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 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 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']
pyplot.tight_layout()
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)