def __call__(self, name, v):
avg, std = np.average(v, axis=0), np.std(v, axis=0)
#if exponent is not None:
exponent = Format.exponents[name]
multi = Format.multi[name]
if exponent:
exponent_str = ('\,10^{%i}' % exponent) if exponent <> 0 else ''
f = math.pow(10., exponent)
s = r'%s \pm %s%s' % (f2l(avg / f * multi, exponential=False),
f2l(std / f * multi,
exponential=False), exponent_str)
else:
s = r'%s \pm %s\,' % (f2l(avg * multi), f2l(std * multi))
return s
def GenerateGlobalMeasurementOutputStrings(dataman, po2group, cachelocation):
prop_list = ['gtv', 'jtv','mro2', 'po2_tissue', 'oef', 'e1', 'e3', 'Jin_root', 'Jout_root', 'Jout_tv', 'Jout_cons']
Get = lambda prop: dataman.obtain_data('detailedPO2_global', prop, po2group, sample_length, cachelocation)
data = map(Get, prop_list)
Fmt = lambda (name, val): r'$%s = %s$ %s' % (Prettyfier.get_sym(name), f2l(val), Prettyfier.get_munit_legacy(name))
result_string = map(Fmt, zip(prop_list, data))
return result_string
def fmt_axis_labels(ax, name, scalefactor=None, hidex=True):
ax.set(
ylabel=(r'$%s$ %s' %
(Prettyfier.get_sym(name), Prettyfier.get_bunit(name))) +
((r' $\times\, %s$' %
f2l(scalefactor, exponential=True)) if scalefactor else ''),
title=Prettyfier.get_label(name))
if hidex: ax.set(xticklabels=[])
else:
xsym = r'\phi' if distance_distribution_name == 'levelset' else r'|x|'
ax.set(xlabel=r'$%s$ [$mm$]' % xsym)
def FormatParameters(root):
from quantities import Prettyfier
parametergroup = root['parameters']
parameters = dict(parametergroup.attrs)
parameters = sorted(parameters.items(), key=lambda (k, v): k)
result = ['++++ Vessel Network Creation Parameters ++++']
for k, v in parameters:
k = 'vgp_' + k
try:
name, sym, unit = Prettyfier.get_label(k), Prettyfier.get_msym(
k), Prettyfier.get_munit(k)
except KeyError:
continue
if isinstance(v, float):
v = f2l(v)
else:
v = str(v)
#name = name.ljust(60)
#print len(name)
result.append('%s:\n%s = $%s$ %s' % (name, sym, v, unit))
return result
def fmt(name, sym, value, unit):
value = f2l(value)
if unit:
value = '%s %s' % (mm_(value), mm_(unit))
return '%s %s = %s' % (name, mm_(sym), value)
fn = 'vessel-single-all.h5'
#os.unlink(fn)
f = h5files.open(fn,'a')
GenerateSingleCapillaryWPo2(dataman, f, 'nair_uptake', 14, singleVesselParameterSets.nair_uptake)
plot_single_capillary(dataman, f['nair_uptake'], useInsets = True)
GenerateSingleCapillaryWPo2(dataman, f, 'nair_release', 14, singleVesselParameterSets.nair_release)
plot_single_capillary(dataman, f['nair_release'], useInsets = True)
grouplist = []
for name in [ 'moschandreou_case%02i' % i for i in xrange(6) ]:
params = getattr(singleVesselParameterSets, name)
r = params.paramsTube['r']
label = 'r = %s' % f2l(r)
GenerateSingleCapillaryWPo2(dataman, f, name, 14, params)
grouplist.append((f[name], label))
compare_single_capillary2(dataman, 'moschandreou_cases', grouplist, None)
plot_single_capillary(dataman, f['moschandreou_case02'], useInsets = True)
GenerateSingleCapillaryWPo2(dataman, f, 'moschandreou_extra_long', 14, singleVesselParameterSets.moschandreou_extra_long)
plot_single_capillary_long(dataman, f['moschandreou_extra_long'], useInsets = True)
# GenerateSingleCapillaryWPo2(dataman, f, 'moschandreou_diag_base', 14, singleVesselParameterSets.moschandreou_diag_base)
# GenerateSingleCapillaryWPo2(dataman, f, 'moschandreou_diag' , 14, singleVesselParameterSets.moschandreou_diag)
# compare_single_capillary2(dataman, 'moschandreou_diag', [(f['moschandreou_diag_base'], 'parallel'),
# (f['moschandreou_diag'], 'diag')], None)
# GenerateSingleCapillaryWPo2(dataman, f, 'test_thin', 7, singleVesselParameterSets.basecase)
# GenerateSingleCapillaryWPo2(dataman, f, 'test_straight', 7, singleVesselParameterSets.test_straight)
def fmt_(b):
for i, c in enumerate('xyz'):
yield '%s = %s .. %s mm' % (c, f2l(b[0][i] * 1.e-3, exponential=False),
f2l(b[1][i] * 1.e-3, exponential=False))
def printstuff_(sym, a, mult, unit=''):
text.append('$%s$ = $%s \pm %s$ %s' %
(sym, f2l(np.average(a) * mult, exponential=False),
f2l(np.std(a) * mult, exponential=False), unit))
def PlotRadialCurves(pdfwriter, bins_spec, snapshotlist, measurementinfo,
world_size):
charsize = 12 / 90.
figscale = mpl_utils.a4size[0] * 0.33
fig, axes = pyplot.subplots(2,
2,
dpi=90,
figsize=(figscale * 2, figscale * 1.5))
mpl_utils.subplots_adjust_abs(fig,
left=4 * charsize,
right=-2 * charsize,
top=-3. * charsize,
bottom=5. * charsize,
hspace=8. * charsize,
wspace=12 * charsize)
axes = axes.ravel()
bins = bins_spec.arange() * 1.e-3
FmtTime = lambda time: 't=' + f2l(round(time))
default_colors = 'rgbcmyk'
def plot(ax,
name,
scalefactor=1.,
label=None,
colors=default_colors,
errorbars=True,
zero_ylim=True):
for color, i, (time, tumor_radius,
curves) in itertools.izip(itertools.cycle(colors),
itertools.count(),
snapshotlist):
curve = myutils.MeanValueArray.fromSummation(
map(lambda x: x.avg, curves[name]))
label = FmtTime(time)
mask = ~curve.avg.mask
if errorbars:
mpl_utils.errorbar(ax,
bins[mask],
scalefactor * curve.avg[mask],
yerr=scalefactor * curve.std_mean[mask],
label=label,
marker=None,
color=color,
every=2)
else:
ax.plot(bins[mask],
scalefactor * curve.avg[mask],
label=label,
color=colors[i])
if zero_ylim:
_, ymax = ax.get_ylim()
ax.set_ylim((0., ymax))
if measurementinfo['distancemap_spec'] == 'levelset':
ax.set(xlim=(-2., 2.))
mpl_utils.add_crosshair(ax, (0., None), ls=':')
else:
mpl_utils.add_crosshair(ax, (0.5e-3 * world_size[0], None))
ax.set(xlim=(0, 0.5e-3 * world_size[0]))
for color, i, (time, tumor_radius,
curves) in itertools.izip(itertools.cycle(colors),
itertools.count(),
snapshotlist):
mpl_utils.add_crosshair(ax, (tumor_radius * 1.e-3, None),
ls=':',
color=color)
def fmt_axis_labels(ax, name, scalefactor=None, hidex=True):
ax.set(
ylabel=(r'$%s$ %s' %
(Prettyfier.get_sym(name), Prettyfier.get_bunit(name))) +
((r' $\times\, %s$' %
f2l(scalefactor, exponential=True)) if scalefactor else ''),
title=Prettyfier.get_label(name))
if hidex: ax.set(xticklabels=[])
else:
xsym = r'\phi' if measurementinfo[
'distancemap_spec'] == 'levelset' else r'|x|'
ax.set(xlabel=r'$%s$ [$mm$]' % xsym)
# if measurementinfo.distancemap_spec == 'radial':
# axtwin = axes[0].twinx()
# plot(axtwin, 'theta_tumor', colors = [(0.5,0.5,0.5)]*7, errorbars=False)
plot(axes[0], 'mvd', label=True)
fmt_axis_labels(axes[0], 'mvd')
theLegend = axes[0].legend(loc='lower right', prop={'size': 6})
#theLegend.get_title().set_fontsize('2')
#pyplot.setp(axes[0].get_legend().get_texts(),fontsize='2')
theLegend.__sizes = [1]
plot(axes[1], 'phi_vessels')
fmt_axis_labels(axes[1], 'phi_vessels')
plot(axes[2], 'radius')
fmt_axis_labels(axes[2], 'radius', hidex=False)
plot(axes[3], 'velocity', scalefactor=1.0e-3)
fmt_axis_labels(axes[3], 'velocity_mm', hidex=False)
pdfwriter.savefig(fig, postfix='_o2radial')
