
ax2 = figg2.add_subplot(111)
pl.xlabel(r'$\text{Potential Shift}\ [K]$', fontsize=20)
pl.ylabel('Bulk Density '+r'$[\si{\angstrom}^{-3}]$', fontsize=20)
pl.grid(True)
pl.xlim([-5.5,0.5])
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 # changes text box above (positive) or below (negative) line
boxSubtract = 1.6
pl.plot([minMus, maxMus], [0.02198, 0.02198], 'k-', lw=3)
pl.annotate('3d SVP', xy=(maxMus - boxSubtract, 0.02198), #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"),
)
# -------------------------------------------------------------------------
# number of particles in film region
pl.figure(3)
pl.xlabel(r'$\text{Potential Shift}\ [K]$', fontsize=20)
pl.ylabel(r'$N_{\text{film}}$', fontsize=20)
pl.grid(True)
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)
# -------------------------------------------------------------------------
# normalized angular winding
pl.figure(4)
pl.xlabel(r'$\text{Potential Shift}\ [K]$', fontsize=20)
pl.ylabel(r'$\Omega$', fontsize=20)
pl.grid(True)
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)
# -------------------------------------------------------------------------
# film density
figg5 = pl.figure(5)
ax5 = figg5.add_subplot(111)
pl.xlabel(r'$\text{Potential Shift}\ [K]$', fontsize=20)
pl.ylabel(r'$\text{Film Density}\ [\si{\angstrom}^{-2}]$', fontsize=20)
pl.ylim([0.03,0.05])
pl.xlim([-5.5,0.5])
pl.grid(True)
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)
# film SVP density line
pl.plot([lowX7, highX7], [0.0432, 0.0432], 'k-', lw=3)
pl.annotate('2d SVP', xy=(-4, 0.0432), #xycoords='data',
xytext=(30, -30), textcoords='offset points',
bbox=dict(boxstyle="round", fc="0.8"),
arrowprops=dict(arrowstyle="->",
connectionstyle="angle,angleA=0,angleB=90,rad=10"),
)
# -------------------------------------------------------------------------
# superfluid fraction
pl.figure(6)
pl.xlabel(r'$\text{Potential Shift}\ [K]$', fontsize=20)
pl.ylabel(r'$\rho_S/\rho$', fontsize=20)
pl.grid(True)
pl.xlim([-5.5,0.5])
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)
# -------------------------------------------------------------------------
# film/bulk densities subplot
pl.figure(7)
ax7a = pl.subplot(211)
pl.ylabel(r'$\text{Film Density}\ [\si{\angstrom}^{-2}]$', fontsize=20)
pl.ylim([0.035,0.05])
pl.grid(True)
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)
# film SVP density line
pl.plot([lowX7, highX7], [0.0432, 0.0432], 'k-', lw=3)
pl.annotate('2d SVP', xy=(-2, 0.0432), #xycoords='data',
xytext=(30, -30), textcoords='offset points',
bbox=dict(boxstyle="round", fc="0.8"),
arrowprops=dict(arrowstyle="->",
connectionstyle="angle,angleA=0,angleB=90,rad=10"),
)
pl.setp(ax7a.get_xticklabels(), visible=False)
ax7b = pl.subplot(212, sharex=ax7a)
pl.xlabel(r'$\text{Potential Shift}\ [K]$', fontsize=20)
pl.ylabel('Bulk Density '+r'$[\si{\angstrom}^{-3}]$', fontsize=20)
pl.grid(True)
pl.xlim([lowX7,highX7])
pl.tick_params(axis='both', which='major', labelsize=16)
pl.tick_params(axis='both', which='minor', labelsize=16)
yticks = ax7b.yaxis.get_major_ticks()
yticks[0].set_visible(False)
# set up bulk SVP densities for plot
bulkVert = 15 # changes text box above (positive) or below (negative) line
boxSubtract = 1.6
pl.plot([lowX7, highX7], [0.02198, 0.02198], 'k-', lw=3)
pl.annotate('3d SVP', xy=(1.2, 0.02198), #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"),
)
# -------------------------------------------------------------------------
# number of particles in bulk
pl.figure(8)
pl.xlabel(r'$\text{Potential Shift}\ [K]$', fontsize=20)
pl.ylabel(r'$N_{\text{bulk}}$', fontsize=20)
pl.grid(True)
pl.xlim([-5.5,0.5])
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)
# -------------------------------------------------------------------------
Tvals = glob.glob('*T*')
Markers = ['-o','-d','-*']
for nT,Tval in enumerate(sorted(Tvals)):
os.chdir(Tval)
T = Tval[1:]
Svals = glob.glob('S*')
# --- loop through known directory structure --------------------------
for nS,Sval in enumerate(sorted(Svals)):
os.chdir(Sval)
Vdirs = glob.glob('*V*')
print os.getcwd()
# store bulk separation value
S = re.search(r'\d+',Sval).group(0)
# get label for plot
if 'distinguishable' in Sval:
labell = 'S = '+str(S)+', Boltzmannons, T='+str(T)
else:
labell = 'S = '+str(S)+', Bosons, T='+str(T)
shortlabell = 'S = '+str(S)+', T='+str(T)
# projected area of film region
projArea = float(S)*Lx
# accessible volume in cell
#accessibleVol = Lx*(Ly*(float(S)+2.0*az) - float(S)*(Ly-2.0*ay))
accessibleVol = 2.0*Lx*Ly*az + 2.0*ay*Lx*float(S)
# multiply angular winding by norm. to be fixed in code later
omegaNorm = 4.0*(float(S)+1.0*Ly)**2
# Arrays to hold all data
Vs = pl.array([])
Films = pl.array([])
Bulks = pl.array([])
filmErrs = pl.array([])
bulkErrs = pl.array([])
Omegas = pl.array([])
omegaErrs = pl.array([])
NumParts = pl.array([])
NumPartErrs = pl.array([])
Supers = pl.array([])
SuperErrs = pl.array([])
# pass potential shift directories for current S value
for Vdir in Vdirs:
os.chdir(Vdir)
# get bipartition file name
f = glob.glob('*Bipart*')[0]
# get angular winding file name
fw = glob.glob('*Ntwind*')[0]
# get estimator file name (includes total number)
fe = glob.glob('*Estimator*')[0]
# get superfrac file name
fs = glob.glob('*Super*')[0]
# build array of film potential shifts from directory names
Vs = pl.append(Vs,float(Vdir[1:]))
# --- Densities -----------------------------------------------
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)
# ---- angular winding ----------------------------------------
omegaAvg,omegaStd,omegaBins = pl.genfromtxt(fw,
unpack=True, usecols=(3,4,5), delimiter=',')
# get rid of any items which are not numbers..
omegaBins = omegaBins[pl.logical_not(pl.isnan(omegaBins))]
omegaStd = omegaStd[pl.logical_not(pl.isnan(omegaStd))]
omegaAvg = omegaAvg[pl.logical_not(pl.isnan(omegaAvg))]
# normalize data.
omegaStd *= omegaNorm
omegaAvg *= omegaNorm
weights = omegaBins/pl.sum(omegaBins)
omegaAvg *= weights
omegaStd *= weights
Omega = pl.sum(omegaAvg)
omegaErr = pl.sum(omegaStd)
Omegas = pl.append(Omegas, Omega)
omegaErrs = pl.append(omegaErrs, omegaErr)
# ---- total number -------------------------------------------
numAvg,numStd,numBins = pl.genfromtxt(fe,
unpack=True, usecols=(12,13,14), delimiter=',')
# get rid of any items which are not numbers..
numBins = numBins[pl.logical_not(pl.isnan(numBins))]
numStd = numStd[pl.logical_not(pl.isnan(numStd))]
numAvg = numAvg[pl.logical_not(pl.isnan(numAvg))]
weights = numBins/pl.sum(numBins)
numAvg *= weights
numStd *= weights
numPart = pl.sum(numAvg)
numPartErr = pl.sum(numStd)
NumParts = pl.append(NumParts, numPart)
NumPartErrs = pl.append(NumPartErrs, numPartErr)
# ---- superfluid fraction ------------------------------------
supAvg,supStd,supBins = pl.genfromtxt(fs,
unpack=True, usecols=(0,1,2), delimiter=',')
# get rid of any items which are not numbers..
supBins = supBins[pl.logical_not(pl.isnan(supBins))]
supStd = supStd[pl.logical_not(pl.isnan(supStd))]
supAvg = supAvg[pl.logical_not(pl.isnan(supAvg))]
# normalize data.
#supStd /= (1.0*accessibleVol)
#supAvg /= (1.0*accessibleVol)
weights = supBins/pl.sum(supBins)
supAvg *= weights
supStd *= weights
supPart = pl.sum(supAvg)
supPartErr = pl.sum(supStd)
Supers = pl.append(Supers, supPart)
SuperErrs = pl.append(SuperErrs, supPartErr)
os.chdir('..')
# Sort data in order of increasing chemical potential.
# This is another bit of magical Python juju.
Vs, Films, Bulks, filmErrs, bulkErrs, Omegas, omegaErrs,\
NumParts, NumPartErrs, Supers, SuperErrs = pl.asarray(
zip(*sorted(zip(Vs, Films, Bulks, filmErrs, bulkErrs,
Omegas, omegaErrs, NumParts, NumPartErrs, Supers,
SuperErrs))))
pl.figure(1)
pl.errorbar(Vs, Films, filmErrs, fmt=Markers[nT], color=colors[nS],
label=labell+', 2d',
markersize=8)
pl.errorbar(Vs, Bulks, bulkErrs, fmt=Markers[nT], mec=colors[nS],
label=labell+', 3d', mfc='None',
markersize=8)
pl.figure(2)
pl.errorbar(Vs, Bulks, bulkErrs, fmt = Markers[nT], color=colors[nS],
label=labell, markersize=8)
pl.figure(3)
pl.errorbar(Vs, Films*projArea, fmt=Markers[nT], color=colors[nS],
label = labell, markersize=8)
pl.figure(4)
pl.errorbar(Vs, Omegas, omegaErrs, fmt=Markers[nT], color=colors[nS],
label = labell, markersize=8)
pl.figure(5)
pl.errorbar(Vs, Films, filmErrs, fmt=Markers[nT], color=colors[nS],
label = labell, markersize=8)
pl.figure(6)
pl.errorbar(Vs, Supers, SuperErrs, fmt=Markers[nT], color=colors[nS],
label = labell, markersize=8)
pl.figure(7)
ax7a.errorbar(Vs, Films, filmErrs, fmt=Markers[nT], color=colors[nS],
label=shortlabell, markersize=8)
ax7b.errorbar(Vs, Bulks, bulkErrs, fmt = Markers[nT], color=colors[nS],
label=shortlabell, markersize=8)
pl.figure(8)
pl.errorbar(Vs, Bulks*bulkVol, bulkErrs*bulkVol,
fmt = Markers[nT], color=colors[nS],
label=labell, markersize=8)
os.chdir('..')
os.chdir('..')
pl.figure(1)
pl.legend(loc=1)
pl.tight_layout()
pl.figure(2)
pl.legend(loc=1)
pl.tight_layout()
pl.figure(3)
pl.legend(loc=1)
pl.tight_layout()
pl.figure(4)
pl.legend(loc=1)
pl.tight_layout()
pl.figure(5)
pl.legend(loc=1)
pl.tight_layout()
pl.figure(6)
pl.legend(loc=1)
pl.tight_layout()
pl.figure(7)
pl.legend(loc=1,bbox_to_anchor=(1.,2.1))
major_formatter = FuncFormatter(my_formatter)
ax7a.yaxis.set_major_formatter(major_formatter)
pl.tight_layout()
if savePDF:
pl.savefig('Bulk_Film_Dens_vs_Vshift_allS_allT_18APR.pdf', format='pdf',
bbox_inches='tight')
pl.figure(8)
pl.legend(loc=1)
pl.tight_layout()
pl.show()
