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 doit(filenames):
dataman = myutils.DataManager(
50,
map(lambda x: x(), detailedo2Analysis.O2DataHandlers) + [
analyzeGeneral.DataTumorTissueSingle(),
analyzeGeneral.DataDistanceFromCenter(),
analyzeGeneral.DataBasicVessel(),
analyzeGeneral.DataVesselSamples(),
analyzeGeneral.DataVesselRadial(),
analyzeGeneral.DataVesselGlobal(),
analyzeBloodFlow.DataTumorBloodFlow()
])
ensemble = EnsembleFiles(dataman, filenames, 'po2/adaption/')
out_prefix, out_suffix = myutils.splitcommonpresuffix(
map(lambda s: basename(s), filenames))
output_base_filename = splitext(out_prefix + out_suffix)[0]
if ensemble.o2ConfigName:
fn_measure = 'detailedo2_%s_common.h5' % ensemble.o2ConfigName
else:
fn_measure = 'detailedo2_common.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)
measurementinfo = MeasurementInfo(sample_length=30.,
cachelocation_callback=cachelocation,
distancemap_spec='radial')
with mpl_utils.PageWriter(output_base_filename + '.pdf',
fileformats=['pdf']) as pdfwriter:
if 0:
compare_tissue_saturation(dataman, ensemble, pdfwriter)
if 0:
#try:
# histogramGroupFinal = f_measure['combinedHistogramsFinal']
# histogramGroupInitial = f_measure['combinedHistogramsInitial']
#except KeyError:
#histogramGroupFinal = f_measure.recreate_group('combinedHistogramsFinal')
histogramGroupInitial = f_measure.recreate_group(
'combinedHistogramsInitial')
#ComputeHistogramsOfPo2Items(dataman, ensemble.items, measurementinfo, histogramGroupFinal)
ComputeHistogramsOfPo2Items(dataman, ensemble.items,
measurementinfo, histogramGroupInitial)
#PlotHistograms(pdfwriter, histogramGroupFinal, 'tum', 'Tumor')
PlotHistograms(pdfwriter, histogramGroupInitial, 'all', 'Initial')
posixpath.join(
splitext(basename(vesselgroup.file.filename))[0],
vesselgroup.name.strip(posixpath.sep))) + (dataname, )
return (f_measure, path)
def cachelocationEnsemble(dataname, groups):
groupchecksum = myutils.checksum(*map(
lambda g: str(g.file.filename + '/' + g.name), sum(groups, [])))
path = ('%s_%s' % (dataname, groupchecksum), )
return (f_measure, path)
dataman = myutils.DataManager(20, [
analyzeGeneral.DataTumorTissueSingle(),
analyzeGeneral.DataDistanceFromCenter(),
analyzeGeneral.DataBasicVessel(),
analyzeGeneral.DataVesselSamples(),
DataPressureMvdCorrelation(200., 5, 30., cachelocation,
cachelocationEnsemble)
])
if not options.picz:
print '-----------computing sammples------------'
localSamples = dataman.obtain_data('intervascular_map_correlations',
thegroups)
globalSamples = dataman.obtain_data(
'intervascular_global_correlations', thegroups)
print '--------------plotting ---------------'
with mpl_utils.PageWriter(outputbasename + '_mvd-grad.pdf',
fileformats=['svg']) as pdfwriter:
fig, axes = pyplot.subplots(1,
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)
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)