def DoIt_single(filenames, options):
fn_measure = basename(commonprefix(filenames))
fn_measure = myutils.strip_from_end(fn_measure, '.h5')
fn_measure = myutils.strip_from_end(fn_measure, '-type')
f_measure = h5files.open('adaption_common.h5', 'a', search = False)
files = [h5files.open(fn, 'r') for fn in filenames]
groups_without_adaption = [f['/adaption/recomputed'] for f in files]
groups_with_adaption = [f['/adaption/vessels_after_adaption'] for f in files]
import analyzeGeneral
dataman = myutils.DataManager(20, [ analyzeGeneral.DataBasicVessel(), analyzeGeneral.DataVesselSamples(), analyzeGeneral.DataVesselGlobal()])
with mpl_utils.PdfWriter(fn_measure+'flow_hist.pdf') as pdfpages:
if 1:
plotFlowHistogram(dataman, f_measure, filenames, options, pdfpages)
with mpl_utils.PdfWriter(fn_measure+'radius_hist.pdf') as pdfpages:
if 1:
plotRadiusHistogram(dataman, f_measure, filenames, options, pdfpages)
with mpl_utils.PdfWriter(fn_measure+'cap_flow_hist.pdf') as pdfpages:
if 1:
plotCapillaryFlowHistogram(dataman, f_measure, filenames, options, pdfpages)
with mpl_utils.PdfWriter(fn_measure+'cap_radius_hist.pdf') as pdfpages:
if 1:
plotCapillaryRadiusHistogram(dataman, f_measure, filenames, options, pdfpages)
def DoIt(filenames, options):
fn_measure = basename(commonprefix(filenames))
fn_measure = myutils.strip_from_end(fn_measure, '.h5')
fn_measure = myutils.strip_from_end(fn_measure, '-type')
f_measure = h5files.open('adaption_common.h5', 'a', search = False)
files = [h5files.open(fn, 'r') for fn in filenames]
groups_without_adaption = [f['/adaption/recomputed'] for f in files]
groups_with_adaption = [f['/adaption/vessels_after_adaption'] for f in files]
with mpl_utils.PdfWriter(fn_measure+'caps.pdf') as pdfpages:
import analyzeGeneral
dataman = myutils.DataManager(20, [ analyzeGeneral.DataBasicVessel(), analyzeGeneral.DataVesselSamples(), analyzeGeneral.DataVesselGlobal()])
# vesselgroups_without = groups_without_adaption
# vesselgroups_with = groups_with_adaption
# geometric_data_before = getGeometricData(groups_without_adaption)
# perfusion_data_before = getTotalPerfusion(groups_without_adaption)*60
# geometric_data_after = getGeometricData(groups_with_adaption)
# perfusion_data_after = getTotalPerfusion(groups_with_adaption)*60
if 1:
plotFlowHistogram(dataman, f_measure, filenames, options, pdfpages)
if 1:
plotRadiusHistogram(dataman, f_measure, filenames, options, pdfpages)
if 1:
plotCapillaryFlowHistogram(dataman, f_measure, filenames, options, pdfpages)
if 1:
plotCapillaryRadiusHistogram(dataman, f_measure, filenames, options, pdfpages)
def plot_single_capillary(dataman, group, useInsets = False):
# used to plot reproduction cases of nair 1989
plt = Plotty(dataman, group, '')
with mpl_utils.PdfWriter('sv-'+os.path.basename(group.name)+'.pdf') as pdfwriter:
fig0, (ax0, ax2) = pyplot.subplots(2,1, figsize = (mpl_utils.a4size[0]*0.35, mpl_utils.a4size[1]*0.30))
fig1, ax1 = pyplot.subplots(1,1, figsize = (mpl_utils.a4size[0]*0.35, mpl_utils.a4size[1]*0.15))
plt.PlotSamples(ax0,'po2', label = 'P', **niceMarker(0, 'k'))
plt.PlotSamples(ax0,'extpo2', label = 'P$_t$', **niceMarker(1, 'k'))
plt.PlotSamples(ax2,'sat', label = None, **niceMarker(2,'k'))
c = niceColors(3)
plt.PlotField(ax1, xcoord = 200, color = c[0], addXMark = True, addYMark = True, marker = 1, label = 'x = 0.2 mm')
plt.PlotField(ax1, xcoord = 2000, color = c[1], addXMark = True, addYMark = True, marker = 1, label = 'x = 1 mm')
plt.PlotField(ax1, xcoord = 3800, color = c[2], addXMark = True, addYMark = True, marker = 1, label = 'x = 3.8 mm')
ax0.legend()
ax1.legend()
ax2.set(xlabel = 'x [mm]', ylabel = 'S')
ax0.set(xlabel = 'x [mm]', ylabel = 'PO$_2$ [mmHg]')
ax1.set(xlabel = 'y$_1$ [$\mu$m]', ylabel = 'PO$_2$ [mmHg]', xlim = (-200., 200.)) #title = 'Tissue $PO_2$ - Transversal' ylim = (40., 100.)
for ax in [ax0, ax1, ax2]:
ax.yaxis.grid(True, which='major', color = '#f0f0f0', ls = '-')
mpl_utils.tight_layout(fig0)
mpl_utils.tight_layout(fig1)
pdfwriter.savefig(fig0, postfix='_long')
pdfwriter.savefig(fig1, postfix='_trans')
plt.AddStatsPage(pdfwriter)
pyplot.close('all')
def plot_single_capillary_long(dataman, group, useInsets = False):
plt = Plotty(dataman, group, '')
with mpl_utils.PdfWriter('sv-'+os.path.basename(group.name)+'.pdf') as pdfwriter:
fig0, ax0 = pyplot.subplots(1,1, figsize = (mpl_utils.a4size[0]*0.35, mpl_utils.a4size[1]*0.15))
fig1, ax1 = pyplot.subplots(1,1, figsize = (mpl_utils.a4size[0]*0.35, mpl_utils.a4size[1]*0.15))
plt.PlotSamples(ax0,'po2', label = 'P', **niceMarker(0, 'k'))
plt.PlotSamples(ax0,'extpo2', label = 'P$_t$', **niceMarker(1, 'k'))
c = niceColors(4)
plt.PlotField(ax1, xcoord = 100, color = c[0], addXMark = True, addYMark = True, marker = 1, label = 'x = 0.1 mm')
plt.PlotField(ax1, xcoord = 1000, color = c[1], addXMark = True, addYMark = True, marker = 1, label = 'x = 1 mm')
plt.PlotField(ax1, xcoord = 4000, color = c[2], addXMark = True, addYMark = True, marker = 1, label = 'x = 4 mm')
plt.PlotField(ax1, xcoord = 48000, color = c[3], addXMark = True, addYMark = True, marker = 1, label = 'x = 48 mm')
ax0.legend()
ax1.legend()
#plt.StyleSamplePlot(ax0, title = '$PO_2$ Longitudinal', yquantity = 'po2')
#plt.StyleTransversalPlot(ax1, title = 'Tissue $PO_2$ - Transversal')
ax0.set(xlabel = 'x [mm]', ylabel = 'PO$_2$ [mmHg]')
ax1.set(xlabel = 'y$_1$ [$\mu$m]', ylabel = 'PO$_2$ [mmHg]', ylim = (40., 100.), xlim = (-200., 200.)) #title = 'Tissue $PO_2$ - Transversal'
for ax in [ax0, ax1]:
ax.yaxis.grid(True, which='major', color = '#f0f0f0', ls = '-')
mpl_utils.tight_layout(fig0)
mpl_utils.tight_layout(fig1)
pdfwriter.savefig(fig0, postfix='_long')
pdfwriter.savefig(fig1, postfix='_trans')
plt.AddStatsPage(pdfwriter)
pyplot.close('all')
def compare_single_capillary3(dataman, outfilename, grouplist, axisticks):
plotties = []
for group, label in grouplist:
plotties.append(Plotty(dataman, group, label))
colors = niceColors(len(grouplist))
with mpl_utils.PdfWriter('comp-' + outfilename + '.pdf') as pdfwriter:
fig, axes = pyplot.subplots(3,
1,
figsize=(mpl_utils.a4size[0] * 0.4,
mpl_utils.a4size[1] * 0.5))
axes = axes.ravel()
for i, plt in enumerate(plotties):
plt.PlotSamples(axes[0], 'extpo2', colors[i], 0, None)
plt.PlotSamples(axes[0], 'po2', colors[i], 1, plt.label)
axes[0].legend(loc=mpl_utils.loc.lower_left)
axes[0].set(xlabel='x [$\mu$m]',
ylabel='mmHg',
title='PO$_2$ - Longitudinal')
axes[0].set(ylim=(50., 100.))
#axes[1].set(xlabel = 'x [$\mu m$]', ylabel = Prettyfier.get_bunit('sat'), title = Prettyfier.get_msym('sat')+' - Longitudinal')
for i, plt in enumerate(plotties):
plt.PlotFieldLongitudinal(axes[1], color=colors[i])
plt.PlotSamples(axes[1], 'po2', colors[i], 1, None)
axes[1].legend(loc=mpl_utils.loc.lower_left)
axes[1].set(xlabel='x [$\mu$m]',
ylabel='mmHg',
title='PO$_2$ - Longitudinal')
for i, plt in enumerate(plotties):
plt.PlotField(axes[2],
sampleIndex=plt.numSamples / 2,
color=colors[i],
addXMark=True,
addYMark=True,
marker=True) #xcoord = 500
#axins.xaxis.set_major_locator(matplotlib.ticker.MaxNLocator(nbins = 3))
#axins.yaxis.set_major_locator(matplotlib.ticker.MaxNLocator(nbins = 3))
axes[2].set(title=r'P$_t$ Transv.')
axes[2].tick_params(axis='both', which='major', labelsize=8)
axes[2].set(xlabel='y$_1$ [$\mu$m]',
ylabel='mmHg',
title='Tissue PO$_2$ - Transversal',
ylim=(0., 100.))
for ax in axes:
ax.yaxis.grid(True, which='major', color='#f0f0f0', ls='-')
pyplot.tight_layout()
pdfwriter.savefig(fig, postfix='_curves')
plotAnalyzeConvergence(dataman, pdfwriter, plotties)
plotties[0].AddStatsPage(pdfwriter)
plotAnalyzeIterativeConvergence(dataman, pdfwriter, plotties)
pyplot.close('all')
def plotit(fn):
out_fn = os.path.splitext(fn)[0]
fd = Filedata1d(fn)
if 1:
data = [(g.attrs['time'], g.attrs['tumor_radius'])
for g in [fd.f[gn] for gn in fd.groupnames]]
if len(fd.groupnames) >= 2:
maxv = max(
np.amax(np.abs(fd.f[gn]['vel_0'])) for gn in fd.groupnames[2:])
data = np.asarray(data).transpose()
from scipy.optimize import leastsq
func = lambda p, x, y: (x * p[0] + p[1] - y)
p, success = leastsq(func, (1, 0), args=(data[0], data[1]))
estv = p[0]
print 'estimated velocity: %f, max vel.: %f' % (estv, maxv)
else:
estv, maxv = 0, 0
if 0:
rc = matplotlib.rc
rc('figure', figsize=(8.3, 11.7))
rc('font', size=13)
#rc('axes', titlesize = 12., labelsize = 12.)
rc('legend', fontsize='small')
else:
dpi = 80
rc = matplotlib.rc
rc('figure',
figsize=list(0.75 / dpi * np.asarray([1024, 768])),
dpi=dpi)
rc('font', size=12.)
rc('axes') #, titlesize = 12., labelsize = 12.)
rc('pdf', compression=6, fonttype=42)
rc(
'figure', **{
'subplot.left': 0.04,
'subplot.right': 1. - 0.04,
'subplot.bottom': 0.06,
'subplot.top': 1. - 0.06,
'subplot.wspace': 0.1,
'subplot.hspace': 0.1
})
rc('savefig', facecolor='none', edgecolor='none', dpi=dpi)
rc('font', **{'family': 'sans-serif'}) #,'sans-serif':['Helvetica']})
rc('path', simplify_threshold=0.01)
with mpl_utils.PdfWriter(out_fn) as pdf:
for g in fd.groupnames:
print fn, g
#title = r'%s ($v_e = %s, v_{max} = %s$)' % (splitext(basename(fn))[0], myutils.f2s(estv), myutils.f2s(maxv))
title = ''
fig = simplePlot(fd, g, title)
pdf.savefig(fig)
plt.close()
def compare_single_capillary2(dataman, outfilename, grouplist, axisticks):
plotties = []
for group, label in grouplist:
plotties.append(Plotty(dataman, group, label))
colors = niceColors(len(grouplist))
with mpl_utils.PdfWriter('comp-'+outfilename+'.pdf') as pdfwriter:
fig, axes = pyplot.subplots(3,1, figsize = (mpl_utils.a4size[0]*0.35, mpl_utils.a4size[1]*0.45))
axes = axes.ravel()
for i, plt in enumerate(plotties):
plt.PlotSamples(axes[0], 'po2', label = plt.label, **niceMarker(0, colors[i]))
plt.PlotSamples(axes[1], 'extpo2', **niceMarker(1, colors[i]))
plt.PlotSamples(axes[2], 'sat', label = plt.label, **niceMarker(2, colors[i]))
axes[0].legend(loc = mpl_utils.loc.lower_left)
axes[0].set(ylabel = r'P [mmHg]')
axes[1].set(ylabel = r'P$_t$ [mmHg]')
axes[2].set(ylabel = r'S')
axes[2].set(ylim = (0.80, 1.), xlabel = '$x$ [mm]')
for ax in axes[0:2]:
ax.set(ylim = (50., 100.))
#ax.xaxis.set(visible=False)
ax.set(xticklabels = [])
for ax in axes:
ax.yaxis.grid(True, which='major', color = '#f0f0f0', ls = '-')
pyplot.tight_layout()
pdfwriter.savefig(fig, postfix='_po2')
fig, ax = pyplot.subplots(1,1, figsize = (mpl_utils.a4size[0]*0.35, mpl_utils.a4size[1]*0.15))
for i, plt in enumerate(plotties):
plt.PlotField(ax, sampleIndex = plt.numSamples/2, color = colors[i], addXMark = False, addYMark = True, marker = True) #xcoord = 500
#ax.set(title = r'$P_t$ Transv.')
#ax.tick_params(axis='both', which='major', labelsize=8)
ax.set(xlabel = 'y$_1$ [$\mu$ m]', ylabel = 'PO$_2$ [mmHg]', ylim = (40., 100.), xlim = (-200., 200.)) #title = 'Tissue $PO_2$ - Transversal'
ax.yaxis.grid(True, which='major', color = '#f0f0f0', ls = '-')
pyplot.tight_layout()
pdfwriter.savefig(fig, postfix='_transv')
# axins = mpl_utils.inset_axes(axes[0],
# width="60%", # width = 30% of parent_bbox
# height="30%", # height : 1 inch
# loc=mpl_utils.loc.upper_right)
# axins.xaxis.set_major_locator(matplotlib.ticker.MaxNLocator(nbins = 3))
# axins.yaxis.set_major_locator(matplotlib.ticker.MaxNLocator(nbins = 3))
# axins.set(title = r'$P_t$ Transv.')
# axins.tick_params(axis='both', which='major', labelsize=8)
plotties[0].AddStatsPage(pdfwriter)
plotAnalyzeIterativeConvergence(dataman, pdfwriter, plotties)
pyplot.close('all')
def plotSingleRun(fn):
out = splitext(basename(fn))[0]
f = h5files.open(fn, 'r')
rc = matplotlib.rc
rc('figure', figsize=(7, 7), dpi=100)
rc('font', size=8.)
rc('axes', titlesize=10., labelsize=10.)
rc('pdf', compression=6, fonttype=42)
rc(
'figure', **{
'subplot.left': 0.02,
'subplot.right': 1. - 0.05,
'subplot.bottom': 0.01,
'subplot.top': 1. - 0.05,
'subplot.wspace': 0.1,
'subplot.hspace': 0.1
})
#rc('savefig', facecolor='none', edgecolor='none', dpi=100)
rc('savefig', dpi=100)
rc('font', **{'family': 'sans-serif'}) #,'sans-serif':['Helvetica']})
rc('path', simplify_threshold=0.01)
#rc('text', usetex=True, **{ 'latex.unicode' : True })
with mpl_utils.PdfWriter(out + ".pdf") as pdfpages:
dataman = myutils.DataManager(100, [
DataTumorTissueSingle(),
DataTumorMeasureCurvature(),
DataTumor3dRendering(),
DataTissueRadial(),
DataTissueRadialAveraged()
])
resultfile = ResultFile(f, dataman)
is3d = resultfile.obtain_data('ld').shape[2] > 1
if is3d:
plot3dRendering(resultfile,
pdfpages,
showVessels=True,
showTumor=False)
plot3dRendering(resultfile, pdfpages)
plot3dRendering(resultfile, pdfpages, showVessels=True)
plotSnapshots(resultfile, pdfpages)
def run_numerical_tests():
convergence_step_widths = [ 0.01, 0.02, 0.04, 0.08, 0.16, 0.32, 0.64, 1. ]
stepper_methods = [ 'euler', 'impeuler', 'vsimexbdf2', 'rk3', 'rk4' ]
gridsizes = [ 30, 5, 1 ]
def run_different_steppers(name, cc):
for stepper in stepper_methods:
c = deepcopy(cc)
c['stepper'] = stepper
if stepper == 'vsimexbdf2':
c['tumor']['timestep_factor'] = 0.2
run(name+'-'+stepper, c)
def run_3d(name, cc, size, stepper):
c = deepcopy(cc)
c['tumor']['timestep_factor'] = 0.1 if (stepper == 'vsimexbdf2') else 1.
c['stepper'] = stepper
c['lattice_size'] = Vec((size,)*3)
c['tumor']['interface_width'] = 30.
c['tend'] = 20
c['out_intervall'] = 20
c['tumor']['write_face_velocities'] = False
run(name+"-s%03i-%s" % (size, stepper), c)
def run_3d_performance_tests():
for l in [ 40, 70, 100, 120, 150 ]:
run_3d('prez3d', cfg, l, 'vsimexbdf2')
run_3d('prez3d', cfg, l, 'impeuler')
def run_lattice_and_stepwidth(name, cc, s, h, stepper):
c = deepcopy(cc)
c['tend'] = 1000.
c['lattice_size'] = Vec((int(100*30/s), 1, 1))
c['lattice_scale'] = s
c['stepper'] = stepper
c['tumor']['timestep_factor'] = h
c['tumor']['elliptic_solver_params'] = { 'use_multigrid' : True }
run(name+"-conv-s%02i-h%0.3f-%s" % (s,h,stepper), c)
def run_convergence_test(name, cc):
cc = deepcopy(cc)
cc['o2_rel_tumor_source_density'] = 1.
name = name+'-maxo2'
for lw in [ 5, 10, 15, 30, 60 ]:
run_lattice_and_stepwidth(name, cc, lw, 1, 'impeuler')
run_lattice_and_stepwidth(name, cc, 5, 1, 'rk4')
# cc['tumor']['interface_width'] = 60.
# for lw in [ 5, 10, 15, 30, 60 ]:
# run_lattice_and_stepwidth(name+'-reg30', cc, lw, 1, 'impeuler')
# run_lattice_and_stepwidth(name+'-reg30', cc, 5, 1, 'rk4')
#
# cc['tumor']['interface_width'] = 120.
# for lw in [ 5, 10, 15, 30, 60 ]:
# run_lattice_and_stepwidth(name+'-reg60', cc, lw, 1, 'impeuler')
# run_lattice_and_stepwidth(name+'-reg60', cc, 5, 1, 'rk4')
if 0:
# test steppers
#run_different_steppers('prez1d', cfg)
#run_3d_performance_tests()
# check how the solution depends on the lattice spacing and time step length
run_convergence_test('prez1d', cfg)
if 1:
rc = matplotlib.rc
rc('font', size = 12)
rc('axes', titlesize = 12., labelsize = 12.)
rc('legend', fontsize = 9)
plt.subplots_adjust(wspace=0.5)
if 0: # plot individuals
for fn in ['prez1d-%s.h5' % n for n in stepper_methods]:
print 'plotting', fn
pdf = mpl_utils.PdfWriter(splitext(fn)[0] + ".pdf")
fd = Filedata1d(fn)
for g in fd.groupnames:
simplePlot(pdf, fd, g, fd.f['parameters'].attrs['stepper'])
pdf.close()
if 0: # plot comparison of different methods
pdf = mpl_utils.PdfWriter("compare1.pdf")
fdnames = ['euler', 'vsimexbdf2', 'rk3']
fds = [ Filedata1d('prez1d-%s.h5' % n) for n in fdnames ]
for g in fds[0].groupnames[-1:]:
print 'plotting', g
comparePlot(pdf, fds, fdnames, g, ['ptc', 'ls', 'vel_0', 'press'], 'Methods')
pdf.close()
if 1: # plot comparison of different time steps
pdf = mpl_utils.PdfWriter("compare3.pdf")
fd_ref = Filedata1d('prez1d-maxo2-conv-s05-h1.000-rk4.h5')
grp_name = fd_ref.groupnames[-1]
def compare(name, title, configs):
title_format = r'$l$=%i, $h=%.1f h_{euler}$, %s'
fds, fdtitles = [], []
for s, h, m in configs:
fn ="%s-conv-s%02i-h%0.3f-%s.h5" % (name, s,h,m)
print 'plotting', fn
fds.append(Filedata1d(fn))
fdtitles.append(title_format % (s,h,m))
iwidth = float(fds[0].f['parameters']['tumor'].attrs.get('interface_width',-1))
iwidth = r"$4 \cdot l$" if iwidth < 0 else (r"$%.1f \mu m$" % iwidth)
fig = comparePlot(fds, fdtitles, grp_name, ['ptc', 'ls', 'vel_0', 'press', 'conc'], 'Stepwidths'+title+(' $\delta=$%s ' % iwidth))
pdf.savefig(fig)
# compare('prez1d', '', [
# (1,1,'vsimexbdf2'),
# (5,1,'vsimexbdf2'),
# (30, 1, 'vsimexbdf2')
# ])
#
# compare('prez1d', '', [
# (1,1.,'vsimexbdf2'),
# (10,1.,'rk4'),
# (30,1., 'rk4')])
stepper = 'impeuler'
stepw = 1.
compare('prez1d-maxo2', ' $max_{o2}$ ', [
(5,stepw,stepper),
(10,stepw,stepper),
(15,stepw,stepper),
(30,stepw,stepper),
(60,stepw,stepper),
(5,1,'rk4'),
])
compare('prez1d-maxo2-reg30', r' $max_{o2}$ ', [
(5,stepw,stepper),
(10,stepw,stepper),
(15,stepw,stepper),
(30,stepw,stepper),
(60,stepw,stepper),
(5,1,'rk4'),
])
compare('prez1d-maxo2-reg60', r' $max_{o2}$ ', [
(5,stepw,stepper),
(10,stepw,stepper),
(15,stepw,stepper),
(30,stepw,stepper),
(60,stepw,stepper),
(5,1,'rk4'),
])
pdf.close()
if 0: # memory and runtime
pdf = mpl_utils.PdfWriter("performance1.pdf")
data = []
for fn in glob.glob('prez3d-*.h5'):
print fn
fd = Filedata1d(fn)
g = fd.f[fd.groupnames[-1]]
s = Struct(dict(
(k, g.attrs[k]) for k in ['mem_vsize', 'mem_rss', 'real_time', 'time'],
))
s['stepper'] = fd.f.attrs['stepper']
s['size'] = fd.f['field_ld'].attrs['SIZEX']
data.append(s)
data2 = collections.defaultdict(list)
for d in data:
data2[(d['stepper'])].append(d)
for k, l in data2.iteritems():
l.sort(key = lambda d: d.size)
l = myutils.zipListOfDicts(l, numpy_output=True)
l['mem_rss'] /= (1024.**2)
l['mem_vsize'] /= (1024.**2)
l['real_time'] /= 1000.
l['dofs'] = l['size']**3
data2[k] = l
fig = plt.figure()
plt.xlabel('lateral size')
plt.ylabel('mem [Mb]')
for stepper, l in data2.iteritems():
plt.plot(l['size'], l['mem_vsize'], label='%s, rss' % (stepper))
plt.plot(l['size'], l['mem_rss'], label='%s, vsize' % (stepper))
plt.legend()
pdf.savefig(fig)
fig = plt.figure()
plt.xlabel('lateral size')
plt.ylabel('mem / sites [bytes]')
for stepper, l in data2.iteritems():
plt.plot(l['size'], l['mem_vsize']*(1024**2)/l['dofs'], label='%s, rss' % (stepper))
plt.plot(l['size'], l['mem_rss']*(1024**2)/l['dofs'], label='%s, vsize' % (stepper))
plt.legend()
pdf.savefig(fig)
fig = plt.figure()
plt.xlabel('lateral size')
plt.ylabel('run time / sim time [s]')
for stepper, l in data2.iteritems():
plt.plot(l['size'], l['real_time']/l['time'], label='%s' % (stepper))
plt.legend()
pdf.savefig(fig)
fig = plt.figure()
plt.xlabel('lateral size')
plt.ylabel('run time / (sim time $\cdot$ sites) [$\mu$s]')
for stepper, l in data2.iteritems():
plt.plot(l['size'], l['real_time']*(1000**2)/l['dofs']/l['time'], label='%s' % (stepper))
plt.legend()
pdf.savefig(fig)
pdf.close()
def DoIt(filenames, options):
fn_measure = basename(commonprefix(filenames))
fn_measure = myutils.strip_from_end(fn_measure, '.h5')
fn_measure = myutils.strip_from_end(fn_measure, '-type')
f_measure = h5files.open('adaption_common.h5', 'a', search = False)
files = [h5files.open(fn, 'r') for fn in filenames]
groups_with_adaption = [f['/vessels_after_adaption'] for f in files]
#this data is stored with the adaption
files_without_adaption = []
for afile in files:
completeFilename=str(afile['/vessels_after_adaption/parameters'].attrs['cwd'])+'/'+str(afile['/vessels_after_adaption/parameters'].attrs['vesselFileName'])
files_without_adaption.append(
h5files.open(completeFilename))
groups_without_adaption = [f['vessels'] for f in files_without_adaption]
with mpl_utils.PdfWriter('adaption_' + fn_measure+'.pdf') as pdfpages:
import analyzeGeneral
dataman = myutils.DataManager(20, [ analyzeGeneral.DataBasicVessel(), analyzeGeneral.DataVesselSamples(), analyzeGeneral.DataVesselGlobal()])
# vesselgroups_without = groups_without_adaption
# vesselgroups_with = groups_with_adaption
geometric_data_before = getGeometricData(groups_without_adaption)
perfusion_data_before = getTotalPerfusion(groups_without_adaption)*60
geometric_data_after = getGeometricData(groups_with_adaption)
perfusion_data_after = getTotalPerfusion(groups_with_adaption)*60
if 1:
res_without = getMultiScatter(300. * len(filenames), groups_without_adaption)
plotMultiScatterBeauty(res_without, pdfpages)
res_with = getMultiScatter(300. * len(filenames), groups_with_adaption)
plotMultiScatterBeauty(res_with, pdfpages, withRadiusPressureRelation=False)
# if 0:
# PlotRadiusHistogram2(dataman, groups_without_adaption, pdfpages)
# PlotRadiusHistogram2(dataman, groups_with_adaption, pdfpages)
#
if 0:
#printBarPlot_rBV(filenames, pdfpages) -->alt
printBarPlot_rBV(dataman, f_measure, filenames, options, pdfpages) #-->mw enginered
if 0:
printBarPlot_rBF(dataman, f_measure, filenames, options, pdfpages)
if 0:
PlotRadiusHistogram_with_cache_by_RC(dataman, f_measure, filenames, options, pdfpages)
#PlotRadiusHistogram_with_cache(dataman, f_measure, filenames, options, pdfpages)
if 0:
printBarPlot_vesseltype_on_root_node_configuration(filenames, pdfpages)
## is it worth to update this ???
if 1:
#importing the murray thing
#DoGetMurray(filenames, pdfpages)
printMurray(dataman, f_measure, filenames, options, pdfpages)
if 1:
printMurray_alphas_effective(dataman, f_measure, filenames, options, pdfpages)
if 1:
text = ["Before adaption"]
text2 = FormatGeometricAndPerfusionData(geometric_data_before, perfusion_data_before)
text = text+text2
def cachelocation(g):
path = posixpath.join('FileCS_'+myutils.checksum(basename(g.file.filename)), g.name.strip(posixpath.sep))
return (f_measure, path)
prop_list2 = ['shearforce', 'velocity']
for name in prop_list2:
data = []
for gvessels in groups_without_adaption:
data.append(dataman.obtain_data('basic_vessel_global', name, gvessels, cachelocation(gvessels)))
text.append(r'$<%s>$ = $%s$%s' %
(Prettyfier.get_sym(name), Format(name, data), Prettyfier.get_munit(name)))
fig, _ = mpl_utils.MakeTextPage(text,figsize = (mpl_utils.a4size[0]*0.8, mpl_utils.a4size[0]*0.8))
pdfpages.savefig(fig, postfix='_vesselsglobal')
text = FormatParameters(groups_without_adaption[0].file)
fig, _ = mpl_utils.MakeTextPage(text,figsize = (mpl_utils.a4size[0]*0.8, mpl_utils.a4size[0]*0.8))
pdfpages.savefig(fig, postfix='_vesselsparams')
text = ["After adaption"]
text2 = FormatGeometricAndPerfusionData(geometric_data_after, perfusion_data_after)
text = text+text2
def cachelocation(g):
path = posixpath.join('FileCS_'+myutils.checksum(basename(g.file.filename)), g.name.strip(posixpath.sep))
return (f_measure, path)
prop_list2 = ['shearforce', 'velocity']
for name in prop_list2:
data = []
for gvessels in groups_with_adaption:
data.append(dataman.obtain_data('basic_vessel_global', name, gvessels, cachelocation(gvessels)))
text.append(r'$<%s>$ = $%s$%s' %
(Prettyfier.get_sym(name), Format(name, data), Prettyfier.get_munit(name)))
fig, _ = mpl_utils.MakeTextPage(text,figsize = (mpl_utils.a4size[0]*0.8, mpl_utils.a4size[0]*0.8))
pdfpages.savefig(fig, postfix='_vesselsglobal')
text = FormatParameters(groups_without_adaption[0].file)
fig, _ = mpl_utils.MakeTextPage(text,figsize = (mpl_utils.a4size[0]*0.8, mpl_utils.a4size[0]*0.8))
pdfpages.savefig(fig, postfix='_vesselsparams')
if 0 and all(map(lambda g: 'data' in g.parent, groups_without_adaption)):
data = VesselData()
for g in groups_without_adaption:
data.add(g.parent['data'])
plot_topological_stats_avg(data, pdfpages)
if 1:
#PlotQdevs_unnormalized(filenames, pdfpages)
PlotQdevs_normalized(filenames, options, pdfpages)
def DoIt(filenames, pattern, with_o2):
fn_measure = basename(commonprefix(filenames))
fn_measure = myutils.strip_from_end(fn_measure, '.h5')
fn_measure = myutils.strip_from_end(fn_measure, '-type')
def cachelocation(g):
path = posixpath.join(
'FileCS_' + myutils.checksum(basename(g.file.filename)),
g.name.strip(posixpath.sep))
return (f_measure, path)
if with_o2:
fn_measure = myutils.strip_from_end(fn_measure, '_detailedpo2')
files = [h5files.open(fn, 'a') for fn in filenames]
f_measure = h5files.open('plotVessels_chache.h5', 'a', search=False)
groups = list(
itertools.chain.from_iterable(
myutils.walkh5(f, pattern, return_h5objects=True) for f in files))
if len(groups) <= 0:
print 'no matching groups in hdf file(s)'
sys.exit(0)
if with_o2:
name = posixpath.commonprefix(map(lambda g: g.name, groups))
name = myutils.strip_from_start(name, '/po2/vessels').replace('/', '-')
fn_measure += name
with mpl_utils.PdfWriter(fn_measure + '.pdf') as pdfpages:
rc = matplotlib.rc
rc('font', size=8.)
rc('axes', titlesize=10., labelsize=8.)
if with_o2:
import detailedo2Analysis as o2analysis
import detailedo2Analysis.plotsForPaper
import detailedo2
dataman = myutils.DataManager(20, [
o2analysis.DataDetailedPO2(),
analyzeGeneral.DataTumorTissueSingle(),
analyzeGeneral.DataDistanceFromCenter(),
analyzeGeneral.DataBasicVessel(),
analyzeGeneral.DataVesselSamples(),
analyzeBloodFlow.DataTumorBloodFlow(),
analyzeGeneral.DataVesselRadial(),
analyzeGeneral.DataVesselGlobal()
])
vesselgroups = list(
detailedo2.OpenVesselAndTumorGroups(g)[0] for g in groups)
#original_vesselgroups = list(h5files.openLink(g, 'SOURCE') for g in vesselgroups)
if 1:
PrintGlobalDataWithOxygen(pdfpages, groups, vesselgroups,
f_measure, dataman)
'''FormatParameters makes the network creation parameters
that does not work, if we have an o2 file'''
#text = FormatParameters(original_vesselgroups[0].file)
text = [' ']
text += detailedo2Analysis.plotsForPaper.FormatParameters(
groups[0])
fig, _ = mpl_utils.MakeTextPage(text,
figsize=(mpl_utils.a4size[0],
mpl_utils.a4size[0]))
pdfpages.savefig(fig, postfix='_vesselsparams')
if 1:
res = getMultiScatter(300. * len(filenames), vesselgroups)
plotMultiScatterBeauty(res, pdfpages)
else:
dataman = myutils.DataManager(20, [
analyzeGeneral.DataTumorTissueSingle(),
analyzeGeneral.DataVesselRadial(),
analyzeGeneral.DataDistanceFromCenter(),
analyzeBloodFlow.DataTumorBloodFlow(),
analyzeGeneral.DataBasicVessel(),
analyzeGeneral.DataVesselSamples(),
analyzeGeneral.DataVesselGlobal()
])
#dataman = myutils.DataManager(20, [ analyzeGeneral.DataBasicVessel(), analyzeGeneral.DataVesselSamples(), analyzeGeneral.DataVesselGlobal()])
vesselgroups = groups
if 0:
res = getMultiScatter(300. * len(filenames), vesselgroups)
plotMultiScatterBeauty(res, pdfpages)
if 0:
PlotRadiusHistogram2(dataman, vesselgroups, pdfpages)
if 0 and all(map(lambda g: 'data' in g.parent, vesselgroups)):
data = VesselData()
for g in vesselgroups:
data.add(g.parent['data'])
plot_topological_stats_avg(data, pdfpages)
if 0: #reproduce swine
plot_geometric_stuff_on_RC(dataman, f_measure, filenames,
options, pdfpages)
if 1:
PrintGlobalData(pdfpages, vesselgroups, f_measure, dataman)
tumorVolumes.append(tumorVolumes_per_time)
fig1 = plt.figure()
ax1 = fig1.add_subplot(111)
# ax1.scatter(timepoints,np.mean(hypoxicVolumes,0),color='r',label=r"hypoxic($PO_2$<%.1f mmHg)" %threshold)
# ax1.scatter(timepoints,np.mean(tumorVolumes,0),color='b',label=r"tumor radius")
ax1.errorbar(timepoints,np.mean(hypoxicVolumes,0),np.std(hypoxicVolumes,0),color='r',label=r"hypoxic($PO_2$<%.1f mmHg)" %threshold)
ax1.scatter(timepoints,np.mean(tumorVolumes,0),color='b',label=r"tumor radius")
ax1.set_xlabel('time/ s')
ax1.set_ylabel(r"volume/$mm^3$")
#ax1.set_ylabel(r"hypoxic volume ($PO_2$<%.1f mmHg)/$mm^3$" %threshold)
ax1.legend(loc=2)
common = os.path.commonprefix([afile.name for afile in goodArguments.oxygenFiles])
ax1.set_title('file: %s' % common)
with mpl_utils.PdfWriter('hypoxic_%s.pdf' % common) as pdfpages:
pdfpages.savefig(fig1, postfix='_vesselsglobal')
def run_out_in_single_file(goodArguments):
print('starting with arguments: %s' % goodArguments)
no_files = len(goodArguments.oxygenFiles)
annoxicVolumes=[]
hypoxicVolumes=[]
normoxicVolumes=[]
tumorVolumes=[]
#threshold = 15
threshold1 = 0.1
threshold2 = 2.5
test = myutils.MeanValueArray.empty()
raise AssertionError('pattern "%s" not found in "%s"!' %
(grp_pattern, fn))
else:
dirs = set.union(dirs, d)
except Exception, e:
print e.message
sys.exit(-1)
print('Resolved groups: %s' % ','.join(dirs))
#create filename due to former standards
filenames = []
for fn in goodArguments.vesselFileNames:
filenames.append(fn.name)
#either specify group, like in tumor simuations or take all from the ensemble
if 0:
for afilename in filenames:
print("got file: %s" % afilename)
if not filenames:
print("python2 got no filenames")
with mpl_utils.PdfWriter('murray' + '.pdf') as pdfpages:
DoGetMurray(filenames, pdfpages)
else:
pattern = goodArguments.grp_pattern
print(pattern)
print(filenames[0])
outfilename = 'murray_for_file_%s' % basename(filenames[0])
with mpl_utils.PdfWriter(outfilename + '.pdf') as pdfpages:
DoGetMurrayForSingleFileGENERAL(filenames[0], pattern, pdfpages)
DoGetMurrayForSingleFileGENERALtwoSingleFigs(
filenames[0], pattern, pdfpages)
'r',
linewidth=1.,
label=(r'$\propto \epsilon ^ {- %s \pm %s }$' %
(myutils.f2s(avg_df, latex=True),
myutils.f2s(std_df, latex=True,
prec=1)))) #label = df_formula(avg_df))
ax.plot(dflist[0].bs,
dflist[0].getY((-avg_df + std_df, avg_y0)),
'r',
linewidth=1.)
ax.set(xscale='log',
yscale='log',
xlabel=r'boxsize $\epsilon$',
ylabel=r'boxcount',
xlim=(10., 10000.))
ax.grid(linestyle=':', linewidth=0.5, color=(0.7, 0.7, 0.7))
ax.legend(loc=3)
# dslee gets 2.52 +/- 0.05
print 'df = %f +/- %f' % (avg_df, std_df)
pyplot.show()
fig.subplots_adjust(left=0.3, right=1. - 0.3, top=1. - 0.3, bottom=0.3)
commonpath = os.path.commonprefix(goodArguments.files)
with mpl_utils.PdfWriter(commonpath + '.pdf') as pdfpages:
pdfpages.savefig(fig, )
(f[g] for g in groupnames) for f in files))
# determination of storage file, copied from analyzeVesselsBulkTumor
prefix, suffix = myutils.splitcommonpresuffix(
map(lambda s: basename(s), filenames))
outputbasename, _ = splitext(prefix + suffix)
fn_measure = 'common-radial-cache.h5'
f_measure = h5files.open(fn_measure, 'a')
def cachelocation(g):
path = posixpath.join(
'FileCS_' + myutils.checksum(basename(g.file.filename)),
g.name.strip(posixpath.sep))
return (f_measure, path)
with mpl_utils.PdfWriter('bloodFlow-%s.pdf' % outputbasename) as pdfpages:
def bloodflow(group):
bv = obtain_averaged_blood_flow(dataman, files, group,
cachelocation)
print 'of ', group, 'bf = ', bv
def printit(id, label, unit):
s = r'$%s$ = $%s \pm %s$ [$%s$]' % (
label, myutils.f2s(bv[id][0], latex=True),
myutils.f2s(bv[id][1], latex=True), unit)
return s
s = '\n'.join([
GetTimeLabel(files[0][group]),
#printit('flow_in', r'\tilde{BF}_{tumor}', '\mu m^3\,Blood\,/\,min'),
if not ax.get_xlabel():
ax.set(xticklabels=[])
text2(ax, '(%s)' % 'abcdefghij'[i])
pdfpages.savefig(fig)
if __name__ == '__main__':
filenames = sys.argv[1:]
if not myutils.is_measurement_file(filenames[0]):
for fn in filenames:
with h5py.File(fn, 'r') as f:
with myutils.MeasurementFile(f, h5files) as fdst:
grps = myutils.getTimeSortedGroups(f['.'], "out")
for grp in grps:
dstgroup = myutils.require_snapshot_group_(fdst, grp)
generate_data(grp, dstgroup)
else:
rc = matplotlib.rc
rc(
'figure', **{
'subplot.left': 0.15,
'subplot.right': 1. - 0.15,
'subplot.bottom': 0.2,
'subplot.top': 1. - 0.1,
'subplot.wspace': 0.2,
'subplot.hspace': 0.2
})
fnmeasure = commonOutputName(filenames)
with mpl_utils.PdfWriter(fnmeasure + '.pdf') as pdfpages:
plot_many(filenames, pdfpages)
parser.add_argument('FileNames',
nargs='*',
type=argparse.FileType('r'),
default=sys.stdin,
help='Vessel file to calculate')
goodArguments, otherArguments = parser.parse_known_args()
#create filename due to former standards
filenames = []
for fn in goodArguments.FileNames:
filenames.append(fn.name)
for fn in filenames:
f = h5py.File(fn)
no_of_iterations = '1'
vesselgrp = f['vessels_after_adaption']
common_filename = os.path.splitext(os.path.basename(fn))[0]
with mpl_utils.PdfWriter(no_of_iterations + '_' + common_filename +
'_stimulies.pdf') as pp:
rc = matplotlib.rc
rc('font', size=8.)
rc('axes', titlesize=10., labelsize=8.)
hydrodynamic_fig = plot_hydrodynamic_stimuli(vesselgrp, pp)
plot_conductive_stimuli(vesselgrp, pp)
plot_hydorodynamic_charicteristics(vesselgrp, pp)
#plot_movie(f,pp=None)
#plot_movie_typeE(f,pp=None)
f.close
default=False,
action='store_true')
goodArguments, otherArguments = parser.parse_known_args()
#create filename due to former standards
filenames = []
for fn in goodArguments.FileNames:
filenames.append(fn.name)
rc = matplotlib.rc
rc('font', size=8.)
rc('axes', titlesize=10., labelsize=8.)
for fn in filenames:
with h5py.File(fn) as f:
no_of_iterations = ''
vesselgrp = f['vessels_after_adaption']
common_filename = os.path.splitext(os.path.basename(fn))[0]
with mpl_utils.PdfWriter(no_of_iterations + '_' + common_filename +
'_hydrodynamic_stimulies.pdf') as pp:
hydrodynamic_fig = plot_hydrodynamic_stimuli(vesselgrp, pp)
with mpl_utils.PdfWriter(no_of_iterations + '_' + common_filename +
'_conductive_stimulies.pdf') as pp:
plot_conductive_stimuli(vesselgrp, pp)
with mpl_utils.PdfWriter(no_of_iterations + '_' + common_filename +
'_hydrodynamics.pdf') as pp:
plot_hydorodynamic_charicteristics(vesselgrp, pp)
#plot_movie(f,pp=None)
#plot_movie_typeE(f,pp=None)
def doit(filenames, pattern):
dataman = myutils.DataManager(20, [
DataTumorTissueSingle(),
DataDistanceFromCenter(),
DataBasicVessel(),
DataVesselSamples(),
DataVesselRadial(),
DataVesselGlobal(),
DataTumorBloodFlow()
])
ensemble = EnsembleFiles(dataman, filenames, pattern)
if ensemble.has_tumor:
print 'paths: ', map(lambda (_t0, path, _t1): path,
ensemble.tumor_snapshots)
else:
print 'paths: ', set(map(lambda e: e.path, ensemble.items))
prefix, suffix = myutils.splitcommonpresuffix(
map(lambda s: basename(s), filenames))
outputbasename, _ = splitext(prefix + suffix)
fn_measure = outputbasename + '-radial-cache.h5'
f_measure = h5files.open(fn_measure, 'a')
def cachelocation(g):
path = posixpath.join(
'FileCS_' + myutils.checksum(basename(g.file.filename)),
g.name.strip(posixpath.sep))
return (f_measure, path)
#name = ensemble.items[0].path.split('/')[-1]
measurementinfo = dict(sample_length=30.,
cachelocation_callback=cachelocation,
distancemap_spec='radial')
print 'getting radial curves'
stuff = []
stuff2 = []
for items, path, time in ensemble.tumor_snapshots:
bins_spec, curves = CollectAllRadialData(dataman, items,
measurementinfo)
tumorradius = GetAverageApproximateTumorRadius(dataman, ensemble,
items)
stuff.append((time, tumorradius, curves))
stuff2.append((time, tumorradius, curves, path))
#output_filename+= '_'+name
with mpl_utils.PdfWriter(outputbasename + '-radial.pdf') as pdfwriter:
PlotRadialCurves(pdfwriter, bins_spec, stuff, measurementinfo,
ensemble.world_size)
with h5py.File(outputbasename + '-radial.h5', 'w') as f:
f.attrs['COMMONPREFIX'] = os.path.commonprefix(
map(lambda s: basename(s), filenames))
f.create_dataset('files', data=filenames)
f.create_dataset('groups',
data=np.asarray(set(
map(lambda item: item.path, ensemble.items)),
dtype=np.str))
for i, (time, tumorradius, curves, path) in enumerate(stuff2):
g = f.create_group(path.strip('/').replace('/', '-'))
g.attrs['TUMORRADIUS'] = tumorradius
g.attrs['TIME'] = time
for name, curve in curves.iteritems():
curve = myutils.MeanValueArray.fromSummation(
map(lambda x: x.avg, curve))
g.create_dataset(name + '/avg', data=np.asarray(curve.avg))
g.create_dataset(name + '/std', data=np.asarray(curve.std))
g.create_dataset(name + '/mask', data=~curve.avg.mask)
g.create_dataset(name + '/std_mean',
data=np.asarray(curve.std_mean))
g.create_dataset('bins', data=bins_spec.arange())
arglist = []
for ii in np.ndindex(shape):
arglist.append(tuple(args[j][i] for j, i in enumerate(ii)))
q = krebsutils.calcBulkTissueSourceTerm(arglist, param_string)
for i, ii in enumerate(np.ndindex(shape)):
q[i] /= args[1][ii[1]]
return q
if __name__ == '__main__':
#print args
#d = generate_data(*args, params=dict())
#print d
with mpl_utils.PdfWriter('bulktissuesources_new.pdf') as pdfpages:
params = dict(source_model_version=3,
o2_necro_threshold=0.1,
o2_prol_threshold=0.4,
time_necrosis=48,
time_death_tumor=240000.,
use_necrotic_regions=False)
if 1:
o2values = [0., 0.11, 0.041, 1.]
fig = pyplot.figure()
plot = fig.add_subplot(
111,
xlabel='$\phi$',
def runStepperConvergenceTests():
out_fn = "stepper_test"
steppersConvergenceTest(out_fn + '.h5')
pdf = mpl_utils.PdfWriter(out_fn + ".pdf")
f = h5py.File(out_fn + '.h5', 'r')
# model_lambda = f.attrs['lambda']
bytime = collections.defaultdict(list)
bymethod = collections.defaultdict(list)
for g in f['/'].itervalues():
if g.name == '/exact':
exact = g
else:
bytime[g.attrs['dt']].append(g)
bymethod[g.attrs['method']].append(g)
for dt, groups in sorted(bytime.iteritems(), key=lambda (k, v): k):
if dt < exact['x'][-1] * 0.01:
continue
fig = pyplot.figure(figsize=(11.7, 8.3))
fig.suptitle('dt = %f' % dt)
plot = fig.add_subplot(1, 1, 1)
plot.set_yscale("log")
plot.set_xlabel('x')
plot.set_ylabel('y')
for marker, g in zip(markers, groups):
plot.plot(np.asarray(g['x']),
np.asarray(g['y']),
label=g.attrs['method'],
marker=marker)
plot.plot(np.asarray(exact['x']),
np.asarray(exact['y']),
label='exact',
linewidth=2)
plot.legend(loc='upper left')
pdf.savefig(fig)
err = collections.defaultdict(collections.defaultdict)
for g in f['/'].itervalues():
if g.name == '/exact': continue
val = abs(g['y'][-1] - exact['y'][-1])
m, t = g.attrs['method'], g.attrs['dt']
err[m][t] = val
print m, t, val
for k, d in err.iteritems():
err[k] = np.asarray(sorted(d.iteritems(), key=lambda
(k, v): k)).transpose()
fig = pyplot.figure(figsize=(11.7, 8.3))
plot = fig.add_subplot(1, 1, 1)
plot.set_yscale("log")
plot.set_xscale("log")
plot.set_xlabel('dt')
plot.set_ylabel('e')
for marker, (method, (t, y)) in zip(markers, err.iteritems()):
plot.plot(t, y, label=method, marker=marker)
plot.legend(loc='lower right')
pdf.savefig(fig)
pdf.close()
if __name__ == '__main__':
filenames = sys.argv[1:]
files = [h5files.open(fn, 'r') for fn in filenames]
fmeasure = h5files.open('analyzeBloodVolumeMeasurements.h5', 'a')
dataman = myutils.DataManager(10, [
DataTumorTissueSingle(),
DataVesselRadial(),
DataVesselSamples(),
DataBasicVessel(),
DataDistanceFromCenter()
])
#allgroups = sorted(filter(lambda k: k.startswith('out'), files[0].keys()))
allgroups = defaultdict(list)
for f in files:
keys = filter(lambda k: k.startswith('out'), f.keys())
for k in keys:
allgroups[k].append(f[k])
allgroups = [(k, allgroups[k]) for k in sorted(allgroups.keys())]
groupslist = [allgroups[-1], allgroups[len(allgroups) / 2]]
outfn = 'bloodVolume-%s.pdf' % splitext(basename(filenames[0]))[0]
pprint(groupslist)
print '-> %s' % (outfn)
with mpl_utils.PdfWriter(outfn) as pdfpages:
if 0:
plot_mvd_in_large_bins1(pdfpages, groupslist)
else:
plot_mvd_in_large_bins2(pdfpages, groupslist)
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)
