def trace_plots(history, burnin):
from rpy import r
h = np.array(history)
print "{} total, {} burning, {} remaining".format(len(history), burnin, len(history)-burnin)
r.par(mfrow=[2,2])
r.acf(h[burnin:])
r.plot(h,xlab='',ylab='',main='')
r.abline(v=burnin, col='blue')
#r.hist(h[burnin:],breaks=30,xlab='',ylab='',main='histogram')
r.plot(r.density(h[burnin:]), xlab='',ylab='',main='density')
def trace_plots(history, burnin):
from rpy import r
h = np.array(history)
print "{} total, {} burning, {} remaining".format(len(history), burnin,
len(history) - burnin)
r.par(mfrow=[2, 2])
r.acf(h[burnin:])
r.plot(h, xlab='', ylab='', main='')
r.abline(v=burnin, col='blue')
#r.hist(h[burnin:],breaks=30,xlab='',ylab='',main='histogram')
r.plot(r.density(h[burnin:]), xlab='', ylab='', main='density')
def plotBundle(self, bundleD, full_filename, colorsD=None, bundlePointsD=None, legendL=None, title=None, y_max=None):
if y_max is None:
y_max = 0.4
if legendL is None:
legendL = bundleD.keys()
legendL.sort()
if title is None:
title = 'data'
bundleIdL = bundleD.keys()
bundleIdL.sort()
if colorsD is None:
colorsL = r.rainbow(len(bundleIdL))
colorsD = dict(zip(bundleIdL, colorsL))
colorsL = [colorsD[x] for x in bundleIdL]
time_min = min([len(bundleD[x]) for x in bundleD.keys()])
timeVec = [0.5 * x for x in range(time_min)]
try:
r.png(full_filename, width=800, height=600)
oldPar = r.par(xpd = True, mar = [x + y for (x,y) in zip(r.par()['mar'], [0,0,0,6])])
print 'plot %s' % full_filename
r.plot(timeVec, timeVec,
type='n',
main=title, ylim=(0, y_max),
xlab="time in hours after transfection", ylab="Relative Cell Counts",
pch=20, lwd=1, lty = 1,
cex=1.0, cex_lab=1.2, cex_main=1.5)
for bundleId in bundleIdL:
if not bundlePointsD is None:
r.points(timeVec, bundlePointsD[bundleId],
col=colorsD[bundleId], pch=20,
lwd=1)
r.lines(timeVec, bundlePointsD[bundleId],
col=colorsD[bundleId],
lwd=1, lty = 1)
r.lines(timeVec, bundleD[bundleId],
col=colorsD[bundleId],
lwd=3, lty = 1)
r.legend(max(timeVec) * 1.1, y_max, legend=legendL, fill=colorsL, cex=1.0, bg= 'whitesmoke')
r.par(oldPar)
r.grid(col="darkgrey")
r.dev_off()
except:
r.dev_off()
print full_filename + ' has not been printed.'
return
# Make the CanonicalProperties
try:
cp = CanonicalProperties(options.muninn_log_file, options.which)
except CanonicalException, e:
print parser.error(e)
# Store all the plotting data
data = []
# Print which is used
print "Using:", cp.fullname
# Plot the required output
r.pdf(options.output, width=options.width, height=options.height)
r.par(cex=options.cex)
inv_beta = arange(options.inv_beta_min, options.inv_beta_max, 0.01)
beta = 1.0/inv_beta
lnZ = vectorize(cp.lnZ)(beta)
r.plot(inv_beta, lnZ, type='l', xlab=r("expression(beta**-1)"), ylab=r("""expression(paste("ln ", Z(beta)))"""))
data.append((cp.number, "lnZ", (inv_beta, lnZ)))
betaF = vectorize(cp.betaF)(beta)
r.plot(inv_beta, betaF, type='l', xlab=r("expression(beta**-1)"), ylab=r("expression(F(beta) * beta)"))
data.append((cp.number, "betaF", (inv_beta, betaF)))
S = vectorize(cp.S)(beta)
r.plot(inv_beta, S, type='l', xlab=r("expression(beta**-1)"), ylab=r("expression(S(beta) / k[B])"))
data.append((cp.number, "S", (inv_beta, S)))
def plot(outfile, data, out_format='png'):
w = int(round(len(data)/4.0))
if out_format == 'png':
r.png(outfile, width=w*100, height=1000, res=72)
elif out_format == 'pdf':
r.pdf(outfile, width=w, height=10)
else:
raise Exception('Unrecognised format: ' + str(out_format))
print("total: " + str(len(data)))
series = []
points = {'translate': [], 'preprocessing': []}
for dat in data:
points['translate'].append(float(dat['translate']))
points['preprocessing'].append(float(dat['preprocessing']))
xlabels = []
for k, v in data[0].iteritems():
if k not in ["problem", 'translate', 'preprocessing']:
series.append(k)
points[k] = []
index = 0
for dat in data:
for k in series:
if dat[k] != 'no-plan':
points[k].append(float(dat[k]) + \
points['translate'][index] + \
points['preprocessing'][index])
else:
points[k].append(-1000)
xlabels.append(dat['problem'])
index += 1
max_value = max(iter([max(iter(points[k])) for k in series]))
yrange = (0, max_value)
legend_labels = []
x = [i for i in range(1,len(points['translate'])+1)]
y = [-1000 for i in x]
r.par(mar=(7,5,4,2))
r.plot(x, y, main='', xlab="", ylab='',
xaxt='n', yaxt='n', pch=0, ylim=yrange,
mgp=(5,1,0))
r.mtext("Problem", side=1, line=5)
r.mtext("CPU Time (s)", side=2, line=3)
pch_start = 1
pch_index = pch_start
# plotting "translate"
#r.plot(x, points['translate'], main='',
# xlab='', ylab='Time (s)',
# xaxt='n', yaxt='n',
# pch=0, ylim=yrange)
#legend_labels.append('translate')
r.lines(x, points['translate'], lty=1)
# preprocessing -- Removed since it's insignificant
#r.points(x, points['preprocessing'], pch=pch_index)
#pch_index =+ 1
# planner output
for k in series:
if k != 'translate' and k != 'preporcessing':
r.points(x, points[k], pch=pch_index)
pch_index += 1
legend_labels.append("FD+" + k.upper())
# put x-axis labels
for i in range(0, len(xlabels)):
r.axis(side=1, at=i+1, labels=xlabels[i], las=2)
# put y-axis labels
base, step = get_y_step(max_value)
print("base: " + str(base) + " -- step: " + str(step))
y = base
for i in range(0, step):
r.axis(side=2, at=y, labels=str(y), las=2)
y += base
# legend
r.legend(1, max_value, legend_labels, pch=[i for i in range(pch_start, pch_index)])
r.dev_off()
def plot(outfile, data, out_format='png'):
w = int(round(len(data) / 4.0))
if out_format == 'png':
r.png(outfile, width=w * 100, height=1000, res=72)
elif out_format == 'pdf':
r.pdf(outfile, width=w, height=10)
else:
raise Exception('Unrecognised format: ' + str(out_format))
print("total: " + str(len(data)))
series = []
points = {'translate': [], 'preprocessing': []}
for dat in data:
points['translate'].append(float(dat['translate']))
points['preprocessing'].append(float(dat['preprocessing']))
xlabels = []
for k, v in data[0].iteritems():
if k not in ["problem", 'translate', 'preprocessing']:
series.append(k)
points[k] = []
index = 0
for dat in data:
for k in series:
if dat[k] != 'no-plan':
points[k].append(float(dat[k]) + \
points['translate'][index] + \
points['preprocessing'][index])
else:
points[k].append(-1000)
xlabels.append(dat['problem'])
index += 1
max_value = max(iter([max(iter(points[k])) for k in series]))
yrange = (0, max_value)
legend_labels = []
x = [i for i in range(1, len(points['translate']) + 1)]
y = [-1000 for i in x]
r.par(mar=(7, 5, 4, 2))
r.plot(x,
y,
main='',
xlab="",
ylab='',
xaxt='n',
yaxt='n',
pch=0,
ylim=yrange,
mgp=(5, 1, 0))
r.mtext("Problem", side=1, line=5)
r.mtext("CPU Time (s)", side=2, line=3)
pch_start = 1
pch_index = pch_start
# plotting "translate"
#r.plot(x, points['translate'], main='',
# xlab='', ylab='Time (s)',
# xaxt='n', yaxt='n',
# pch=0, ylim=yrange)
#legend_labels.append('translate')
r.lines(x, points['translate'], lty=1)
# preprocessing -- Removed since it's insignificant
#r.points(x, points['preprocessing'], pch=pch_index)
#pch_index =+ 1
# planner output
for k in series:
if k != 'translate' and k != 'preporcessing':
r.points(x, points[k], pch=pch_index)
pch_index += 1
legend_labels.append("FD+" + k.upper())
# put x-axis labels
for i in range(0, len(xlabels)):
r.axis(side=1, at=i + 1, labels=xlabels[i], las=2)
# put y-axis labels
base, step = get_y_step(max_value)
print("base: " + str(base) + " -- step: " + str(step))
y = base
for i in range(0, step):
r.axis(side=2, at=y, labels=str(y), las=2)
y += base
# legend
r.legend(1,
max_value,
legend_labels,
pch=[i for i in range(pch_start, pch_index)])
r.dev_off()