def plot_oxy(ax):
ax.set(title='$O_2$')
imshow_cb(ax, oxy, ld, matplotlib.cm.Reds, (0., oxy.max()), 1.)
contour(ax,
oxy,
ld,
levels=[param_oxy_death, param_oxy_prol],
colors=('r', 'b'),
worldscaling=1.e-3)
def plot_vesselvolume(ax):
data = resultfile.obtain_data('fieldvariable', 'phi_vessels',
groupname, 'imslice')
ax.set(title=ur'$\phi_v$')
imshow_cb(ax, data, ld, matplotlib.cm.coolwarm, (0., 1.), 1.)
contour(ax,
oxy,
ld,
levels=[param_oxy_death],
colors='r',
worldscaling=1.e-3)
def plot_phi_cells(ax):
data = resultfile.obtain_data('fieldvariable', 'phi_viabletumor',
groupname, 'imslice')
ax.set(title=ur'$\phi$')
imshow_cb(ax, data, ld, matplotlib.cm.coolwarm, (0.4, 0.5), 1.)
contour(ax,
oxy,
ld,
levels=[param_oxy_death, param_oxy_prol],
colors=('r', 'b'),
worldscaling=1.e-3)
def plot_sources(ax):
data = resultfile.obtain_data('fieldvariable', 'sources',
groupname, 'imslice')
ax.set(title=ur'$\Gamma_\phi$')
imshow_cb(ax, data, ld, matplotlib.cm.coolwarm, 'zero-centered',
1.e3)
contour(ax,
oxy,
ld,
levels=[param_oxy_death, param_oxy_prol],
colors=('r', 'b'),
worldscaling=1.e-3)
def plotSnapshotMatrix(resultfile_matrix, pdfpages, xlabels=[], ylabels=[]):
nrows, ncols = resultfile_matrix.shape
fig, axes = pyplot.subplots(nrows,
ncols,
sharex=True,
sharey=True,
figsize=(ncols * 3. + 2., nrows * 3 + 2.))
for ax, resultfile in zip(axes.ravel(), resultfile_matrix.ravel()):
ld = resultfile.obtain_data('ld')
data = resultfile.obtain_data('fieldvariable', 'dist_tumor',
resultfile.groupnames[-1], 'imslice')
#imshow_(ax, data, ld, matplotlib.cm.coolwarm, None, 1.)
contour(ax, data, ld, levels=[0.], colors='k')
ax.set(xticklabels=[], yticklabels=[])
for xlabel, ax in zip(xlabels, axes[-1, :]):
ax.set(xlabel=xlabel)
for ylabel, ax in zip(ylabels, axes[:, 0]):
ax.set(ylabel=ylabel)
pdfpages.savefig(fig)
def analyzeO2_with_tumor(f):
dataman = myutils.DataManager(100,
[plotBulkTissue2d.DataTumorTissueSingle()])
resultfile = plotBulkTissue2d.ResultFile(f, dataman)
group = resultfile.groupnames[-1]
ld = resultfile.obtain_data('ld')
print group
arterial_reference = 12.
theta_tumor = resultfile.obtain_data('fieldvariable', 'theta_tumor', group)
o2 = arterial_reference * resultfile.obtain_data('fieldvariable', 'oxy',
group)
dist_tumor = resultfile.obtain_data('fieldvariable', 'dist_tumor', group)
def tumorAndTissueAverages(field):
tumor = field[theta_tumor > 0.5]
normal = field[theta_tumor < 0.5]
tissueavg = (np.average(normal), np.std(normal))
tumoravg = (np.average(tumor), np.std(tumor))
return tissueavg, tumoravg
cons_rate_normal = float(f['parameters'].attrs['o2_cons_coeff_normal'])
cons_rate_tumor = float(f['parameters'].attrs['o2_cons_coeff_tumor'])
consumption = (theta_tumor * cons_rate_tumor +
(1. - theta_tumor) * cons_rate_normal) * o2
solubility = 0.00021 # ml O2 / ml Tissue / kPa
consfactors = 2000. * 60 * solubility * 1.e2 # 2000. from diff koff, 60 from s to min conversion.
consumption *= consfactors
avgStr = lambda t: r'$%s \pm %s$' % tuple(f2l(q) for q in t)
outfn = 'oxycheck-' + splitext(basename(str(f.filename)))[0] + '.pdf'
with mpl_utils.PdfWriter(outfn) as pdfwriter:
fig = pyplot.figure(figsize=(5, 5))
ax = fig.add_axes([0.05, 0.05, 4.2 / 5, 4.2 / 5])
img = plotBulkTissue.imshow(ax,
plotBulkTissue.imslice(o2),
ld,
vmin=0.,
vmax=9.,
cmap=matplotlib.cm.Reds,
worldscaling=1.e-3)
plotBulkTissue.colorbar(ax.get_figure(), ax, img)
plotBulkTissue.contour(ax,
plotBulkTissue.imslice(dist_tumor),
ld,
levels=[0.],
colors=['k'],
worldscaling=1.e-3)
tissueavg, tumoravg = tumorAndTissueAverages(o2)
print 'normal po2: %f +/- %f' % tissueavg
print 'tumor po2: %f +/- %f' % tumoravg
fig.suptitle(r'O2 [kPa] (arterial $P_{O_2} = 12 kPa$)')
fig.text(
0.1, 0.90, '<tissue> ' + avgStr(tissueavg) + '\n' + '<tumor> ' +
avgStr(tumoravg))
pdfwriter.savefig(fig)
fig = pyplot.figure(figsize=(5, 5))
ax = fig.add_axes([0.05, 0.05, 4.2 / 5, 4.2 / 5])
img = plotBulkTissue.imshow(ax,
plotBulkTissue.imslice(consumption),
ld,
vmin=consumption.min(),
vmax=consumption.max(),
cmap=matplotlib.cm.Reds,
worldscaling=1.e-3)
plotBulkTissue.colorbar(ax.get_figure(), ax, img)
plotBulkTissue.contour(ax,
plotBulkTissue.imslice(dist_tumor),
ld,
levels=[0.],
colors=['k'],
worldscaling=1.e-3)
tissueavg, tumoravg = tumorAndTissueAverages(consumption)
print 'mro2 normal o2: %f +/- %f' % tissueavg
print 'mro2 tumor o2: %f +/- %f' % tumoravg
fig.suptitle(r'$MRO_2$ [ml $O_2$ / 100 ml Tissue min]')
fig.text(
0.1, 0.90, '<tissue> ' + avgStr(tissueavg) + '\n' + '<tumor> ' +
avgStr(tumoravg))
pdfwriter.savefig(fig)