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')
for filename in filenames:
f = h5files.open(filename)
print 'opened -- ', filename, '/',
paths_before = myutils.walkh5(f['.'], pattern_before)
paths_after = myutils.walkh5(f['.'], pattern_after)
print paths_before, paths_after
for path_before, path_after in zip(paths_before, paths_after):
vesselgroup_before = f[path_before]
vesselgroup_after = f[path_after]
tumorgroup = analyzeGeneral.try_find_tumor_group_from_vesselgroup(
vesselgroup_after)
assert tumorgroup
thegroups.append(
[vesselgroup_before, vesselgroup_after, tumorgroup])
prefix, suffix = myutils.splitcommonpresuffix(
map(lambda s: basename(s), filenames))
outputbasename, _ = splitext(prefix + suffix)
fn_measure = join(dirname(outputbasename), 'common-mvd-grad-map-cache.h5')
f_measure = h5files.open(fn_measure, 'a')
def cachelocation(dataname, vesselgroup, tumorgroup, version):
path = myutils.splitPath(
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, [])))
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())