def plot_curv(ax):
data = imslice(
resultfile.obtain_data('field_curvatureradius', groupname))
delta = imslice(
resultfile.obtain_data('field_interfacedelta',
groupname)) * ld.scale
ax.set(title='$\kappa$')
imshow_cb(ax, data * delta, ld, matplotlib.cm.coolwarm,
'zero-centered', 1.)
def obtain_volumes(f, group, dataman):
ld = dataman('ld', f)
dist_viabletumor = dataman('fieldvariable', f, 'dist_viabletumor', group)
dist_tumor = dataman('fieldvariable', f, 'dist_tumor', group)
pyplot.imshow(imslice(dist_viabletumor))
pyplot.colorbar()
pyplot.show()
nsites_total = np.prod(ld.shape)
nsites_tumor = np.sum(np.asarray(dist_tumor < 0., dtype=np.uint))
nsites_viabletumor = np.sum(
np.asarray(dist_viabletumor < 0., dtype=np.uint))
return np.prod(ld.GetWorldSize()), float(
nsites_tumor) / nsites_total, float(nsites_viabletumor) / nsites_total
def obtain_data(self, dataman, dataname, *args):
if dataname == 'ld':
f, = args
ld = krebsutils.read_lattice_data_from_hdf(f['field_ld'])
return ld
####
if dataname == 'time':
if len (args) == 1:
group, = args
else:
group = args[0][args[1]] # f, group = args
return group.attrs['time'] # in hours
#####
if dataname == 'fieldvariable':
f, fieldname, group = args[:3]
g = f[group] # group name to actual group
tumor_path = tumor_path_(f, group)
ld = dataman.obtain_data('ld', f)
def get(name):
return self.obtain_data(dataman, 'fieldvariable', f, name, group)
if fieldname == 'phi_cells':
data = f[tumor_path]['conc']
elif fieldname == 'dist_tumor_':
data = f[tumor_path]['ls']
elif fieldname == 'theta_tumor':
gtumor = f[tumor_path]
if gtumor.attrs['TYPE'] == 'faketumor':
data = gtumor['tc_density']
else:
data = gtumor['ptc']
elif fieldname == 'phi_necro':
data = f[tumor_path]['necro']
elif fieldname in ('oxy'):
try:
data = g['oxy']
except KeyError:
data = g['fieldOxy']
elif fieldname in ('gf'):
data = g['fieldGf']
elif fieldname in ('press', 'sources', 'vel'):
data = f[tumor_path][fieldname]
elif fieldname == 'phi_viabletumor':
theta_tumor = get('theta_tumor')
phi_cells = get('phi_cells')
phi_necro = get('phi_necro')
data = theta_tumor * (phi_cells - phi_necro)
elif fieldname == 'phi_tumor':
theta_tumor = get('theta_tumor')
phi_cells = get('phi_cells')
data = theta_tumor * phi_cells
elif fieldname == 'dist_necro':
phi_necro = get('phi_necro')
phi_cells = get('phi_cells')
tumor_contour_level = 0.5*np.average(phi_cells)
data = calc_distmap(phi_necro, ld, tumor_contour_level)
elif fieldname == 'dist_viabletumor':
def read(gmeasure, dsname):
return np.asarray(gmeasure[dsname])
def write(gmeasure, dsname):
dist_tumor = get('dist_tumor')
dist_necro = get('dist_necro')
data = np.maximum(dist_tumor, -dist_necro)
gmeasure.create_dataset(dsname, data = data, compression = 9)
fm = myutils.MeasurementFile(f, h5files)
data = myutils.hdf_data_caching(read, write, fm, (tumor_path, 'dist_viabletumor'), (0,1))
elif fieldname == 'phi_vessels':
def read(gmeasure, name):
return np.asarray(gmeasure[name])
def write(gmeasure, name):
phi_vessels = CalcPhiVessels(dataman, f[group]['vessels'], ld, scaling = 1.)
gmeasure.create_dataset(name, data = phi_vessels, compression = 9)
fm = myutils.MeasurementFile(f, h5files)
data = myutils.hdf_data_caching(read, write, fm, (group, 'phi_vessels'), (0,1))
elif fieldname == 'dist_tumor':
def read(gmeasure, name):
return np.asarray(gmeasure[name])
def write(gmeasure, name):
ls = get('dist_tumor_')
ls = -ls
dist = calc_distmap(np.asarray(ls < 0., dtype=np.float32), ld, 0.5)
gmeasure.create_dataset(name, data = dist, compression = 9)
fm = myutils.MeasurementFile(f, h5files)
data = myutils.hdf_data_caching(read, write, fm, (tumor_path, 'dist_tumor_full'), (0,1))
else:
raise RuntimeError('unkown field %s' % fieldname)
if len(args)>3 and args[3] == 'imslice':
import plotBulkTissue
return plotBulkTissue.imslice(data)
else:
return np.asarray(data)
if dataname == 'fieldvariable_radial':
property_name, tumorgroup, bins_spec, distance_distribution_name, cachelocation = args
def write(gmeasure, groupname):
distmap, ld = obtain_distmap_(dataman, tumorgroup, distance_distribution_name)
data = dataman.obtain_data('fieldvariable', tumorgroup.file, property_name, tumorgroup.name)
bins = bins_spec.arange()
a = myutils.MeanValueArray.fromHistogram1d(bins, np.ravel(distmap), np.ravel(data))
ds = a.write(gmeasure, groupname)
ds.attrs['BINS_SPEC'] = str(bins_spec)
def read(gmeasure, groupname):
assert groupname == property_name
return myutils.MeanValueArray.read(gmeasure, groupname)
return HdfCacheRadialDistribution((read, write), property_name, bins_spec, distance_distribution_name, cachelocation, 1)
if dataname == 'approximate_tumor_radius':
tumorgroup, = args
def write(gmeasure, groupname):
rad = ApproximateTumorRadius(dataman, tumorgroup)
ds = gmeasure.create_dataset(groupname, data = rad)
def read(gmeasure, groupname):
return np.asscalar(gmeasure[groupname][()])
return myutils.hdf_data_caching(read, write, tumorgroup, ('approximate_tumor_radius',), (1,))
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)