def plot_times(ax, name, **kwargs):
f = kwargs.pop('value_prefactor', 1.)
colors = 'rgbmk'
markers = 'os<>d'
for i, t in enumerate(times):
avg, std = radial[name, t]
avg, std = avg[mask], std[mask]
#ax.errorbar(bins[mask], f*avg, yerr=f*std, label = 't = %s' % f2s(t), **kwargs)
mpl_utils.errorbar(ax,
bins[mask],
f * avg,
yerr=f * std,
label='t = $%s$' % f2s(t),
marker=markers[i],
color=colors[i],
every=5,
**kwargs)
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 plt_rad(ax):
ax.set(ylabel='[mm]', xlabel='t [h]')
mpl_utils.errorbar(ax,
times,
1.e-3 * radius,
yerr=1.e-3 * radius_std,
label='r',
lw=0.,
marker='x',
color='k',
markersize=5.)
label = u'$r_0 + v_{fit} t$\n$v_{fit} = %s$ [\u03BCm/h]' % f2s(
velocity)
ax.plot(times,
1.e-3 * (p[1] + p[0] * times),
label=label,
color='r')
ax.legend()
def plot(ax,
name,
scalefactor=1.,
label=None,
colors=default_colors,
errorbars=True,
zero_ylim=True):
for i, path in enumerate(pathorder):
curve = curves_by_path_and_name[path, name]
label = labels_by_path[path] if label else None
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=colors[i],
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 distance_distribution_name == 'levelset':
ax.set(xlim=(-2., 2.))
mpl_utils.add_crosshair(ax, (0., None), ls=':')
else:
ax.set(xlim=(0., rmax))
for i, path in enumerate(pathorder):
mpl_utils.add_crosshair(
ax,
(curves_by_path_and_name[path,
'approximate_tumor_radius'].avg *
1.e-3, None),
ls=':')
def plot(name, **kwargs):
f = kwargs.pop('value_prefactor', 1.)
avg, std = radial[name]
avg, std = avg[mask], std[mask]
kwargs.update(every=5,
marker=kwargs.pop('marker', 's'),
color=kwargs.pop('color', 'k'))
ret = mpl_utils.errorbar(ax,
bins[mask],
f * avg,
yerr=f * std,
**kwargs)
return ret