def run(x0=None):
try:
result = calculate([x0], **run_params)
#result = {k: [v] for k, v in result.items()}
fields.save_fields(name, save_params, x=[x0], **result)
except: # Exception, RuntimeError:
print "x = %s: Error occured, continuing without saving." %x0
print traceback.print_exc()
result = None
return result
def run(params=params):
X = np.array([0.])
Y = np.array([0.])
Z = np.array([params["z0"]])
J1 = np.array([])
T = np.array([])
avgbind=params["avgbind"]
P_bind=params["P_bind"]
ffa = True
i=0
while i<maxiter and Z[-1]>=-hpore/2.-2.:
xi_x=gauss(0.,1.)
xi_y=gauss(0.,1.)
xi_z=gauss(0.,1.)
Force = F
[[Dxfac, Dyfac, Dzfac],[DDx,DDy,DDz]]=D(X[-1],Y[-1],Z[-1])
# x_new = X[-1] + coeff*xi_x*math.sqrt(abs(Dxfac)) + C*Force[0]*Dxfac + DDx*tau*Dmol
# y_new = Y[-1] + coeff*xi_y*math.sqrt(abs(Dyfac)) + C*Force[1]*Dyfac + DDy*tau*Dmol
# z_new = Z[-1] + coeff*xi_z*math.sqrt(abs(Dzfac)) + C*Force[2]*Dzfac + DDz*tau*Dmol
x_new = X[-1] + coeff*xi_x + C*Force[0]
y_new = Y[-1] + coeff*xi_y + C*Force[1]
z_new = Z[-1] + coeff*xi_z + C*Force[2]
if dis(argument(x_new,y_new,z_new)) < rMolecule:
x_new = X[-1]
y_new = Y[-1]
z_new = Z[-1]
if ffa and np.random.binomial(1,P_bind)==1 and Z[-1]<=hpore/2.-h2 and Z[-1]>=-hpore/2.+1.:
add=expovariate(lambd=1./avgbind)
else:
add=0.
ffa = False
elif dis(argument(x_new,y_new,z_new)) < rMolecule + beps:
pass
else:
ffa = True
add=tau
X = np.append(X,x_new)
Y = np.append(Y,y_new)
Z = np.append(Z,z_new)
if abs(Z[-1])>30.:
print 'Traceback fehler????????????'
J1=np.append(J1,J(Z[-1]))
T =np.append(T,add)
i+=1
if not (Z[i]<=H/2. and Z[i]>=-H/2 and X[i] <=R/2 and X[i] >=-R/2 and Y[i] <=R/2 and Y[i] >=-R/2):
break
if i>=maxiter:
print 'randomwalk: more than 1e6 steps!'
X=[list(X)]
Y=[list(Y)]
Z=[list(Z)]
T=[list(T)]
J1=[list(J1)]
print 'savefield'
fields.save_fields("randomwalk7",params,T=T,J=J1)
def run(params=params):
X = np.array([0.])
Y = np.array([0.])
Z = np.array([params["z0"]])
J1 = np.array([])
T = np.array([])
avgbind = params["avgbind"]
P_bind = params["P_bind"]
i = 0
while i < maxiter and Z[-1] >= -hpore / 2. - 2.:
xi_x = gauss(0., 1.)
xi_y = gauss(0., 1.)
xi_z = gauss(0., 1.)
Force = F
Dfac = f(Z[-1])
x_new = X[-1] + coeff * xi_x + C * Force[0]
y_new = Y[-1] + coeff * xi_y + C * Force[1]
z_new = Z[-1] + coeff * xi_z * math.sqrt(
Dfac) + C * Force[2] * Dfac + fp(Z[i]) * Dmol * tau
if dis(argument(x_new, y_new, z_new)) < rMolecule:
x_new = X[-1]
y_new = Y[-1]
z_new = Z[-1]
if np.random.binomial(
1, P_bind
) == 1 and Z[-1] <= hpore / 2. - h2 and Z[-1] >= -hpore / 2. + 1.:
add = expovariate(lambd=1. / avgbind)
else:
add = 0.
else:
add = tau
X = np.append(X, x_new)
Y = np.append(Y, y_new)
Z = np.append(Z, z_new)
J1 = np.append(J1, J(Z[-1]))
T = np.append(T, add)
i += 1
if not (Z[i] <= H / 2. and Z[i] >= -H / 2 and X[i] <= R / 2
and X[i] >= -R / 2 and Y[i] <= R / 2 and Y[i] >= -R / 2):
break
if i >= maxiter:
print 'randomwalk: more than 1e6 steps!'
X = [list(X)]
Y = [list(Y)]
Z = [list(Z)]
T = [list(T)]
J1 = [list(J1)]
fields.save_fields("randomwalk3", params, X=X, Y=Y, Z=Z, T=T, J=J1)
def save(self, name="rw"):
if "N" in self.params:
self.params.pop("N")
optional = dict()
if self.record_positions:
optional.update(positions=self.positions,
timetraces=self.timetraces)
if hasattr(self, "binding_zone_forces"):
optional.update(binding_zone_forces=self.binding_zone_forces)
fields.save_fields(name,
self.params,
times=self.times,
success=self.success,
fail=self.fail,
bind_times=self.bind_times,
attempts=self.attempts,
bindings=self.bindings,
attempt_times=self.attempt_times,
**optional)
fields.update()
def save_fig_filter(params, fieldsname, i):
data = f.get_fields(fieldsname, **params)
T_ = data["T"][i].load()
J_ = data["J"][i].load()
T = np.array([])
J = np.array([])
for k in range(T_.shape[0]):
time = T_[k]
if time == 1.:
T = np.append(T, 1.)
J = np.append(J, J_[k])
else:
vec = np.ones(int(time))
T = np.append(T, vec)
J = np.append(J, J_[k] * vec)
T = np.append(T, np.ones(1000000))
J = np.append(J, np.ones(1000000) * J[0])
for k in range(1, T.shape[0]):
T[k] += T[k - 1]
# T*=1e-9
J *= 1e12
zero = J[0]
J -= zero
from scipy import signal
b, a = signal.bessel(2, 1. / 5. * 1e-3, analog=False)
sig_ff = signal.lfilter(b, a, J)
amp = data["a"][i]
ftime = np.min(np.where(sig_ff > 1e-10)) - 1
ampf = abs(np.sum(sig_ff[:ftime])) / float(ftime) / curr * 100
print 'without filter: %.1f' % amp
print 'with filter: %.1f' % ampf
tau_off = float(ftime)
figname = fieldsname + '_filter_' + '%.8f' % (
tau_off * 1e-6) + '_%04d' % i + '_%.1e_%.1e_%.1e_%.1e' % (
params["avgbind1"], params["avgbind2"], params["P_bind1"],
params["P_bind2"]) + str(params["z0"])
af = [ampf]
tf = [tau_off]
f.save_fields(fieldsname, params, tf5=tf, af5=af)
plt.figure(figsize=(6, 4), dpi=80)
plt.title('Current signal')
ax = plt.gca()
if tau_off < 1e3:
t = np.linspace(0., tau_off, 3)
fac = 1.
ax.set_xlabel('time [$ns$]')
elif tau_off < 1e6 and tau_off >= 1e3:
t = np.linspace(0., tau_off * 1e-3, 3)
fac = 1e-3
ax.set_xlabel(r'time [$\mu s$]')
else:
t = np.linspace(0., tau_off * 1e-6, 3)
fac = 1e-6
ax.set_xlabel('time [$ms$]')
T = T * fac
plt.plot(T, J + zero, color='#000000', label='Original')
plt.plot(T, sig_ff + zero, linewidth=2., color='#ff6600', label='Filtered')
plt.legend(loc='best')
yt = np.linspace(580., 760, 4)
ax.set_ylabel(r'A [$pA$]')
ax.set_yticks(yt)
ax.set_xticks(t)
xfmt = FormatStrFormatter('%.1f')
ax.xaxis.set_major_formatter(xfmt)
ax.set_xlim([-4e-2 * tau_off * fac, (1. + 4e-2) * tau_off * fac])
plt.tight_layout()
nano.savefigs(name=figname, DIR='/home/bstadlbau/plots/')
print 'savefig: %s' % figname
plt.close("all")
def run(params,fieldsname,outcome,outside,b1,b2):
def area1(x,y,z):
for seg in b1:
h=np.array([p[1] for p in seg])
if np.min(h)<=z and z<=np.max(h):
return True
return False
def area2(x,y,z):
for seg in b2:
h=np.array([p[1] for p in seg])
if np.min(h)<=z and z<=np.max(h):
return True
return False
z0 = params["z0"]
avgbind1=params["avgbind1"]
P_bind1=params["P_bind1"]
avgbind2=params["avgbind2"]
P_bind2=params["P_bind2"]
should_restart = True
while should_restart:
should_restart = False
X = np.array([0.])
Y = np.array([0.])
Z = np.array([z0])
J1 = np.array([])
T = np.array([])
Nc = 0
ffa = True
i=0
ood = False
while i<maxiter and Z[-1]>=-hpore/2.-4.:
add=tau
xi_x=gauss(0.,1.)
xi_y=gauss(0.,1.)
xi_z=gauss(0.,1.)
arg = argument(X[-1],Y[-1],Z[-1])
F = Force(X[-1],Y[-1],Z[-1])
D = [Dx(arg)*1e9,Dy(arg)*1e9,Dz(arg)*1e9]
dD = [dxDx(arg)*1e9,dyDy(arg)*1e9,dzDz(arg)*1e9]
# x_new = X[-1] + coeff*xi_x*math.sqrt(abs(Dxfac)) + C*Force[0]*Dxfac + DDx*tau*Dmol
# y_new = Y[-1] + coeff*xi_y*math.sqrt(abs(Dyfac)) + C*Force[1]*Dyfac + DDy*tau*Dmol
# z_new = Z[-1] + coeff*xi_z*math.sqrt(abs(Dzfac)) + C*Force[2]*Dzfac + DDz*tau*Dmol
# x_new = X[-1] + coeff*xi_x + C*Force[0]
# y_new = Y[-1] + coeff*xi_y + C*Force[1]
# z_new = Z[-1] + coeff*xi_z + C*Force[2]
x_new = X[-1] + sqrt(2*D[0]*tau)*xi_x + F[0]*D[0]*1e-9*tau/kT+dD[0]*tau
y_new = Y[-1] + sqrt(2*D[1]*tau)*xi_y + F[1]*D[1]*1e-9*tau/kT+dD[1]*tau
z_new = Z[-1] + sqrt(2*D[2]*tau)*xi_z + F[2]*D[2]*1e-9*tau/kT+dD[2]*tau
if dis(argument(x_new,y_new,z_new)) < rMolecule:
x_new = X[-1]
y_new = Y[-1]
z_new = Z[-1]
if ffa and area2(0.,0.,Z[-1]): Nc+=1
if ffa and np.random.binomial(1,P_bind1)==1 and area2(0.,0.,Z[-1]):
add+=expovariate(lambd=1./avgbind1)
elif ffa and np.random.binomial(1,P_bind2)==1 and area1(0.,0.,Z[-1]):
add+=expovariate(lambd=1./avgbind2)
else:
add+=0.
ffa = False
elif dis(argument(x_new,y_new,z_new)) < rMolecule + beps:
pass
else:
ffa = True
X = np.append(X,x_new)
Y = np.append(Y,y_new)
Z = np.append(Z,z_new)
if abs(Z[-1])>35. or abs(X[-1])>10. or abs(Y[-1])>10.:
print 'Out of domain!'
ood = True
if not outside or np.unique(J1).shape[0]==1:
should_restart = True
print 'restart!'
break
Jx=Current(X[-1],Y[-1],Z[-1])
if math.isnan(Jx):
if add<=tau:
Jx = J1[-1]
else:
print 'current at binding position is NaN!!!'
print 'current = %.1e A'%Jx
print 'X = %.8f'%X[-1]
print 'Y = %.8f'%Y[-1]
print 'Z = %.8f'%Z[-1]
print 'add = %.2f nanoseconds'%add
exit()
J1=np.append(J1,Jx)
T =np.append(T,add)
i+=1
if i>=maxiter:
print 'randomwalk: more than 1e6 steps!'
fields.save_fields(fieldsname,params,Nc=[Nc])
if outcome=='type' or outcome=='both':
tau_off = np.sum(T)*1e-6
curr = 7.523849e-10
amp = (curr-np.inner(T*1e-6,J1)/tau_off)/curr*100.
if math.isnan(amp):
np.save('T',T)
np.save('J1',J1)
file=open('nanerror.txt','w')
file.write('tau_off = %.10f\n'% tau_off)
file.write('amp = %.10f\n'% amp)
file.close()
exit()
t=[tau_off]
a=[amp]
if ood:
ood=[1]
else:
ood=[0]
fields.save_fields(fieldsname,params,t=t,a=a,ood=ood)
if outcome=='traj' or outcome=='both':
X=[X]
Y=[Y]
Z=[Z]
T=[T]
J1=[J1]
fields.save_fields(fieldsname,params,X=X, Y=Y, Z=Z, T=T, J=J1)
stokesLU=True,
)
fields.update()
# TODO calculate in parallel
# TODO calculate current as well
# TODO retrieve x points based on a given model and save file link
# to data field
X = fields.get_entry("xforce", "X")
N = len(X)
if fields.exists("force3D", **params):
Xdone = fields.get_field("force3D", "x", **params)
X = [x0 for x0 in X if x0 not in Xdone]
print "Existing force file found, %d/%d points remaining." % (len(X), N)
Xfailed = []
for x in X:
x0 = [x[0], 0., x[1]]
try:
F, Fel, Fdrag = Howorka.F_explicit3D([x0], **solver_params)
fields.save_fields("force3D", params, x=[x], F=F, Fel=Fel, Fdrag=Fdrag)
except RuntimeError:
print "RuntimeError occured, continuing without saving."
Xfailed.append(x)
print "failed:"
print Xfailed
print "%d of %d force calculations failed." % (len(Xfailed), len(X))
fields.update()
rMolecule = geop.rMolecule
params=dict(avgbind1=17.2e6,avgbind2=3e4,P_bind1=0.193,P_bind2=3e-1,z0=hpore/2.+0.)
#params=dict(avgbind1=17.2e6,avgbind2=3e4,P_bind1=0.020,P_bind2=3e-1,z0=hpore/2.+0.)
#b1 = [[[l3/2.,-hpore/2.],[l3/2.,hpore/2.-h2],[l2/2.,hpore/2.-h2],[l2/2.,hpore/2.-h1],[l1/2.,hpore/2.-h1],[l1/2.,hpore/2.]]]
#b2 = [[[2.5, hpore/2.-h2-24.], [2.5, hpore/2.-h2-32.]]]
b2 = [[[l3/2.,-hpore/2.],[l3/2.,hpore/2.-h2],[l2/2.,hpore/2.-h2],[l2/2.,hpore/2.-h1],[l1/2.,hpore/2.-h1],[l1/2.,hpore/2.]]]
b1 = []
#b2 = []
outside=True
#outside=False
for i in range(samples):
run(params,fieldsname,outcome,outside,b1,b2)
print '%i out of '%i+num
try: fields.get_fields(fieldsname,**params)["b1"]
except: fields.save_fields(fieldsname,params,b1=b1)
try: fields.get_fields(fieldsname,**params)["b2"]
except: fields.save_fields(fieldsname,params,b2=b2)
print 'field updates'
if outcome=='type' or outcome=='both':
from create_plot_type import *
f.update()
save_fig_type(params,fieldsname)
plt.close("all")
from create_plot_traj import *
save_fig_traj(params,fieldsname,0,False)
if outcome=='traj' or outcome=='both':
if outcome!='both':
from create_plot_traj import *
Dyy_ = [D[1][1] for D in data["D"]]
Dzz_ = [D[2][2] for D in data["D"]]
Dxx = smooth(smooth(Dxx_, 3), 5)
Dyy = smooth(smooth(Dyy_, 3), 5)
Dzz = smooth(smooth(Dzz_, 3), 5)
def matrix(d):
return [[d[0], 0., 0.], [0., d[1], 0.], [0., 0., d[2]]]
data = dict(x=x, D=map(matrix, zip(Dxx, Dyy, Dzz)))
if not fields.exists("pugh_diff_pore", rMolecule=rMolecule):
print "SAVING..."
fields.save_fields("pugh_diff_pore", dict(rMolecule=rMolecule), **data)
fields.update()
plt.figure()
plt.plot(x, Dxx_, "o:b")
plt.plot(x, Dxx, "-b", label=r"$D_x$")
plt.plot(x, Dyy_, "o:r")
plt.plot(x, Dyy, "-r", label=r"$D_y$")
plt.plot(x, Dzz_, "o:g")
plt.plot(x, Dzz, "-g", label=r"$D_z$")
plt.xlabel('x distance from pore wall [nm]')
plt.ylabel('diffusivity relative to bulk')
plt.legend(loc='lower right')
plt.tight_layout()
def run(params,fieldsname):
z0 = params["z0"]
X = np.array([0.])
Y = np.array([0.])
Z = np.array([z0])
J1 = np.array([])
T = np.array([])
bind1 = 0
bind2 = 0
avgbind1=params["avgbind1"]
P_bind1=params["P_bind1"]
avgbind2=params["avgbind2"]
P_bind2=params["P_bind2"]
ffa = True
i=0
ood = False
while i<maxiter and Z[-1]>=-hpore/2.-2.:
if ood:
bind1 = 0
bind2 = 0
i=0
ood = False
ffa = True
X = np.array([0.])
Y = np.array([0.])
Z = np.array([z0])
T = np.array([])
J1 = np.array([])
add=tau
xi_x=gauss(0.,1.)
xi_y=gauss(0.,1.)
xi_z=gauss(0.,1.)
arg = argument(X[-1],Y[-1],Z[-1])
F = Force(X[-1],Y[-1],Z[-1])
D = [Dx(arg)*1e9,Dy(arg)*1e9,Dz(arg)*1e9]
dD = [dxDx(arg)*1e9,dyDy(arg)*1e9,dzDz(arg)*1e9]
# x_new = X[-1] + coeff*xi_x*math.sqrt(abs(Dxfac)) + C*Force[0]*Dxfac + DDx*tau*Dmol
# y_new = Y[-1] + coeff*xi_y*math.sqrt(abs(Dyfac)) + C*Force[1]*Dyfac + DDy*tau*Dmol
# z_new = Z[-1] + coeff*xi_z*math.sqrt(abs(Dzfac)) + C*Force[2]*Dzfac + DDz*tau*Dmol
# x_new = X[-1] + coeff*xi_x + C*Force[0]
# y_new = Y[-1] + coeff*xi_y + C*Force[1]
# z_new = Z[-1] + coeff*xi_z + C*Force[2]
x_new = X[-1] + sqrt(2*D[0]*tau)*xi_x + F[0]*D[0]*1e-9*tau/kT+dD[0]*tau
y_new = Y[-1] + sqrt(2*D[1]*tau)*xi_y + F[1]*D[1]*1e-9*tau/kT+dD[1]*tau
z_new = Z[-1] + sqrt(2*D[2]*tau)*xi_z + F[2]*D[2]*1e-9*tau/kT+dD[2]*tau
if dis(argument(x_new,y_new,z_new)) < rMolecule:
x_new = X[-1]
y_new = Y[-1]
z_new = Z[-1]
if ffa and np.random.binomial(1,P_bind1)==1 and Z[-1]<=hpore/2.-h2-5 and Z[-1]>=-hpore/2.+h4:
add+=expovariate(lambd=1./avgbind1)
# print add
bind1+=1
elif ffa and np.random.binomial(1,P_bind2)==1 and ((Z[-1]<=-hpore/2.+h4 and Z[-1]>=-hpore/2.+0.) or (Z[-1]<=hpore/2.-h2 and Z[-1]>=hpore/2.-h2-5.)):
add+=expovariate(lambd=1./avgbind2)
bind2+=1
else:
add+=0.
ffa = False
elif dis(argument(x_new,y_new,z_new)) < rMolecule + beps:
pass
else:
ffa = True
X = np.append(X,x_new)
Y = np.append(Y,y_new)
Z = np.append(Z,z_new)
if abs(Z[-1])>35. or abs(X[-1])>10. or abs(Y[-1])>10.:
print 'Out of domain!'
ood = True
X[-1]=0.
Y[-1]=0.
Z[-1]=0.
Jx=Current(X[-1],Y[-1],Z[-1])
if math.isnan(Jx):
if add<=tau:
Jx = J1[-1]
else:
print 'current at binding position is NaN!!!'
print 'current = %.1e A'%Jx
print 'X = %.8f'%X[-1]
print 'Y = %.8f'%Y[-1]
print 'Z = %.8f'%Z[-1]
print 'add = %.2f nanoseconds'%add
exit()
J1=np.append(J1,Jx)
T =np.append(T,add)
i+=1
if not (Z[i]<=H/2. and Z[i]>=-H/2 and X[i] <=R/2 and X[i] >=-R/2 and Y[i] <=R/2 and Y[i] >=-R/2):
break
if i>=maxiter:
print 'randomwalk: more than 1e6 steps!'
# tau_off = np.sum(T)*1e-6
# curr = 7.523849e-10
# amp = (curr-np.inner(T*1e-6,J1)/tau_off)/curr*100.
# if tau_off<1.:
# t1 = [tau_off]
# a1 = [amp]
# t2 = []
# a2 = []
# else:
# t2 = [tau_off]
# a2 = [amp]
# t1 = []
# a1 = []
X=[list(X)]
Y=[list(Y)]
Z=[list(Z)]
T=[list(T)]
J1=[list(J1)]
fields.save_fields(fieldsname,params,X=X, Y=Y, Z=Z, T=T, J=J1)
new = ['events3_onlyone_%i_new' % i for i in [1, 2, 3, 4, 5]]
hpore = 46.
params = dict(avgbind1=2e7,
avgbind2=3e4,
P_bind1=8.e-2,
P_bind2=0 * 3e-1,
z0=hpore / 2. + 0.)
for i in range(5):
print i
data = fields.get_fields(old[i], **params)
for k in range(len(data["X"])):
if k % 10 == 0:
print k
saveX = [np.array(data["X"][k])]
saveY = [np.array(data["Y"][k])]
saveZ = [np.array(data["Z"][k])]
fields.save_fields(new[i], params, X=saveX, Y=saveY, Z=saveZ)
fields.save_fields(new[i],
params,
a=data["a"],
ood=data["ood"],
J=data["J"],
t=data["t"],
b1=data["b1"],
Fzavg=data["Fzavg"],
b2=data["b2"],
Dzavg=data["Dzavg"],
T=data["T"])
fields.update()