def add_conductance(G, kind, invivo, edges=None):
"""Adds conductance values to the edges of the graph (consult the relevant
functions in the physiology module for more information.
INPUT: G: Vascular graph in iGraph format.
kind: The vessel kind. This can be either 'a' for artery or 'v' for
vein.
invivo: Boolean, whether the physiological blood characteristics
are calculated using the invivo (=True) or invitro (=False)
equations
edges: (Optional.) The indices of the edges to be given a
conductance value. If no indices are supplied, all edges are
considered.
"""
P = Physiology(G['defaultUnits'])
if edges is None:
edgelist = G.es
else:
edgelist = G.es(edges)
#for e in edgelist:
#print('')
#print(e['diameter'])
#print(e['length'])
#print(P.dynamic_blood_viscosity(e['diameter'],invivo,kind))
G.es(edgelist.indices)['conductance'] = \
[P.conductance(e['diameter'],e['length'],
P.dynamic_blood_viscosity(e['diameter'],
invivo,kind))
for e in edgelist]
def add_conductance(G,kind,invivo,edges=None):
"""Adds conductance values to the edges of the graph (consult the relevant
functions in the physiology module for more information.
INPUT: G: Vascular graph in iGraph format.
kind: The vessel kind. This can be either 'a' for artery or 'v' for
vein.
invivo: Boolean, whether the physiological blood characteristics
are calculated using the invivo (=True) or invitro (=False)
equations
edges: (Optional.) The indices of the edges to be given a
conductance value. If no indices are supplied, all edges are
considered.
"""
P = Physiology(G['defaultUnits'])
if edges is None:
edgelist = G.es
else:
edgelist = G.es(edges)
#for e in edgelist:
#print('')
#print(e['diameter'])
#print(e['length'])
#print(P.dynamic_blood_viscosity(e['diameter'],invivo,kind))
G.es(edgelist.indices)['conductance'] = \
[P.conductance(e['diameter'],e['length'],
P.dynamic_blood_viscosity(e['diameter'],
invivo,kind))
for e in edgelist]
def __init__(self, G, **kwargs):
"""Constructs the linear system A x = b where the matrix A contains the
conductance information of the vascular graph, the vector b specifies
the boundary conditions and the vector x holds the pressures at the
vertices (for which the system needs to be solved).
pBC should be given in mmHG and pressure will be output in mmHg
INPUT: G: Vascular graph in iGraph format.(the pBC should be given in mmHg)
withRBC: boolean if a fixed distribution of RBCs should be considered
if 'htt' is an edge attribute the current distribution is used.
Otherwise the value to be assinged for all vessels should be given.
For arteries and veins a empirical value which is a function of
the diameter is assigned.
resistanceLength: boolean if diameter is not considered for the restistance
and hence the resistance is only a function of the vessel length
OUTPUT: A: Matrix A of the linear system, holding the conductance
information.
b: Vector b of the linear system, holding the boundary
conditions.
"""
self._G = G
self._P = Physiology(G['defaultUnits'])
self._muPlasma = self._P.dynamic_plasma_viscosity()
#Check if a arbirtrary distribution of RBCs should be considered
if kwargs.has_key('withRBC'):
if kwargs['withRBC']!=0:
self._withRBC = kwargs['withRBC']
else:
self._withRBC = 0
else:
self._withRBC = 0
if kwargs.has_key('invivo'):
if kwargs['invivo']!=0:
self._invivo = kwargs['invivo']
else:
self._invivo = 0
else:
self._invivo = 0
if kwargs.has_key('resistanceLength'):
if kwargs['resistanceLength']==1:
self._resistanceLength = 1
print('Diameter not considered for calculation of resistance')
else:
self._resistanceLength = 0
else:
self._resistanceLength = 0
if self._withRBC != 0:
if 'htt' not in G.es.attribute_names():
G.es['htt']=[self._withRBC]*G.ecount()
self._withRBC = 1
httNone = G.es(htt_eq=None).indices
if len(httNone) > 0:
G.es[httNone]['htt']=[self._withRBC]*len(httNone)
self.update(G)
self._eps = np.finfo(float).eps
def add_pBCs(G,kind,vertices):
"""Adds pressure boundary conditions to the vascular graph. Pressure values
are taken from literature (see function 'blood_pressure').
The pressure boundary vertices recieve a kind tag of either 'a' or 'v'
to classify them as arteries or veins respectively.
INPUT: G: Vascular graph in iGraph format.
kind: The vertex kind. This can be either 'a' for arterial or 'v'
for venous.
vertices: The indices of the vertices for which the pressure
boundary conditions are to be set.
OUTPUT: G is modified in place.
"""
P = Physiology(G['defaultUnits'])
for vertex in vertices:
diameter = max([G.es[x]['diameter'] for x in G.adjacent(vertex,'all')])
G.vs[vertex]['pBC'] = P.blood_pressure(diameter,kind)
G.vs[vertex]['kind'] = kind
def add_pBCs(G, kind, vertices):
"""Adds pressure boundary conditions to the vascular graph. Pressure values
are taken from literature (see function 'blood_pressure').
The pressure boundary vertices recieve a kind tag of either 'a' or 'v'
to classify them as arteries or veins respectively.
INPUT: G: Vascular graph in iGraph format.
kind: The vertex kind. This can be either 'a' for arterial or 'v'
for venous.
vertices: The indices of the vertices for which the pressure
boundary conditions are to be set.
OUTPUT: G is modified in place.
"""
P = Physiology(G['defaultUnits'])
for vertex in vertices:
diameter = max(
[G.es[x]['diameter'] for x in G.adjacent(vertex, 'all')])
G.vs[vertex]['pBC'] = P.blood_pressure(diameter, kind)
G.vs[vertex]['kind'] = kind
def __init__(self, G, withRBC = 0, invivo = 0, dMin_empirical = 3.5, htdMax_empirical = 0.6, verbose = True,
diameterOverTime=[],**kwargs):
"""
Computes the flow and pressure field of a vascular graph without RBC tracking.
It can be chosen between pure plasma flow, constant hematocrit or a given htt/htd
distribution.
The pressure boundary conditions (pBC) should be given in mmHG and pressure will be output in mmHg
INPUT: G: Vascular graph in iGraph format.(the pBC should be given in mmHg)
invivo: boolean if the invivo or invitro empirical functions are used (default = 0)
withRBC: = 0: no RBCs, pure plasma Flow (default)
0 < withRBC < 1 & 'htt' not in edgeAttributes: the given value is assigned as htt to all edges.
0 < withRBC < 1 & 'htt' in edgeAttributes: the given value is assigned as htt to all edges where htt = None.
NOTE: Htd will be computed from htt and used to compute the resistance.
If htd is already in the edge attributes, it will be overwritten.
dMin_empiricial: lower limit for the diameter that is used to compute nurel (effective viscosity). The aim of the limit
is to avoid using the empirical equations in a range where no data exists (default = 3.5).
htdMax_empirical: upper limit for htd that is used to compute nurel (effective viscosity). The aim of the limit
is to avoid using the empirical equations in a range where no data exists (default = 0.6). Maximum has to be 1.
verbose: Bool if WARNINGS and setup information (INFO) is printed
diameterOverTime: list of the diameterChanges over time. The length of the list is the number of time steps with diameter change.
each diameter change should be provided as a tuple, e.g. two diameterChanges at the same time, a single diameter change afterwards.
[[[edgeIndex1, edgeIndex1],[newDiameter1, newDiameter2]],[[edgeIndex3], [newDiameter3]]]
OUTPUT: None, the edge properties htt is assgined and the function update is executed (see description for more details)
"""
self._G = G
nVertices = G.vcount()
self._b = np.zeros(nVertices)
self._A = lil_matrix((nVertices,nVertices),dtype=float)
self._eps = np.finfo(float).eps
self._P = Physiology(G['defaultUnits'])
self._muPlasma = self._P.dynamic_plasma_viscosity()
self._withRBC = withRBC
self._invivo = invivo
self._verbose = verbose
self._dMin_empirical = dMin_empirical
self._htdMax_empirical = htdMax_empirical
self._diameterOverTime = diameterOverTime
self._timeSteps = len(diameterOverTime)
self._scalingFactor = vgm.units.scaling_factor_du('mmHg',G['defaultUnits'])
if self._verbose:
print('INFO: The limits for the compuation of the effective viscosity are set to')
print('Minimum diameter %.2f' %self._dMin_empirical)
print('Maximum discharge %.2f' %self._htdMax_empirical)
if self._withRBC != 0:
if self._withRBC < 1.:
if 'htt' not in G.es.attribute_names():
G.es['htt']=[self._withRBC]*G.ecount()
else:
httNone = G.es(htt_eq=None).indices
if len(httNone) > 0:
G.es[httNone]['htt']=[self._withRBC]*len(httNone)
else:
if self._verbose:
print('WARNING: htt is already an edge attribute. \n Existing values are not overwritten!'+\
'\n If new values should be assigned htt has to be deleted beforehand!')
else:
print('ERROR: 0 < withRBC < 1')
if 'rBC' not in G.vs.attribute_names():
G.vs['rBC'] = [None]*G.vcount()
if 'pBC' not in G.vs.attribute_names():
G.vs['pBC'] = [None]*G.vcount()
#Convert 'pBC' ['mmHG'] to default Units
for v in G.vs(pBC_ne=None):
v['pBC']=v['pBC']*self._scalingFactor
if len(G.vs(pBC_ne=None)) > 0:
if self._verbose:
print('INFO: Pressure boundary conditions changed from mmHg --> default Units')
self.update()
class LinearSystemTimeCourse(object):
def __init__(self, G, withRBC = 0, invivo = 0, dMin_empirical = 3.5, htdMax_empirical = 0.6, verbose = True,
diameterOverTime=[],**kwargs):
"""
Computes the flow and pressure field of a vascular graph without RBC tracking.
It can be chosen between pure plasma flow, constant hematocrit or a given htt/htd
distribution.
The pressure boundary conditions (pBC) should be given in mmHG and pressure will be output in mmHg
INPUT: G: Vascular graph in iGraph format.(the pBC should be given in mmHg)
invivo: boolean if the invivo or invitro empirical functions are used (default = 0)
withRBC: = 0: no RBCs, pure plasma Flow (default)
0 < withRBC < 1 & 'htt' not in edgeAttributes: the given value is assigned as htt to all edges.
0 < withRBC < 1 & 'htt' in edgeAttributes: the given value is assigned as htt to all edges where htt = None.
NOTE: Htd will be computed from htt and used to compute the resistance.
If htd is already in the edge attributes, it will be overwritten.
dMin_empiricial: lower limit for the diameter that is used to compute nurel (effective viscosity). The aim of the limit
is to avoid using the empirical equations in a range where no data exists (default = 3.5).
htdMax_empirical: upper limit for htd that is used to compute nurel (effective viscosity). The aim of the limit
is to avoid using the empirical equations in a range where no data exists (default = 0.6). Maximum has to be 1.
verbose: Bool if WARNINGS and setup information (INFO) is printed
diameterOverTime: list of the diameterChanges over time. The length of the list is the number of time steps with diameter change.
each diameter change should be provided as a tuple, e.g. two diameterChanges at the same time, a single diameter change afterwards.
[[[edgeIndex1, edgeIndex1],[newDiameter1, newDiameter2]],[[edgeIndex3], [newDiameter3]]]
OUTPUT: None, the edge properties htt is assgined and the function update is executed (see description for more details)
"""
self._G = G
nVertices = G.vcount()
self._b = np.zeros(nVertices)
self._A = lil_matrix((nVertices,nVertices),dtype=float)
self._eps = np.finfo(float).eps
self._P = Physiology(G['defaultUnits'])
self._muPlasma = self._P.dynamic_plasma_viscosity()
self._withRBC = withRBC
self._invivo = invivo
self._verbose = verbose
self._dMin_empirical = dMin_empirical
self._htdMax_empirical = htdMax_empirical
self._diameterOverTime = diameterOverTime
self._timeSteps = len(diameterOverTime)
self._scalingFactor = vgm.units.scaling_factor_du('mmHg',G['defaultUnits'])
if self._verbose:
print('INFO: The limits for the compuation of the effective viscosity are set to')
print('Minimum diameter %.2f' %self._dMin_empirical)
print('Maximum discharge %.2f' %self._htdMax_empirical)
if self._withRBC != 0:
if self._withRBC < 1.:
if 'htt' not in G.es.attribute_names():
G.es['htt']=[self._withRBC]*G.ecount()
else:
httNone = G.es(htt_eq=None).indices
if len(httNone) > 0:
G.es[httNone]['htt']=[self._withRBC]*len(httNone)
else:
if self._verbose:
print('WARNING: htt is already an edge attribute. \n Existing values are not overwritten!'+\
'\n If new values should be assigned htt has to be deleted beforehand!')
else:
print('ERROR: 0 < withRBC < 1')
if 'rBC' not in G.vs.attribute_names():
G.vs['rBC'] = [None]*G.vcount()
if 'pBC' not in G.vs.attribute_names():
G.vs['pBC'] = [None]*G.vcount()
#Convert 'pBC' ['mmHG'] to default Units
for v in G.vs(pBC_ne=None):
v['pBC']=v['pBC']*self._scalingFactor
if len(G.vs(pBC_ne=None)) > 0:
if self._verbose:
print('INFO: Pressure boundary conditions changed from mmHg --> default Units')
self.update()
#--------------------------------------------------------------------------
def update(self,esequence=None):
"""Constructs the linear system A x = b where the matrix A contains the
conductance information of the vascular graph, the vector b specifies
the boundary conditions and the vector x holds the pressures at the
vertices (for which the system needs to be solved).
INPUT: esequence: list of edges which need to be updated. Default=None, i.e. all edges will be updated
OUTPUT: matrix A and vector b
"""
G = self._G
A = self._A
b = self._b
htt2htd = self._P.tube_to_discharge_hematocrit
nurel = self._P.relative_apparent_blood_viscosity
# Compute nominal and specific resistance:
self._update_nominal_and_specific_resistance(esequence=esequence)
#edge and vertex List that need to be updated
if esequence is None:
es = G.es
vertexList = G.vs
else:
es = G.es(esequence)
vertexList = []
for edge in esequence:
e = G.es[edge]
vertexList.append(e.source)
vertexList.append(e.target)
vertexList = [int(v) for v in np.unique(vertexList)]
vertexList = G.vs[vertexList]
#if with RBCs compute effective resistance
if self._withRBC:
es['htd'] = [min(htt2htd(htt, d, self._invivo), 1.0) for htt,d in zip(es['htt'],es['diameter'])]
es['effResistance'] =[ e['resistance'] * nurel(max(self._dMin_empirical,e['diameter']),\
min(e['htd'],self._htdMax_empirical),self._invivo) for e in es]
es['conductance']=1/np.array(es['effResistance'])
else:
es['conductance'] = [1/e['resistance'] for e in es]
for vertex in vertexList:
i = vertex.index
A.data[i] = []
A.rows[i] = []
b[i] = 0.0
if vertex['pBC'] is not None:
A[i,i] = 1.0
b[i] = vertex['pBC']
else:
aDummy=0
k=0
neighbors=[]
for edge in G.incident(i,'all'):
if G.is_loop(edge):
continue
j=G.neighbors(i)[k]
k += 1
conductance = G.es[edge]['conductance']
neighbor = G.vs[j]
# +=, -= account for multiedges
aDummy += conductance
if neighbor['pBC'] is not None:
b[i] = b[i] + neighbor['pBC'] * conductance
#elif neighbor['rBC'] is not None:
# b[i] = b[i] + neighbor['rBC']
else:
if j not in neighbors:
A[i,j] = - conductance
else:
A[i,j] = A[i,j] - conductance
neighbors.append(j)
if vertex['rBC'] is not None:
b[i] += vertex['rBC']
A[i,i]=aDummy
self._A = A
self._b = b
#--------------------------------------------------------------------------
def solve(self, method):
"""Solves the linear system A x = b for the vector of unknown pressures
x, either using a direct solver (obsolete) or an iterative GMRES solver. From the
pressures, the flow field is computed.
INPUT: method: This can be either 'direct' or 'iterative2'
OUTPUT: None - G is modified in place.
G_final.pkl & G_final.vtp: are save as output
sampledict.pkl: is saved as output
"""
b = self._b
A = self._A
G = self._G
htt2htd = self._P.tube_to_discharge_hematocrit
A = self._A.tocsr()
if method == 'direct':
linalg.use_solver(useUmfpack=True)
x = linalg.spsolve(A, b)
elif method == 'iterative2':
ml = rootnode_solver(A, smooth=('energy', {'degree':2}), strength='evolution' )
M = ml.aspreconditioner(cycle='V')
# Solve pressure system
x,info = gmres(A, self._b, tol=1000*self._eps, maxiter=200, M=M)
if info != 0:
print('ERROR in Solving the Matrix')
print(info)
G.vs['pressure'] = x
G.es['flow'] = [abs(G.vs[edge.source]['pressure'] - G.vs[edge.target]['pressure']) * \
edge['conductance'] for edge in G.es]
#Default Units - mmHg for pressure
G.vs['pressure'] = [v['pressure']/self._scalingFactor for v in G.vs]
if self._withRBC:
G.es['v']=[e['htd']/e['htt']*e['flow']/(0.25*np.pi*e['diameter']**2) for e in G.es]
else:
G.es['v']=[e['flow']/(0.25*np.pi*e['diameter']**2) for e in G.es]
#--------------------------------------------------------------------------
def _update_nominal_and_specific_resistance(self, esequence=None):
"""Updates the nominal and specific resistance of a given edge
sequence.
INPUT: esequence: Sequence of edge indices which have to be updated. If not provided, all
edges are updated.
OUTPUT: None, the edge properties 'resistance' and 'specificResistance'
are updated (or created).
"""
G = self._G
if esequence is None:
es = G.es
else:
es = G.es(esequence)
es['specificResistance'] = [128 * self._muPlasma / (np.pi * d**4)
for d in es['diameter']]
es['resistance'] = [l * sr for l, sr in zip(es['length'],
es['specificResistance'])]
#--------------------------------------------------------------------------
def evolve(self):
""" The flow field is recomputed for changing vessel diameters over time. The changing vessel
diameters have been provided as input in _init_ (diameterOverTime).
OUTPUT: None - G is modified in place.
sampledict.pkl: which saves the pressure for all vertices over time and
flow, diameter and RBC velocity for all edges over time.
"""
G = self._G
diameterOverTime = self._diameterOverTime
flow_time_edges=[]
diameter_time_edges=[]
v_time_edges=[]
pressure_time_edges=[]
#First iteration for initial diameter distribution
self.solve('iterative2')
flow_time_edges.append(G.es['flow'])
diameter_time_edges.append(G.es['diameter'])
v_time_edges.append(G.es['v'])
pressure_time_edges.append(G.vs['pressure'])
for timeStep in range(self._timeSteps):
edgeSequence = diameterOverTime[timeStep][0]
G.es[edgeSequence]['diameter'] = diameterOverTime[timeStep][1]
self.update(esequence=edgeSequence)
self.solve('iterative2')
flow_time_edges.append(G.es['flow'])
diameter_time_edges.append(G.es['diameter'])
v_time_edges.append(G.es['v'])
pressure_time_edges.append(G.vs['pressure'])
vgm.write_pkl(G,'G_'+str(timeStep)+'.pkl')
flow_edges_time = np.transpose(flow_time_edges)
diameter_edges_time = np.transpose(diameter_time_edges)
v_edges_time = np.transpose(v_time_edges)
pressure_edges_time = np.transpose(pressure_time_edges)
#Write Output
sampledict={}
sampledict['flow']=flow_edges_time
sampledict['diameter']=diameter_edges_time
sampledict['v']=v_edges_time
sampledict['pressure']=pressure_edges_time
g_output.write_pkl(sampledict, 'sampledict.pkl')
#Convert 'pBC' from default Units to mmHg
pBCneNone=G.vs(pBC_ne=None)
pBCneNone['pBC']=np.array(pBCneNone['pBC'])*(1/self._scalingFactor)
if len(pBCneNone) > 0:
if self._verbose:
print('INFO: Pressure boundary conditions changed from default Units --> mmHg')
class LinearSystem(object):
def __init__(self, G, **kwargs):
"""Constructs the linear system A x = b where the matrix A contains the
conductance information of the vascular graph, the vector b specifies
the boundary conditions and the vector x holds the pressures at the
vertices (for which the system needs to be solved).
pBC should be given in mmHG and pressure will be output in mmHg
INPUT: G: Vascular graph in iGraph format.(the pBC should be given in mmHg)
withRBC: boolean if a fixed distribution of RBCs should be considered
if 'htt' is an edge attribute the current distribution is used.
Otherwise the value to be assinged for all vessels should be given.
For arteries and veins a empirical value which is a function of
the diameter is assigned.
resistanceLength: boolean if diameter is not considered for the restistance
and hence the resistance is only a function of the vessel length
OUTPUT: A: Matrix A of the linear system, holding the conductance
information.
b: Vector b of the linear system, holding the boundary
conditions.
"""
self._G = G
self._P = Physiology(G['defaultUnits'])
self._muPlasma = self._P.dynamic_plasma_viscosity()
#Check if a arbirtrary distribution of RBCs should be considered
if kwargs.has_key('withRBC'):
if kwargs['withRBC']!=0:
self._withRBC = kwargs['withRBC']
else:
self._withRBC = 0
else:
self._withRBC = 0
if kwargs.has_key('invivo'):
if kwargs['invivo']!=0:
self._invivo = kwargs['invivo']
else:
self._invivo = 0
else:
self._invivo = 0
if kwargs.has_key('resistanceLength'):
if kwargs['resistanceLength']==1:
self._resistanceLength = 1
print('Diameter not considered for calculation of resistance')
else:
self._resistanceLength = 0
else:
self._resistanceLength = 0
if self._withRBC != 0:
if 'htt' not in G.es.attribute_names():
G.es['htt']=[self._withRBC]*G.ecount()
self._withRBC = 1
httNone = G.es(htt_eq=None).indices
if len(httNone) > 0:
G.es[httNone]['htt']=[self._withRBC]*len(httNone)
self.update(G)
self._eps = np.finfo(float).eps
#--------------------------------------------------------------------------
def update(self, newGraph=None):
"""Constructs the linear system A x = b where the matrix A contains the
conductance information of the vascular graph, the vector b specifies
the boundary conditions and the vector x holds the pressures at the
vertices (for which the system needs to be solved). x will have the
same units of [pressure] as the pBC vertices.
Note that in this approach, A and b contain a mixture of dimensions,
i.e. A and b have dimensions of [1.0] and [pressure] in the pBC case,
[conductance] and [conductance*pressure] otherwise, the latter being
rBCs. This has the advantage that no re-indexing is required as the
matrices contain all vertices.
INPUT: newGraph: Vascular graph in iGraph format to replace the
previous self._G. (Optional, default=None.)
OUTPUT: A: Matrix A of the linear system, holding the conductance
information.
b: Vector b of the linear system, holding the boundary
conditions.
"""
htt2htd = self._P.tube_to_discharge_hematocrit
nurel = self._P.relative_apparent_blood_viscosity
if newGraph is not None:
self._G = newGraph
G = self._G
if not G.vs[0].attributes().has_key('pBC'):
G.vs[0]['pBC'] = None
if not G.vs[0].attributes().has_key('rBC'):
G.vs[0]['rBC'] = None
#Convert 'pBC' ['mmHG'] to default Units
pBCneNone=G.vs(pBC_ne=None).indices
for i in pBCneNone:
v=G.vs[i]
v['pBC']=v['pBC']*vgm.units.scaling_factor_du('mmHg',G['defaultUnits'])
nVertices = G.vcount()
b = np.zeros(nVertices)
A = lil_matrix((nVertices,nVertices),dtype=float)
# Compute nominal and specific resistance:
self._update_nominal_and_specific_resistance()
#if with RBCs compute effective resistance
if self._withRBC:
dischargeHt = [min(htt2htd(e, d, self._invivo), 1.0) for e,d in zip(G.es['htt'],G.es['diameter'])]
G.es['effResistance'] =[ res * nurel(max(4.0,d),min(dHt,0.6),self._invivo) for res,dHt,d in zip(G.es['resistance'], \
dischargeHt,G.es['diameter'])]
G.es['conductance']=1/np.array(G.es['effResistance'])
else:
# Compute conductance
for e in G.es:
e['conductance']=1/e['resistance']
#if not bound_cond is None:
# self._conductance = [max(min(c, bound_cond[1]), bound_cond[0])
# for c in G.es['conductance']]
#else:
# self._conductance = G.es['conductance']
self._conductance = G.es['conductance']
for vertex in G.vs:
i = vertex.index
A.data[i] = []
A.rows[i] = []
b[i] = 0.0
if vertex['pBC'] is not None:
A[i,i] = 1.0
b[i] = vertex['pBC']
else:
aDummy=0
k=0
neighbors=[]
for edge in G.adjacent(i,'all'):
if G.is_loop(edge):
continue
j=G.neighbors(i)[k]
k += 1
conductance = G.es[edge]['conductance']
neighbor = G.vs[j]
# +=, -= account for multiedges
aDummy += conductance
if neighbor['pBC'] is not None:
b[i] = b[i] + neighbor['pBC'] * conductance
#elif neighbor['rBC'] is not None:
# b[i] = b[i] + neighbor['rBC']
else:
if j not in neighbors:
A[i,j] = - conductance
else:
A[i,j] = A[i,j] - conductance
neighbors.append(j)
if vertex['rBC'] is not None:
b[i] += vertex['rBC']
A[i,i]=aDummy
self._A = A
self._b = b
#--------------------------------------------------------------------------
def solve(self, method, **kwargs):
"""Solves the linear system A x = b for the vector of unknown pressures
x, either using a direct solver or an iterative AMG solver. From the
pressures, the flow field is computed.
INPUT: method: This can be either 'direct' or 'iterative'
**kwargs
precision: The accuracy to which the ls is to be solved. If not
supplied, machine accuracy will be used.
maxiter: The maximum number of iterations. The default value for
the iterative solver is 250.
OUTPUT: None - G is modified in place.
"""
b = self._b
G = self._G
htt2htd = self._P.tube_to_discharge_hematocrit
A = self._A.tocsr()
if method == 'direct':
linalg.use_solver(useUmfpack=True)
x = linalg.spsolve(A, b)
elif method == 'iterative':
if kwargs.has_key('precision'):
eps = kwargs['precision']
else:
eps = self._eps
if kwargs.has_key('maxiter'):
maxiter = kwargs['maxiter']
else:
maxiter = 250
AA = pyamg.smoothed_aggregation_solver(A, max_levels=10, max_coarse=500)
x = abs(AA.solve(self._b, x0=None, tol=eps, accel='cg', cycle='V', maxiter=maxiter))
# abs required, as (small) negative pressures may arise
elif method == 'iterative2':
# Set linear solver
ml = rootnode_solver(A, smooth=('energy', {'degree':2}), strength='evolution' )
M = ml.aspreconditioner(cycle='V')
# Solve pressure system
x,info = gmres(A, self._b, tol=self._eps, maxiter=50, M=M)
if info != 0:
print('ERROR in Solving the Matrix')
G.vs['pressure'] = x
self._x = x
conductance = self._conductance
G.es['flow'] = [abs(G.vs[edge.source]['pressure'] - \
G.vs[edge.target]['pressure']) * \
conductance[i] for i, edge in enumerate(G.es)]
for v in G.vs:
v['pressure']=v['pressure']/vgm.units.scaling_factor_du('mmHg',G['defaultUnits'])
if self._withRBC:
for e in G.es:
dischargeHt = min(htt2htd(e['htt'], e['diameter'], self._invivo), 1.0)
e['v']=dischargeHt/e['htt']*e['flow']/(0.25*np.pi*e['diameter']**2)
else:
for e in G.es:
e['v']=e['flow']/(0.25*np.pi*e['diameter']**2)
#Convert 'pBC' from default Units to mmHg
pBCneNone=G.vs(pBC_ne=None).indices
if 'diamCalcEff' in G.es.attribute_names():
del(G.es['diamCalcEff'])
if 'effResistance' in G.es.attribute_names():
del(G.es['effResistance'])
if 'conductance' in G.es.attribute_names():
del(G.es['conductance'])
if 'resistance' in G.es.attribute_names():
del(G.es['resistance'])
G.vs[pBCneNone]['pBC']=np.array(G.vs[pBCneNone]['pBC'])*(1/vgm.units.scaling_factor_du('mmHg',G['defaultUnits']))
vgm.write_pkl(G, 'G_final.pkl')
vgm.write_vtp(G, 'G_final.vtp',False)
#Write Output
sampledict={}
for eprop in ['flow', 'v']:
if not eprop in sampledict.keys():
sampledict[eprop] = []
sampledict[eprop].append(G.es[eprop])
for vprop in ['pressure']:
if not vprop in sampledict.keys():
sampledict[vprop] = []
sampledict[vprop].append(G.vs[vprop])
g_output.write_pkl(sampledict, 'sampledict.pkl')
#--------------------------------------------------------------------------
def _verify_mass_balance(self):
"""Computes the mass balance, i.e. sum of flows at each node and adds
the result as a vertex property 'flowSum'.
INPUT: None
OUTPUT: None (result added as vertex property)
"""
G = self._G
G.vs['flowSum'] = [sum([G.es[e]['flow'] * np.sign(G.vs[v]['pressure'] -
G.vs[n]['pressure'])
for e, n in zip(G.adjacent(v), G.neighbors(v))])
for v in xrange(G.vcount())]
for i in range(G.vcount()):
if G.vs[i]['flowSum'] > self._eps:
print('')
print(i)
print(G.vs['flowSum'][i])
#print(self._res[i])
print('ERROR')
for j in G.adjacent(i):
print(G.es['flow'][j])
#--------------------------------------------------------------------------
def _verify_p_consistency(self):
"""Checks for local pressure maxima at non-pBC vertices.
INPUT: None.
OUTPUT: A list of local pressure maxima vertices and the maximum
pressure difference to their respective neighbors."""
G = self._G
localMaxima = []
for i, v in enumerate(G.vs):
if v['pBC'] is None:
pdiff = [v['pressure'] - n['pressure']
for n in G.vs[G.neighbors(i)]]
if min(pdiff) > 0:
localMaxima.append((i, max(pdiff)))
return localMaxima
#--------------------------------------------------------------------------
def _residual_norm(self):
"""Computes the norm of the current residual.
"""
return np.linalg.norm(self._A * self._x - self._b)
#--------------------------------------------------------------------------
def _update_nominal_and_specific_resistance(self, esequence=None):
"""Updates the nominal and specific resistance of a given edge
sequence.
INPUT: es: Sequence of edge indices as tuple. If not provided, all
edges are updated.
OUTPUT: None, the edge properties 'resistance' and 'specificResistance'
are updated (or created).
"""
G = self._G
if esequence is None:
es = G.es
else:
es = G.es(esequence)
if self._resistanceLength:
G.es['specificResistance'] = [1]*G.ecount()
else:
G.es['specificResistance'] = [128 * self._muPlasma / (np.pi * d**4)
for d in G.es['diameter']]
G.es['resistance'] = [l * sr for l, sr in zip(G.es['length'],
G.es['specificResistance'])]
def __init__(self,
G,
invivo=True,
assert_pBCs=True,
resetHtd=True,
**kwargs):
"""Initializes a LinearSystemPries instance.
INPUT: G: Vascular graph in iGraph format.
invivo: Boolean, whether the physiological blood characteristics
are calculated using the invivo (=True) or invitro (=False)
equations
assert_pBCs: (Optional, default=True.) Boolean whether or not to
check components for correct pressure boundary
conditions.
resetHtd: (Optional, default=True.) Boolean whether or not to
reset the discharge hematocrit of the VascularGraph at
initialization. It may be useful to preserve the
original htd-distribution, if only a minor change from
the current state is to be expected (faster to obtain
the solution).
**kwargs:
httBC: tube hematocrit boundary condition at inflow (edge)
htdBC: discharge hematocrit boundary condition at inflow (vertex)
plasmaType: if it is not given, the default value is used. option two: --> francesco: plasma value of francescos simulations
species: what type of animal we are dealing with --> relevant for the rbc volume that is used, default is rat
OUTPUT: None
"""
self._G = G
self._invivo = invivo
self._P = Physiology(G['defaultUnits'])
self._eps = 1e-7 #finfo(float).eps * 1e4
htt2htd = self._P.tube_to_discharge_hematocrit
if kwargs.has_key('httBC'):
for vi in G['av']:
for ei in G.adjacent(vi):
G.es[ei]['httBC'] = kwargs['httBC']
htdBC = htt2htd(kwargs['httBC'], G.es[ei]['diameter'],
invivo)
G.vs[vi]['htdBC'] = htdBC
if kwargs.has_key('htdBC'):
for vi in G['av']:
G.vs[vi]['htdBC'] = kwargs['htdBC']
if kwargs.has_key('plasmaType'):
self._plasmaType = kwargs['plasmaType']
else:
self._plasmaType = 'default'
if kwargs.has_key('species'):
self._species = kwargs['species']
else:
self._species = 'rat'
# Discharge hematocrit boundary conditions:
if not 'htdBC' in G.vs.attribute_names():
for vi in G['av']:
htdlist = []
for ei in G.adjacent(vi):
if 'httBC' in G.es.attribute_names():
if G.es[ei]['httBC'] != None:
htdBC = htt2htd(G.es[ei]['httBC'],
G.es[ei]['diameter'], invivo)
G.vs[vi]['htdBC'] = htdBC
else:
for ei in G.adjacent(vi):
htdlist.append(
self._P.discharge_hematocrit(
G.es[ei]['diameter'], 'a'))
G.vs[vi]['htdBC'] = np.mean(htdlist)
else:
for ei in G.adjacent(vi):
htdlist.append(
self._P.discharge_hematocrit(
G.es[ei]['diameter'], 'a'))
G.vs[vi]['htdBC'] = np.mean(htdlist)
#Convert 'pBC' ['mmHg'] to default Units
for v in G.vs:
if v['pBC'] != None:
v['pBC'] = v['pBC'] * vgm.units.scaling_factor_du(
'mmHg', G['defaultUnits'])
# Initial RBC flow, hematocrit, conductance, pressure and flow:
G.vs['pressure'] = [0.0 for v in G.vs]
G.es['rbcFlow'] = [0.0 for e in G.es]
if resetHtd:
G.es['htd'] = [0.0 for e in G.es]
if not G.vs[0].attributes().has_key('pBC'):
G.vs[0]['pBC'] = None
if not G.vs[0].attributes().has_key('rBC'):
G.vs[0]['rBC'] = None
nVertices = G.vcount()
self._b = zeros(nVertices)
self._A = lil_matrix((nVertices, nVertices), dtype=float)
self._update_conductance_and_LS(G, assert_pBCs)
self._linear_analysis('iterative2')
self._rheological_analysis(None, True, 1.0)
self._linear_analysis('iterative2')
class LinearSystemPries(object):
"""Solves a VascularGraph for pressure and flow. The influence of red blood
cells is taken explicitly into account.
This is an iterative method consisting of two main parts: the linear
analysis and the rheological analysis. In the linear analysis part (which
gives this class its name), a linear system Ax = b is constructed from
current vessel conduction values and solved to yield pressure and flow.
In the rheological analysis part, a hematocrit redistribution is performed
based on current flow values and the empirical relations found by Pries et
al. (1990).
Note that the method as a whole is non-linear.
"""
def __init__(self,
G,
invivo=True,
assert_pBCs=True,
resetHtd=True,
**kwargs):
"""Initializes a LinearSystemPries instance.
INPUT: G: Vascular graph in iGraph format.
invivo: Boolean, whether the physiological blood characteristics
are calculated using the invivo (=True) or invitro (=False)
equations
assert_pBCs: (Optional, default=True.) Boolean whether or not to
check components for correct pressure boundary
conditions.
resetHtd: (Optional, default=True.) Boolean whether or not to
reset the discharge hematocrit of the VascularGraph at
initialization. It may be useful to preserve the
original htd-distribution, if only a minor change from
the current state is to be expected (faster to obtain
the solution).
**kwargs:
httBC: tube hematocrit boundary condition at inflow (edge)
htdBC: discharge hematocrit boundary condition at inflow (vertex)
plasmaType: if it is not given, the default value is used. option two: --> francesco: plasma value of francescos simulations
species: what type of animal we are dealing with --> relevant for the rbc volume that is used, default is rat
OUTPUT: None
"""
self._G = G
self._invivo = invivo
self._P = Physiology(G['defaultUnits'])
self._eps = 1e-7 #finfo(float).eps * 1e4
htt2htd = self._P.tube_to_discharge_hematocrit
if kwargs.has_key('httBC'):
for vi in G['av']:
for ei in G.adjacent(vi):
G.es[ei]['httBC'] = kwargs['httBC']
htdBC = htt2htd(kwargs['httBC'], G.es[ei]['diameter'],
invivo)
G.vs[vi]['htdBC'] = htdBC
if kwargs.has_key('htdBC'):
for vi in G['av']:
G.vs[vi]['htdBC'] = kwargs['htdBC']
if kwargs.has_key('plasmaType'):
self._plasmaType = kwargs['plasmaType']
else:
self._plasmaType = 'default'
if kwargs.has_key('species'):
self._species = kwargs['species']
else:
self._species = 'rat'
# Discharge hematocrit boundary conditions:
if not 'htdBC' in G.vs.attribute_names():
for vi in G['av']:
htdlist = []
for ei in G.adjacent(vi):
if 'httBC' in G.es.attribute_names():
if G.es[ei]['httBC'] != None:
htdBC = htt2htd(G.es[ei]['httBC'],
G.es[ei]['diameter'], invivo)
G.vs[vi]['htdBC'] = htdBC
else:
for ei in G.adjacent(vi):
htdlist.append(
self._P.discharge_hematocrit(
G.es[ei]['diameter'], 'a'))
G.vs[vi]['htdBC'] = np.mean(htdlist)
else:
for ei in G.adjacent(vi):
htdlist.append(
self._P.discharge_hematocrit(
G.es[ei]['diameter'], 'a'))
G.vs[vi]['htdBC'] = np.mean(htdlist)
#Convert 'pBC' ['mmHg'] to default Units
for v in G.vs:
if v['pBC'] != None:
v['pBC'] = v['pBC'] * vgm.units.scaling_factor_du(
'mmHg', G['defaultUnits'])
# Initial RBC flow, hematocrit, conductance, pressure and flow:
G.vs['pressure'] = [0.0 for v in G.vs]
G.es['rbcFlow'] = [0.0 for e in G.es]
if resetHtd:
G.es['htd'] = [0.0 for e in G.es]
if not G.vs[0].attributes().has_key('pBC'):
G.vs[0]['pBC'] = None
if not G.vs[0].attributes().has_key('rBC'):
G.vs[0]['rBC'] = None
nVertices = G.vcount()
self._b = zeros(nVertices)
self._A = lil_matrix((nVertices, nVertices), dtype=float)
self._update_conductance_and_LS(G, assert_pBCs)
self._linear_analysis('iterative2')
self._rheological_analysis(None, True, 1.0)
self._linear_analysis('iterative2')
#--------------------------------------------------------------------------
def _update_conductance_and_LS(self, newGraph=None, assert_pBCs=True):
"""Constructs the linear system A x = b where the matrix A contains the
conductance information of the vascular graph, the vector b specifies
the boundary conditions and the vector x holds the pressures at the
vertices (for which the system needs to be solved). x will have the
same units of [pressure] as the pBC vertices.
Note that in this approach, A and b contain a mixture of dimensions,
i.e. A and b have dimensions of [1.0] and [pressure] in the pBC case,
[conductance] and [conductance*pressure] otherwise, the latter being
rBCs. This has the advantage that no re-indexing is required as the
matrices contain all vertices.
INPUT: newGraph: Vascular graph in iGraph format to replace the
previous self._G. (Optional, default=None.)
assert_pBCs: (Optional, default=True.) Boolean whether or not to
check components for correct pressure boundary
conditions.
OUTPUT: A: Matrix A of the linear system, holding the conductance
information.
b: Vector b of the linear system, holding the boundary
conditions.
"""
if newGraph is not None:
self._G = newGraph
G = self._G
P = self._P
invivo = self._invivo
cond = P.conductance
nublood = P.dynamic_blood_viscosity
G.es['conductance'] = [
cond(
e['diameter'], e['length'],
nublood(e['diameter'],
invivo,
discharge_ht=e['htd'],
plasmaType=self._plasmaType)) for e in G.es
]
G.es['conductance'] = [
max(min(c, 1e5), 1e-5) for c in G.es['conductance']
]
A = self._A
b = self._b
if assert_pBCs:
# Ensure that the problem is well posed in terms of BCs.
# This takes care of unconnected nodes as well as connected
# components of the graph that have not been assigned a minimum of
# one pressure boundary condition:
for component in G.components():
if all(map(lambda x: x is None, G.vs(component)['pBC'])):
i = component[0]
G.vs[i]['pBC'] = 0.0
for vertex in G.vs:
i = vertex.index
A.data[i] = []
A.rows[i] = []
b[i] = 0.0
if vertex['pBC'] is not None:
A[i, i] = 1.0
b[i] = vertex['pBC']
else:
aDummy = 0
k = 0
neighbors = []
for edge in G.adjacent(i, 'all'):
if G.is_loop(edge):
continue
j = G.neighbors(i)[k]
k += 1
conductance = G.es[edge]['conductance']
neighbor = G.vs[j]
# +=, -= account for multiedges
aDummy += conductance
if neighbor['pBC'] is not None:
b[i] = b[i] + neighbor['pBC'] * conductance
#elif neighbor['rBC'] is not None:
# b[i] = b[i] + neighbor['rBC']
else:
if j not in neighbors:
A[i, j] = -conductance
else:
A[i, j] = A[i, j] - conductance
neighbors.append(j)
if vertex['rBC'] is not None:
b[i] += vertex['rBC']
A[i, i] = aDummy
self._A = A
self._b = b
self._G = G
#--------------------------------------------------------------------------
def _update_rbc_flow(self, limiter=0.5):
"""Traverses all vertices ordered by pressure from high to low.
Distributes the red cell flow of the mother vessel to the daughter
vessels according to an empirical relation.
Note that plasma and RBC flow are conserved at bifurcations, however,
discharge hematocrit is not a conservative quantity.
INPUT: limiter: Limits change from one iteration level to the next, if
< 1.0, at limiter == 1.0 the change is unmodified.
OUTPUT: None, edge properties 'rbcFlow' and 'htd' are modified
in-place.
"""
# This limit ensures that the hematocrit stays within the physically
# possible bounds:
htdLimit = 0.99
# Short notation:
G = self._G
eps = self._eps
pse = self._P.phase_separation_effect
#pse = self._P.phase_separation_effect_step
# Copy current htd:
oldRbcFlow = copy.deepcopy(G.es['rbcFlow'])
G.es['rbcFlow'] = [0.0 for e in G.es]
G.es['htd'] = [0.0 for e in G.es]
# Vertices sorted by pressure:
pSortedVertices = sorted([(v['pressure'], v.index) for v in G.vs],
reverse=True)
# Loop through vertices, distributing discharge hematocrit:
for vertexPressure, vertex in pSortedVertices:
# Determine in- and outflow edges:
outEdges = []
inEdges = []
for neighbor, edge in zip(G.neighbors(vertex, 'all'),
G.adjacent(vertex, 'all')):
if G.vs[neighbor]['pressure'] < vertexPressure - eps:
outEdges.append(edge)
elif G.vs[neighbor]['pressure'] > vertexPressure + eps:
inEdges.append(edge)
# The rbc flow is computed from htdBC and the red cells entering
# from the mother vessels (if any exist). In case of multiple
# mother vessels, each is distributed according to the empirical
# relation. If there are more than two daughter edges, the
# emperical relation is applied to all possible pairings and the
# final fractional flow to each daughter is computed in a
# hierarchical fashion (see below).
trimmedOutEdges = copy.deepcopy(outEdges)
for outEdge in outEdges:
if G.es[outEdge]['flow'] <= eps:
trimmedOutEdges.remove(outEdge)
elif not (G.vs[vertex]['htdBC'] is None):
G.es[outEdge]['rbcFlow'] = G.vs[vertex]['htdBC'] * \
G.es[outEdge]['flow']
G.es[outEdge]['htd'] = G.vs[vertex]['htdBC']
trimmedOutEdges.remove(outEdge)
# Only edges without hematocrit BC are included in the distribution
# algorithm:
outEdges = copy.deepcopy(trimmedOutEdges)
NoOutEdges = len(outEdges)
if len(outEdges) == 0:
continue
elif len(outEdges) == 1:
outEdge = outEdges[0]
rbcFlowIn = 0.0
FlowIn = 0.0
for inEdge in inEdges:
rbcFlowIn += G.es[inEdge]['rbcFlow']
FlowIn += G.es[inEdge]['flow']
if len(inEdges) == 0:
G.es[outEdge]['htd'] = G.es[outEdge]['htdBC']
G.es[outEdge]['rbcFlow'] = G.es[outEdge]['flow'] * G.es[
outEdge]['htd']
else:
G.es[outEdge]['rbcFlow'] = rbcFlowIn
G.es[outEdge]['htd'] = min(rbcFlowIn / FlowIn, htdLimit)
if G.es[outEdge]['htd'] < 0:
print('ERROR 1 htd smaller than 0')
else:
rbcFlowIn = 0.0
edgepairs = list(itertools.combinations(outEdges, 2))
for inEdge in inEdges:
df = G.es[inEdge]['diameter']
htdIn = G.es[inEdge]['htd']
rbcFlowIn += G.es[inEdge]['rbcFlow']
outFractions = dict(zip(outEdges, [[] for e in outEdges]))
for edgepair in edgepairs:
oe0, oe1 = G.es[edgepair]
flowSum = sum(G.es[edgepair]['flow'])
if flowSum > 0.0:
relativeValue = oe0['flow'] / flowSum
#stdout.write("\r oe0/flowSum = %f \n" % relativeValue)
#if oe0['flow']/flowSum > 0.49 and oe0['flow']/flowSum < 0.51:
#stdout.write("\r NOW \n")
f0 = pse(oe0['flow'] / flowSum, oe0['diameter'],
oe1['diameter'], df, htdIn)
#f0 = pse(oe0['flow'] / flowSum,
# 0.7, 0.7, 0.7, 0.64)
f1 = 1 - f0
else:
f0 = 0
f1 = 0
outFractions[oe0.index].append(f0)
outFractions[oe1.index].append(f1)
# Sort out-edges from highest to lowest out fraction and
# distribute RBC flow accordingly:
sortedOutEdges = sorted(zip(
map(sum, outFractions.values()), outFractions.keys()),
reverse=True)
remainingFraction = 1.0
for i, soe in enumerate(sortedOutEdges[:-1]):
outEdge = soe[1]
outFractions[outEdge] = remainingFraction * \
sorted(outFractions[outEdge])[i]
remainingFraction -= outFractions[outEdge]
remainingFraction = max(remainingFraction, 0.0)
outFractions[sortedOutEdges[-1][1]] = remainingFraction
#Outflow in second outEdge is calculated by the difference of inFlow and OutFlow first
#outEdge
count = 0
for outEdge in outEdges:
G.es[outEdge]['rbcFlow'] += G.es[inEdge]['rbcFlow'] * \
outFractions[outEdge]
#stdout.write("\r outEdge = %g \n" %outEdge)
#stdout.write("\r G.es[outEdge]['rbcFlow'] = %f \n" %G.es[outEdge]['rbcFlow'])
if count == 0:
G.es[outEdge]['rbcFlow'] = oldRbcFlow[outEdge] + \
(G.es[outEdge]['rbcFlow'] - oldRbcFlow[outEdge]) * limiter
elif count == 1:
G.es[outEdge]['rbcFlow'] = G.es[inEdge][
'rbcFlow'] - G.es[outEdges[0]]['rbcFlow']
#stdout.write("\r LIMITED: G.es[outEdge]['rbcFlow'] = %f \n" %G.es[outEdge]['rbcFlow'])
count += 1
# Limit change between iteration levels for numerical
# stability. Note that this is only applied to the diverging
# bifurcations:
for outEdge in outEdges:
if G.es[outEdge]['flow'] > eps:
G.es[outEdge]['htd'] = min(G.es[outEdge]['rbcFlow'] / \
G.es[outEdge]['flow'], htdLimit)
if G.es[outEdge]['htd'] < 0:
print('ERROR 2 htd smaller than 0')
#stdout.write("\r outEdge = %g \n" %outEdge)
#stdout.write("\r outFractions[outEdge] = %f \n" %outFractions[outEdge])
#stdout.write("\r G.es[outEdge]['flow'] = %f \n" %G.es[outEdge]['flow'])
#stdout.write("\r G.es[outEdge]['htd'] = %f \n" %G.es[outEdge]['htd'])
#--------------------------------------------------------------------------
def _linear_analysis(self, method, **kwargs):
"""Performs the linear analysis, in which the pressure and flow fields
are computed.
INPUT: method: This can be either 'direct' or 'iterative'
**kwargs
precision: The accuracy to which the ls is to be solved. If not
supplied, machine accuracy will be used. (This only
applies to the iterative solver)
OUTPUT: The maximum, mean, and median pressure change. Moreover,
pressure and flow are modified in-place.
"""
G = self._G
A = self._A.tocsr()
if method == 'direct':
linalg.use_solver(useUmfpack=True)
x = linalg.spsolve(A, self._b)
elif method == 'iterative':
if kwargs.has_key('precision'):
eps = kwargs['precision']
else:
eps = self._eps
AA = smoothed_aggregation_solver(A, max_levels=10, max_coarse=500)
x = abs(
AA.solve(self._b,
x0=None,
tol=eps,
accel='cg',
cycle='V',
maxiter=150))
# abs required, as (small) negative pressures may arise
elif method == 'iterative2':
# Set linear solver
ml = rootnode_solver(A,
smooth=('energy', {
'degree': 2
}),
strength='evolution')
M = ml.aspreconditioner(cycle='V')
# Solve pressure system
#x,info = gmres(A, self._b, tol=self._eps, maxiter=50, M=M, x0=self._x)
#x,info = gmres(A, self._b, tol=self._eps/10000000000000, maxiter=50, M=M)
x, info = gmres(A, self._b, tol=self._eps / 10000, maxiter=50, M=M)
if info != 0:
print('SOLVEERROR in Solving the Matrix')
pdiff = map(abs, [(p - xx) / p if p > 0 else 0.0
for p, xx in zip(G.vs['pressure'], x)])
maxPDiff = max(pdiff)
meanPDiff = np.mean(pdiff)
medianPDiff = np.median(pdiff)
log.debug(np.nonzero(np.array(pdiff) == maxPDiff)[0])
G.vs['pressure'] = x
G.es['flow'] = [abs(G.vs[edge.source]['pressure'] - \
G.vs[edge.target]['pressure']) * \
edge['conductance'] for edge in G.es]
self._maxPDiff = maxPDiff
self._meanPDiff = meanPDiff
self._medianPDiff = medianPDiff
return maxPDiff, meanPDiff, medianPDiff
#--------------------------------------------------------------------------
def _rheological_analysis(self,
newGraph=None,
assert_pBCs=True,
limiter=0.5):
"""Performs the rheological analysis, in which the discharge hematocrit
and apparent viscosity (and thus the new conductances of the vessels)
are computed.
INPUT: newGraph: Vascular graph in iGraph format to replace the
previous self._G. (Optional, default=None.)
assert_pBCs: (Optional, default=True.) Boolean whether or not to
check components for correct pressure boundary
conditions.
limiter: Limits change from one iteration level to the next, if
> 1.0, at limiter == 1.0 the change is unmodified.
OUTPUT: None, edge properties 'htd' and 'conductance' are modified
in-place.
"""
self._update_rbc_flow(limiter)
self._update_conductance_and_LS(newGraph, assert_pBCs)
#--------------------------------------------------------------------------
def solve(self,
method,
precision=None,
maxIterations=1e4,
limiter=0.5,
**kwargs):
"""Solve for pressure, flow, hematocrit and conductance by iterating
over linear and rheological analysis. Stop when either the desired
accuracy has been achieved or the maximum number of iterations have
been performed.
INPUT: method: This can be either 'direct' or 'iterative'
precision: Desired precision, measured in the maximum amount by
which the pressure may change from one iteration
level to the next. If the maximum change is below
threshold, the iteration is aborted.
maxIterations: The maximum number of iterations to perform.
limiter: Limits change from one iteration level to the next, if
> 1.0, at limiter == 1.0 the change is unmodified.
**kwargs
precisionLS: The accuracy to which the ls is to be solved. If
not supplied, machine accuracy will be used. (This
only applies to the iterative solver)
OUTPUT: None, the vascular graph is modified in-place.
"""
G = self._G
P = self._P
invivo = self._invivo
if precision is None: precision = self._eps
iterationCount = 0
maxPDiff = 1e200
filename = 'iter_' + str(iterationCount) + '.vtp'
g_output.write_vtp(G, filename, False)
filenames = [filename]
convergenceHistory = []
while iterationCount < maxIterations and maxPDiff > precision:
stdout.write("\rITERATION %g \n" % iterationCount)
self._rheological_analysis(None, False, limiter=0.5)
maxPDiff, meanPDiff, medianPDiff = self._linear_analysis(
method, **kwargs)
#maxPDiff=self._maxPDiff
#meanPDiff=self._meanPDiff
#medianPDiff=self._medianPDiff
log.info('Iteration %i of %i' %
(iterationCount + 1, maxIterations))
log.info('maximum pressure change: %.2e' % maxPDiff)
log.info('mean pressure change: %.2e' % meanPDiff)
log.info('median pressure change: %.2e\n' % medianPDiff)
convergenceHistory.append((maxPDiff, meanPDiff, medianPDiff))
iterationCount += 1
G.es['htt'] = [
P.discharge_to_tube_hematocrit(e['htd'], e['diameter'], invivo)
for e in G.es
]
vrbc = P.rbc_volume(self._species)
G.es['nMax'] = [
np.pi * e['diameter']**2 / 4 * e['length'] / vrbc for e in G.es
]
G.es['minDist'] = [e['length'] / e['nMax'] for e in G.es]
G.es['nRBC'] = [
e['htt'] * e['length'] / e['minDist'] for e in G.es
]
filename = 'iter_' + str(iterationCount) + '.vtp'
g_output.write_vtp(G, filename, False)
filenames.append(filename)
if iterationCount >= maxIterations:
stdout.write("\rMaximum number of iterations reached\n")
elif maxPDiff <= precision:
stdout.write("\rPrecision limit is reached\n")
self.integrity_check()
G.es['htt'] = [
P.discharge_to_tube_hematocrit(e['htd'], e['diameter'], invivo)
for e in G.es
]
vrbc = P.rbc_volume(self._species)
G.es['nMax'] = [
np.pi * e['diameter']**2 / 4 * e['length'] / vrbc for e in G.es
]
G.es['minDist'] = [e['length'] / e['nMax'] for e in G.es]
G.es['nRBC'] = [e['htt'] * e['length'] / e['minDist'] for e in G.es]
G.es['v'] = [
4 * e['flow'] *
P.velocity_factor(e['diameter'], invivo, tube_ht=e['htt']) /
(np.pi * e['diameter']**2) if e['htt'] > 0 else 4 * e['flow'] /
(np.pi * e['diameter']**2) for e in G.es
]
for v in G.vs:
v['pressure'] = v['pressure'] / vgm.units.scaling_factor_du(
'mmHg', G['defaultUnits'])
filename = 'iter_final.vtp'
g_output.write_vtp(G, filename, False)
filenames.append(filename)
g_output.write_pvd_time_series('sequence.pvd', filenames)
vgm.write_pkl(G, 'G_final.pkl')
stdout.flush()
return convergenceHistory
return G
#--------------------------------------------------------------------------
def integrity_check(self):
"""Asserts that mass conservation is honored by writing the sum of in-
and outflow as well as the sum of the in- and outgoing discharge
hematocrit to the graph vertices as 'flowSum' and 'htdSum'
INPUT: None
OUTPUT: None, flow and htd sums added as vertex properties in-place.
"""
G = self._G
eps = self._eps
G.es['plasmaFlow'] = [(1 - e['htd']) * e['flow'] for e in G.es]
for v in G.vs:
vertex = v.index
vertexPressure = v['pressure']
outEdges = []
inEdges = []
for neighbor, edge in zip(G.neighbors(vertex, 'all'),
G.adjacent(vertex, 'all')):
if G.vs[neighbor]['pressure'] < vertexPressure:
outEdges.append(edge)
elif G.vs[neighbor]['pressure'] >= vertexPressure:
inEdges.append(edge)
v['flowSum'] = sum(G.es[outEdges]['flow']) - \
sum(G.es[inEdges]['flow'])
v['rbcFlowSum'] = sum(G.es[outEdges]['rbcFlow']) - \
sum(G.es[inEdges]['rbcFlow'])
v['plasmaFlowSum'] = sum(G.es[outEdges]['plasmaFlow']) - \
sum(G.es[inEdges]['plasmaFlow'])
for e in G.es:
e['errorHtd'] = abs(e['rbcFlow'] / e['flow'] - e['htd']) \
if e['flow'] > eps else 0.0
log.info('Max error htd: %.1g' % max(G.es['errorHtd']))
def __init__(self, G, invivo=True,assert_pBCs=True, resetHtd=True,**kwargs):
"""Initializes a LinearSystemPries instance.
INPUT: G: Vascular graph in iGraph format.
invivo: Boolean, whether the physiological blood characteristics
are calculated using the invivo (=True) or invitro (=False)
equations
assert_pBCs: (Optional, default=True.) Boolean whether or not to
check components for correct pressure boundary
conditions.
resetHtd: (Optional, default=True.) Boolean whether or not to
reset the discharge hematocrit of the VascularGraph at
initialization. It may be useful to preserve the
original htd-distribution, if only a minor change from
the current state is to be expected (faster to obtain
the solution).
**kwargs:
httBC: tube hematocrit boundary condition at inflow (edge)
htdBC: discharge hematocrit boundary condition at inflow (vertex)
plasmaType: if it is not given, the default value is used. option two: --> francesco: plasma value of francescos simulations
species: what type of animal we are dealing with --> relevant for the rbc volume that is used, default is rat
OUTPUT: None
"""
self._G = G
self._invivo=invivo
self._P = Physiology(G['defaultUnits'])
self._eps = 1e-7 #finfo(float).eps * 1e4
htt2htd=self._P.tube_to_discharge_hematocrit
if kwargs.has_key('httBC'):
for vi in G['av']:
for ei in G.adjacent(vi):
G.es[ei]['httBC']=kwargs['httBC']
htdBC = htt2htd(kwargs['httBC'],G.es[ei]['diameter'],invivo)
G.vs[vi]['htdBC']=htdBC
if kwargs.has_key('htdBC'):
for vi in G['av']:
G.vs[vi]['htdBC']=kwargs['htdBC']
if kwargs.has_key('plasmaType'):
self._plasmaType=kwargs['plasmaType']
else:
self._plasmaType='default'
if kwargs.has_key('species'):
self._species=kwargs['species']
else:
self._species='rat'
# Discharge hematocrit boundary conditions:
if not 'htdBC' in G.vs.attribute_names():
for vi in G['av']:
htdlist = []
for ei in G.adjacent(vi):
if 'httBC' in G.es.attribute_names():
if G.es[ei]['httBC'] != None:
htdBC = htt2htd(G.es[ei]['httBC'],G.es[ei]['diameter'],invivo)
G.vs[vi]['htdBC']=htdBC
else:
for ei in G.adjacent(vi):
htdlist.append(self._P.discharge_hematocrit(
G.es[ei]['diameter'], 'a'))
G.vs[vi]['htdBC'] = np.mean(htdlist)
else:
for ei in G.adjacent(vi):
htdlist.append(self._P.discharge_hematocrit(
G.es[ei]['diameter'], 'a'))
G.vs[vi]['htdBC'] = np.mean(htdlist)
#Convert 'pBC' ['mmHg'] to default Units
for v in G.vs:
if v['pBC'] != None:
v['pBC']=v['pBC']*vgm.units.scaling_factor_du('mmHg',G['defaultUnits'])
# Initial RBC flow, hematocrit, conductance, pressure and flow:
G.vs['pressure'] = [0.0 for v in G.vs]
G.es['rbcFlow'] = [0.0 for e in G.es]
if resetHtd:
G.es['htd'] = [0.0 for e in G.es]
if not G.vs[0].attributes().has_key('pBC'):
G.vs[0]['pBC'] = None
if not G.vs[0].attributes().has_key('rBC'):
G.vs[0]['rBC'] = None
nVertices = G.vcount()
self._b = zeros(nVertices)
self._A = lil_matrix((nVertices,nVertices),dtype=float)
self._update_conductance_and_LS(G, assert_pBCs)
self._linear_analysis('iterative2')
self._rheological_analysis(None, True, 1.0)
self._linear_analysis('iterative2')
class LinearSystemPries(object):
"""Solves a VascularGraph for pressure and flow. The influence of red blood
cells is taken explicitly into account.
This is an iterative method consisting of two main parts: the linear
analysis and the rheological analysis. In the linear analysis part (which
gives this class its name), a linear system Ax = b is constructed from
current vessel conduction values and solved to yield pressure and flow.
In the rheological analysis part, a hematocrit redistribution is performed
based on current flow values and the empirical relations found by Pries et
al. (1990).
Note that the method as a whole is non-linear.
"""
def __init__(self, G, invivo=True,assert_pBCs=True, resetHtd=True,**kwargs):
"""Initializes a LinearSystemPries instance.
INPUT: G: Vascular graph in iGraph format.
invivo: Boolean, whether the physiological blood characteristics
are calculated using the invivo (=True) or invitro (=False)
equations
assert_pBCs: (Optional, default=True.) Boolean whether or not to
check components for correct pressure boundary
conditions.
resetHtd: (Optional, default=True.) Boolean whether or not to
reset the discharge hematocrit of the VascularGraph at
initialization. It may be useful to preserve the
original htd-distribution, if only a minor change from
the current state is to be expected (faster to obtain
the solution).
**kwargs:
httBC: tube hematocrit boundary condition at inflow (edge)
htdBC: discharge hematocrit boundary condition at inflow (vertex)
plasmaType: if it is not given, the default value is used. option two: --> francesco: plasma value of francescos simulations
species: what type of animal we are dealing with --> relevant for the rbc volume that is used, default is rat
OUTPUT: None
"""
self._G = G
self._invivo=invivo
self._P = Physiology(G['defaultUnits'])
self._eps = 1e-7 #finfo(float).eps * 1e4
htt2htd=self._P.tube_to_discharge_hematocrit
if kwargs.has_key('httBC'):
for vi in G['av']:
for ei in G.adjacent(vi):
G.es[ei]['httBC']=kwargs['httBC']
htdBC = htt2htd(kwargs['httBC'],G.es[ei]['diameter'],invivo)
G.vs[vi]['htdBC']=htdBC
if kwargs.has_key('htdBC'):
for vi in G['av']:
G.vs[vi]['htdBC']=kwargs['htdBC']
if kwargs.has_key('plasmaType'):
self._plasmaType=kwargs['plasmaType']
else:
self._plasmaType='default'
if kwargs.has_key('species'):
self._species=kwargs['species']
else:
self._species='rat'
# Discharge hematocrit boundary conditions:
if not 'htdBC' in G.vs.attribute_names():
for vi in G['av']:
htdlist = []
for ei in G.adjacent(vi):
if 'httBC' in G.es.attribute_names():
if G.es[ei]['httBC'] != None:
htdBC = htt2htd(G.es[ei]['httBC'],G.es[ei]['diameter'],invivo)
G.vs[vi]['htdBC']=htdBC
else:
for ei in G.adjacent(vi):
htdlist.append(self._P.discharge_hematocrit(
G.es[ei]['diameter'], 'a'))
G.vs[vi]['htdBC'] = np.mean(htdlist)
else:
for ei in G.adjacent(vi):
htdlist.append(self._P.discharge_hematocrit(
G.es[ei]['diameter'], 'a'))
G.vs[vi]['htdBC'] = np.mean(htdlist)
#Convert 'pBC' ['mmHg'] to default Units
for v in G.vs:
if v['pBC'] != None:
v['pBC']=v['pBC']*vgm.units.scaling_factor_du('mmHg',G['defaultUnits'])
# Initial RBC flow, hematocrit, conductance, pressure and flow:
G.vs['pressure'] = [0.0 for v in G.vs]
G.es['rbcFlow'] = [0.0 for e in G.es]
if resetHtd:
G.es['htd'] = [0.0 for e in G.es]
if not G.vs[0].attributes().has_key('pBC'):
G.vs[0]['pBC'] = None
if not G.vs[0].attributes().has_key('rBC'):
G.vs[0]['rBC'] = None
nVertices = G.vcount()
self._b = zeros(nVertices)
self._A = lil_matrix((nVertices,nVertices),dtype=float)
self._update_conductance_and_LS(G, assert_pBCs)
self._linear_analysis('iterative2')
self._rheological_analysis(None, True, 1.0)
self._linear_analysis('iterative2')
#--------------------------------------------------------------------------
def _update_conductance_and_LS(self, newGraph=None, assert_pBCs=True):
"""Constructs the linear system A x = b where the matrix A contains the
conductance information of the vascular graph, the vector b specifies
the boundary conditions and the vector x holds the pressures at the
vertices (for which the system needs to be solved). x will have the
same units of [pressure] as the pBC vertices.
Note that in this approach, A and b contain a mixture of dimensions,
i.e. A and b have dimensions of [1.0] and [pressure] in the pBC case,
[conductance] and [conductance*pressure] otherwise, the latter being
rBCs. This has the advantage that no re-indexing is required as the
matrices contain all vertices.
INPUT: newGraph: Vascular graph in iGraph format to replace the
previous self._G. (Optional, default=None.)
assert_pBCs: (Optional, default=True.) Boolean whether or not to
check components for correct pressure boundary
conditions.
OUTPUT: A: Matrix A of the linear system, holding the conductance
information.
b: Vector b of the linear system, holding the boundary
conditions.
"""
if newGraph is not None:
self._G = newGraph
G = self._G
P = self._P
invivo=self._invivo
cond = P.conductance
nublood = P.dynamic_blood_viscosity
G.es['conductance'] = [cond(e['diameter'], e['length'],
nublood(e['diameter'], invivo,discharge_ht=e['htd'],plasmaType=self._plasmaType))
for e in G.es]
G.es['conductance'] = [max(min(c, 1e5), 1e-5)
for c in G.es['conductance']]
A = self._A
b = self._b
if assert_pBCs:
# Ensure that the problem is well posed in terms of BCs.
# This takes care of unconnected nodes as well as connected
# components of the graph that have not been assigned a minimum of
# one pressure boundary condition:
for component in G.components():
if all(map(lambda x: x is None, G.vs(component)['pBC'])):
i = component[0]
G.vs[i]['pBC'] = 0.0
for vertex in G.vs:
i = vertex.index
A.data[i] = []
A.rows[i] = []
b[i] = 0.0
if vertex['pBC'] is not None:
A[i,i] = 1.0
b[i] = vertex['pBC']
else:
aDummy=0
k=0
neighbors=[]
for edge in G.adjacent(i,'all'):
if G.is_loop(edge):
continue
j=G.neighbors(i)[k]
k += 1
conductance = G.es[edge]['conductance']
neighbor = G.vs[j]
# +=, -= account for multiedges
aDummy += conductance
if neighbor['pBC'] is not None:
b[i] = b[i] + neighbor['pBC'] * conductance
#elif neighbor['rBC'] is not None:
# b[i] = b[i] + neighbor['rBC']
else:
if j not in neighbors:
A[i,j] = - conductance
else:
A[i,j] = A[i,j] - conductance
neighbors.append(j)
if vertex['rBC'] is not None:
b[i] += vertex['rBC']
A[i,i]=aDummy
self._A = A
self._b = b
self._G = G
#--------------------------------------------------------------------------
def _update_rbc_flow(self, limiter=0.5):
"""Traverses all vertices ordered by pressure from high to low.
Distributes the red cell flow of the mother vessel to the daughter
vessels according to an empirical relation.
Note that plasma and RBC flow are conserved at bifurcations, however,
discharge hematocrit is not a conservative quantity.
INPUT: limiter: Limits change from one iteration level to the next, if
< 1.0, at limiter == 1.0 the change is unmodified.
OUTPUT: None, edge properties 'rbcFlow' and 'htd' are modified
in-place.
"""
# This limit ensures that the hematocrit stays within the physically
# possible bounds:
htdLimit = 0.99
# Short notation:
G = self._G
eps = self._eps
pse = self._P.phase_separation_effect
#pse = self._P.phase_separation_effect_step
# Copy current htd:
oldRbcFlow = copy.deepcopy(G.es['rbcFlow'])
G.es['rbcFlow'] = [0.0 for e in G.es]
G.es['htd'] = [0.0 for e in G.es]
# Vertices sorted by pressure:
pSortedVertices = sorted([(v['pressure'], v.index) for v in G.vs],
reverse=True)
# Loop through vertices, distributing discharge hematocrit:
for vertexPressure, vertex in pSortedVertices:
# Determine in- and outflow edges:
outEdges = []
inEdges = []
for neighbor, edge in zip(G.neighbors(vertex, 'all'), G.adjacent(vertex, 'all')):
if G.vs[neighbor]['pressure'] < vertexPressure - eps:
outEdges.append(edge)
elif G.vs[neighbor]['pressure'] > vertexPressure + eps:
inEdges.append(edge)
# The rbc flow is computed from htdBC and the red cells entering
# from the mother vessels (if any exist). In case of multiple
# mother vessels, each is distributed according to the empirical
# relation. If there are more than two daughter edges, the
# emperical relation is applied to all possible pairings and the
# final fractional flow to each daughter is computed in a
# hierarchical fashion (see below).
trimmedOutEdges = copy.deepcopy(outEdges)
for outEdge in outEdges:
if G.es[outEdge]['flow'] <= eps:
trimmedOutEdges.remove(outEdge)
elif not (G.vs[vertex]['htdBC'] is None):
G.es[outEdge]['rbcFlow'] = G.vs[vertex]['htdBC'] * \
G.es[outEdge]['flow']
G.es[outEdge]['htd'] = G.vs[vertex]['htdBC']
trimmedOutEdges.remove(outEdge)
# Only edges without hematocrit BC are included in the distribution
# algorithm:
outEdges = copy.deepcopy(trimmedOutEdges)
NoOutEdges=len(outEdges)
if len(outEdges) == 0:
continue
elif len(outEdges) == 1:
outEdge = outEdges[0]
rbcFlowIn = 0.0
FlowIn = 0.0
for inEdge in inEdges:
rbcFlowIn += G.es[inEdge]['rbcFlow']
FlowIn += G.es[inEdge]['flow']
if len(inEdges) == 0:
G.es[outEdge]['htd']=G.es[outEdge]['htdBC']
G.es[outEdge]['rbcFlow']=G.es[outEdge]['flow']*G.es[outEdge]['htd']
else:
G.es[outEdge]['rbcFlow'] = rbcFlowIn
G.es[outEdge]['htd'] = min(rbcFlowIn/FlowIn,htdLimit)
if G.es[outEdge]['htd'] < 0:
print('ERROR 1 htd smaller than 0')
else:
rbcFlowIn = 0.0
edgepairs = list(itertools.combinations(outEdges, 2))
for inEdge in inEdges:
df = G.es[inEdge]['diameter']
htdIn = G.es[inEdge]['htd']
rbcFlowIn += G.es[inEdge]['rbcFlow']
outFractions = dict(zip(outEdges, [[] for e in outEdges]))
for edgepair in edgepairs:
oe0, oe1 = G.es[edgepair]
flowSum = sum(G.es[edgepair]['flow'])
if flowSum > 0.0:
relativeValue=oe0['flow']/flowSum
#stdout.write("\r oe0/flowSum = %f \n" % relativeValue)
#if oe0['flow']/flowSum > 0.49 and oe0['flow']/flowSum < 0.51:
#stdout.write("\r NOW \n")
f0 = pse(oe0['flow'] / flowSum,
oe0['diameter'], oe1['diameter'],
df, htdIn)
#f0 = pse(oe0['flow'] / flowSum,
# 0.7, 0.7, 0.7, 0.64)
f1 = 1 - f0
else:
f0 = 0
f1 = 0
outFractions[oe0.index].append(f0)
outFractions[oe1.index].append(f1)
# Sort out-edges from highest to lowest out fraction and
# distribute RBC flow accordingly:
sortedOutEdges = sorted(zip(map(sum, outFractions.values()),
outFractions.keys()),
reverse=True)
remainingFraction = 1.0
for i, soe in enumerate(sortedOutEdges[:-1]):
outEdge = soe[1]
outFractions[outEdge] = remainingFraction * \
sorted(outFractions[outEdge])[i]
remainingFraction -= outFractions[outEdge]
remainingFraction = max(remainingFraction, 0.0)
outFractions[sortedOutEdges[-1][1]] = remainingFraction
#Outflow in second outEdge is calculated by the difference of inFlow and OutFlow first
#outEdge
count = 0
for outEdge in outEdges:
G.es[outEdge]['rbcFlow'] += G.es[inEdge]['rbcFlow'] * \
outFractions[outEdge]
#stdout.write("\r outEdge = %g \n" %outEdge)
#stdout.write("\r G.es[outEdge]['rbcFlow'] = %f \n" %G.es[outEdge]['rbcFlow'])
if count == 0:
G.es[outEdge]['rbcFlow'] = oldRbcFlow[outEdge] + \
(G.es[outEdge]['rbcFlow'] - oldRbcFlow[outEdge]) * limiter
elif count == 1:
G.es[outEdge]['rbcFlow']=G.es[inEdge]['rbcFlow']-G.es[outEdges[0]]['rbcFlow']
#stdout.write("\r LIMITED: G.es[outEdge]['rbcFlow'] = %f \n" %G.es[outEdge]['rbcFlow'])
count += 1
# Limit change between iteration levels for numerical
# stability. Note that this is only applied to the diverging
# bifurcations:
for outEdge in outEdges:
if G.es[outEdge]['flow'] > eps:
G.es[outEdge]['htd'] = min(G.es[outEdge]['rbcFlow'] / \
G.es[outEdge]['flow'], htdLimit)
if G.es[outEdge]['htd'] < 0:
print('ERROR 2 htd smaller than 0')
#stdout.write("\r outEdge = %g \n" %outEdge)
#stdout.write("\r outFractions[outEdge] = %f \n" %outFractions[outEdge])
#stdout.write("\r G.es[outEdge]['flow'] = %f \n" %G.es[outEdge]['flow'])
#stdout.write("\r G.es[outEdge]['htd'] = %f \n" %G.es[outEdge]['htd'])
#--------------------------------------------------------------------------
def _linear_analysis(self, method, **kwargs):
"""Performs the linear analysis, in which the pressure and flow fields
are computed.
INPUT: method: This can be either 'direct' or 'iterative'
**kwargs
precision: The accuracy to which the ls is to be solved. If not
supplied, machine accuracy will be used. (This only
applies to the iterative solver)
OUTPUT: The maximum, mean, and median pressure change. Moreover,
pressure and flow are modified in-place.
"""
G = self._G
A = self._A.tocsr()
if method == 'direct':
linalg.use_solver(useUmfpack=True)
x = linalg.spsolve(A, self._b)
elif method == 'iterative':
if kwargs.has_key('precision'):
eps = kwargs['precision']
else:
eps = self._eps
AA = smoothed_aggregation_solver(A, max_levels=10, max_coarse=500)
x = abs(AA.solve(self._b, x0=None, tol=eps, accel='cg', cycle='V', maxiter=150))
# abs required, as (small) negative pressures may arise
elif method == 'iterative2':
# Set linear solver
ml = rootnode_solver(A, smooth=('energy', {'degree':2}), strength='evolution' )
M = ml.aspreconditioner(cycle='V')
# Solve pressure system
#x,info = gmres(A, self._b, tol=self._eps, maxiter=50, M=M, x0=self._x)
#x,info = gmres(A, self._b, tol=self._eps/10000000000000, maxiter=50, M=M)
x,info = gmres(A, self._b, tol=self._eps/10000, maxiter=50, M=M)
if info != 0:
print('SOLVEERROR in Solving the Matrix')
pdiff = map(abs, [(p - xx) / p if p > 0 else 0.0
for p, xx in zip(G.vs['pressure'], x)])
maxPDiff = max(pdiff)
meanPDiff = np.mean(pdiff)
medianPDiff = np.median(pdiff)
log.debug(np.nonzero(np.array(pdiff) == maxPDiff)[0])
G.vs['pressure'] = x
G.es['flow'] = [abs(G.vs[edge.source]['pressure'] - \
G.vs[edge.target]['pressure']) * \
edge['conductance'] for edge in G.es]
self._maxPDiff=maxPDiff
self._meanPDiff=meanPDiff
self._medianPDiff=medianPDiff
return maxPDiff, meanPDiff, medianPDiff
#--------------------------------------------------------------------------
def _rheological_analysis(self, newGraph=None, assert_pBCs=True,
limiter=0.5):
"""Performs the rheological analysis, in which the discharge hematocrit
and apparent viscosity (and thus the new conductances of the vessels)
are computed.
INPUT: newGraph: Vascular graph in iGraph format to replace the
previous self._G. (Optional, default=None.)
assert_pBCs: (Optional, default=True.) Boolean whether or not to
check components for correct pressure boundary
conditions.
limiter: Limits change from one iteration level to the next, if
> 1.0, at limiter == 1.0 the change is unmodified.
OUTPUT: None, edge properties 'htd' and 'conductance' are modified
in-place.
"""
self._update_rbc_flow(limiter)
self._update_conductance_and_LS(newGraph, assert_pBCs)
#--------------------------------------------------------------------------
def solve(self, method, precision=None, maxIterations=1e4, limiter=0.5,
**kwargs):
"""Solve for pressure, flow, hematocrit and conductance by iterating
over linear and rheological analysis. Stop when either the desired
accuracy has been achieved or the maximum number of iterations have
been performed.
INPUT: method: This can be either 'direct' or 'iterative'
precision: Desired precision, measured in the maximum amount by
which the pressure may change from one iteration
level to the next. If the maximum change is below
threshold, the iteration is aborted.
maxIterations: The maximum number of iterations to perform.
limiter: Limits change from one iteration level to the next, if
> 1.0, at limiter == 1.0 the change is unmodified.
**kwargs
precisionLS: The accuracy to which the ls is to be solved. If
not supplied, machine accuracy will be used. (This
only applies to the iterative solver)
OUTPUT: None, the vascular graph is modified in-place.
"""
G = self._G
P = self._P
invivo=self._invivo
if precision is None: precision = self._eps
iterationCount = 0
maxPDiff = 1e200
filename = 'iter_'+str(iterationCount)+'.vtp'
g_output.write_vtp(G, filename, False)
filenames = [filename]
convergenceHistory = []
while iterationCount < maxIterations and maxPDiff > precision:
stdout.write("\rITERATION %g \n" %iterationCount)
self._rheological_analysis(None, False, limiter=0.5)
maxPDiff, meanPDiff, medianPDiff = self._linear_analysis(method, **kwargs)
#maxPDiff=self._maxPDiff
#meanPDiff=self._meanPDiff
#medianPDiff=self._medianPDiff
log.info('Iteration %i of %i' % (iterationCount+1, maxIterations))
log.info('maximum pressure change: %.2e' % maxPDiff)
log.info('mean pressure change: %.2e' % meanPDiff)
log.info('median pressure change: %.2e\n' % medianPDiff)
convergenceHistory.append((maxPDiff, meanPDiff, medianPDiff))
iterationCount += 1
G.es['htt'] = [P.discharge_to_tube_hematocrit(e['htd'], e['diameter'],invivo)
for e in G.es]
vrbc = P.rbc_volume(self._species)
G.es['nMax'] = [np.pi * e['diameter']**2 / 4 * e['length'] / vrbc
for e in G.es]
G.es['minDist'] = [e['length'] / e['nMax'] for e in G.es]
G.es['nRBC'] =[e['htt']*e['length']/e['minDist'] for e in G.es]
filename = 'iter_'+str(iterationCount)+'.vtp'
g_output.write_vtp(G, filename, False)
filenames.append(filename)
if iterationCount >= maxIterations:
stdout.write("\rMaximum number of iterations reached\n")
elif maxPDiff <= precision :
stdout.write("\rPrecision limit is reached\n")
self.integrity_check()
G.es['htt'] = [P.discharge_to_tube_hematocrit(e['htd'], e['diameter'],invivo)
for e in G.es]
vrbc = P.rbc_volume(self._species)
G.es['nMax'] = [np.pi * e['diameter']**2 / 4 * e['length'] / vrbc
for e in G.es]
G.es['minDist'] = [e['length'] / e['nMax'] for e in G.es]
G.es['nRBC'] =[e['htt']*e['length']/e['minDist'] for e in G.es]
G.es['v']=[4 * e['flow'] * P.velocity_factor(e['diameter'], invivo, tube_ht=e['htt']) /
(np.pi * e['diameter']**2) if e['htt'] > 0 else
4 * e['flow'] / (np.pi * e['diameter']**2)
for e in G.es]
for v in G.vs:
v['pressure']=v['pressure']/vgm.units.scaling_factor_du('mmHg',G['defaultUnits'])
filename = 'iter_final.vtp'
g_output.write_vtp(G, filename, False)
filenames.append(filename)
g_output.write_pvd_time_series('sequence.pvd', filenames)
vgm.write_pkl(G, 'G_final.pkl')
stdout.flush()
return convergenceHistory
return G
#--------------------------------------------------------------------------
def integrity_check(self):
"""Asserts that mass conservation is honored by writing the sum of in-
and outflow as well as the sum of the in- and outgoing discharge
hematocrit to the graph vertices as 'flowSum' and 'htdSum'
INPUT: None
OUTPUT: None, flow and htd sums added as vertex properties in-place.
"""
G = self._G
eps = self._eps
G.es['plasmaFlow'] = [(1 - e['htd']) * e['flow'] for e in G.es]
for v in G.vs:
vertex = v.index
vertexPressure = v['pressure']
outEdges = []
inEdges = []
for neighbor, edge in zip(G.neighbors(vertex, 'all'),
G.adjacent(vertex, 'all')):
if G.vs[neighbor]['pressure'] < vertexPressure:
outEdges.append(edge)
elif G.vs[neighbor]['pressure'] >= vertexPressure:
inEdges.append(edge)
v['flowSum'] = sum(G.es[outEdges]['flow']) - \
sum(G.es[inEdges]['flow'])
v['rbcFlowSum'] = sum(G.es[outEdges]['rbcFlow']) - \
sum(G.es[inEdges]['rbcFlow'])
v['plasmaFlowSum'] = sum(G.es[outEdges]['plasmaFlow']) - \
sum(G.es[inEdges]['plasmaFlow'])
for e in G.es:
e['errorHtd'] = abs(e['rbcFlow'] / e['flow'] - e['htd']) \
if e['flow'] > eps else 0.0
log.info('Max error htd: %.1g' % max(G.es['errorHtd']))
def __init__(self,
G,
withRBC=0,
invivo=0,
dMin_empirical=3.5,
htdMax_empirical=0.6,
verbose=True,
**kwargs):
"""
Computes the flow and pressure field of a vascular graph without RBC tracking.
It can be chosen between pure plasma flow, constant hematocrit or a given htt/htd
distribution.
The pressure boundary conditions (pBC) should be given in mmHG and pressure will be output in mmHg
INPUT: G: Vascular graph in iGraph format.(the pBC should be given in mmHg)
invivo: boolean if the invivo or invitro empirical functions are used (default = 0)
withRBC: = 0: no RBCs, pure plasma Flow (default)
0 < withRBC < 1 & 'htt' not in edgeAttributes: the given value is assigned as htt to all edges.
0 < withRBC < 1 & 'htt' in edgeAttributes: the given value is assigned as htt to all edges where htt = None.
NOTE: If htd is not in the edge attributes, Htd will be computed from htt and used to compute the resistance.
If htd is already in the edge attributes, it won't be recomputed but the current htd values will be used.
dMin_empiricial: lower limit for the diameter that is used to compute nurel (effective viscosity). The aim of the limit
is to avoid using the empirical equations in a range where no data exists (default = 3.5).
htdMax_empirical: upper limit for htd that is used to compute nurel (effective viscosity). The aim of the limit
is to avoid using the empirical equations in a range where no data exists (default = 0.6). Maximum has to be 1.
verbose: Bool if WARNINGS and setup information is printed
OUTPUT: None, the edge properties htt is assgined and the function update is executed (see description for more details)
"""
self._G = G
self._eps = np.finfo(float).eps
self._P = Physiology(G['defaultUnits'])
self._muPlasma = self._P.dynamic_plasma_viscosity()
self._withRBC = withRBC
self._invivo = invivo
self._verbose = verbose
self._dMin_empirical = dMin_empirical
self._htdMax_empirical = htdMax_empirical
if self._verbose:
print(
'INFO: The limits for the compuation of the effective viscosity are set to'
)
print('Minimum diameter %.2f' % self._dMin_empirical)
print('Maximum discharge %.2f' % self._htdMax_empirical)
if self._withRBC != 0:
if self._withRBC < 1.:
if 'htt' not in G.es.attribute_names():
G.es['htt'] = [self._withRBC] * G.ecount()
else:
httNone = G.es(htt_eq=None).indices
if len(httNone) > 0:
G.es[httNone]['htt'] = [self._withRBC] * len(httNone)
else:
if self._verbose:
print('WARNING: htt is already an edge attribute. \n Existing values are not overwritten!'+\
'\n If new values should be assigned htt has to be deleted beforehand!')
else:
print('ERROR: 0 < withRBC < 1')
if 'rBC' not in G.vs.attribute_names():
G.vs['rBC'] = [None] * G.vcount()
if 'pBC' not in G.vs.attribute_names():
G.vs['pBC'] = [None] * G.vcount()
self.update()
class LinearSystem(object):
def __init__(self,
G,
withRBC=0,
invivo=0,
dMin_empirical=3.5,
htdMax_empirical=0.6,
verbose=True,
**kwargs):
"""
Computes the flow and pressure field of a vascular graph without RBC tracking.
It can be chosen between pure plasma flow, constant hematocrit or a given htt/htd
distribution.
The pressure boundary conditions (pBC) should be given in mmHG and pressure will be output in mmHg
INPUT: G: Vascular graph in iGraph format.(the pBC should be given in mmHg)
invivo: boolean if the invivo or invitro empirical functions are used (default = 0)
withRBC: = 0: no RBCs, pure plasma Flow (default)
0 < withRBC < 1 & 'htt' not in edgeAttributes: the given value is assigned as htt to all edges.
0 < withRBC < 1 & 'htt' in edgeAttributes: the given value is assigned as htt to all edges where htt = None.
NOTE: If htd is not in the edge attributes, Htd will be computed from htt and used to compute the resistance.
If htd is already in the edge attributes, it won't be recomputed but the current htd values will be used.
dMin_empiricial: lower limit for the diameter that is used to compute nurel (effective viscosity). The aim of the limit
is to avoid using the empirical equations in a range where no data exists (default = 3.5).
htdMax_empirical: upper limit for htd that is used to compute nurel (effective viscosity). The aim of the limit
is to avoid using the empirical equations in a range where no data exists (default = 0.6). Maximum has to be 1.
verbose: Bool if WARNINGS and setup information is printed
OUTPUT: None, the edge properties htt is assgined and the function update is executed (see description for more details)
"""
self._G = G
self._eps = np.finfo(float).eps
self._P = Physiology(G['defaultUnits'])
self._muPlasma = self._P.dynamic_plasma_viscosity()
self._withRBC = withRBC
self._invivo = invivo
self._verbose = verbose
self._dMin_empirical = dMin_empirical
self._htdMax_empirical = htdMax_empirical
if self._verbose:
print(
'INFO: The limits for the compuation of the effective viscosity are set to'
)
print('Minimum diameter %.2f' % self._dMin_empirical)
print('Maximum discharge %.2f' % self._htdMax_empirical)
if self._withRBC != 0:
if self._withRBC < 1.:
if 'htt' not in G.es.attribute_names():
G.es['htt'] = [self._withRBC] * G.ecount()
else:
httNone = G.es(htt_eq=None).indices
if len(httNone) > 0:
G.es[httNone]['htt'] = [self._withRBC] * len(httNone)
else:
if self._verbose:
print('WARNING: htt is already an edge attribute. \n Existing values are not overwritten!'+\
'\n If new values should be assigned htt has to be deleted beforehand!')
else:
print('ERROR: 0 < withRBC < 1')
if 'rBC' not in G.vs.attribute_names():
G.vs['rBC'] = [None] * G.vcount()
if 'pBC' not in G.vs.attribute_names():
G.vs['pBC'] = [None] * G.vcount()
self.update()
#--------------------------------------------------------------------------
def update(self):
"""Constructs the linear system A x = b where the matrix A contains the
conductance information of the vascular graph, the vector b specifies
the boundary conditions and the vector x holds the pressures at the
vertices (for which the system needs to be solved).
OUTPUT: matrix A and vector b
"""
htt2htd = self._P.tube_to_discharge_hematocrit
nurel = self._P.relative_apparent_blood_viscosity
G = self._G
#Convert 'pBC' ['mmHG'] to default Units
for v in G.vs(pBC_ne=None):
v['pBC'] = v['pBC'] * vgm.units.scaling_factor_du(
'mmHg', G['defaultUnits'])
nVertices = G.vcount()
b = np.zeros(nVertices)
A = lil_matrix((nVertices, nVertices), dtype=float)
# Compute nominal and specific resistance:
self._update_nominal_and_specific_resistance()
#if with RBCs compute effective resistance
if self._withRBC:
if 'htd' not in G.es.attribute_names():
dischargeHt = [
min(htt2htd(htt, d, self._invivo), 1.0)
for htt, d in zip(G.es['htt'], G.es['diameter'])
]
G.es['htd'] = dischargeHt
else:
dischargeHt = G.es['htd']
if self._verbose:
print('WARNING: htd is already an edge attribute. \n Existing values are not overwritten!'+\
'\n If new values should be assigned htd has to be deleted beforehand!')
G.es['effResistance'] =[ res * nurel(max(self._dMin_empirical,d),min(dHt,self._htdMax_empirical),self._invivo) \
for res,dHt,d in zip(G.es['resistance'], dischargeHt,G.es['diameter'])]
G.es['conductance'] = 1 / np.array(G.es['effResistance'])
else:
G.es['conductance'] = [1 / e['resistance'] for e in G.es]
self._conductance = G.es['conductance']
for vertex in G.vs:
i = vertex.index
A.data[i] = []
A.rows[i] = []
b[i] = 0.0
if vertex['pBC'] is not None:
A[i, i] = 1.0
b[i] = vertex['pBC']
else:
aDummy = 0
k = 0
neighbors = []
for edge in G.incident(i, 'all'):
if G.is_loop(edge):
continue
j = G.neighbors(i)[k]
k += 1
conductance = G.es[edge]['conductance']
neighbor = G.vs[j]
# +=, -= account for multiedges
aDummy += conductance
if neighbor['pBC'] is not None:
b[i] = b[i] + neighbor['pBC'] * conductance
#elif neighbor['rBC'] is not None:
# b[i] = b[i] + neighbor['rBC']
else:
if j not in neighbors:
A[i, j] = -conductance
else:
A[i, j] = A[i, j] - conductance
neighbors.append(j)
if vertex['rBC'] is not None:
b[i] += vertex['rBC']
A[i, i] = aDummy
self._A = A
self._b = b
#--------------------------------------------------------------------------
def solve(self, method, **kwargs):
"""Solves the linear system A x = b for the vector of unknown pressures
x, either using a direct solver (obsolete) or an iterative GMRES solver. From the
pressures, the flow field is computed.
INPUT: method: This can be either 'direct' or 'iterative2'
OUTPUT: None - G is modified in place.
G_final.pkl & G_final.vtp: are save as output
sampledict.pkl: is saved as output
"""
b = self._b
G = self._G
htt2htd = self._P.tube_to_discharge_hematocrit
A = self._A.tocsr()
if method == 'direct':
linalg.use_solver(useUmfpack=True)
x = linalg.spsolve(A, b)
elif method == 'iterative2':
ml = rootnode_solver(A,
smooth=('energy', {
'degree': 2
}),
strength='evolution')
M = ml.aspreconditioner(cycle='V')
# Solve pressure system
#x,info = gmres(A, self._b, tol=self._eps, maxiter=1000, M=M)
x, info = gmres(A, self._b, tol=10 * self._eps, M=M)
if info != 0:
print('ERROR in Solving the Matrix')
print(info)
G.vs['pressure'] = x
self._x = x
conductance = self._conductance
G.es['flow'] = [abs(G.vs[edge.source]['pressure'] - G.vs[edge.target]['pressure']) * \
conductance[i] for i, edge in enumerate(G.es)]
#Default Units - mmHg for pressure
for v in G.vs:
v['pressure'] = v['pressure'] / vgm.units.scaling_factor_du(
'mmHg', G['defaultUnits'])
if self._withRBC:
G.es['v'] = [
e['htd'] / e['htt'] * e['flow'] /
(0.25 * np.pi * e['diameter']**2) for e in G.es
]
else:
G.es['v'] = [
e['flow'] / (0.25 * np.pi * e['diameter']**2) for e in G.es
]
#Convert 'pBC' from default Units to mmHg
pBCneNone = G.vs(pBC_ne=None).indices
G.vs[pBCneNone]['pBC'] = np.array(G.vs[pBCneNone]['pBC']) * (
1 / vgm.units.scaling_factor_du('mmHg', G['defaultUnits']))
vgm.write_pkl(G, 'G_final.pkl')
vgm.write_vtp(G, 'G_final.vtp', False)
#Write Output
sampledict = {}
for eprop in ['flow', 'v']:
if not eprop in sampledict.keys():
sampledict[eprop] = []
sampledict[eprop].append(G.es[eprop])
for vprop in ['pressure']:
if not vprop in sampledict.keys():
sampledict[vprop] = []
sampledict[vprop].append(G.vs[vprop])
g_output.write_pkl(sampledict, 'sampledict.pkl')
#--------------------------------------------------------------------------
def _update_nominal_and_specific_resistance(self, esequence=None):
"""Updates the nominal and specific resistance of a given edge
sequence.
INPUT: es: Sequence of edge indices as tuple. If not provided, all
edges are updated.
OUTPUT: None, the edge properties 'resistance' and 'specificResistance'
are updated (or created).
"""
G = self._G
if esequence is None:
es = G.es
else:
es = G.es(esequence)
G.es['specificResistance'] = [
128 * self._muPlasma / (np.pi * d**4) for d in G.es['diameter']
]
G.es['resistance'] = [
l * sr for l, sr in zip(G.es['length'], G.es['specificResistance'])
]
