def ProduceData(fitParameters, filename):
from krebs.analyzeGeneral import DataBasicVessel, DataVesselSamples, DataVesselGlobal
from krebs.detailedo2Analysis import DataDetailedPO2
import krebs.detailedo2Analysis.singleVesselCases as singleVesselCases
paramspo2Override = dict(
massTransferCoefficientModelNumber=1,
conductivity_coeff1=fitParameters[0],
conductivity_coeff2=fitParameters[1],
conductivity_coeff3=fitParameters[2],
)
f = h5files.open(filename, 'a')
krebsutils.set_num_threads(2)
dataman = myutils.DataManager(20, [
DataDetailedPO2(),
DataBasicVessel(),
DataVesselSamples(),
DataVesselGlobal()
])
for k, params in fitCases:
params = deepcopy(params)
params.paramspo2.update(paramspo2Override)
singleVesselCases.GenerateSingleCapillaryWPo2(dataman, f, k, 16,
params)
return f
plotAnalyzeConvergence(dataman, pdfwriter, plotties)
plotties[0].AddStatsPage(pdfwriter)
plotAnalyzeIterativeConvergence(dataman, pdfwriter, plotties)
pyplot.close('all')
if __name__ == '__main__':
krebsutils.set_num_threads(2)
dataman = myutils.DataManager(20, [DataDetailedPO2(),DataBasicVessel(), DataVesselSamples(), DataVesselGlobal()])
fn = 'vessel-single-all.h5'
#os.unlink(fn)
f = h5files.open(fn,'a')
GenerateSingleCapillaryWPo2(dataman, f, 'nair_uptake', 14, singleVesselParameterSets.nair_uptake)
plot_single_capillary(dataman, f['nair_uptake'], useInsets = True)
GenerateSingleCapillaryWPo2(dataman, f, 'nair_release', 14, singleVesselParameterSets.nair_release)
plot_single_capillary(dataman, f['nair_release'], useInsets = True)
grouplist = []
for name in [ 'moschandreou_case%02i' % i for i in xrange(6) ]:
params = getattr(singleVesselParameterSets, name)
r = params.paramsTube['r']
plotAnalyzeConvergence(dataman, pdfwriter, plotties)
plotties[0].AddStatsPage(pdfwriter)
plotAnalyzeIterativeConvergence(dataman, pdfwriter, plotties)
pyplot.close('all')
if __name__ == '__main__':
krebsutils.set_num_threads(2)
dataman = myutils.DataManager(20, [
DataDetailedPO2(),
DataBasicVessel(),
DataVesselSamples(),
DataVesselGlobal()
])
fn = 'vessel-single-all.h5'
#os.unlink(fn)
f = h5files.open(fn, 'a')
GenerateSingleCapillaryWPo2(dataman, f, 'nair_uptake', 14,
singleVesselParameterSets.nair_uptake)
plot_single_capillary(dataman, f['nair_uptake'], useInsets=True)
GenerateSingleCapillaryWPo2(dataman, f, 'nair_release', 14,
singleVesselParameterSets.nair_release)
plot_single_capillary(dataman, f['nair_release'], useInsets=True)
grouplist = []
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())