def f4d(f=2e-3, new=0, title='f4d.eps'):
print "\nFigure 4D"
#dxe=plm(gx=0,xt=1600,two=0,tt=3000,f4d=f,neww=0,graph=1,p=(10**(-3))/(F),pkcc=1e-3/F)
dxe = plm(gx=0, xt=360, two=0, tt=1800, f4d=f, neww=0)
a0, a1, a2 = minithreefig([
dxe[11][1:-1], dxe[14][1:-1], dxe[13][1:-1], dxe[16][1:-1],
dxe[10][1:-1], dxe[23][1:-1]
],
xcolor,
yl=[[-100, -70], [1.85e-12, 2.0e-12], [0, 0.09]])
print(dxe[16][-1] - dxe[14][-1])
#print (dxe[16][350]-dxe[14][350])
if new == 1:
delta = plm(gx=0, xt=360, two=0, tt=1800, f4d=f, neww=1, graph=0)
a0.plot(delta[11][1:-1],
delta[14][1:-1],
color=clcolor,
linestyle='--')
a0.plot(delta[11][1:-1], delta[13][1:-1], color=kcolor, ls='--')
a0.plot(delta[11][1:-1], delta[16][1:-1], 'k', ls='--')
a1.plot(delta[11][1:-1], delta[10][1:-1], color=wcolor,
ls='--') #volume
a2.plot(delta[11][1:-1], delta[23][1:-1], color=xcolor,
ls='--') #conc X
plt.savefig(title)
plt.show()
return dxe
def f4e(Z=range(-120, -50), moldelt=1e-12
): # extra function needed in figure 5
molinit = plm(gx=1e-8, xt=25, tt=100, two=1, paratwo=True, moldelt=moldelt)
zee = zp(Z=Z, molinit=molinit, moldelt=moldelt)
newx = []
for i in zee[9]:
newx.append(i)
return zee[0], Z, zee, newx
def f1c(new=0, l='-', g1=0, g2=0, g3=0):
g = 1
if new != 0:
g = 2
print("\nFigure 1C")
offpump = plm(graph=g,
ton=3000,
toff=9000,
tt=12000,
title='f1c.eps',
neww=new,
ls=l,
a0=g1,
a1=g2,
a2=g3)
return offpump
def f4a(init_x=[40e-3, 80e-3, 120e-3, 160e-3]):
print "Figure 4A"
plt.figure()
for i in range(len(init_x)):
endcl = plm(xinit=init_x[i], tt=1800, k_init=0, osmofix=True)
plt.subplot(2, 1, 1)
plt.plot(endcl[11][1:-1],
endcl[20][1:-1],
color=xcolor,
linestyle=sym[i])
plt.subplot(2, 1, 2)
plt.plot(endcl[11][0:-1], endcl[10][0:-1], 'k' + sym[i])
#plt.savefig('f4a.eps')
plt.show()
return endcl[20], endcl
def sf4fa():
XF = [5e-14, 1e-13, 2e-13]
GX = [-10, -9, -8]
col = ['mo', 'bo', 'go']
plt.figure()
for a in XF:
for g in GX:
dez = plm(tt=2500,
moldelt=a,
two=1,
gx=10**g,
xt=25,
xend=0,
ratio=0.8)
#minithreefig([dez[11][1:-1],dez[14][1:-1],dez[13][1:-1],dez[16][1:-1],dez[10][1:-1],dez[22][1:-1]],'k')
plt.plot(g, dez[22][-1], col[XF.index(a)])
plt.show()
return
def f3a():
dg = plm(tk=120, tt=600)
print "Figure 3A"
a1, a2, a3 = minithreefig([
dg[11][4:-1], dg[14][4:-1], dg[13][4:-1], dg[16][4:-1], dg[10][4:-1],
dg[24][4:-1]
],
'k',
yl=[[-100, -70], [1.92e-12, 1.98e-12], [0,
6e-7]])
#plt.savefig('f3a.eps')
plt.show()
print dg[24][-1]
print dg[14][0]
print dg[17][0]
print dg[17][-1]
print dg[14][-1]
return dg
def f3d(new=0, title='f3d.eps'): #doubles as f6f when new==1
molint = plm(gx=1e-8, xt=25, tt=100, two=1, paratwo=True, moldelt=0)
if new == 0:
gp = zplm(molinit=molint)
print "\nFigure 3D"
minifig([gp[0], gp[3], gp[2], [], [], gp[5], gp[11]], x=0)
else:
gp = zplm()
gp_nokcc = zplm(gkcc=0)
print "\nFigure 6F"
plt.figure()
plt.plot(gp[-1], np.subtract(gp[5], gp[3]), color='k')
plt.plot(gp_nokcc[-1],
np.subtract(gp_nokcc[5], gp_nokcc[3]),
linestyle='--',
color='k')
#plt.savefig(title)
plt.show()
return
def f4b(init_x=range(25, 586, 40), new=0, l='-', title='f4b.eps', a=0, b=0):
endv = []
endk = []
endcl = []
endw = []
print "\nFigure 4B"
for i in init_x:
end = plm(xinit=i * 1e-3,
k_init=0,
tt=2000,
osmofix=False,
neww=new,
ls=l)
endv.append(end[9])
endk.append(end[6])
endcl.append(end[7])
endw.append(end[21])
print end[9]
print end[6]
print end[7]
plt.figure()
gs = gridspec.GridSpec(2, 1, height_ratios=[1.5, 1])
if a == 0:
a = plt.subplot(gs[0])
a.plot(init_x, endcl, color=clcolor, linestyle=l)
a.plot(init_x, endcl, 'bo', linestyle=l)
a.plot(init_x, endk, color=kcolor, linestyle=l)
a.plot(init_x, endk, 'ro', linestyle=l)
a.plot(init_x, endv, 'k', linestyle=l)
a.plot(init_x, endv, 'ko', linestyle=l)
a.set_ylim([-100, -70])
if b == 0:
b = plt.subplot(gs[1])
b.plot(init_x, endw, color=wcolor, linestyle=l)
b.plot(init_x, endw, 'ko', linestyle=l)
b.set_ylim([0, 8e-12])
#plt.savefig(title)
plt.show()
return a, b
def sf4fb():
XF = [5e-14, 1e-13, 2e-13]
rat = [0.2, 0.4, 0.6, 0.8]
GX = [-10, -9, -8]
col = ['mo', 'bo', 'go', 'ro']
plt.figure()
for s in XF:
for r in rat:
dez = plm(tt=500,
moldelt=s,
two=1,
gx=10**(-8),
xt=25,
xend=0,
ratio=r)
#minithreefig([dez[11][1:-1],dez[14][1:-1],dez[13][1:-1],dez[16][1:-1],dez[10][1:-1],dez[22][1:-1]],'k')
plt.plot(r, dez[22][-1], col[XF.index(s)])
#plt.ylim((-0.83,-0.85))
plt.show()
return
def f1b(init_cl=[1e-3, 15e-3, 50e-3, 90e-3], ham=0):
leg = []
print("Figure 1B")
plt.figure()
for i in range(len(init_cl)):
endcl = plm(clinit=init_cl[i],
tt=1800,
osmofix=False,
k_init=0,
hamada=ham)
plt.subplot(2, 1, 1)
plt.plot(endcl[11][13:-1],
endcl[17][13:-1],
color=clcolor,
linestyle=sym[i])
plt.subplot(2, 1, 2)
plt.plot(endcl[11][13:-1], endcl[10][13:-1], 'k' + sym[i])
leg.append(str(init_cl[i] * 1000) + ' mM')
plt.savefig('f1bham.eps')
plt.show()
return
def sf4c(GX=[5e-10, 1e-9, 5e-9, 7e-9, 1e-8, 2e-8],
tt=600,
xt=25,
ratio=0.98,
xend=0):
deltecl = []
maxdeltecl = []
deltw = []
deltx = []
for i in GX:
dex = plm(gx=i, xt=xt, tt=tt, ratio=ratio, xend=xend)
deltecl.append(dex[14][-1] - dex[14][1])
maxdeltecl.append(max(np.absolute((dex[14] - dex[14][1]))))
deltw.append((dex[10][-1]) / dex[10][1])
deltx.append(max(np.absolute((dex[20] - dex[20][1]))))
minithreefig([
dex[11][1:-1], dex[14][1:-1], dex[13][1:-1], dex[16][1:-1],
dex[10][1:-1], dex[20][1:-1]
], xcolor)
print maxdeltecl
twoaxes(GX, deltecl, maxdeltecl, deltx, deltw)
return
def f6b(moldelt=0):
print "\nFigure 6B"
XF = [
-1.20, -1.15, -1.1, -1.05, -1.0, -0.95, -0.9, -0.85, -0.8, -0.75, -0.7,
-0.65, -0.6, -0.55, -0.501
]
XFp = []
cl = []
vm = []
k = []
w = []
Z = []
x = []
df = []
newpr = []
nai = []
ki = []
cli = []
xi = []
vm = []
pi = []
ena = []
ek = []
ecl = []
exi = []
ev = []
df2 = []
nai2 = []
pi, zi, zee, newx = f4e(moldelt=moldelt)
for i in range(len(zi)):
for b in XF:
if zi[i] / 100.0 == b:
#XFp.append(pi[i])
XFp.append(default_p)
for a in range(len(XFp)):
dez = plm(z=XF[a],
tt=1000,
p=(10**(XFp[a])) / F,
graph=0,
osmofix=True,
xinit=0)
newpr.append(dez[-3])
cl.append(dez[7])
k.append(dez[6])
vm.append(dez[9])
w.append(dez[10][-1])
Z.append(dez[22][-1] * 100)
x.append(dez[3])
df.append(dez[9] - dez[7])
pi = []
pi1 = []
pi2 = []
df3 = []
for m, o in enumerate(newpr, 0):
q = 10**(default_P / 10000.0) / (F * R)
pi1.append(q * R)
z = XF[m]
theta = (-z * (ose) + np.sqrt(
z**2 * (ose)**2 + 4 *
(1 - z**2) * cle * np.exp(-2 * q * gkcc * beta) *
(nae * np.exp(-3 * q / gna) + ke * np.exp(2 * q *
(gcl + gkcc) * beta)))
) / (2 * (1 - z) * ((nae * np.exp(-3 * q / gna) +
ke * np.exp(2 * q *
(gcl + gkcc) * beta))))
v = (-np.log(theta)) * R
nai2.append(nae * np.exp(-v / R - 3 * q / gna))
pi2.append(np.log10(F * R * q / (((np.exp(-v / R - 3 * q / gna)))**3)))
df3.append(1000.0 * v - 1000 * R *
np.log(cle * np.exp(+v / R - 2 * q * gkcc * beta) / cle))
for m, o in enumerate(newpr, 0):
q = 10**(o) / (F * R)
z = XF[m]
theta = (-z * (ose) + np.sqrt(
z**2 * (ose)**2 + 4 *
(1 - z**2) * cle * np.exp(-2 * q * gkcc * beta) *
(nae * np.exp(-3 * q / gna) + ke * np.exp(2 * q *
(gcl + gkcc) * beta)))
) / (2 * (1 - z) * ((nae * np.exp(-3 * q / gna) +
ke * np.exp(2 * q *
(gcl + gkcc) * beta))))
v = (-np.log(theta)) * R
nai.append(nae * np.exp(-v / R - 3 * q / gna))
ki.append(ke * np.exp(-v / R + 2 * q * (gcl + gkcc) * beta))
cli.append(cle * np.exp(+v / R - 2 * q * gkcc * beta))
xi.append(ose - nai[-1] - cli[-1] - ki[-1])
pi.append(q * R)
ek.append(1000 * R * np.log(ke / ki[-1]))
ena.append(1000 * R * np.log(nae / nai[-1]))
ecl.append(1000 * R * np.log(cli[-1] / cle))
exi.append(z * 1000 * R * np.log(xe / xi[-1]))
ev.append(1000.0 * v)
df2.append(ev[-1] - ecl[-1])
Z = XF[0:14]
twoaxes(Z, pi, pi1, nai)
plt.figure()
plt.plot(Z, df2, 'k')
plt.plot(Z, df3, 'k--')
#plt.savefig('f6b.eps')
plt.show()
return
def delta_gs3(Gk=[70], Gna=[20], Gkcc=[20], Gcl=[20], molinit=0):
vm = []
cli = []
nai = []
ki = []
xi = []
w = []
ek = []
ecl = []
ev = []
exi = []
ena = []
df = []
chosen = []
kpflux = []
kaflux = []
kaflux1 = []
kaflux2 = []
molinit = plm(gx=1e-8, xt=25, tt=100, two=1, paratwo=True, moldelt=0)
# only operate on conductance which is changing
for i in range(max(len(Gcl), len(Gkcc), len(Gk), len(Gna))):
for a in Gk, Gna, Gkcc, Gcl:
if len(a) <= i:
a.append(a[-1])
if len(a) > i + 1:
chosen = a
gkcc = Gkcc[i] * 1.0e-4 / F
gna = Gna[i] * 1.0e-4 / F
gk = Gk[i] * 1.0e-4 / F
gcl = Gcl[i] * 1.0e-4 / F
found = False
# run through all pump rates (analytical solution)
for l in prange:
q = np.float64(10**(l / 10000.0) / (F * R))
if gk * gcl + gkcc * gcl + gk * gkcc != 0:
beta = 1.0 / (gk * gcl + gkcc * gcl + gk * gkcc)
else:
print "invalid conductances: division by 0"
if z == -1:
theta = 0.5 * ose / (nae * np.exp(-3 * q / gna) +
ke * np.exp(2 * q * (gcl + gkcc) * beta))
else:
theta = (-z * ose + np.sqrt(
z**2 * ose**2 + 4 *
(1 - z**2) * cle * np.exp(-2 * q * gkcc * beta) *
(nae * np.exp(-3 * q / gna) +
ke * np.exp(2 * q *
(gcl + gkcc) * beta)))) / (2 * (1 - z) * (
(nae * np.exp(-3 * q / gna) +
ke * np.exp(2 * q *
(gcl + gkcc) * beta))))
v = (-np.log(theta)) * R
# efficient coding for expression pi=np.log10(F*R*q/(((np.exp(-v/R-3*q/gna)))**3))
pi = np.exp(-v / R - 3 * q / gna)
pi = -3 * np.log10(pi)
pi += np.log10(F * R * q)
# match constant pump rates to determine which pair (l, na) produce (very close to) the default pump rate
# append to arrays
if np.abs(pi - default_p) < 0.01 and found == False:
vm.append(v)
nai.append(nae * np.exp(-v / R - 3 * q / gna))
ki.append(ke * np.exp(-v / R + 2 * q * (gcl + gkcc) * beta))
cli.append(cle * np.exp(+v / R - 2 * q * gkcc * beta))
xi.append(ose - nai[-1] - cli[-1] - ki[-1])
w.append((molinit) / xi[-1])
ek.append(1000 * R * np.log(ke / ki[-1]))
ena.append(1000 * R * np.log(nae / nai[-1]))
ecl.append(1000 * R * np.log(cli[-1] / cle))
exi.append(z * 1000 * R * np.log(xe / xi[-1]))
ev.append(1000.0 * v)
df.append(ev[-1] - ecl[-1])
found = True
kpflux.append(gk * (v - ek[-1] / 1000.0))
kaflux.append(-(2 * q * R + gkcc *
(ek[-1] - ecl[-1]) / 1000.0))
kaflux1.append(-(2 * q * R))
kaflux2.append(-(gkcc * (ek[-1] - ecl[-1]) / 1000.0))
# if when checking for correct pair (l, na) none is found, notify (this will mean that no figure is generated)
if found == False:
print 'no match', i, pi, default_p
# may be fixed by increasing bounds of prange (i.e. larger search space)
return np.log10(
chosen), ecl, ek, ena, ki, ev, w, kpflux, kaflux, kaflux1, kaflux2
def delta_gs(Gk=[70], Gna=[20], Gkcc=[20], Gcl=[20], molinit=0):
vm = []
cli = []
nai = []
ki = []
xi = []
w = []
ek = []
ecl = []
ev = []
exi = []
ena = []
df = []
chosen = []
q = 10**(default_P / 10000.0)
for i in range(max(len(Gcl), len(Gkcc), len(Gk), len(Gna))):
for a in Gk, Gna, Gkcc, Gcl:
if len(a) <= i:
a.append(a[-1])
if len(a) > i + 1:
chosen = a
gkcc = Gkcc[i] * 1.0e-4 / F
gna = Gna[i] * 1.0e-4 / F
gk = Gk[i] * 1.0e-4 / F
gcl = Gcl[i] * 1.0e-4 / F
molinit = plm(gx=1e-8, xt=25, tt=100, two=1, paratwo=True, moldelt=0)
if gk * gcl + gkcc * gcl + gk * gkcc != 0:
beta = 1.0 / (gk * gcl + gkcc * gcl + gk * gkcc)
else:
print "invalid conductances: division by 0"
if z == -1:
theta = 0.5 * ose / (nae * np.exp(-3 * q / gna) +
ke * np.exp(2 * q * (gcl + gkcc) * beta))
else:
theta = (-z * ose +
np.sqrt(z**2 * ose**2 + 4 *
(1 - z**2) * cle * np.exp(-2 * q * gkcc * beta) *
(nae * np.exp(-3 * q / gna) +
ke * np.exp(2 * q * (gcl + gkcc) * beta)))) / (
2 * (1 - z) *
((nae * np.exp(-3 * q / gna) +
ke * np.exp(2 * q * (gcl + gkcc) * beta))))
v = (-np.log(theta)) * R
vm.append(v)
nai.append(nae * np.exp(-v / R - 3 * q / gna))
ki.append(ke * np.exp(-v / R + 2 * q * (gcl + gkcc) * beta))
cli.append(cle * np.exp(+v / R - 2 * q * gkcc * beta))
xi.append(ose - nai[-1] - cli[-1] - ki[-1])
w.append((molinit) / xi[-1])
ek.append(1000 * R * np.log(ke / ki[-1]))
ena.append(1000 * R * np.log(nae / nai[-1]))
ecl.append(1000 * R * np.log(cli[-1] / cle))
exi.append(z * 1000 * R * np.log(xe / xi[-1]))
ev.append(1000.0 * v)
df.append(ev[-1] - ecl[-1])
print ecl[-1], ek[-1], ev[-1]
return np.log10(chosen), ecl, ek, ena, df, ev, w, kpflux, kaflux
def f1d(time=25000, ham=0, l=0):
T = [
-7000, -6000, -5000, -4500, -4000, -3500, -3000, -2000, -1000, 0, 1000,
2000
]
#T=[-2000,0,1000,2000]
ti = [[], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [],
[], [], [], [], [], [], [], [], []]
# numerical solutions
if ham == 0:
for k in T:
q = 10**(k / 1000.0) / F
# optimisations (alternative is to start sims at any values and allow each to run for a long time)
if k == -7000:
a = plm(p=q,
tt=time,
graph=0,
k_init=0,
xinit=30e-3,
clinit=120e-3,
na_init=140e-3,
f1d=True,
lin=l)
elif k == -6000:
a = plm(p=q,
tt=time,
graph=0,
k_init=0,
xinit=36e-3,
clinit=113e-3,
na_init=140e-3,
f1d=True,
lin=l)
else:
a = plm(p=q,
tt=time,
graph=0,
k_init=0,
xinit=75e-3,
clinit=75e-3,
na_init=135e-3,
f1d=True,
lin=l)
for i in range(25):
ti[i].append(a[i])
else:
L = []
for a in T:
L.append((a - 3500.0) / 4.5) #y=(x-3500)/4.5
for k in T:
q = 10**(k / 1000.0 - default_p)
# optimisations (alternative is to start sims at any values and allow each to run for a long time)
if k == -7000:
a = plm(p=q,
tt=time,
graph=1,
k_init=0,
xinit=30e-3,
clinit=120e-3,
na_init=140e-3,
f1d=True,
hamada=q)
elif k == -6000:
a = plm(p=q,
tt=time,
graph=1,
k_init=0,
xinit=33e-3,
clinit=118e-3,
na_init=142e-3,
f1d=True,
hamada=q)
elif -3000 > k:
a = plm(p=q,
tt=time,
graph=1,
k_init=0,
xinit=45e-3,
clinit=105e-3,
na_init=140e-3,
f1d=True,
hamada=q)
elif k == -3000:
a = plm(p=q,
tt=time,
graph=1,
k_init=0,
xinit=50e-3,
clinit=100e-3,
na_init=140e-3,
f1d=True,
hamada=q)
elif k == -2000:
a = plm(p=q,
tt=time,
graph=1,
k_init=0,
xinit=120e-3,
clinit=35e-3,
na_init=135e-3,
f1d=True,
hamada=q)
else:
a = plm(p=q,
tt=5000,
graph=1,
k_init=0,
xinit=75e-3,
clinit=75e-3,
na_init=135e-3,
f1d=True,
hamada=q)
for i in range(25):
ti[i].append(a[i])
# parametric solutions
molinit = plm(gx=1e-8, xt=25, tt=100, two=1, paratwo=True, moldelt=0)
para = zplm(molinit=molinit)
# plotting
print("\nFigure 1D")
gs = gridspec.GridSpec(3, 1, height_ratios=[1.5, 1, 1])
plt.subplot(gs[0])
plt.plot(para[0], para[8], color=clcolor, linestyle='-')
plt.plot(para[0], para[7], color=kcolor, linestyle='-')
plt.plot(para[0], para[6], color=nacolor, linestyle='-')
plt.plot(para[0], para[9], color=xcolor, linestyle='-')
plt.plot(T, ti[0], 'o--', color=nacolor)
plt.plot(T, ti[1], 'o--', color=kcolor)
plt.plot(T, ti[2], 'o--', color=clcolor)
plt.plot(T, ti[3], 'o--', color=xcolor)
plt.subplot(gs[1])
plt.plot(para[0], para[10], 'k-')
plt.plot(T, ti[4], 'ko--')
plt.subplot(gs[2])
plt.plot(para[0], para[11], color=wcolor, linestyle='-')
plt.plot(T, ti[21], 'ko--')
plt.savefig('f1d.eps')
plt.show()
return ti
def f4c(gX=1e-8,
tt=3600,
xt=360,
xend=420,
xflux=4e-7,
new=0,
title='f4c.eps',
ham=0): #doubles as f6c when new!=0
dex = plm(gx=gX, xt=xt, tt=tt, xflux=xflux, xend=xend, graph=0, hamada=0)
delta = []
delta1 = []
if new == 0:
print "\nFigure 4C"
ax0, ax1, ax2 = minithreefig([
dex[11][1:-1], dex[14][1:-1], dex[13][1:-1], dex[16][1:-1],
dex[10][1:-1], dex[20][1:-1]
],
xcolor,
yl=[[-100, -70], [1.9e-12, 2.5e-12],
[154, 157]])
else:
print "\nFigure 6C"
ax0, ax1, ax2 = minithreefig([
dex[11][1:-1], dex[14][1:-1], dex[13][1:-1], dex[16][1:-1],
dex[18][1:-1], dex[10][1:-1]
],
'k',
yl=[[-100, -70], [13, 19],
[1.8e-12, 2.2e-12]])
delta = plm(gx=gX,
xt=xt,
tt=tt,
xflux=xflux,
xend=xend,
neww=3,
graph=0,
hamada=ham)
ax0.plot(delta[11][1:-1],
delta[14][1:-1],
color=clcolor,
linestyle='--')
ax0.plot(delta[11][1:-1], delta[13][1:-1], color=kcolor, ls='--')
ax0.plot(delta[11][1:-1], delta[16][1:-1], 'k', ls='--')
print(delta[16][-1] - delta[14][-1])
print(delta[16][350] - delta[14][350])
print len(delta[16])
ax1.plot(delta[11][1:-1], delta[18][1:-1], color=nacolor,
ls='--') #nai
ax2.plot(delta[11][1:-1], delta[10][1:-1], color='k', ls='--') #volume
delta1 = plm(gx=gX,
xt=xt,
tt=tt,
xflux=xflux,
xend=xend,
neww=5,
graph=0,
hamada=ham)
if ham == 1:
delta2 = plm(gx=gX,
xt=xt,
tt=tt,
xflux=xflux,
xend=xend,
graph=0,
hamada=ham)
ax0.plot(delta2[11][1:-1],
delta2[14][1:-1],
color=clcolor,
linestyle=':')
ax0.plot(delta2[11][1:-1], delta2[13][1:-1], color=kcolor, ls=':')
ax0.plot(delta2[11][1:-1], delta2[16][1:-1], 'k', ls=':')
ax1.plot(delta2[11][1:-1], delta2[18][1:-1], color=nacolor,
ls=':') #nai
ax2.plot(delta2[11][1:-1], delta2[10][1:-1], color='k',
ls=':') #volume
print(delta2[16][-1] - delta2[14][-1])
print len(delta2[16])
ax0.plot(delta1[11][1:-1],
delta1[14][1:-1],
color=clcolor,
linestyle='-.')
ax0.plot(delta1[11][1:-1], delta1[13][1:-1], color=kcolor, ls='-.')
ax0.plot(delta1[11][1:-1], delta1[16][1:-1], 'k', ls='-.')
ax1.plot(delta1[11][1:-1], delta1[18][1:-1], color=nacolor,
ls='-.') #nai
ax2.plot(delta1[11][1:-1], delta1[10][1:-1], color='k',
ls='-.') #volume
print(delta1[16][-1] - delta1[14][-1])
print len(delta1[16])
plt.savefig(title)
plt.show()
return dex, delta, delta1
def f6d(os=range(-100, 100)):
print "\nFigure 6D"
nai = []
ki = []
cli = []
ecl = []
ev = []
pi = []
os2 = []
os3 = []
q = 10**(default_P / 10000.0) / (F * R)
for o in os:
theta = (-z * (ose + o / 10000.0) + np.sqrt(
z**2 * (ose + o / 10000.0)**2 + 4 *
(1 - z**2) * cle * np.exp(-2 * q * gkcc * beta) *
(nae * np.exp(-3 * q / gna) + ke * np.exp(2 * q *
(gcl + gkcc) * beta)))
) / (2 * (1 - z) * ((nae * np.exp(-3 * q / gna) +
ke * np.exp(2 * q *
(gcl + gkcc) * beta))))
v = (-np.log(theta)) * R
nai.append(nae * np.exp(-v / R - 3 * q / gna))
ki.append(ke * np.exp(-v / R + 2 * q * (gcl + gkcc) * beta))
cli.append(cle * np.exp(+v / R - 2 * q * gkcc * beta))
pi.append(q * R)
ecl.append(1000.0 * R * np.log(cli[-1] / cle))
ev.append(1000.0 * v)
on = [-0.1, -0.075, -0.05, -0.025, -0.01, 0, 0.01, 0.025, 0.05, 0.075, 0.1]
p_plm_osmo = []
for a, i in enumerate(os, 0):
for b in on:
if a == b * 1000:
p_plm_osmo.append(pi[i])
for m in (2, 7):
if m == 1:
shape = '*'
elif m == 2:
shape = 's'
elif m == 3:
shape = 'o'
for l, o in enumerate(on, 0):
d = plm(neww=m,
osmofix=True,
os_choose=o / 10.0,
tt=1000,
areascale=1,
xflux=o * 1e-5,
xt=180,
xend=180,
graph=0)
oh = (d[-2] - d[-1]) * 10000
os2.append(10**(d[-3]) / (F))
os3.append(oh)
nai2 = []
ki2 = []
cli2 = []
xi2 = []
ecl2 = []
ev2 = []
pi2 = []
for x, o in enumerate(os3, 0):
q = os2[x] / R
theta = (-z * (ose + o / 10000.0) + np.sqrt(
z**2 * (ose + o / 10000.0)**2 + 4 *
(1 - z**2) * cle * np.exp(-2 * q * gkcc * beta) *
(nae * np.exp(-3 * q / gna) + ke * np.exp(2 * q *
(gcl + gkcc) * beta)))
) / (2 * (1 - z) * ((nae * np.exp(-3 * q / gna) +
ke * np.exp(2 * q *
(gcl + gkcc) * beta))))
v = (-np.log(theta)) * R
nai2.append(nae * np.exp(-v / R - 3 * q / gna))
ki2.append(ke * np.exp(-v / R + 2 * q * (gcl + gkcc) * beta))
cli2.append(cle * np.exp(+v / R - 2 * q * gkcc * beta))
pi2.append(np.log10(F * R * q / (((np.exp(-v / R - 3 * q / gna)))**3)))
ecl2.append(1000.0 * R * np.log(cli2[-1] / cle))
ev2.append(1000.0 * v)
twoaxes(os, pi, os2, nai, os3)
plt.figure()
plt.plot(os, np.array(ev) - np.array(ecl), color='k')
plt.plot(os3, np.array(ev2) - np.array(ecl2), 'k--')
#plt.savefig('f6d.eps')
plt.show()
return
