import numpy as np
[docs]
def fit_for_pressureBC():
#Available Data --> see Schmid et al. 2017 for references: https://doi.org/10.1371/journal.pcbi.1005392
#Werber 1984, Pial Vessels Rat (normotensive Wistar Kyoto rat)
aP=[[60.],[3.0]] #[[list of mean pressures],[list of associated stds]]
aD=[[77.],[4.]] #[[list of mean diameters],[list of associated stds]]
vP=[[8.],[1.0]]
vD=[[180.],[16.]]
#Harper 1984, Pial Rat (normotensive Wistar Kyoto rat)
aP1=[[55.6,51.8,34.6,27.2],[5.0,3.3,3.1,1.0]]
aD1=[[51.2,34.3,21.6,12.5],[3.3,2.2,1.3,0.6]]
vP1=[[12.2,13.8,15.8],[1.,1.,1.2]]
vD1=[[160.6,76.2,34.2],[10.7,8.1,2.5]]
#Hudetz 1987 Rat Pial (normotensive)
aP2=[[79.7,72.7,63.7,60.7,57],[0,0,0,0,0]]
aD2=[[191,168,106,71,50],[7,22,22,19,12]]
#Shapiro 1971 Cat pial
aP3=[[40.6,47.5,50.0,52,52.8],[0,0,0,0,0]]
aD3=[[25,30,35,40,50],[0,0,0,0,0]]
#estimate value for D = 0 for the fit based on the smallest arteriole and venule diameter
#min(vD)=34.2, vP=15.8 --> 15.8 = m*(34.2) + b
#min(aD)=-12.5, aP=27.2 --> 27.2 = m*(-12.5) + b
m = - 0.244; b = 24.145
f_x = lambda x,m,b: x*m+b
d0=0.; p0=f_x(0,m,b)
#Data to fit:
#Artery
aPall=np.concatenate([aP[0],aP1[0],aP2[0],aP3[0],[p0]])
aDall=np.concatenate([aD[0],aD1[0],aD2[0],aD3[0],[d0]])
z=np.polyfit(aDall,aPall,3)
#Vein
vPall=np.concatenate([vP[0],vP1[0],[p0]])
vDall=np.concatenate([vD[0],vD1[0],[d0]])
z2=np.polyfit(vDall,vPall,3)
polynomial_a = np.poly1d(z)
polynomial_v = np.poly1d(z2) #NOTE I don't use the venule fit but assign a constant value of 10 mmHg
return polynomial_a, polynomial_v
[docs]
def assign_pressureBC(G,polynomial_a):
G.vs['degree'] = G.degree()
G.vs['pBC'] = [None]*G.vcount()
G.vs(degree_eq=1,nkind_eq=3)['pBC'] = 10. #NOTE nkind = 3 --> label for venules
for v in G.vs(degree_eq=1,nkind_ne=3): #NOTE nkind != 3, i.e. nkind = 2 --> label for arterioles and nkind = 4 for capillaries/everything else
v['pBC'] = np.polyval(polynomial_a,G.es[G.incident(v)[0]]['diameter'])
return G
