def main():
# parse the command line options
args = docopt(__doc__)
fileNames = args['<file>']
skip = int(args['--skip'])
period = int(args['--period'])
estimator = args['--estimator']
leglabel = args['--legend'] and args['--legend']
error = args['--error'] and float(args['--error'])
val = args['--hline'] and float(args['--hline'])
# if labels are not assigned, we default to the PIMCID
if not leglabel:
leglabel = []
for n,fileName in enumerate(fileNames):
leglabel.append(fileName[-13:-4])
# We count the number of lines in the estimator file to make sure we have
# some data and grab the headers
headers = pimchelp.getHeadersDict(fileNames[0])
# If we don't choose an estimator, provide a list of possible ones
if estimator not in headers:
errorString = "Need to specify one of:\n"
for head,index in headers.iteritems():
errorString += "\"%s\"" % head + " "
parser.error(errorString)
numFiles = len(fileNames)
col = list([headers[estimator]])
# Attempt to find a 'pretty name' for the label, otherwise just default to
# the column heading
label = pimchelp.Description()
try:
yLong = label.estimatorLongName[estimator]
except:
yLong = estimator
try:
yShort = label.estimatorShortName[estimator]
except:
yShort = estimator
# get a label for a possible horizontal line
if args['--hline']:
if args['--hlabel']:
hlabel = args['--hlabel']
else:
hlabel = yShort.split()[0] + ' = ' + args['--hline']
# First we load and store all the data
data = []
for fileName in fileNames:
dataFile = open(fileName,'r');
dataLines = dataFile.readlines();
dataFile.close()
if len(dataLines) > 2:
data.append(loadtxt(fileName,usecols=col,unpack=True))
# ============================================================================
# Figure 1 : column vs. MC Steps
# ============================================================================
figure(1)
connect('key_press_event',kevent.press)
colors = loadgmt.getColorList('cw/1','cw1-029',max(numFiles,2))
#colors = loadgmt.getColorList('cw/1','cw1-013',max(numFiles,2))
#colors = loadgmt.getColorList('oc','rainbow',max(numFiles,2))
#colors = loadgmt.getColorList('grass','bgyr',max(numFiles,2))
colors = ["#70D44A", "#BE5AD4", "#D04537", "#81D0D5", "#393A2E", "#C49ECA",
"#C5CB7A", "#523767", "#D39139", "#C8488C", "#CB817A", "#73D999",
"#6F2836", "#6978CF", "#588569", "#CDC3AD", "#5C7890", "#7F5327",
"#D0D33D", "#5B7D2E"]
colors = ["#53B0AD", "#D74C20", "#CD53DA", "#58C038", "#5F4B7A", "#49622A",
"#CE4379", "#D2912E", "#7970D2", "#749AC9", "#7D5121", "#5EAC72",
"#CB85AA", "#853B46", "#396465", "#A5A33E", "#D47F5B", "#BA4EA5",
"#C93F44", "#5D9937"]
colors =["#CAC5E8", "#E1D273", "#82DFCE", "#F1AF92", "#E1E7CF", "#92C798",
"#CDF197", "#DDD199", "#ECB1D1", "#91C7DE", "#E3AF6E", "#ECAAAC",
"#CEB29C", "#B2BCA9", "#B0C778", "#DBCDD7", "#B5DEE0", "#A5ECB4",
"#C5E6C0", "#E5CCC0"]
colors = ["#688EAF", "#FC991D", "#7DEB74", "#FA6781", "#8B981D", "#BB7548",
"#AD8FE4", "#96E4AA", "#D669B0", "#E1C947", "#A78200", "#7C9FE4",
"#957DA6", "#75BF38", "#C3B059", "#51C17A", "#79AEBB", "#2790AC",
"#688ECE", "#749DB7"]
colors += colors
colors += colors
colors += colors
print len(colors)
for n,cdata in enumerate(data):
plot(cdata[skip:],marker='s',color=colors[n],markeredgecolor=colors[n],\
markersize=4,linestyle='-',linewidth=1.0)
ylabel(yLong)
xlabel("MC Bin Number")
# ============================================================================
# Figure 2 : running average of column vs. MC Bins
# ============================================================================
figure(2)
connect('key_press_event',kevent.press)
n = 0
for n,cdata in enumerate(data):
if size(cdata) > 1:
# Get the cumulative moving average
if args['--error']:
cma = cumulativeMovingAverage(cdata[skip:])
sem = error*ones_like(cma)
elif args['--nobin']:
cma,sem = cumulativeMovingAverageWithError(cdata[skip:])
else:
cma = cumulativeMovingAverage(cdata[skip:])
ave,err = getStats(cdata[skip:])
sem = err*ones_like(cma)
print '%s: %s = %8.4E +- %8.4E' % (leglabel[n],yShort, ave,err)
sma = simpleMovingAverage(50,cdata[skip:])
x = range(int(0.10*len(cma)),len(cma))
plot(x,cma[x],color=colors[n],linewidth=1.0,marker='None',linestyle='-',
label=leglabel[n])
fill_between(x, cma[x]-sem[x], cma[x]+sem[x],color=colors[n], alpha=0.1)
n += 1
# Add a possible horizontal line indicating some value
if args['--hline']:
axhline(y=val,color='gray',linewidth=2.5, label=hlabel)
ylabel(yLong)
xlabel("MC Bin Number")
tight_layout()
leg = legend(loc='best', frameon=False, prop={'size':16},markerscale=2, ncol=2)
for l in leg.get_lines():
l.set_linewidth(4.0)
# Perform a Welch's t-test
if args['--ttest']:
# We only perform the Welch's t test if we have multiple samples we are
# comparing
N = len(data)
if N > 1:
tval = zeros([N,N])
p = zeros([N,N])
for i in range(N):
for j in range(i+1,N):
tval[i,j],p[i,j] = stats.ttest_ind(data[i][skip:], data[j][skip:],
equal_var=False)
# ============================================================================
# Figure 3 : plot the estimator histogram along with t-test values
# ============================================================================
fig = figure(3)
connect('key_press_event',kevent.press)
for i in range(N):
n, bins, patches = hist(data[i], 100, normed=True, facecolor=colors[i],
alpha=0.75, label=leglabel[i],
edgecolor='w')
# Add the p-values from the t-test
y = 0.92
if N > 1:
figtext(0.78, y, 't-test p values', horizontalalignment='center',
verticalalignment='top', fontsize=15, backgroundcolor='white')
for i in range(N):
for j in range(i+1,N):
y -= 0.03
lab = 'p(' + leglabel[i] + ' - ' + leglabel[j] + ') = ' + '%4.2f'%p[i,j]
figtext(0.78, y, lab, horizontalalignment='center',
verticalalignment='top', fontsize=12,
backgroundcolor='white')
legend(loc='upper left', fontsize=15, frameon=False)
xlabel(yLong)
ylabel(r'$P($' + estimator + r'$)$')
show()
def main():
# setup the command line parser options
parser = argparse.ArgumentParser(description='Plot binning analysis for \
MC Data for Scalar Estimators.')
parser.add_argument('fileNames', help='Scalar estimator files', nargs='+')
parser.add_argument('--estimator',
'-e',
help='A list of estimator names \
that are to be plotted.',
type=str)
parser.add_argument('--skip',
'-s',
help='Number of measurements to be \
skipped in the binning analysis.',
type=int,
default=0)
parser.add_argument('--scale',
help='Option to compare binning results \
for different parameters',
action='store_true')
args = parser.parse_args()
fileNames = args.fileNames
scale = args.scale
if len(fileNames) < 1:
parser.error("Need to specify at least one scalar estimator file")
# We count the number of lines in the estimator file to make sure we have
# some data and grab the headers
headers = pimchelp.getHeadersDict(fileNames[0])
# If we don't choose an estimator, provide a list of possible ones
if not args.estimator or args.estimator not in headers:
errorString = "Need to specify one of:\n"
for head, index in headers.iteritems():
errorString += "\"%s\"" % head + " "
parser.error(errorString)
numFiles = len(fileNames)
col = list([headers[args.estimator]])
# Attempt to find a 'pretty name' for the label, otherwise just default to
# the column heading
label = pimchelp.Description()
try:
yLong = label.estimatorLongName[args.estimator]
except:
yLong = args.estimator
try:
yShort = label.estimatorShortName[args.estimator]
except:
yShort = args.estimator
# ============================================================================
# Figure 1 : Error vs. bin level
# ============================================================================
figure(1)
connect('key_press_event', kevent.press)
colors = loadgmt.getColorList('cw/1', 'cw1-029', max(numFiles, 2))
n = 0
for fileName in fileNames:
dataFile = open(fileName, 'r')
dataLines = dataFile.readlines()
dataFile.close()
if len(dataLines) > 2:
data = loadtxt(fileName, usecols=col)
if not pyutils.isList(data):
data = list([data])
delta = MCstat.bin(data[args.skip:])
if n == 0:
delta_ar = np.zeros((numFiles, delta.shape[0]))
delta_ar[n, :] = delta.T
#delta_ar[n,:len(delta)] = delta.T
n += 1
if n > 1:
if scale:
for m in range(n):
plot(np.arange(len(delta_ar)),delta_ar[m],marker='s',markersize=4,\
linestyle='-',linewidth=1.0,color=colors[m],\
markeredgecolor=colors[m])
else:
Delta = np.average(delta_ar, 0)
dDelta = np.std(delta_ar, 0) / np.sqrt(n)
errorbar(np.arange(len(Delta)),Delta,dDelta,marker='s',markersize=4,\
linestyle='-',linewidth=1.0,color=colors[0],\
markeredgecolor=colors[0])
bin_ac = MCstat.bin_ac(Delta, dDelta)
bin_conv = MCstat.bin_conv(Delta, dDelta)
print 'Convergence Ratio: %1.2f+/-%1.2f' % (bin_conv['CF'],
bin_conv['dCF'])
print 'autocorrlelation time: %2.1f+/-%2.1f' % \
(bin_ac['tau'],bin_ac['dtau'])
else:
plot(delta,marker='s',markersize=4,linestyle='-',linewidth=1.0,\
color=colors[0],markeredgecolor=colors[0])
print 'Convergence Ratio: %1.3f' % MCstat.bin_conv(delta)['CF']
print 'autocorrlelation time: %3.3f' % MCstat.bin_ac(delta)['tau']
ylabel(r"$\Delta_l$")
xlabel("$l$")
title("Bin scaling: " + yLong)
show()
def main():
# Read in the command line arguments
args = docopt(__doc__)
fileName = args['<input-file>']
estName = args['--estimator']
# Open up the tensor file, and determine the number of grid boxes in each
# dimension and the column headers
with open(fileName,'r') as inFile:
N = int(inFile.readline().split()[1])
# Get the spatial dimensions, otherwise just use N
L = [N,N,N]
for n,l in enumerate(['--Lx','--Ly','--Lz']):
if args[l]:
L[n] = float(args[l])
gridSize = []
for cL in L:
gridSize.append(cL/(1.0*N))
dV = pl.product(gridSize)
# Get the column headers from the data file
headers = pimchelp.getHeadersDict(fileName,skipLines=1)
# determine which column we will plot
plotColumn = headers.get(estName,0)
# Assuming a 3d data set, load the data
data = pl.loadtxt(fileName,ndmin=2)[:,plotColumn].reshape([N,N,N])
# assuming translational symmetry in the z-axis
rhoxy = pl.average(data,axis=2)
# For the particular case of A^2 we need to
num_rad_sep = int(N/2)
dr = 0.5*L[0]/num_rad_sep
rho = pl.zeros(num_rad_sep)
counts = pl.zeros_like(rho)
l = range(N)
for i,j in itertools.product(l,l):
x = -0.5*L[0] + (i+0.5)*gridSize[0]
y = -0.5*L[1] + (j+0.5)*gridSize[1]
r = pl.sqrt(x*x + y*y)
n = int(r/dr)
if n < num_rad_sep:
if abs(rho[n]) < 1000.0:
if 'A^2' in estName:
rho[n] += rhoxy[i,j]/(r*r)
else:
rho[n] += rhoxy[i,j]
counts[n] += 1
if 'A^2' in estName and r > 0.8:
rhoxy[i,j] /= r*r
elif 'A^2' in estName and r < 0.8:
rhoxy[i,j] = 0.0
elif 'A:rho_s' in estName and r < 1.0:
rhoxy[i,j] = 0.0
for n in range(num_rad_sep):
if counts[n] > 0:
rho[n] /= counts[n]
# plot histograms in all three projections
pl.figure(1)
pl.imshow(rhoxy, cmap=cmap,
extent=[-0.5*L[0],0.5*L[0],-0.5*L[1],0.5*L[1]],
interpolation='None')
pl.xlabel(r'$y\ [\AA]$')
pl.ylabel(r'$x\ [\AA]$')
pl.title('Particle Density Projection (X-Y)')
pl.colorbar(shrink=0.4)
r = pl.linspace(0,0.5*L[0],num_rad_sep)
# ar = pl.loadtxt('r.dat')
pl.figure(2)
pl.plot(r,rho,'ro-', markersize=8)
# pl.plot(ar[:,0],ar[:,1],'r-', linewidth=2)
for i,cr in enumerate(r):
print '%16.8E%16.8E' % (cr,rho[i])
# Now we locally average the results
loc_ave_data = pl.zeros_like(rhoxy)
for i,j in itertools.product(l,l):
x = -0.5*L[0] + i*gridSize[0]
y = -0.5*L[1] + j*gridSize[1]
r = pl.sqrt(x*x + y*y)
if r < 0.5*L[0]:
count = 0
for k,l in itertools.product(range(-1,2),range(-1,2)):
if i+k < N and j+l < N:
loc_ave_data[i,j] += rhoxy[i+k,j+l]
count += 1
if count > 0:
loc_ave_data[i,j] /= count
else:
loc_ave_data[i,j] = rhoxy[i,j]
pl.figure(3)
pl.imshow(loc_ave_data, cmap=cmap,
extent=[-0.5*L[0],0.5*L[0],-0.5*L[1],0.5*L[1]])
pl.xlabel(r'$y\ [\AA]$')
pl.ylabel(r'$x\ [\AA]$')
pl.title('Particle Density Projection (X-Y)')
pl.colorbar(shrink=0.4)
# pl.figure(2)
# pl.imshow(I, cmap=cmap, extent=[-0.5*L[0],0.5*L[0],-0.5*L[1],0.5*L[1]])
# pl.xlabel(r'$y\ [\AA]$')
# pl.ylabel(r'$x\ [\AA]$')
# pl.title('Particle Density Projection (X-Y)')
# pl.colorbar(shrink=0.4)
# pl.figure(3)
# # pl.imshow(R2, cmap=cmap,
# # extent=[-0.5*L[0],0.5*L[0],-0.5*L[1],0.5*L[1]])
# pl.imshow(pl.sum(data,axis=1), cmap=cmap,
# extent=[-0.5*L[2],0.5*L[2],-0.5*L[0],0.5*L[0]])
# pl.xlabel(r'$z\ [\AA]$')
# pl.ylabel(r'$x\ [\AA]$')
# pl.title('Particle Density Projection (X-Z)')
# pl.colorbar(shrink=0.4)
#
# pl.figure(4)
# pl.imshow(pl.sum(data,axis=0), cmap=cmap,
# extent=[-0.5*L[2],0.5*L[2],-0.5*L[1],0.5*L[1]])
# pl.xlabel(r'$z\ [\AA]$')
# pl.ylabel(r'$y\ [\AA]$')
# pl.title('Particle Density Projection (Y-Z)')
# pl.colorbar(shrink=0.4)
# pl.savefig('plot.svg')
pl.show()
def main():
# parse the command line options
args = docopt(__doc__)
fileNames = args['<file>']
skip = int(args['--skip'])
period = int(args['--period'])
estimator = args['--estimator']
leglabel = args['--legend'] and args['--legend']
error = args['--error'] and float(args['--error'])
val = args['--hline'] and float(args['--hline'])
# if labels are not assigned, we default to the PIMCID
if not leglabel:
leglabel = []
for n, fileName in enumerate(fileNames):
leglabel.append(fileName[-13:-4])
# We count the number of lines in the estimator file to make sure we have
# some data and grab the headers
headers = pimchelp.getHeadersDict(fileNames[0])
# If we don't choose an estimator, provide a list of possible ones
if estimator not in headers:
errorString = "Need to specify one of:\n"
for head, index in headers.iteritems():
errorString += "\"%s\"" % head + " "
parser.error(errorString)
numFiles = len(fileNames)
col = list([headers[estimator]])
# Attempt to find a 'pretty name' for the label, otherwise just default to
# the column heading
label = pimchelp.Description()
try:
yLong = label.estimatorLongName[estimator]
except:
yLong = estimator
try:
yShort = label.estimatorShortName[estimator]
except:
yShort = estimator
# get a label for a possible horizontal line
if args['--hline']:
if args['--hlabel']:
hlabel = args['--hlabel']
else:
hlabel = yShort.split()[0] + ' = ' + args['--hline']
# First we load and store all the data
data = []
for fileName in fileNames:
dataFile = open(fileName, 'r')
dataLines = dataFile.readlines()
dataFile.close()
if len(dataLines) > 2:
data.append(loadtxt(fileName, usecols=col, unpack=True))
# ============================================================================
# Figure 1 : column vs. MC Steps
# ============================================================================
figure(1)
connect('key_press_event', kevent.press)
colors = loadgmt.getColorList('cw/1', 'cw1-029', max(numFiles, 2))
#colors = loadgmt.getColorList('cw/1','cw1-013',max(numFiles,2))
#colors = loadgmt.getColorList('oc','rainbow',max(numFiles,2))
#colors = loadgmt.getColorList('grass','bgyr',max(numFiles,2))
colors = [
"#70D44A", "#BE5AD4", "#D04537", "#81D0D5", "#393A2E", "#C49ECA",
"#C5CB7A", "#523767", "#D39139", "#C8488C", "#CB817A", "#73D999",
"#6F2836", "#6978CF", "#588569", "#CDC3AD", "#5C7890", "#7F5327",
"#D0D33D", "#5B7D2E"
]
colors = [
"#53B0AD", "#D74C20", "#CD53DA", "#58C038", "#5F4B7A", "#49622A",
"#CE4379", "#D2912E", "#7970D2", "#749AC9", "#7D5121", "#5EAC72",
"#CB85AA", "#853B46", "#396465", "#A5A33E", "#D47F5B", "#BA4EA5",
"#C93F44", "#5D9937"
]
colors = [
"#CAC5E8", "#E1D273", "#82DFCE", "#F1AF92", "#E1E7CF", "#92C798",
"#CDF197", "#DDD199", "#ECB1D1", "#91C7DE", "#E3AF6E", "#ECAAAC",
"#CEB29C", "#B2BCA9", "#B0C778", "#DBCDD7", "#B5DEE0", "#A5ECB4",
"#C5E6C0", "#E5CCC0"
]
colors = [
"#688EAF", "#FC991D", "#7DEB74", "#FA6781", "#8B981D", "#BB7548",
"#AD8FE4", "#96E4AA", "#D669B0", "#E1C947", "#A78200", "#7C9FE4",
"#957DA6", "#75BF38", "#C3B059", "#51C17A", "#79AEBB", "#2790AC",
"#688ECE", "#749DB7"
]
colors += colors
colors += colors
colors += colors
print len(colors)
for n, cdata in enumerate(data):
plot(cdata[skip:],marker='s',color=colors[n],markeredgecolor=colors[n],\
markersize=4,linestyle='-',linewidth=1.0)
ylabel(yLong)
xlabel("MC Bin Number")
# ============================================================================
# Figure 2 : running average of column vs. MC Bins
# ============================================================================
figure(2)
connect('key_press_event', kevent.press)
n = 0
for n, cdata in enumerate(data):
if size(cdata) > 1:
# Get the cumulative moving average
if args['--error']:
cma = cumulativeMovingAverage(cdata[skip:])
sem = error * ones_like(cma)
elif args['--nobin']:
cma, sem = cumulativeMovingAverageWithError(cdata[skip:])
else:
cma = cumulativeMovingAverage(cdata[skip:])
ave, err = getStats(cdata[skip:])
sem = err * ones_like(cma)
print '%s: %s = %8.4E +- %8.4E' % (leglabel[n], yShort, ave,
err)
sma = simpleMovingAverage(50, cdata[skip:])
x = range(int(0.10 * len(cma)), len(cma))
plot(x,
cma[x],
color=colors[n],
linewidth=1.0,
marker='None',
linestyle='-',
label=leglabel[n])
fill_between(x,
cma[x] - sem[x],
cma[x] + sem[x],
color=colors[n],
alpha=0.1)
n += 1
# Add a possible horizontal line indicating some value
if args['--hline']:
axhline(y=val, color='gray', linewidth=2.5, label=hlabel)
ylabel(yLong)
xlabel("MC Bin Number")
tight_layout()
leg = legend(loc='best',
frameon=False,
prop={'size': 16},
markerscale=2,
ncol=2)
for l in leg.get_lines():
l.set_linewidth(4.0)
# Perform a Welch's t-test
if args['--ttest']:
# We only perform the Welch's t test if we have multiple samples we are
# comparing
N = len(data)
if N > 1:
tval = zeros([N, N])
p = zeros([N, N])
for i in range(N):
for j in range(i + 1, N):
tval[i, j], p[i, j] = stats.ttest_ind(data[i][skip:],
data[j][skip:],
equal_var=False)
# ============================================================================
# Figure 3 : plot the estimator histogram along with t-test values
# ============================================================================
fig = figure(3)
connect('key_press_event', kevent.press)
for i in range(N):
n, bins, patches = hist(data[i],
100,
normed=True,
facecolor=colors[i],
alpha=0.75,
label=leglabel[i],
edgecolor='w')
# Add the p-values from the t-test
y = 0.92
if N > 1:
figtext(0.78,
y,
't-test p values',
horizontalalignment='center',
verticalalignment='top',
fontsize=15,
backgroundcolor='white')
for i in range(N):
for j in range(i + 1, N):
y -= 0.03
lab = 'p(' + leglabel[i] + ' - ' + leglabel[
j] + ') = ' + '%4.2f' % p[i, j]
figtext(0.78,
y,
lab,
horizontalalignment='center',
verticalalignment='top',
fontsize=12,
backgroundcolor='white')
legend(loc='upper left', fontsize=15, frameon=False)
xlabel(yLong)
ylabel(r'$P($' + estimator + r'$)$')
show()
def main():
# setup the command line parser options
parser = argparse.ArgumentParser(description='Plot binning analysis for \
MC Data for Scalar Estimators.')
parser.add_argument('fileNames', help='Scalar estimator files', nargs='+')
parser.add_argument('--estimator','-e', help='A list of estimator names \
that are to be plotted.', type=str)
parser.add_argument('--skip','-s', help='Number of measurements to be \
skipped in the binning analysis.', type=int, default=0)
parser.add_argument('--scale', help='Option to compare binning results \
for different parameters', action='store_true')
args = parser.parse_args()
fileNames = args.fileNames
scale = args.scale
if len(fileNames) < 1:
parser.error("Need to specify at least one scalar estimator file")
# We count the number of lines in the estimator file to make sure we have
# some data and grab the headers
headers = pimchelp.getHeadersDict(fileNames[0])
# If we don't choose an estimator, provide a list of possible ones
if not args.estimator or args.estimator not in headers:
errorString = "Need to specify one of:\n"
for head,index in headers.iteritems():
errorString += "\"%s\"" % head + " "
parser.error(errorString)
numFiles = len(fileNames)
col = list([headers[args.estimator]])
# Attempt to find a 'pretty name' for the label, otherwise just default to
# the column heading
label = pimchelp.Description()
try:
yLong = label.estimatorLongName[args.estimator]
except:
yLong = args.estimator
try:
yShort = label.estimatorShortName[args.estimator]
except:
yShort = args.estimator
# ============================================================================
# Figure 1 : Error vs. bin level
# ============================================================================
figure(1)
connect('key_press_event',kevent.press)
colors = loadgmt.getColorList('cw/1','cw1-029',max(numFiles,2))
n = 0
for fileName in fileNames:
dataFile = open(fileName,'r');
dataLines = dataFile.readlines();
dataFile.close()
if len(dataLines) > 2:
data = loadtxt(fileName,usecols=col)
if not pyutils.isList(data):
data = list([data])
delta = MCstat.bin(data[args.skip:])
if n == 0:
delta_ar = np.zeros((numFiles,delta.shape[0]))
delta_ar[n,:] = delta.T
#delta_ar[n,:len(delta)] = delta.T
n += 1
if n > 1:
if scale:
for m in range(n):
plot(np.arange(len(delta_ar)),delta_ar[m],marker='s',markersize=4,\
linestyle='-',linewidth=1.0,color=colors[m],\
markeredgecolor=colors[m])
else:
Delta = np.average(delta_ar,0)
dDelta = np.std(delta_ar,0)/np.sqrt(n)
errorbar(np.arange(len(Delta)),Delta,dDelta,marker='s',markersize=4,\
linestyle='-',linewidth=1.0,color=colors[0],\
markeredgecolor=colors[0])
bin_ac = MCstat.bin_ac(Delta,dDelta)
bin_conv = MCstat.bin_conv(Delta,dDelta)
print 'Convergence Ratio: %1.2f+/-%1.2f'%(bin_conv['CF'],bin_conv['dCF'])
print 'autocorrlelation time: %2.1f+/-%2.1f' % \
(bin_ac['tau'],bin_ac['dtau'])
else:
plot(delta,marker='s',markersize=4,linestyle='-',linewidth=1.0,\
color=colors[0],markeredgecolor=colors[0])
print 'Convergence Ratio: %1.3f' % MCstat.bin_conv(delta)['CF']
print 'autocorrlelation time: %3.3f' % MCstat.bin_ac(delta)['tau']
ylabel(r"$\Delta_l$")
xlabel("$l$")
title("Bin scaling: "+yLong)
show()
def main():
# parse the command line options
args = docopt(__doc__)
fileNames = args['<file>']
skip = int(args['--skip'])
period = int(args['--period'])
estimators = args['--estimator']
leglabel = args['--legend'] and args['--legend']
error = args['--error'] and float(args['--error'])
# number of estimators must equal number of filenames given
if len(fileNames) != len(estimators):
print "\nNumber of estimators must equal number of filenames supplied."
print "Note that these must be in the same order!\n"
sys.exit()
# if labels are not assigned, we default to the PIMCID
if not leglabel:
leglabel = []
for n,fileName in enumerate(fileNames):
leglabel.append(fileName[-13:-4])
numFiles = len(fileNames)
# first load all of data from all files into a list
col = []
data = []
for numEst in range(len(estimators)):
headers = pimchelp.getHeadersDict(fileNames[numEst])
col = list([headers[estimators[numEst]]])
dataFile = open(fileNames[numEst],'r');
dataLines = dataFile.readlines();
dataFile.close()
if len(dataLines) > 2:
data.append(loadtxt(fileNames[numEst],usecols=col,unpack=True))
# loop over files, loading them and plotting all data wanted.
for numEst in range(len(estimators)):
# We count the number of lines in the estimator file to make sure we have
# some data and grab the headers
headers = pimchelp.getHeadersDict(fileNames[numEst])
# If we don't choose an estimator, provide a list of possible ones
if estimators[numEst] not in headers:
errorString = "Need to specify one of:\n"
for head,index in headers.iteritems():
errorString += "\"%s\"" % head + " "
parser.error(errorString)
# Attempt to find a 'pretty name' for the label, otherwise just default to
# the column heading
label = pimchelp.Description()
try:
yLong = label.estimatorLongName[estimators[numEst]]
except:
yLong = estimators[numEst]
try:
yShort = label.estimatorShortName[estimators[numEst]]
except:
yShort = estimators[numEst]
# ============================================================================
# Figure 1 : column vs. MC Steps
# ============================================================================
figNum = 1
if args['--pdf']:
figure(figNum, dpi=40, figsize=(6,3.8))
else:
figure(figNum)
connect('key_press_event',kevent.press)
colors = loadgmt.getColorList('oc','rainbow',max(numFiles,2))
for n,cdata in enumerate(data):
plot(cdata[skip:],marker='s',color=colors[n],markeredgecolor=colors[n],\
markersize=4,linestyle='-',linewidth=1.0, label=leglabel[n])
ylabel(yLong)
xlabel("MC Bin Number")
if numEst == 0:
legend(loc=3, frameon=False, ncol=2)
if args['--pdf']:
savefig('col_vs_MCSteps.pdf', format='pdf',
bbox_inches='tight')
savefig('col_vs_MCSteps_trans.pdf', format='pdf',
bbox_inches='tight', transparent=True, dpi=40)
else:
savefig('col_vs_MCSteps_trans.png', format='png',
bbox_inches='tight', transparent=True)
# ============================================================================
# Figure 2 : running average of column vs. MC Bins
# ============================================================================
figNum += 1
if args['--pdf']:
figure(figNum, dpi=40, figsize=(6,3.8))
else:
figure(figNum, figsize=(6,3.8))
connect('key_press_event',kevent.press)
n = 0
for n,cdata in enumerate(data):
if size(cdata) > 1:
# Get the cumulative moving average
if args['--error']:
cma = cumulativeMovingAverage(cdata[skip:])
sem = error*ones_like(cma)
elif args['--bin']:
cma = cumulativeMovingAverage(cdata[skip:])
ave,err = getStats(cdata[skip:])
sem = err*ones_like(cma)
print '%s: %s = %8.4E +- %8.4E' % (leglabel[n],yShort, ave,err)
else:
cma,sem = cumulativeMovingAverageWithError(cdata[skip:])
sma = simpleMovingAverage(50,cdata[skip:])
x = range(int(0.10*len(cma)),len(cma))
plot(x,cma[x],color=colors[n],linewidth=1.0,marker='None',linestyle='-',
label=leglabel[n])
fill_between(x, cma[x]-sem[x], cma[x]+sem[x],color=colors[n], alpha=0.1)
#n += 1
ylabel(yLong)
xlabel("MC Bin Number")
if numEst == 0:
leg = legend(loc='best', frameon=False, prop={'size':12},markerscale=2, ncol=2)
for l in leg.get_lines():
l.set_linewidth(4.0)
if args['--pdf']:
savefig('runAve_vs_MCSteps.pdf', format='pdf',
bbox_inches='tight')
savefig('runAve_vs_MCSteps_trans.pdf', format='pdf',
bbox_inches='tight',transparent=True, dpi=40)
else:
savefig('runAve_vs_MCSteps_trans.png', format='png',
bbox_inches='tight',transparent=True)
# Perform a Welch's t-test
if args['--ttest']:
# We only perform the Welch's t test if we have multiple samples we are
# comparing
N = len(data)
if N > 1:
tval = zeros([N,N])
p = zeros([N,N])
for i in range(N):
for j in range(i+1,N):
tval[i,j],p[i,j] = stats.ttest_ind(data[i][skip:], data[j][skip:],
equal_var=False)
figNum += 1
# ============================================================================
# Figure 3 : plot the estimator histogram along with t-test values
# ============================================================================
if args['--pdf']:
fig = figure(figNum, dpi=40, figsize=(6,3.8))
else:
fig = figure(figNum)
connect('key_press_event',kevent.press)
for i in range(N):
n, bins, patches = hist(data[i], 100, normed=True, facecolor=colors[i],
alpha=0.75, label=leglabel[i],
edgecolor='w')
'''# Add the p-values from the t-test
y = 0.92
if N > 1:
figtext(0.78, y, 't-test p values', horizontalalignment='center',
verticalalignment='top', fontsize=15, backgroundcolor='white')
for i in range(N):
for j in range(i+1,N):
y -= 0.03
lab = 'p(' + leglabel[i] + ' - ' + leglabel[j] + ') = ' + '%4.2f'%p[i,j]
figtext(0.78, y, lab, horizontalalignment='center',
verticalalignment='top', fontsize=12,
backgroundcolor='white')'''
#legend(loc='upper left', fontsize=15, frameon=False)
if numEst == 0:
legend(loc='upper left', frameon=False)
xlabel(yLong)
yLabb = estimators[numEst]
if yLabb == 'Ecv/N':
yLabb = 'E/N'
ylabel(r'$P($' + yLabb + r'$)$')
if args['--pdf']:
savefig('ttest_histogram.pdf', format='pdf',
bbox_inches='tight')
savefig('ttest_histogram_trans.pdf', format='pdf',
bbox_inches='tight', transparent=True, dpi=40)
else:
savefig('ttest_histogram_trans.png', format='png',
bbox_inches='tight', transparent=True)
# ============================================================================
# Figure 4 : autocorrelation
# ============================================================================
figNum += 1
if args['--pdf']:
figure(figNum, dpi=40, figsize=(6,3.8))
else:
figure(figNum)
connect('key_press_event',kevent.press)
colors = loadgmt.getColorList('oc','rainbow',max(numFiles,2))
mcTime = arange(0,len(cdata[skip:]),1)
for n,cdata in enumerate(data):
plot(mcTime,estimated_autocorrelation(cdata[skip:]),
marker='s',color=colors[n],markeredgecolor=colors[n],
markersize=4,linestyle='-',linewidth=1.0, label=leglabel[n])
ylabel("Autocorrelation")
xlabel("Data")
if numEst == 0:
legend(loc=3, frameon=False, ncol=2)
'''if args['--pdf']:
savefig('autocorrelation.pdf', format='pdf',
bbox_inches='tight')
savefig('autocorrelation_trans.pdf', format='pdf',
bbox_inches='tight', transparent=True, dpi=40)
else:
savefig('autocorrelation_trans.png', format='png',
bbox_inches='tight', transparent=True)
'''
show()
