def boxcountInitialVessels(fn, opts):
items_to_analyze = [
'arteries', 'veins', 'capillaries', 'complete', 'arteriovenous'
]
with h5py.File(fn, 'r') as f:
data = {}
for item_to_analyze in items_to_analyze:
calcBoxCounts(data, f['vessels'], item_to_analyze, opts)
with myutils.MeasurementFile(fn, h5files, opts.prefix) as fdst:
gdst = fdst.recreate_group('fractaldim')
myutils.hdf_write_dict_hierarchy(gdst, '.', data)
def computeAdaption_(gdst, vesselgroup, parameters):
#myutils.buildLink(gdst, 'SOURCE_VESSELS', vesselgroup)
#gdst.attrs['PARAMETER_CHECKSUM'] = myutils.checksum(parameters)
#gdst.attrs['UUID'] = myutils.uuidstr()
#writing parameters as acsii string. but i think it is better to store them as hdf datasets directly
#ds = gdst.create_dataset('PARAMETERS_AS_JSON', data = np.string_(json.dumps(parameters, indent=2)))
#myutils.hdf_write_dict_hierarchy(gdst, 'parameters', parameters)
#gdst.file.flush()
# now we can compute Adaption. The c++ routine can a.t.m. only read from hdf so it has to use our new file
r = adaption_cpp.computeAdaption(vesselgroup, parameters['adaption'], parameters['calcflow'], gdst)
myutils.hdf_write_dict_hierarchy(gdst, 'parameters', parameters)
gdst.file.flush()
def doit_optimize(vesselFileName,adaptParams,BfParams):
returns = adaption_cpp.doAdaptionOptimization(vesselFileName,adaptParams,BfParams)
print("should be optimized vaules:")
print(returns)
print('from file: %s' %vesselFileName)
f_results = h5files.open("optimize_results.h5", 'a')
a_uuid = str(uuid.uuid4())
g = f_results.create_group(vesselFileName + '_' + a_uuid)
g.create_dataset('x_opt', data=returns)
myutils.hdf_write_dict_hierarchy(g, 'adaptParams', adaptParams)
myutils.hdf_write_dict_hierarchy(g, 'BfParams', BfParams)
g.file.flush()
#f_results.close()
return returns
def boxcountTumorVessels(fn, opts):
items_to_analyze = ['complete', 'withintumor']
with h5py.File(fn, 'r') as f:
groups = myutils.getTimeSortedGroups(f['.'], "out")
for g in groups[-1:]:
data = {}
print '%s : %s' % (fn, g.name)
for item_to_analyze in items_to_analyze:
calcBoxCounts(data, g['vessels'], item_to_analyze, opts)
with myutils.MeasurementFile(fn, h5files, opts.prefix) as fdst:
dstgroup = myutils.require_snapshot_group_(fdst, g)
fdst.attrs.create('tumorcode_file_type',
data='fractaldim_analyze_bulktum')
#gdst = fdst.require_snapshot_group_(g)
#gdst = gdst.recreate_group('fractaldim')
#myutils.hdf_write_dict_hierarchy(gdst, '.', data)
myutils.hdf_write_dict_hierarchy(dstgroup, 'fractaldim', data)
def createMostBasic_world(fn):
points = []
points.append([-5, 0, 0])
p1 = [0, 0, 0]
p2 = [2.5, 3, 0]
p3 = [7.5, 3, 0]
p4 = [10, 0, 0]
p5 = [2.5, -3, 0]
p6 = [7.5, -3, 0]
p7 = [15, 0, 0]
points.append(p1)
points.append(p2)
points.append(p3)
points.append(p4)
points.append(p5)
points.append(p6)
points.append(p7)
node_a_index = [0, 1, 2, 3, 1, 5, 6, 4]
node_b_index = [1, 2, 3, 4, 5, 6, 4, 7]
flags = []
radii = []
flags.append(int(ku.ARTERY)) #0
radii.append(7)
flags.append(int(ku.ARTERY)) #1
radii.append(5)
flags.append(int(ku.CAPILLARY)) #2
radii.append(3)
flags.append(int(ku.VEIN)) #3
radii.append(6)
flags.append(int(ku.ARTERY)) #4
radii.append(5)
flags.append(int(ku.CAPILLARY)) #5
radii.append(3)
flags.append(int(ku.VEIN)) #6
radii.append(6)
flags.append(int(ku.VEIN)) #7
radii.append(10)
# flags.append(int(ku.VEIN))#8
# radii.append(level1)
# flags.append(int(ku.ARTERY))#9
# radii.append(level2)
# flags.append(int(ku.ARTERY))#10
# radii.append(level3)
# flags.append(int(ku.CAPILLARY))#11
# radii.append(level3)
# flags.append(int(ku.VEIN))#12
# radii.append(level3)
# flags.append(int(ku.ARTERY))#13
# radii.append(level3)
# flags.append(int(ku.CAPILLARY))#14
# radii.append(level3)
# flags.append(int(ku.VEIN))#15
# radii.append(level3)
# flags.append(int(ku.VEIN))#16
# radii.append(level2)
# flags.append(int(ku.CAPILLARY))#17
# radii.append(level3)
# #mirror x axis
# for i in range(len(points)):
# points.append([points[i][0],-points[i][1],points[i][2]])
#
#
# #double flags
# for i in range(len(flags)):
# flags.append(flags[i])
# radii.append(radii[i])
#
# #add entrence
# flags.append(int(ku.ARTERY))
# radii.append(level0)
# points.append([-13,0,0])
# flags.append(int(ku.VEIN))
# radii.append(level0)
# points.append([13,0,0])
#
# def reverse_xy(points):
# points2 = []
# scale = 100
# for point in points:
# points2.append([point[1]*scale,point[0]*scale,point[2]*scale])
# return points2
#
# #points = reverse_xy(points)
# points = scale_points(points)
# #add circulated flag
# flags2=[]
# for aflag in flags:
# flags2.append(np.bitwise_or(ku.CIRCULATED,aflag))
#
# #this datalist is after successfull adaption with all signals
# datalist=[13.301738,9.563003,7.1691427,7.108207,5.7596083,5.6054125,5.584573,6.330745,8.065095,9.596836,7.166868,
# 5.7524166,5.6175594,7.166868,5.7524166,5.6175594,6.354387,5.704988,13.301738,9.563003,7.1691427,7.108207,
# 5.7596083,5.6054125,5.584573,6.330745,8.065095,9.596836,7.166868,5.7524166,5.6175594,7.166868,5.7524166,
# 5.6175594,6.354387,5.704988,17.057396,9.972254]
# #radii = np.array(datalist)
# #radii=10*np.ones(len(node_a_index))
# #radii=6.5*np.ones(len(node_a_index))
# #write the data to a h5 file
# #f2 = h5files.open(fn,'w')
f2 = fn
f2.create_group('mostBasic_world/vessels')
#edge stuff
N_edges = len(radii)
edgegrp = f2['mostBasic_world/vessels'].create_group("edges")
edgegrp.attrs.create('COUNT', N_edges)
ds_nodeA = edgegrp.create_dataset('node_a_index', data=node_a_index)
ds_nodeB = edgegrp.create_dataset('node_b_index', data=node_b_index)
ds_radius = edgegrp.create_dataset('radius', data=radii)
#ds_hema = edgegrp.create_dataset('hematocrit', data=hema)
#ds_flow = edgegrp.create_dataset('flow', data=flow)
#mw_vessel_flags = get_flags_from_other_file()
ds_vesselflags = edgegrp.create_dataset('flags', data=flags)
#node stuff
N_nodes = len(points)
nodegrp = f2['mostBasic_world/vessels'].create_group("nodes")
nodegrp.attrs.create('COUNT', N_nodes)
ds_roots = f2['mostBasic_world/vessels/nodes'].create_dataset('roots',
data=[0, 7])
ds_bc_node_index = f2['mostBasic_world/vessels/nodes'].create_dataset(
'bc_node_index', data=[0, 7])
#dummy pressure for compatiblility
#ds_pressure = f3['vessels/nodes'].create_dataset('roots_pressure', data = roots_pressure)
#this works
#ds_value_of_bc = f2['symetricA/vessels/nodes'].create_dataset('bc_value', data=[70*0.133,10*0.133])
ds_value_of_bc = f2['mostBasic_world/vessels/nodes'].create_dataset(
'bc_value', data=[5, 3])
ds_bctyp_of_roots = f2['mostBasic_world/vessels/nodes'].create_dataset(
'bc_type', data=[1, 1])
ds_world_pos = f2['mostBasic_world/vessels/nodes'].create_dataset(
'world_pos', data=np.array(points))
f2['mostBasic_world/vessels'].attrs.create('CLASS', 'REALWORLD')
import krebsjobs.parameters.parameterSetsAdaption
#set_name = 'adaption_symetric_world'
set_name = 'adaption_symetricA_world_break'
#set_name = 'adaption_symetric_world'
adaptionParams = getattr(krebsjobs.parameters.parameterSetsAdaption,
set_name)
#CALCULATE!!!!
myutils.hdf_write_dict_hierarchy(f2['/'], 'mostBasic_world/parameters',
adaptionParams)
f2['mostBasic_world/parameters'].attrs.create('name', set_name)
f2['/'].file.flush()
dd = ku.calc_vessel_hydrodynamics(
f2['mostBasic_world/vessels'],
adaptionParams['calcflow']['includePhaseSeparationEffect'], False,
None, adaptionParams['calcflow'])
print('Calcflow done')
def createSymetricIrregular_world(fn):
points = []
points.append([-10, 0, 0])
p1 = [-6, 4, 0]
p2 = [-3, 6, 0]
p3 = [-1, 7, 0]
p4 = [-1, 5, 0]
p5 = [-3, 2, 0]
p6 = [-1, 3, 0]
p7 = [-1, 1, 0]
points.append(p1)
points.append(p2)
points.append(p3)
points.append(p4)
points.append(p5)
points.append(p6)
points.append(p7)
#mirror y axis
for i in range(len(points)):
points.append([-points[i][0], points[i][1], points[i][2]])
node_a_index = [
0, 1, 2, 2, 3, 11, 12, 10, 9, 1, 5, 6, 14, 5, 7, 15, 13, 4, 0, 17, 18,
18, 19, 27, 28, 26, 25, 17, 21, 22, 30, 21, 23, 31, 29, 20, 32, 8
]
node_b_index = [
1, 2, 3, 4, 11, 10, 10, 9, 8, 5, 6, 14, 13, 7, 15, 13, 9, 12, 17, 18,
19, 20, 27, 26, 26, 25, 8, 21, 22, 30, 29, 23, 31, 29, 25, 28, 0, 33
]
flags = []
radii = []
flags.append(int(ku.ARTERY)) #0
radii.append(9.5)
flags.append(int(ku.ARTERY)) #1
radii.append(8)
flags.append(int(ku.ARTERY)) #2
radii.append(7.5)
flags.append(int(ku.ARTERY)) #3
radii.append(7.)
flags.append(int(ku.CAPILLARY)) #4
radii.append(6.5)
flags.append(int(ku.VEIN)) #5
radii.append(7.)
flags.append(int(ku.VEIN)) #6
radii.append(7.5)
flags.append(int(ku.VEIN)) #7
radii.append(8)
flags.append(int(ku.VEIN)) #8
radii.append(9.5)
flags.append(int(ku.ARTERY)) #9
radii.append(8)
flags.append(int(ku.ARTERY)) #10
radii.append(7.5)
flags.append(int(ku.CAPILLARY)) #11
radii.append(6.5)
flags.append(int(ku.VEIN)) #12
radii.append(7.5)
flags.append(int(ku.ARTERY)) #13
radii.append(7.5)
flags.append(int(ku.CAPILLARY)) #14
radii.append(6.5)
flags.append(int(ku.VEIN)) #15
radii.append(7.5)
flags.append(int(ku.VEIN)) #16
radii.append(8)
flags.append(int(ku.CAPILLARY)) #17
radii.append(6.5)
#mirror x axis
for i in range(len(points)):
points.append([points[i][0], -points[i][1], points[i][2]])
#double flags
for i in range(len(flags)):
flags.append(flags[i])
radii.append(radii[i])
#add entrence
flags.append(int(ku.ARTERY))
radii.append(11.5)
points.append([-13, 0, 0])
flags.append(int(ku.VEIN))
radii.append(11.5)
points.append([13, 0, 0])
def reverse_xy(points):
points2 = []
scale = 100
for point in points:
points2.append([point[1] * scale, point[0] * scale, point[2]] *
scale)
return points2
def scale_points(points):
points2 = []
scale = 100
for point in points:
points2.append([point[0] * scale, point[1] * scale, point[2]] *
scale)
return points2
#points = reverse_xy(points)
points = scale_points(points)
#add circulated flag
flags2 = []
for aflag in flags:
flags2.append(np.bitwise_or(ku.CIRCULATED, aflag))
#radii=30*np.ones(len(node_a_index))
#radii=10*np.ones(len(node_a_index))
#now we will randomize the points a little bit
def screw_points(points, variation):
points2 = []
for point in points:
points2.append([
point[0] + point[0] * np.random.normal(0, variation),
point[1] + point[1] * np.random.normal(0, 2 * variation),
point[2] + point[2] * np.random.normal(0, variation)
])
return points2
if os.path.isfile('screwed_points.h5'):
f_out = h5py.File('screwed_points.h5', 'r')
points = np.asarray(f_out['screwed_points'])
else:
points = screw_points(points, 0.05)
f_out = h5py.File('screwed_points.h5', 'w')
f_out.create_dataset('screwed_points', data=points)
f_out.close()
#write the data to a h5 file
#f2 = h5files.open(fn,'w')
f2 = fn
f2.create_group('symetricIrregular/vessels')
#edge stuff
N_edges = len(radii)
edgegrp = f2['symetricIrregular/vessels'].create_group("edges")
edgegrp.attrs.create('COUNT', N_edges)
ds_nodeA = edgegrp.create_dataset('node_a_index', data=node_a_index)
ds_nodeB = edgegrp.create_dataset('node_b_index', data=node_b_index)
ds_radius = edgegrp.create_dataset('radius', data=radii)
#ds_hema = edgegrp.create_dataset('hematocrit', data=hema)
#ds_flow = edgegrp.create_dataset('flow', data=flow)
#mw_vessel_flags = get_flags_from_other_file()
ds_vesselflags = edgegrp.create_dataset('flags', data=flags2)
#node stuff
N_nodes = len(points)
nodegrp = f2['symetricIrregular/vessels'].create_group("nodes")
nodegrp.attrs.create('COUNT', N_nodes)
ds_roots = f2['symetricIrregular/vessels/nodes'].create_dataset(
'roots', data=[32, 33])
ds_bc_node_index = f2['symetricIrregular/vessels/nodes'].create_dataset(
'bc_node_index', data=[32, 33])
#dummy pressure for compatiblility
#ds_pressure = f3['vessels/nodes'].create_dataset('roots_pressure', data = roots_pressure)
ds_value_of_bc = f2['symetricIrregular/vessels/nodes'].create_dataset(
'bc_value', data=[70 * 0.133, 2.5e6])
ds_bctyp_of_roots = f2['symetricIrregular/vessels/nodes'].create_dataset(
'bc_type', data=[1, 2])
ds_world_pos = f2['symetricIrregular/vessels/nodes'].create_dataset(
'world_pos', data=np.array(points))
f2['symetricIrregular/vessels'].attrs.create('CLASS', 'REALWORLD')
import krebsjobs.parameters.parameterSetsAdaption
set_name = 'adaption_symetricIrregular_world'
adaptionParams = getattr(krebsjobs.parameters.parameterSetsAdaption,
set_name)
#CALCULATE!!!!
myutils.hdf_write_dict_hierarchy(f2['/'], 'symetricIrregular/parameters',
adaptionParams)
f2['symetricIrregular/parameters'].attrs.create('name', set_name)
f2['/'].file.flush()
dd = ku.calc_vessel_hydrodynamics(
f2['symetricIrregular/vessels'],
adaptionParams['calcflow']['includePhaseSeparationEffect'], False,
None, adaptionParams['calcflow'])
print('Calcflow done')
#r = adap.computeAdaption(f3['vessels'], None, adaptionParams, adaptionParams['calcflow'], f3['/'] )
#dst_grp = f2['/symetric'].create_group('symetric/vessels_after_adaption')
dd = adap.computeAdaption_(
f2['/symetricIrregular'].create_group('vessels_after_adaption'),
f2['symetricIrregular/vessels'], adaptionParams)
a = os.system(
"python2 /localdisk/thierry/tumorcode/py/krebs/hdfvessels2vtk.py test_configs.h5 /symetricIrregular/vessels_after_adaption/vessels_after_adaption"
)
os.system(
"python2 /localdisk/thierry/tumorcode/py/krebs/povrayRenderVessels.py test_configs.h5 /symetricIrregular/vessels_after_adaption/vessels_after_adaption"
)
def createSymetric_worldB(fn):
points = []
points.append([-10, 0, 0])
p1 = [-6, 4, 0]
p2 = [-3, 6, 0]
p3 = [-1, 7, 0]
p4 = [-1, 5, 0]
p5 = [-3, 2, 0]
p6 = [-1, 3, 0]
p7 = [-1, 1, 0]
points.append(p1)
points.append(p2)
points.append(p3)
points.append(p4)
points.append(p5)
points.append(p6)
points.append(p7)
#mirror y axis
for i in range(len(points)):
points.append([-points[i][0], points[i][1], points[i][2]])
node_a_index = [
0, 1, 2, 2, 3, 11, 12, 10, 9, 1, 5, 6, 14, 5, 7, 15, 13, 4, 0, 17, 18,
18, 19, 27, 28, 26, 25, 17, 21, 22, 30, 21, 23, 31, 29, 20, 32, 8
]
node_b_index = [
1, 2, 3, 4, 11, 10, 10, 9, 8, 5, 6, 14, 13, 7, 15, 13, 9, 12, 17, 18,
19, 20, 27, 26, 26, 25, 8, 21, 22, 30, 29, 23, 31, 29, 25, 28, 0, 33
]
flags = []
radii = []
# level0 = 12.
# level1 = 10.1
# level2 = 8.5
# level3 = 7.1
level0 = 11.95
level1 = 9.55
level2 = 7.55
level3 = 6.0
flags.append(int(ku.ARTERY)) #0
radii.append(level1)
flags.append(int(ku.ARTERY)) #1
radii.append(level2)
flags.append(int(ku.ARTERY)) #2
radii.append(level3)
flags.append(int(ku.ARTERY)) #3
radii.append(level3)
flags.append(int(ku.CAPILLARY)) #4
radii.append(level3)
flags.append(int(ku.VEIN)) #5
radii.append(level3)
flags.append(int(ku.VEIN)) #6
radii.append(level3)
flags.append(int(ku.VEIN)) #7
radii.append(level2)
flags.append(int(ku.VEIN)) #8
radii.append(level1)
flags.append(int(ku.ARTERY)) #9
radii.append(level2)
flags.append(int(ku.ARTERY)) #10
radii.append(level3)
flags.append(int(ku.CAPILLARY)) #11
radii.append(level3)
flags.append(int(ku.VEIN)) #12
radii.append(level3)
flags.append(int(ku.ARTERY)) #13
radii.append(level3)
flags.append(int(ku.CAPILLARY)) #14
radii.append(level3)
flags.append(int(ku.VEIN)) #15
radii.append(level3)
flags.append(int(ku.VEIN)) #16
radii.append(level2)
flags.append(int(ku.CAPILLARY)) #17
radii.append(level3)
#mirror x axis
for i in range(len(points)):
points.append([points[i][0], -points[i][1], points[i][2]])
#double flags
for i in range(len(flags)):
flags.append(flags[i])
radii.append(radii[i])
#add entrence
flags.append(int(ku.ARTERY))
radii.append(level0)
points.append([-13, 0, 0])
flags.append(int(ku.VEIN))
radii.append(level0)
points.append([13, 0, 0])
def reverse_xy(points):
points2 = []
scale = 100
for point in points:
points2.append([point[1] * scale, point[0] * scale, point[2]] *
scale)
return points2
#points = reverse_xy(points)
points = scale_points(points)
#add circulated flag
flags2 = []
for aflag in flags:
flags2.append(np.bitwise_or(ku.CIRCULATED, aflag))
#this datalist is after successfull adaption with all signals
datalist = [
13.301738, 9.563003, 7.1691427, 7.108207, 5.7596083, 5.6054125,
5.584573, 6.330745, 8.065095, 9.596836, 7.166868, 5.7524166, 5.6175594,
7.166868, 5.7524166, 5.6175594, 6.354387, 5.704988, 13.301738,
9.563003, 7.1691427, 7.108207, 5.7596083, 5.6054125, 5.584573,
6.330745, 8.065095, 9.596836, 7.166868, 5.7524166, 5.6175594, 7.166868,
5.7524166, 5.6175594, 6.354387, 5.704988, 17.057396, 9.972254
]
#radii = np.array(datalist)
#radii=10*np.ones(len(node_a_index))
#radii=6.5*np.ones(len(node_a_index))
#write the data to a h5 file
#f2 = h5files.open(fn,'w')
f2 = fn
f2.create_group('symetricB/vessels')
#edge stuff
N_edges = len(radii)
edgegrp = f2['symetricB/vessels'].create_group("edges")
edgegrp.attrs.create('COUNT', N_edges)
ds_nodeA = edgegrp.create_dataset('node_a_index', data=node_a_index)
ds_nodeB = edgegrp.create_dataset('node_b_index', data=node_b_index)
ds_radius = edgegrp.create_dataset('radius', data=radii)
#ds_hema = edgegrp.create_dataset('hematocrit', data=hema)
#ds_flow = edgegrp.create_dataset('flow', data=flow)
#mw_vessel_flags = get_flags_from_other_file()
ds_vesselflags = edgegrp.create_dataset('flags', data=flags2)
#node stuff
N_nodes = len(points)
nodegrp = f2['symetricB/vessels'].create_group("nodes")
nodegrp.attrs.create('COUNT', N_nodes)
ds_roots = f2['symetricB/vessels/nodes'].create_dataset('roots',
data=[32, 33])
ds_bc_node_index = f2['symetricB/vessels/nodes'].create_dataset(
'bc_node_index', data=[32, 33])
#dummy pressure for compatiblility
#ds_pressure = f3['vessels/nodes'].create_dataset('roots_pressure', data = roots_pressure)
ds_value_of_bc = f2['symetricB/vessels/nodes'].create_dataset(
'bc_value', data=[70 * 0.133, 0.5e6])
ds_bctyp_of_roots = f2['symetricB/vessels/nodes'].create_dataset(
'bc_type', data=[1, 2])
ds_world_pos = f2['symetricB/vessels/nodes'].create_dataset(
'world_pos', data=np.array(points))
f2['symetricB/vessels'].attrs.create('CLASS', 'REALWORLD')
import krebsjobs.parameters.parameterSetsAdaption
set_name = 'adaption_symetricB_world'
adaptionParams = getattr(krebsjobs.parameters.parameterSetsAdaption,
set_name)
#CALCULATE!!!!
myutils.hdf_write_dict_hierarchy(f2['/'], 'symetricB/parameters',
adaptionParams)
f2['symetricB/parameters'].attrs.create('name', set_name)
f2['/'].file.flush()
dd = ku.calc_vessel_hydrodynamics(
f2['symetricB/vessels'],
adaptionParams['calcflow']['includePhaseSeparationEffect'], False,
None, adaptionParams['calcflow'])
print('Calcflow done')
#r = adap.computeAdaption(f3['vessels'], None, adaptionParams, adaptionParams['calcflow'], f3['/'] )
#dst_grp = f2['/symetric'].create_group('symetric/vessels_after_adaption')
dd = adap.computeAdaption_(
f2['/symetricB'].create_group('vessels_after_adaption'),
f2['symetricB/vessels'], adaptionParams)
a = os.system(
"python2 /localdisk/thierry/tumorcode/py/krebs/hdfvessels2vtk.py test_configs.h5 /symetricB/vessels_after_adaption/vessels_after_adaption"
)
os.system(
"python2 /localdisk/thierry/tumorcode/py/krebs/povrayRenderVessels.py test_configs.h5 /symetricB/vessels_after_adaption/vessels_after_adaption"
)
def createAsymetric_world(fn):
points = []
points.append([0, 0, 0])
points.append([200, 0, 0])
points.append([400, 0, 0])
points.append([600, 0, 0])
points.append([800, 0, 0])
points.append([1000, 0, 0])
points.append([1200, 0, 0])
points.append([1400, 0, 0])
points.append([1450, 120, 0])
points.append([1500, 240, 0])
points.append([1500, 360, 0])
points.append([1450, 480, 0])
points.append([1400, 600, 0])
points.append([1350, 480, 0])
points.append([1300, 360, 0])
points.append([1300, 240, 0])
points.append([1350, 120, 0])
points.append([1200, 600, 0])
points.append([1000, 600, 0])
points.append([800, 600, 0])
points.append([600, 600, 0])
points.append([400, 600, 0])
points.append([200, 600, 0])
points.append([0, 600, 0])
points.append([1200, 360, 0])
points.append([1000, 360, 0])
points.append([800, 360, 0])
points.append([600, 360, 0])
points.append([400, 360, 0])
points.append([200, 360, 0])
points.append([1200, 240, 0])
points.append([1000, 240, 0])
points.append([800, 240, 0])
points.append([600, 240, 0])
points.append([400, 240, 0])
points.append([200, 240, 0])
def scale_points(points):
points2 = []
scale = 1
for point in points:
points2.append([-point[0] * scale, -point[1] * scale, point[2]] *
scale)
return points2
#points = reverse_xy(points)
points = scale_points(points)
node_a_index = []
node_b_index = []
flags = []
radii = []
#1
node_a_index.append(0)
node_b_index.append(1)
flags.append(int(ku.ARTERY))
radii.append(7.5)
#2
node_a_index.append(1)
node_b_index.append(2)
flags.append(int(ku.ARTERY))
radii.append(7.)
#3
node_a_index.append(2)
node_b_index.append(3)
flags.append(int(ku.ARTERY))
radii.append(6.5)
#4
node_a_index.append(3)
node_b_index.append(4)
flags.append(int(ku.ARTERY))
radii.append(6.0)
#5
node_a_index.append(4)
node_b_index.append(5)
flags.append(int(ku.ARTERY))
radii.append(5.8)
#6
node_a_index.append(5)
node_b_index.append(6)
flags.append(int(ku.ARTERY))
radii.append(5.6)
#7
node_a_index.append(6)
node_b_index.append(7)
flags.append(int(ku.ARTERY))
radii.append(5.4)
#8
node_a_index.append(7)
node_b_index.append(8)
flags.append(int(ku.ARTERY))
radii.append(5.2)
#9
node_a_index.append(8)
node_b_index.append(9)
flags.append(int(ku.ARTERY))
radii.append(5.)
#10
node_a_index.append(9)
node_b_index.append(10)
flags.append(int(ku.CAPILLARY))
radii.append(4.5)
#11
node_a_index.append(10)
node_b_index.append(11)
flags.append(int(ku.VEIN))
radii.append(7.)
#12
node_a_index.append(11)
node_b_index.append(12)
flags.append(int(ku.VEIN))
radii.append(7.)
#13
node_a_index.append(12)
node_b_index.append(13)
flags.append(int(ku.VEIN))
radii.append(7.)
#14
node_a_index.append(13)
node_b_index.append(14)
flags.append(int(ku.VEIN))
radii.append(7.)
#15
node_a_index.append(14)
node_b_index.append(15)
flags.append(int(ku.CAPILLARY))
radii.append(4.5)
#16
node_a_index.append(15)
node_b_index.append(16)
flags.append(int(ku.ARTERY))
radii.append(7.)
#17
node_a_index.append(16)
node_b_index.append(7)
flags.append(int(ku.ARTERY))
radii.append(7.)
#18
node_a_index.append(12)
node_b_index.append(17)
flags.append(int(ku.VEIN))
radii.append(7.)
#19
node_a_index.append(17)
node_b_index.append(18)
flags.append(int(ku.VEIN))
radii.append(7.)
#20
node_a_index.append(18)
node_b_index.append(19)
flags.append(int(ku.VEIN))
radii.append(7.)
#21
node_a_index.append(19)
node_b_index.append(20)
flags.append(int(ku.VEIN))
radii.append(7.)
#22
node_a_index.append(20)
node_b_index.append(21)
flags.append(int(ku.VEIN))
radii.append(7.)
#23
node_a_index.append(21)
node_b_index.append(22)
flags.append(int(ku.VEIN))
radii.append(7.)
#24
node_a_index.append(22)
node_b_index.append(23)
flags.append(int(ku.VEIN))
radii.append(7.)
#25
node_a_index.append(17)
node_b_index.append(24)
flags.append(int(ku.VEIN))
radii.append(7.)
#26
node_a_index.append(18)
node_b_index.append(25)
flags.append(int(ku.VEIN))
radii.append(7.)
#27
node_a_index.append(19)
node_b_index.append(26)
flags.append(int(ku.VEIN))
radii.append(7.)
#28
node_a_index.append(20)
node_b_index.append(27)
flags.append(int(ku.VEIN))
radii.append(7.)
#29
node_a_index.append(21)
node_b_index.append(28)
flags.append(int(ku.VEIN))
radii.append(7.)
#30
node_a_index.append(22)
node_b_index.append(29)
flags.append(int(ku.VEIN))
radii.append(7.)
#31
node_a_index.append(24)
node_b_index.append(30)
flags.append(int(ku.CAPILLARY))
radii.append(4.5)
#32
node_a_index.append(25)
node_b_index.append(31)
flags.append(int(ku.CAPILLARY))
radii.append(4.5)
#33
node_a_index.append(26)
node_b_index.append(32)
flags.append(int(ku.CAPILLARY))
radii.append(4.5)
#34
node_a_index.append(27)
node_b_index.append(33)
flags.append(int(ku.CAPILLARY))
radii.append(4.5)
#35
node_a_index.append(28)
node_b_index.append(34)
flags.append(int(ku.CAPILLARY))
radii.append(4.5)
#36
node_a_index.append(29)
node_b_index.append(35)
flags.append(int(ku.CAPILLARY))
radii.append(4.5)
#37
node_a_index.append(30)
node_b_index.append(6)
flags.append(int(ku.ARTERY))
radii.append(7.)
#38
node_a_index.append(31)
node_b_index.append(5)
flags.append(int(ku.ARTERY))
radii.append(7.)
#39
node_a_index.append(32)
node_b_index.append(4)
flags.append(int(ku.ARTERY))
radii.append(7.)
#40
node_a_index.append(33)
node_b_index.append(3)
flags.append(int(ku.ARTERY))
radii.append(7.)
#41
node_a_index.append(34)
node_b_index.append(2)
flags.append(int(ku.ARTERY))
radii.append(7.)
#42
node_a_index.append(35)
node_b_index.append(1)
flags.append(int(ku.ARTERY))
radii.append(7.)
#add circulated flag
flags2 = []
for aflag in flags:
flags2.append(np.bitwise_or(ku.CIRCULATED, aflag))
#radii=30*np.ones(len(node_a_index))
#write the data to a h5 file
#f3 = h5files.open(fn,'w')
f3 = fn
f3.create_group('Asymetric/vessels')
#edge stuff
N_edges = len(radii)
edgegrp = f3['Asymetric/vessels'].create_group("edges")
edgegrp.attrs.create('COUNT', N_edges)
ds_nodeA = edgegrp.create_dataset('node_a_index', data=node_a_index)
ds_nodeB = edgegrp.create_dataset('node_b_index', data=node_b_index)
ds_radius = edgegrp.create_dataset('radius', data=radii)
#ds_hema = edgegrp.create_dataset('hematocrit', data=hema)
#ds_flow = edgegrp.create_dataset('flow', data=flow)
#mw_vessel_flags = get_flags_from_other_file()
ds_vesselflags = edgegrp.create_dataset('flags', data=flags2)
#node stuff
N_nodes = len(points)
nodegrp = f3['Asymetric/vessels'].create_group("nodes")
nodegrp.attrs.create('COUNT', N_nodes)
ds_bc_node_index = f3['Asymetric/vessels/nodes'].create_dataset(
'bc_node_index', data=[0, 23])
ds_roots = f3['Asymetric/vessels/nodes'].create_dataset('roots',
data=[0, 23])
#dummy pressure for compatiblility
#ds_pressure = f3['vessels/nodes'].create_dataset('roots_pressure', data = roots_pressure)
ds_value_of_bc = f3['Asymetric/vessels/nodes'].create_dataset(
'bc_value', data=[70 * 0.133, 2.5e6])
""" Michael 1: press, 2, flow,
Secomb 0: press, 2, flow
"""
ds_bctyp_of_roots = f3['Asymetric/vessels/nodes'].create_dataset(
'bc_type', data=[1, 2])
ds_world_pos = f3['Asymetric/vessels/nodes'].create_dataset(
'world_pos', data=np.array(points))
f3['Asymetric/vessels'].attrs.create('CLASS', 'REALWORLD')
import krebsjobs.parameters.parameterSetsAdaption
set_name = 'adaption_asymetric_world'
adaptionParams = getattr(krebsjobs.parameters.parameterSetsAdaption,
set_name)
#CALCULATE!!!!
myutils.hdf_write_dict_hierarchy(f3['/'], 'Asymetric/parameters',
adaptionParams)
f3['Asymetric/parameters'].attrs.create('name', set_name)
f3['/'].file.flush()
dd = ku.calc_vessel_hydrodynamics(
f3['Asymetric/vessels'],
adaptionParams['calcflow']['includePhaseSeparationEffect'], False,
None, adaptionParams['calcflow'])
print('Calcflow done')
#r = adap.computeAdaption(f3['vessels'], None, adaptionParams, adaptionParams['calcflow'], f3['/'] )
dd = adap.computeAdaption_(
f3['Asymetric'].create_group('vessels_after_adaption'),
f3['Asymetric/vessels'], adaptionParams)
def createMostBasic_world(fn):
points = []
points.append([-5, 0, 0])
p1 = [0, 0, 0]
p2 = [2.5, 3, 0]
p3 = [7.5, 3, 0]
p4 = [10, 0, 0]
p5 = [2.5, -3, 0]
p6 = [7.5, -3, 0]
p7 = [15, 0, 0]
points.append(p1)
points.append(p2)
points.append(p3)
points.append(p4)
points.append(p5)
points.append(p6)
points.append(p7)
node_a_index = [0, 1, 2, 3, 1, 5, 6, 4]
node_b_index = [1, 2, 3, 4, 5, 6, 4, 7]
flags = []
radii = []
flags.append(int(ku.ARTERY)) #0
radii.append(7)
flags.append(int(ku.ARTERY)) #1
radii.append(5)
flags.append(int(ku.CAPILLARY)) #2
radii.append(3)
flags.append(int(ku.VEIN)) #3
radii.append(6)
flags.append(int(ku.ARTERY)) #4
radii.append(5)
flags.append(int(ku.CAPILLARY)) #5
radii.append(3)
flags.append(int(ku.VEIN)) #6
radii.append(6)
flags.append(int(ku.VEIN)) #7
radii.append(10)
f2 = fn
f2.create_group('mostBasic_world/vessels')
#edge stuff
N_edges = len(radii)
edgegrp = f2['mostBasic_world/vessels'].create_group("edges")
edgegrp.attrs.create('COUNT', N_edges)
ds_nodeA = edgegrp.create_dataset('node_a_index', data=node_a_index)
ds_nodeB = edgegrp.create_dataset('node_b_index', data=node_b_index)
ds_radius = edgegrp.create_dataset('radius', data=radii)
#ds_hema = edgegrp.create_dataset('hematocrit', data=hema)
#ds_flow = edgegrp.create_dataset('flow', data=flow)
ds_vesselflags = edgegrp.create_dataset('flags', data=flags)
#node stuff
N_nodes = len(points)
nodegrp = f2['mostBasic_world/vessels'].create_group("nodes")
nodegrp.attrs.create('COUNT', N_nodes)
ds_roots = f2['mostBasic_world/vessels/nodes'].create_dataset('roots',
data=[0, 7])
ds_bc_node_index = f2['mostBasic_world/vessels/nodes'].create_dataset(
'bc_node_index', data=[0, 7])
ds_value_of_bc = f2['mostBasic_world/vessels/nodes'].create_dataset(
'bc_value', data=[5, 3])
ds_bctyp_of_roots = f2['mostBasic_world/vessels/nodes'].create_dataset(
'bc_type', data=[1, 1])
ds_world_pos = f2['mostBasic_world/vessels/nodes'].create_dataset(
'world_pos', data=np.array(points))
f2['mostBasic_world/vessels'].attrs.create('CLASS', 'REALWORLD')
'''get some hydrodynamics parameters'''
import krebsjobs.parameters.parameterSetsVesselGen
set_name = 'default'
vesselGenParams = getattr(krebsjobs.parameters.parameterSetsVesselGen,
set_name)
#CALCULATE!!!!
myutils.hdf_write_dict_hierarchy(f2['/'], 'mostBasic_world/parameters',
vesselGenParams['calcflow'])
f2['mostBasic_world/parameters'].attrs.create('name', set_name)
f2['/'].file.flush()
#calc_vessel_hydrodynamics(vesselgroup, calc_hematocrit=False, return_flags=False, override_hematocrit = None, bloodflowparams = dict(),storeCalculationInHDF=False)
dd = ku.calc_vessel_hydrodynamics(
f2['mostBasic_world/vessels'],
bloodflowparams=vesselGenParams['calcflow'],
storeCalculationInHDF=True)
print('Calcflow done')
def generate_data(statedata, dstgroup):
"""
main routine that does all the measurement.
Enable Individual parts. Computed date overwrites old data.
"""
print statedata.file.filename, statedata.name
time = statedata.attrs['time']
tum_grp = statedata['tumor']
tum_ld = get_tumld(tum_grp)
cellvol = tum_ld.scale**3
distmap = calc_distmap(tum_grp)
radialmap = krebsutils.make_radial_field(tum_ld)
axial_max_rad = np.max(np.abs(tum_ld.worldBox.ravel()))
ptc = np.asarray(tum_grp['ptc'])
if 1:
## shape data: rim surface area, volume
vtkds, = extractVtkFields.Extractor(statedata['tumor'],
['ls']).asVtkDataSets()
area = integrate_surface(vtkds)
del vtkds
vol = np.sum(ptc) * cellvol
radius = np.average(radialmap[np.nonzero(
np.logical_and(distmap > -2 * tum_ld.scale,
distmap < 2 * tum_ld.scale))])
sphere_equiv_radius = math.pow(vol * 3. / (4. * math.pi), 1. / 3.)
sphere_equiv_area = 4. * math.pi * (sphere_equiv_radius**2)
sphericity = sphere_equiv_area / area
#cylinder vol = pi r^2 h
cylinder_equiv_radius = math.sqrt(vol / tum_ld.GetWorldSize()[2] /
math.pi)
cylinder_equiv_area = 2. * math.pi * cylinder_equiv_radius * tum_ld.GetWorldSize(
)[2]
cylindericity = cylinder_equiv_area / area
d = dict(time=time,
area=area,
volume=vol,
radius=radius,
sphericity=sphericity,
cylindericity=cylindericity,
sphere_equiv_radius=sphere_equiv_radius,
sphere_equiv_area=sphere_equiv_area,
cylinder_equiv_radius=cylinder_equiv_radius,
cylinder_equiv_area=cylinder_equiv_area)
pprint(d)
print 'geometry data for %s/%s' % (statedata.file.filename,
statedata.name)
g = dstgroup.recreate_group('geometry')
myutils.hdf_write_dict_hierarchy(g, '.', d)
if 1:
print 'regenerating radial for %s/%s' % (statedata.file.filename,
statedata.name)
dstdata = dstgroup.recreate_group('radial')
## radial data; vessels
vessels = krebsutils.read_vesselgraph(statedata['vessels'], [
'position', 'flags', 'shearforce', 'radius', 'flow', 'maturation'
])
vessels = vessels.get_filtered(
edge_indices=(vessels.edges['flags'] & krebsutils.CIRCULATED != 0))
sample_length = 50.
far = 1.e10
s = plotVessels.generate_samples(vessels, 'position', 'nodes',
sample_length)
dist = krebsutils.sample_field(s,
distmap,
tum_ld,
linear_interpolation=True,
extrapolation_value=far)
rad = krebsutils.sample_field(s,
radialmap,
tum_ld,
linear_interpolation=True,
extrapolation_value=far)
del s
dstdata.recreate_group('vs_r').create_dataset(
'bins', data=np.average((bins_rad[:-1], bins_rad[1:]), axis=0))
dstdata.recreate_group('vs_dr').create_dataset(
'bins', data=np.average((bins_dist[:-1], bins_dist[1:]), axis=0))
# dstdata['vs_r'] = dict(bins = np.average((bins_rad[:-1],bins_rad[1:]), axis=0))
# dstdata['vs_dr'] = dict(bins = np.average((bins_dist[:-1],bins_dist[1:]), axis=0))
# def add_histogram_data(dst, name, (s_avg, s_std, s_sqr)):
# dst[name] = dict(avg = s_avg, std = s_std, sqr = s_sqr)
for name in ['shearforce', 'radius', 'flow', 'maturation']:
s = plotVessels.generate_samples(vessels, name, 'edges',
sample_length)
myutils.MeanValueArray.fromHistogram1d(bins_rad, rad, s).write(
dstdata['vs_r'], name)
myutils.MeanValueArray.fromHistogram1d(bins_dist, dist, s).write(
dstdata['vs_dr'], name)
# d = myutils.scatter_histogram(rad, s, bins_rad, 1.)
# add_histogram_data(dstdata['vs_r'], name, d)
# d = myutils.scatter_histogram(dist, s, bins_dist, 1.)
# add_histogram_data(dstdata['vs_dr'], name, d)
def make_mvd(flavor, s, bins, smap, mask_bound):
a = myutils.MeanValueArray.fromHistogram1d(bins, s,
np.ones_like(s))
b = myutils.MeanValueArray.fromHistogram1d(
bins, smap.ravel(), np.ones_like(smap.ravel()))
a.cnt = b.cnt.copy()
a.sum *= sample_length / cellvol
a.sqr *= a.sum**2
a.write(dstdata[flavor], 'mvd')
# d = myutils.scatter_histogram(xdata, np.ones_like(xdata), bins=bins, xdata2 = np.ravel(xdata2))
# m = dstdata[flavor]['bins'] > mask_bound
# for x in d[:2]:
# x *= sample_length/cellvol
# x.mask |= m
# for x in d[2:]:
# x *= (sample_length/cellvol)**2
# x.mask |= m
# add_histogram_data(dstdata[flavor], 'mvd', d)
make_mvd('vs_r', rad, bins_rad, radialmap, axial_max_rad)
make_mvd('vs_dr', dist, bins_dist, distmap, 1000.)
# pyplot.errorbar(dstdata['vs_dr']['bins'], dstdata['vs_dr']['mvd_avg'], yerr=dstdata['vs_dr']['mvd_std'])
# pyplot.show()
del rad, dist
for name in ['conc', 'sources', 'ptc', 'press']:
a = np.ravel(np.asarray(tum_grp[name]))
myutils.MeanValueArray.fromHistogram1d(
bins_rad, np.ravel(radialmap), a).write(dstdata['vs_r'], name)
myutils.MeanValueArray.fromHistogram1d(bins_dist,
np.ravel(distmap), a).write(
dstdata['vs_dr'], name)
# d = myutils.scatter_histogram(np.ravel(distmap), np.ravel(np.asarray(tum_grp[name])), bins_dist, 1.)
# add_histogram_data(dstdata['vs_dr'], name, d)
# d = myutils.scatter_histogram(np.ravel(radialmap), np.ravel(np.asarray(tum_grp[name])), bins_rad, 1.)
# add_histogram_data(dstdata['vs_r'], name, d)
# velocities, projected radially outward
vel_field = tuple(
np.asarray(tum_grp['vel'][:, :, :, i]) for i in range(3))
vnorm = np.zeros(distmap.shape, dtype=np.float32)
dgrad = krebsutils.field_gradient(radialmap, spacing=tum_ld.scale)
for vv, g in zip(vel_field, dgrad):
vnorm += vv * g
del vel_field, dgrad
phi_tumor = ptc * np.asarray(tum_grp['conc'])
oxy = np.asarray(statedata['fieldOxy'])
gf = np.array(statedata['fieldGf'])
for name, field in [('phi_tumor', phi_tumor), ('vel', vnorm),
('oxy', oxy), ('gf', gf)]:
a = np.ravel(field)
myutils.MeanValueArray.fromHistogram1d(
bins_rad, np.ravel(radialmap), a).write(dstdata['vs_r'], name)
myutils.MeanValueArray.fromHistogram1d(bins_dist,
np.ravel(distmap), a).write(
dstdata['vs_dr'], name)