def write(gmeasure, name):
gvessels, gtumor = detailedo2.OpenVesselAndTumorGroups(
po2group)
weight_smpl = dataman.obtain_data('basic_vessel_samples',
'weight', gvessels,
sample_length)
flags = dataman.obtain_data('basic_vessel_samples', 'flags',
gvessels, sample_length)
# get teh radial distance function (either distance from tumor border or distance from center)
dist_smpl, distmap, mask, tumor_ld = dataman.obtain_data(
'distancemap_samples', gvessels, gtumor, sample_length,
distance_distribution_name, None)
# tumor_ld might actually be a unrelated lattice
#filter uncirculated
mask = mask & myutils.bbitwise_and(flags,
krebsutils.CIRCULATED)
dist_smpl = dist_smpl[mask]
weight_smpl = weight_smpl[mask]
bins = bins_spec.arange()
gmeasure = gmeasure.create_group(name)
for name in ['po2', 'extpo2', 'jtv', 'sat', 'gtv', 'dS_dx']:
smpl = dataman.obtain_data('detailedPO2_samples', name,
po2group, sample_length, None,
samplelocation)
myutils.MeanValueArray.fromHistogram1d(
bins, dist_smpl, smpl[mask],
w=weight_smpl).write(gmeasure, name)
del smpl
_, po2ld, po2field, parameters = dataman.obtain_data(
'detailedPO2', po2group)
po2field = krebsutils.resample_field(np.asarray(po2field),
po2ld.worldBox,
tumor_ld.shape,
tumor_ld.worldBox,
order=1,
mode='nearest')
myutils.MeanValueArray.fromHistogram1d(bins, distmap.ravel(),
po2field.ravel()).write(
gmeasure,
'po2_tissue')
del po2field
uptakefield = detailedo2.computeO2Uptake(po2group, gtumor)
uptakefield = krebsutils.resample_field(uptakefield,
po2ld.worldBox,
tumor_ld.shape,
tumor_ld.worldBox,
order=1,
mode='nearest')
myutils.MeanValueArray.fromHistogram1d(
bins, distmap.ravel(),
uptakefield.ravel()).write(gmeasure, 'mro2')
del uptakefield
hema = dataman.obtain_data('basic_vessel_samples',
'hematocrit', gvessels,
sample_length)[mask]
sat = dataman.obtain_data('detailedPO2_samples', 'sat',
po2group, sample_length, None,
samplelocation)[mask]
rad = dataman.obtain_data('basic_vessel_samples', 'radius',
gvessels, sample_length)[mask]
hbvolume = weight_smpl * rad * rad * math.pi * hema
vol_per_bin = myutils.MeanValueArray.fromHistogram1d(
bins, distmap.ravel(), np.ones_like(
distmap.ravel())).cnt * (tumor_ld.scale**3)
tmp = myutils.MeanValueArray.fromHistogram1d(
bins, dist_smpl, hbvolume * sat)
tmp.cnt = vol_per_bin.copy()
tmp.write(gmeasure, 'vfhb_oxy')
tmp = myutils.MeanValueArray.fromHistogram1d(
bins, dist_smpl, hbvolume * (1. - sat))
tmp.cnt = vol_per_bin.copy()
tmp.write(gmeasure, 'vfhb_deoxy')
del tmp, hbvolume, vol_per_bin
def obtain_data(self, dataman, dataname, *args):
if dataname == 'detailedPO2Parameters':
po2group, = args
return detailedo2.readParameters(po2group)
if dataname == 'detailedPO2':
po2group, = args
a = np.asarray(po2group['po2vessels'])
if a.shape[0] <> 2: a = np.transpose(a)
po2field = po2group['po2field']
ld = krebsutils.read_lattice_data_from_hdf(po2group['field_ld'])
parameters = dataman.obtain_data('detailedPO2Parameters', po2group)
return a, ld, po2field, parameters
if dataname == 'detailedPO2_consumption':
po2group, tumorgroup = args
return detailedo2.computeO2Uptake(po2group, tumorgroup)
if dataname == 'detailedPO2_samples' or dataname == 'detailedPO2_total_fluxes':
prop, po2group, sample_length, every, cachelocation = args
def read(gmeasure, name):
gmeasure = gmeasure[name]
if dataname == 'detailedPO2_samples':
if prop == 'gtv':
parameters = dataman.obtain_data(
'detailedPO2Parameters', po2group)
#po2 = dataman.obtain_data('detailedPO2_samples','po2', *args[1:])
#extpo2 = dataman.obtain_data('detailedPO2_samples','extpo2', *args[1:])
#return (po2-extpo2)/(parameters['grid_lattice_const']*parameters.get('transvascular_ring_size',0.5))
gvessels, gtumor = detailedo2.OpenVesselAndTumorGroups(
po2group)
jtv = dataman.obtain_data('detailedPO2_samples', 'jtv',
*args[1:])
#radius = dataman.obtain_data('basic_vessel_samples', 'radius', gvessels, sample_length)
#radius = radius * (60.e-4*math.pi*2.* parameters['kD_tissue']*parameters['alpha_t'])
radius = (
60.e-4 *
parameters.get('D_tissue',
parameters.get('kD_tissue', None)) *
parameters.get('solubility_tissue',
parameters.get('alpha_t', None)))
gtv = jtv / radius
return gtv
elif prop == 'sat':
po2 = dataman.obtain_data('detailedPO2_samples', 'po2',
*args[1:])
parameters = dataman.obtain_data(
'detailedPO2Parameters', po2group)
return detailedo2.PO2ToSaturation(po2, parameters)
else:
ds = gmeasure['smpl_' + prop]
ds = ds[...] if every is None else ds[::every]
return ds
else:
keys = filter(lambda k: k.startswith('flux_'),
gmeasure.keys())
fluxes = dict(map(lambda k: (k[5:], gmeasure[k][()]),
keys))
fluxes['e1'] = abs(
100. * (fluxes['Jin_root'] - fluxes['Jout_root'] -
fluxes['Jout_tv']) / fluxes['Jin_root'])
fluxes['e2'] = abs(
100. *
(fluxes['Jin_root'] - fluxes['Jout_root'] -
fluxes['Jout_cons'] - fluxes.get('tv_cons', 0.)) /
fluxes['Jin_root'])
fluxes['e3'] = abs(
100. * (fluxes['Jout_tv'] - fluxes['Jout_cons'] -
fluxes.get('tv_cons', 0.)) / fluxes['Jout_tv'])
return fluxes
def write(gmeasure, name):
# do the sampling
gvessels, gtumor = detailedo2.OpenVesselAndTumorGroups(
po2group)
smpl, fluxes = detailedo2.sampleVessels(
po2group, gvessels, gtumor, sample_length)
#cons = dataman.detailedPO2_consumption(po2group, gtumor)
cons = dataman.obtain_data('detailedPO2_consumption', po2group,
gtumor)
# get the raw data, just for the consumption flux
po2vessels, ld, po2field, parameters = dataman.obtain_data(
'detailedPO2', po2group)
avgCons = np.asarray(myutils.largeDatasetAverage(cons),
dtype=np.float64)
fluxes['Jout_cons'] = avgCons * np.product(
cons.shape) * (ld.scale * 1.e-4)**3
del po2vessels, po2field, ld
gmeasure = gmeasure.create_group(name)
for k, v in smpl.iteritems():
gmeasure.create_dataset('smpl_' + k,
data=v,
compression=9,
dtype=np.float32)
for k, v in fluxes.iteritems():
gmeasure.create_dataset('flux_' + k,
data=v) # scalar dataset
version_id = myutils.checksum(sample_length, 3, getuuid_(po2group))
ret = myutils.hdf_data_caching(
read, write, cachelocation[0],
(cachelocation[1], 'samples_and_fluxes'), (None, version_id))
return ret
if dataname == 'detailedPO2_global':
prop, po2group, sample_length, cachelocation = args
samplelocation = MakeSampleLocation(po2group)
def write(gmeasure, measurename):
assert prop == measurename
gvessels, gtumor = detailedo2.OpenVesselAndTumorGroups(
po2group)
if prop in ['po2', 'sat', 'gtv', 'jtv']:
w = dataman.obtain_data('basic_vessel_samples', 'weight',
gvessels, sample_length)
d = dataman.obtain_data('detailedPO2_samples', prop,
po2group, sample_length, 1,
samplelocation)
gmeasure.create_dataset(prop,
data=myutils.WeightedAverageStd(
d, weights=w))
elif prop in ['sat_vein', 'sat_capi', 'sat_art']:
w = dataman.obtain_data('basic_vessel_samples', 'weight',
gvessels, sample_length)
d = dataman.obtain_data('detailedPO2_samples', 'sat',
po2group, sample_length, 1,
samplelocation)
f = dataman.obtain_data('basic_vessel_samples', 'flags',
gvessels, sample_length)
mask = ~myutils.bbitwise_and(
f, krebsutils.WITHIN_TUMOR) & myutils.bbitwise_and(
f, krebsutils.CIRCULATED)
m = {
'sat_vein': krebsutils.VEIN,
'sat_capi': krebsutils.CAPILLARY,
'sat_art': krebsutils.ARTERY
}
mask &= myutils.bbitwise_and(f, m[prop])
d, w = d[mask], w[mask]
gmeasure.create_dataset(prop,
data=myutils.WeightedAverageStd(
d, weights=w))
elif prop in [
'e1', 'e2', 'e3', 'Jin_root', 'Jout_root', 'Jout_tv',
'tv_cons', 'Jout_cons'
]:
d = dataman.obtain_data('detailedPO2_total_fluxes', prop,
po2group, sample_length, 1,
samplelocation)
gmeasure.create_dataset(prop, data=[d[prop], 0])
elif prop == 'po2_tissue':
_, po2ld, po2field, parameters = dataman.obtain_data(
'detailedPO2', po2group)
d = myutils.largeDatasetAverageAndStd(po2field)
gmeasure.create_dataset(prop, data=d)
elif prop == 'mro2':
uptakefield = detailedo2.computeO2Uptake(po2group, gtumor)
d = myutils.largeDatasetAverageAndStd(uptakefield)
gmeasure.create_dataset(prop, data=d)
elif prop in ('sat_via_hb_ratio', 'vfhb_oxy', 'vfhb_deoxy',
'vfhb'):
weight = dataman.obtain_data('basic_vessel_samples',
'weight', gvessels,
sample_length)
flags = dataman.obtain_data('basic_vessel_samples',
'flags', gvessels,
sample_length)
mask = myutils.bbitwise_and(flags, krebsutils.CIRCULATED)
hema = dataman.obtain_data('basic_vessel_samples',
'hematocrit', gvessels,
sample_length)[mask]
sat = dataman.obtain_data('detailedPO2_samples', 'sat',
po2group, sample_length, None,
samplelocation)[mask]
rad = dataman.obtain_data('basic_vessel_samples', 'radius',
gvessels, sample_length)[mask]
weight = weight[mask]
hbvolume = weight * rad * rad * math.pi * hema
ld = krebsutils.read_lattice_data_from_hdf(
po2group['field_ld'])
volume = np.product(ld.GetWorldSize())
if prop == 'sat_via_hb_ratio':
result = np.sum(hbvolume * sat) / np.sum(hbvolume)
elif prop == 'vfhb_oxy':
result = np.sum(hbvolume * sat) / volume
elif prop == 'vfhb_deoxy':
result = np.sum(hbvolume * (1. - sat)) / volume
elif prop == 'vfhb':
result = np.sum(hbvolume) / volume
gmeasure.create_dataset(prop, data=[result, 0.])
elif prop in ('chb_oxy', 'chb_deoxy', 'chb'):
m = {
'chb_oxy': 'vfhb_oxy',
'chb_deoxy': 'vfhb_deoxy',
'chb': 'vfhb'
}
result = dataman.obtain_data('detailedPO2_global', m[prop],
po2group, sample_length,
cachelocation)
result = result * detailedo2.chb_of_rbcs
gmeasure.create_dataset(prop, data=[result, 0.])
elif prop == 'mro2_by_j':
fluxes = dataman.obtain_data('detailedPO2_total_fluxes',
prop, po2group, sample_length,
1)
ld = krebsutils.read_lattice_data_from_hdf(
po2group['field_ld'])
worldbb = ld.worldBox
result = fluxes['Jout_tv'] / np.prod(worldbb[1] -
worldbb[0]) * 1.e12
gmeasure.create_dataset(prop, data=[result, 0.])
elif prop == 'oef':
fluxes = dataman.obtain_data('detailedPO2_total_fluxes',
prop, po2group, sample_length,
1, samplelocation)
result = (fluxes['Jin_root'] -
fluxes['Jout_root']) / fluxes['Jin_root']
gmeasure.create_dataset(prop, data=[result, 0.])
else:
assert False
def read(gmeasure, measurename):
d = np.asarray(gmeasure[measurename])
if measurename in ('chb_oxy', 'chb', 'chb_deoxy'):
d *= 1.e6
return d[0] # its a tuple (avg, std), we want avg now.
version_num = collections.defaultdict(lambda: 3)
version_id = myutils.checksum(sample_length, version_num[prop],
getuuid_(po2group))
#version_id = myutils.checksum(sample_length, (2 if prop in else 1))
return myutils.hdf_data_caching(read, write, cachelocation[0],
('global', cachelocation[1], prop),
(1, 1, version_id))
if dataname == 'detailedPO2_radial':
po2group, sample_length, bins_spec, distance_distribution_name, cachelocation = args
samplelocation = MakeSampleLocation(po2group)
# we assume that there is a tumor. without this measurement makes little sense
def read(gmeasure, name):
d = dict(gmeasure[name].items())
d = dict(
(k, myutils.MeanValueArray.read(v)) for k, v in d.items())
hbo = d['vfhb_oxy']
hbd = d['vfhb_deoxy']
hb = myutils.MeanValueArray(hbo.cnt, hbo.sum + hbd.sum,
hbo.sqr + hbd.sqr)
d['vfhb'] = hb
sat = hbo.avg / hb.avg
d['sat_via_hb_ratio'] = myutils.MeanValueArray(
np.ones_like(hb.cnt), sat, sat * sat)
d['chb_oxy'] = d['vfhb_oxy'] * detailedo2.chb_of_rbcs * 1.e6
d['chb_deoxy'] = d['vfhb_deoxy'] * detailedo2.chb_of_rbcs * 1.e6
d['chb'] = d['vfhb'] * detailedo2.chb_of_rbcs * 1.e6
return d
def write(gmeasure, name):
gvessels, gtumor = detailedo2.OpenVesselAndTumorGroups(
po2group)
weight_smpl = dataman.obtain_data('basic_vessel_samples',
'weight', gvessels,
sample_length)
flags = dataman.obtain_data('basic_vessel_samples', 'flags',
gvessels, sample_length)
# get teh radial distance function (either distance from tumor border or distance from center)
dist_smpl, distmap, mask, tumor_ld = dataman.obtain_data(
'distancemap_samples', gvessels, gtumor, sample_length,
distance_distribution_name, None)
# tumor_ld might actually be a unrelated lattice
#filter uncirculated
mask = mask & myutils.bbitwise_and(flags,
krebsutils.CIRCULATED)
dist_smpl = dist_smpl[mask]
weight_smpl = weight_smpl[mask]
bins = bins_spec.arange()
gmeasure = gmeasure.create_group(name)
for name in ['po2', 'extpo2', 'jtv', 'sat', 'gtv', 'dS_dx']:
smpl = dataman.obtain_data('detailedPO2_samples', name,
po2group, sample_length, None,
samplelocation)
myutils.MeanValueArray.fromHistogram1d(
bins, dist_smpl, smpl[mask],
w=weight_smpl).write(gmeasure, name)
del smpl
_, po2ld, po2field, parameters = dataman.obtain_data(
'detailedPO2', po2group)
po2field = krebsutils.resample_field(np.asarray(po2field),
po2ld.worldBox,
tumor_ld.shape,
tumor_ld.worldBox,
order=1,
mode='nearest')
myutils.MeanValueArray.fromHistogram1d(bins, distmap.ravel(),
po2field.ravel()).write(
gmeasure,
'po2_tissue')
del po2field
uptakefield = detailedo2.computeO2Uptake(po2group, gtumor)
uptakefield = krebsutils.resample_field(uptakefield,
po2ld.worldBox,
tumor_ld.shape,
tumor_ld.worldBox,
order=1,
mode='nearest')
myutils.MeanValueArray.fromHistogram1d(
bins, distmap.ravel(),
uptakefield.ravel()).write(gmeasure, 'mro2')
del uptakefield
hema = dataman.obtain_data('basic_vessel_samples',
'hematocrit', gvessels,
sample_length)[mask]
sat = dataman.obtain_data('detailedPO2_samples', 'sat',
po2group, sample_length, None,
samplelocation)[mask]
rad = dataman.obtain_data('basic_vessel_samples', 'radius',
gvessels, sample_length)[mask]
hbvolume = weight_smpl * rad * rad * math.pi * hema
vol_per_bin = myutils.MeanValueArray.fromHistogram1d(
bins, distmap.ravel(), np.ones_like(
distmap.ravel())).cnt * (tumor_ld.scale**3)
tmp = myutils.MeanValueArray.fromHistogram1d(
bins, dist_smpl, hbvolume * sat)
tmp.cnt = vol_per_bin.copy()
tmp.write(gmeasure, 'vfhb_oxy')
tmp = myutils.MeanValueArray.fromHistogram1d(
bins, dist_smpl, hbvolume * (1. - sat))
tmp.cnt = vol_per_bin.copy()
tmp.write(gmeasure, 'vfhb_deoxy')
del tmp, hbvolume, vol_per_bin
version = getuuid_(po2group)
ret = analyzeGeneral.HdfCacheRadialDistribution(
(read, write), 'po2', bins_spec, distance_distribution_name,
cachelocation, version)
return ret
assert False
def write(gmeasure, measurename):
assert prop == measurename
gvessels, gtumor = detailedo2.OpenVesselAndTumorGroups(
po2group)
if prop in ['po2', 'sat', 'gtv', 'jtv']:
w = dataman.obtain_data('basic_vessel_samples', 'weight',
gvessels, sample_length)
d = dataman.obtain_data('detailedPO2_samples', prop,
po2group, sample_length, 1,
samplelocation)
gmeasure.create_dataset(prop,
data=myutils.WeightedAverageStd(
d, weights=w))
elif prop in ['sat_vein', 'sat_capi', 'sat_art']:
w = dataman.obtain_data('basic_vessel_samples', 'weight',
gvessels, sample_length)
d = dataman.obtain_data('detailedPO2_samples', 'sat',
po2group, sample_length, 1,
samplelocation)
f = dataman.obtain_data('basic_vessel_samples', 'flags',
gvessels, sample_length)
mask = ~myutils.bbitwise_and(
f, krebsutils.WITHIN_TUMOR) & myutils.bbitwise_and(
f, krebsutils.CIRCULATED)
m = {
'sat_vein': krebsutils.VEIN,
'sat_capi': krebsutils.CAPILLARY,
'sat_art': krebsutils.ARTERY
}
mask &= myutils.bbitwise_and(f, m[prop])
d, w = d[mask], w[mask]
gmeasure.create_dataset(prop,
data=myutils.WeightedAverageStd(
d, weights=w))
elif prop in [
'e1', 'e2', 'e3', 'Jin_root', 'Jout_root', 'Jout_tv',
'tv_cons', 'Jout_cons'
]:
d = dataman.obtain_data('detailedPO2_total_fluxes', prop,
po2group, sample_length, 1,
samplelocation)
gmeasure.create_dataset(prop, data=[d[prop], 0])
elif prop == 'po2_tissue':
_, po2ld, po2field, parameters = dataman.obtain_data(
'detailedPO2', po2group)
d = myutils.largeDatasetAverageAndStd(po2field)
gmeasure.create_dataset(prop, data=d)
elif prop == 'mro2':
uptakefield = detailedo2.computeO2Uptake(po2group, gtumor)
d = myutils.largeDatasetAverageAndStd(uptakefield)
gmeasure.create_dataset(prop, data=d)
elif prop in ('sat_via_hb_ratio', 'vfhb_oxy', 'vfhb_deoxy',
'vfhb'):
weight = dataman.obtain_data('basic_vessel_samples',
'weight', gvessels,
sample_length)
flags = dataman.obtain_data('basic_vessel_samples',
'flags', gvessels,
sample_length)
mask = myutils.bbitwise_and(flags, krebsutils.CIRCULATED)
hema = dataman.obtain_data('basic_vessel_samples',
'hematocrit', gvessels,
sample_length)[mask]
sat = dataman.obtain_data('detailedPO2_samples', 'sat',
po2group, sample_length, None,
samplelocation)[mask]
rad = dataman.obtain_data('basic_vessel_samples', 'radius',
gvessels, sample_length)[mask]
weight = weight[mask]
hbvolume = weight * rad * rad * math.pi * hema
ld = krebsutils.read_lattice_data_from_hdf(
po2group['field_ld'])
volume = np.product(ld.GetWorldSize())
if prop == 'sat_via_hb_ratio':
result = np.sum(hbvolume * sat) / np.sum(hbvolume)
elif prop == 'vfhb_oxy':
result = np.sum(hbvolume * sat) / volume
elif prop == 'vfhb_deoxy':
result = np.sum(hbvolume * (1. - sat)) / volume
elif prop == 'vfhb':
result = np.sum(hbvolume) / volume
gmeasure.create_dataset(prop, data=[result, 0.])
elif prop in ('chb_oxy', 'chb_deoxy', 'chb'):
m = {
'chb_oxy': 'vfhb_oxy',
'chb_deoxy': 'vfhb_deoxy',
'chb': 'vfhb'
}
result = dataman.obtain_data('detailedPO2_global', m[prop],
po2group, sample_length,
cachelocation)
result = result * detailedo2.chb_of_rbcs
gmeasure.create_dataset(prop, data=[result, 0.])
elif prop == 'mro2_by_j':
fluxes = dataman.obtain_data('detailedPO2_total_fluxes',
prop, po2group, sample_length,
1)
ld = krebsutils.read_lattice_data_from_hdf(
po2group['field_ld'])
worldbb = ld.worldBox
result = fluxes['Jout_tv'] / np.prod(worldbb[1] -
worldbb[0]) * 1.e12
gmeasure.create_dataset(prop, data=[result, 0.])
elif prop == 'oef':
fluxes = dataman.obtain_data('detailedPO2_total_fluxes',
prop, po2group, sample_length,
1, samplelocation)
result = (fluxes['Jin_root'] -
fluxes['Jout_root']) / fluxes['Jin_root']
gmeasure.create_dataset(prop, data=[result, 0.])
else:
assert False