def estimate_ratio_hypoxic(oxy_grp,threshold):
dataman = myutils.DataManager(2, [DataDetailedPO2(), DataBasicVessel()])
gvessels, gtumor = OpenVesselAndTumorGroups(oxy_grp)
po2vessels, po2field_ld, po2field, parameters = dataman('detailedPO2', oxy_grp)
po2vessels = np.average(po2vessels, axis=0)
print 'po2vessels:', po2vessels.min(), po2vessels.max()
print 'po2field:', np.amin(po2field), np.amax(po2field)
#tissueOxygen = np.asarray(oxy_grp['po2field'])
#print(tissueOxygen)
#I neglect 5 entries from the boarder
oxy_np_field= np.asarray(po2field)
border=15
cropped_oxy = oxy_np_field[border:-border,border:-border,border:-border]
hypoxic_Tissue = cropped_oxy<threshold
hypoxic_counts = np.sum(hypoxic_Tissue[:])
number_of_boxes = cropped_oxy.shape[0]*cropped_oxy.shape[1]*cropped_oxy.shape[2]
#times volume of each box
cropped_volume = number_of_boxes*np.power(po2field_ld.scale,3)
print('considerd volume of: %f mum^3' % cropped_volume)
print('considerd volume of: %f mm^3' % (cropped_volume/1e9))
hypoxic_fraction = float(hypoxic_counts)/float(number_of_boxes)
print('hypoxic fraction: %s ' % hypoxic_fraction)
hypoxic_volume = hypoxic_counts*np.power(po2field_ld.scale,3)
#to mm
hypoxic_volume = hypoxic_volume/1e9
return hypoxic_fraction,hypoxic_volume
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
def renderScene(drug_grp, imagefn, options):
imagefn, ext = splitext(imagefn)
ext = '.' + options.format
options.imageFileName = imagefn+'_iff_drug'+ext
max_conc = getMaxConcentration(drug_grp.file)
dataman = myutils.DataManager(2, [DataBasicVessel()])
timepoint = drug_grp.attrs['time'] #comes in seconds
timepoint = timepoint/3600.
#gvessels = drug_grp.parent['iff/vessels']
gvessels = drug_grp.parent['vessels']
iff_pressure_field = drug_grp.parent['iff/iff_pressure']
drug_conc_field = drug_grp['conc']
cell_drug_conc_field = drug_grp['conc_cell']
ex_drug_conc_field = drug_grp['conc_ex']
#ex_drug_auc_field = drug_grp.parent['measurements/drug_local_integral']['auc_ex']
#in_drug_auc_field = drug_grp.parent['measurements/drug_local_integral']['auc_in']
#iff_ld = krebsutils.read_lattice_data_from_hdf_by_filename(drug_grp.parent['field_ld'])
iff_ld = krebsutils.read_lattice_data_from_hdf_by_filename(str(drug_grp.file.filename),str(drug_grp.parent.name)+'field_ld')
#ld = iffgroup['lattice']
#po2vessels, po2field_ld, po2field, parameters = dataman('detailedPO2', po2group)
#po2vessels = np.average(po2vessels, axis=0)
#print 'po2vessels:', po2vessels.min(), po2vessels.max()
print 'ifpfield:', np.amin(iff_pressure_field), np.amax(iff_pressure_field)
print 'drug_conc_field:', np.amin(drug_conc_field), np.amax(drug_conc_field)
#vessel_ld = krebsutils.read_lattice_data_from_hdf(gvessels['lattice'])
vessel_ld = krebsutils.read_lattice_data_from_hdf_by_filename(str(gvessels.file.filename),str(gvessels.name)+'/lattice')
vessel_graph = dataman('vessel_graph', gvessels, ['position', 'flags', 'radius'])
#vessel_graph.edges['po2vessels'] = po2vessels
#vessel_graph.edges['saturation'] = PO2ToSaturation(po2vessels, parameters)
#vessel_graph.edges['hboconc'] = vessel_graph.edges['saturation']*vessel_graph.edges['hematocrit']*chb_of_rbcs*1.0e3
vessel_graph = vessel_graph.get_filtered(edge_indices = myutils.bbitwise_and(vessel_graph['flags'], krebsutils.CIRCULATED))
if options.filterradiuslowpass>0.0:
print("lowpass filter activated:")
vessel_graph = vessel_graph.get_filtered(edge_indices = vessel_graph['radius']< kwargs['filterradiuslowpass'])
imagefn, ext = splitext(imagefn)
ext = '.' + options.format
if 1:
if timepoint==0:
renderSliceWithDistribution((vessel_ld, vessel_graph, 'iff_pressure'), (iff_ld, iff_pressure_field), (imagefn+'_iff_pressure_t%0.1fh'%timepoint )+ext, 'IF pressure t=%.1f h'%timepoint, options, max_conc=max_conc)
renderSliceWithDistribution((vessel_ld, vessel_graph, 'drug_conc'), (iff_ld, drug_conc_field), (imagefn+'_iff_drug_t%0.1fh'%timepoint )+ext, '%s t=%.1f h'%(drug_grp.file.attrs.get('MESSAGE'),timepoint), options, max_conc=max_conc)
#renderSliceWithDistribution((vessel_ld, vessel_graph, 'drug_conc'), (iff_ld, cell_drug_conc_field), (imagefn+'_iff_drug_incell_t%0.1fh'%timepoint)+ext, 'Tr. intr. t=%.1f h'%timepoint, kwargs)
#renderSliceWithDistribution((vessel_ld, vessel_graph, 'drug_conc'), (iff_ld, ex_drug_conc_field), (imagefn+'_iff_drug_excell_t%0.1fh'%timepoint)+ext, 'Tr. extr. t=%.1f h'%timepoint, kwargs)
if options.plot_auc:
renderSliceWithDistribution((vessel_ld, vessel_graph, 'auc'), (iff_ld, ex_drug_auc_field), imagefn+'_iff_ex_drug_auc'+ext, 'Tr. extr. t=%.1f h'%timepoint, options, max_conc=max_conc)
#renderSliceWithDistribution((vessel_ld, vessel_graph, 'auc'), (iff_ld, in_drug_auc_field), imagefn+'_iff_in_drug_auc'+ext, 'Tr. intr. t=%.1f h'%timepoint, kwargs)
if 0:
renderSlice((vessel_ld, vessel_graph, 'radius'), (iff_ld, iff_pressure_field), imagefn+'_iff_pressure'+ext, '', options)
renderSlice((vessel_ld, vessel_graph, 'radius'), (iff_ld, drug_conc_field), imagefn+'_iff_drug'+ext, '', options)
renderSlice((vessel_ld, vessel_graph, 'radius'), (iff_ld, cell_drug_conc_field), imagefn+'_iff_drug_incell'+ext, '', options)
renderSlice((vessel_ld, vessel_graph, 'radius'), (iff_ld, ex_drug_conc_field), imagefn+'_iff_drug_excell'+ext, '', options)
def estimate_annoxic_hypoxic_normoxic(oxy_grp, tumorradius,threshold1,threshold2):
dataman = myutils.DataManager(2, [DataDetailedPO2(), DataBasicVessel()])
gvessels, gtumor = OpenVesselAndTumorGroups(oxy_grp)
po2vessels, po2field_ld, po2field, parameters = dataman('detailedPO2', oxy_grp)
po2vessels = np.average(po2vessels, axis=0)
print 'po2vessels:', po2vessels.min(), po2vessels.max()
print 'po2field:', np.amin(po2field), np.amax(po2field)
#tissueOxygen = np.asarray(oxy_grp['po2field'])
#print(tissueOxygen)
#I neglect 5 entries from the boarder
oxy_np_field= np.asarray(po2field)
#find minimal dimension
min_dim = np.min(oxy_np_field.shape)
print("tumorradius: %f, ld: %f" %(tumorradius,po2field_ld.scale))
border0=int(np.floor(oxy_np_field.shape[0]/2)-np.ceil(tumorradius/po2field_ld.scale))
border1=int(np.floor(oxy_np_field.shape[1]/2)-np.ceil(tumorradius/po2field_ld.scale))
border2=int(np.floor(oxy_np_field.shape[2]/2)-np.ceil(tumorradius/po2field_ld.scale))
print("border0: %i" %border0)
print("border1: %i" %border1)
print("border2: %i" %border2)
cropped_oxy = oxy_np_field[border0:-border0,border1:-border1,border2:-border2]
annoxic_Tissue = cropped_oxy<threshold1
annoxic_counts = np.sum(annoxic_Tissue[:])
hypoxic_Tissue = np.logical_and(cropped_oxy<threshold2, cropped_oxy>threshold1)
#hypoxic_Tissue = hypoxic_Tissue>threshold1
hypoxic_counts = np.sum(hypoxic_Tissue[:])
normoxic_Tissue = cropped_oxy>threshold2
normoxic_counts = np.sum(normoxic_Tissue[:])
number_of_boxes = cropped_oxy.shape[0]*cropped_oxy.shape[1]*cropped_oxy.shape[2]
''' volume correction for not sampling a sphere, but a cube
\frac{volume(sphere)}{volume(cube of 2 times radius)} = \pi/6
'''
volume_correction_factor = np.pi/6.
#volume_correction_factor = 1.
#times volume of each box
cropped_volume = number_of_boxes*np.power(po2field_ld.scale,3)
print('considerd volume of: %f mum^3' % cropped_volume)
print('considerd volume of: %f mm^3' % (cropped_volume/1e9))
hypoxic_fraction = float(hypoxic_counts)/float(number_of_boxes)
print('hypoxic fraction: %s ' % hypoxic_fraction)
annoxic_volume = annoxic_counts*np.power(po2field_ld.scale,3) * volume_correction_factor
#to mm
annoxic_volume = annoxic_volume/1e9
normoxic_volume = normoxic_counts*np.power(po2field_ld.scale,3) * volume_correction_factor
#to mm
normoxic_volume = normoxic_volume/1e9
hypoxic_volume = hypoxic_counts*np.power(po2field_ld.scale,3) * volume_correction_factor
#to mm
hypoxic_volume = hypoxic_volume/1e9
#return hypoxic_fraction,hypoxic_volume
return annoxic_volume,hypoxic_volume,normoxic_volume
def AddAverageConductivity(vesselgroup, parameters):
dataman = myutils.DataManager(100, [DataBasicVessel()])
vessels = dataman.obtain_data('vessel_graph', vesselgroup, ['flow', 'pressure', 'flags', 'radius','nodeflags'])
def DoBC():
conductivities, avgVenousPressure, avgArterialPressure, totalFlow = ComputeVascularTreeBloodFlowResistances(vessels)
avgConductivity = (totalFlow/(avgArterialPressure-avgVenousPressure))
print 'avgVenousPressure', avgVenousPressure
print 'avgArterialPressure', avgArterialPressure
print 'totalFlow', totalFlow
print 'avgConductivity', avgConductivity
return avgConductivity
avgConductivity = DoBC()
parameters['adaption']['avgRootNodeConductivity'] = avgConductivity
return parameters
def renderScene(po2group, imagefn, options):
dataman = myutils.DataManager(2, [DataDetailedPO2(), DataBasicVessel()])
gvessels, gtumor = OpenVesselAndTumorGroups(po2group)
po2vessels, po2field_ld, po2field, parameters = dataman(
'detailedPO2', po2group)
po2vessels = np.average(po2vessels, axis=0)
print 'po2vessels:', po2vessels.min(), po2vessels.max()
print 'po2field:', np.amin(po2field), np.amax(po2field)
#vessel_ld = krebsutils.read_lattice_data_from_hdf(gvessels['lattice'])
vessel_graph = dataman('vessel_graph', gvessels,
['position', 'flags', 'radius', 'hematocrit'])
vessel_graph.edges['po2_vessels'] = po2vessels
print(parameters)
vessel_graph.edges['saturation'] = PO2ToSaturation(po2vessels, parameters)
vessel_graph.edges['hboconc'] = vessel_graph.edges[
'saturation'] * vessel_graph.edges['hematocrit'] * chb_of_rbcs * 1.0e3
vessel_graph = vessel_graph.get_filtered(edge_indices=myutils.bbitwise_and(
vessel_graph['flags'], krebsutils.CIRCULATED))
if options.filterradiuslowpass > 0:
print("lowpass filter activated:")
vessel_graph = vessel_graph.get_filtered(
edge_indices=vessel_graph['radius'] < options.filterradiuslowpass)
imagefn, ext = splitext(imagefn)
ext = '.' + options.format
#renderSliceWithDistribution((vessel_ld, vessel_graph, 'po2vessels'), (po2field_ld, po2field), imagefn+'_po2vessels'+ext, '', options)
#renderSlice((vessel_ld, vessel_graph, 'saturation'), (None, None), imagefn+'_saturation'+ext, '', options)
#renderSlice((vessel_ld, vessel_graph, 'hboconc'), (None, None), imagefn+'_hboconc'+ext, 'HbO [mmol/l blood]', options)
#try world
options.imageFileName = imagefn + '_po2vessels' + ext
renderSliceWithDistribution((po2field_ld, vessel_graph, 'po2_vessels'),
(po2field_ld, po2field), '', options)
options.imageFileName = imagefn + '_saturation' + ext
renderSlice((po2field_ld, vessel_graph, 'saturation'), (None, None), '',
options)
options.imageFileName = imagefn + '_hboconc' + ext
renderSlice((po2field_ld, vessel_graph, 'hboconc'), (None, None),
'HbO [mmol/l blood]', options)
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']
pdfwriter.savefig(fig, postfix='_curves')
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)
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())