def save_fig(params, fieldsname):
figname = fieldsname + '_%.1e_%.1e_%.1e_%.1e_%.1e_%.1e' % (
params["avgbind1"], params["avgbind2"], params["avgbind3"],
params["P_bind1"], params["P_bind2"], params["P_bind3"]) + str(
params["z0"])
data = f.get_fields(fieldsname, **params)
t1 = data["t1"]
a1 = data["a1"]
t2 = data["t2"]
a2 = data["a2"]
t0 = data["t0"]
a0 = data["a0"]
print len(t1)
print len(t2)
lendata = len(t1) + len(t2)
for i in range(len(t1)):
if math.isnan(a1[i]): print 'a1: NaN %i' % i
plt.figure(figsize=(6, 4), dpi=80)
plt.plot([3e-6, .7], [0., 0.], linewidth=2, color='lightgreen')
plt.plot([1., 1e2], [0., 0.], linewidth=2, color='green')
ax = plt.gca()
plt.scatter(t1, a1, color='lightgreen', s=8)
plt.scatter(t2, a2, color='green', s=8)
plt.scatter(t0,
a0,
marker='o',
s=50,
facecolors='none',
edgecolors='#ff0000')
#ax.text(t1[k],a1[k],'%i'%k,fontsize=9)
xfmt = FormatStrFormatter('%g')
ax.set_xlim([1e-6, 500.])
ax.set_ylim([-2., 25.0])
ax.set_xscale('log')
ax.xaxis.set_major_formatter(xfmt)
ax.invert_yaxis()
ax.set_xlabel(r'$\tau_{off}$ [ms]', fontsize=15)
ax.set_ylabel(r'A/I$_0$ [%]', fontsize=15)
ax.text(.001, -0.03, 'I', fontsize=15)
ax.text(5., -0.03, 'II', fontsize=15)
#ax.text(1.,5.,lendata,fontsize=25)
plt.tight_layout()
nano.savefigs(name=figname, DIR='/home/lv70496/benjamin/plots/')
print 'savefig:'
print figname
def save_fig(params):
figname = 'type_'+str(params["avgbind1"])+'_'+str(params["avgbind2"])+'_'+str(params["P_bind1"])+'_'+str(params["P_bind2"])+'_'+str(params["z0"])+'.eps'
data=f.get_fields("rw_2_bind",**params)
t1 = np.array([])
t2 = np.array([])
a1 = np.array([])
a2 = np.array([])
T=data["T"]
J=data["J"]
# print '#data = %i'%len(T)
for i in range(len(T)):
T_=np.array(T[i])*1e-6
J_=np.array(J[i])
tau_off=np.sum(T_)
amp = (2060.-np.inner(J_,T_)/tau_off)/2060.*100
if number: ax.text(tau_off,amp,'%i'%i,fontsize=9)
if tau_off<.1:
t1=np.append(t1,np.array([tau_off]))
a1=np.append(a1,np.array([amp]))
else:
t2=np.append(t2,np.array([tau_off]))
a2=np.append(a2,np.array([amp]))
plt.plot([5e-4,.1],[0.,0.],linewidth=2,color='lightgreen')
plt.plot([.2,1e2],[0.,0.],linewidth=2,color='green')
ax=plt.gca()
plt.scatter(t1,a1,color='lightgreen')
plt.scatter(t2,a2,color='green')
ax.set_xlim([1e-4,500.])
ax.set_ylim([-0.40,4.0])
ax.set_xscale('log')
ax.invert_yaxis()
ax.set_xlabel(r'$\tau_{off}$ [ms]',fontsize=15)
ax.set_ylabel(r'A/I$_0$ [%]',fontsize=15)
ax.text(.011,-0.03,'I',fontsize=15)
ax.text(5.,-0.03,'II',fontsize=15)
plt.tight_layout()
nano.savefigs(name=figname,DIR='/home/lv70496/benjamin/plots/')
plt.xlabel(r"$\tau$ off [ms]")
#plt.xlim(xmin=0.5)
plt.legend(loc="lower right", frameon=False)
print "CDF fit:", T
plt.figure("data_gammafit_pdf", figsize=(4, 3))
#########
for i in T:
T[i].plot_pdf(tt,
label=kaformat % kastr[i],
std=std,
log=log,
color=colors[i],
linestyle=linestyles[i])
plt.bar(tc, epdf, 0.8 * np.diff(t1), alpha=0.5, label="Experiment")
plt.xscale("log")
plt.ylabel("Rel. frequency")
plt.xlabel(r"$\tau$ off [ms]")
#plt.xlim(xmin=0.1)
plt.legend(loc="upper right", frameon=False)
plt.figure("data_gammafit_error", figsize=(4, 3))
plt.semilogx(ka, error, "o")
plt.xlabel(r"$k_a$")
plt.ylabel("Fitting error")
import folders
nanopores.savefigs("rw_cyl", folders.FIGDIR + "/pugh", ending=".pdf")
def save_fig_type(params, fieldsname):
plotlin = False
# cmap=matplotlib.cm.get_cmap('plasma')
data = f.get_fields(fieldsname, **params)
figname = fieldsname + '_%.1e_%.1e_%.1e_%.1e' % (
params["avgbind1"], params["avgbind2"], params["P_bind1"],
params["P_bind2"]) + str(params["z0"])
t = data["t"]
tdata = np.array(t)
a = data["a"]
ood = data["ood"]
lendata = len(t)
fac = 1.
if max(t) < 1e-2:
fac = 1e3
t = [x * 1e3 for x in t]
P_bind1 = params["P_bind1"]
P_bind2 = params["P_bind2"]
avgbind1 = params["avgbind1"] * 1e-6
avgbind2 = params["avgbind2"] * 1e-6
color2 = 'green'
color1 = 'lightgreen'
color3 = 'red'
if plotlin:
plt.figure(figsize=(10, 5), dpi=80)
else:
plt.figure(figsize=(7, 5), dpi=80)
if plotlin:
gs = gridspec.GridSpec(2,
3,
width_ratios=[4, 2, 1],
height_ratios=[1, 2.5])
else:
gs = gridspec.GridSpec(2,
2,
width_ratios=[4, 1],
height_ratios=[1, 2.5])
gs.update(wspace=0., hspace=0.)
minperc = 0.
maxperc = 30.
plt1 = plt.subplot(gs[1, 0])
for k in range(lendata):
if ood[k] == 0:
type1 = plt1.scatter([t[k]], [a[k]], color=color2, s=8)
else:
type0 = plt1.scatter([t[k]], [a[k]], color=color3, s=8)
xfmt = FormatStrFormatter('%g')
plt1.set_xlim([.2 * min(t), max(t) * 5.])
plt1.set_ylim([minperc, maxperc])
plt1.set_xscale('log')
plt1.xaxis.set_major_formatter(xfmt)
plt1.invert_yaxis()
plt1.set_ylabel(r'A/I$_0$ [%]', fontsize=15)
# plt1.text(.2,23.,str(float(np.where(tdata>0.2)[0].shape[0])/float(tdata.shape[0]))+'='+str(np.where(tdata>0.2)[0].shape[0])+'/'+str(tdata.shape[0]),fontsize=9)
if fac == 1.:
if P_bind1 != 0.:
plt1.text(avgbind1 * .5,
27.,
'Long binding',
fontsize=9,
horizontalalignment='center')
k = 0.5
# plt1.add_patch(matplotlib.patches.Rectangle((avgbind1*10**(-k*2),0.),avgbind1*(10**(k)-10**(-k)),maxperc,facecolor=cmap(.7),alpha=.15))
if P_bind2 != 0.:
plt1.text(0.002,
27.,
'Short binding',
fontsize=9,
horizontalalignment='center')
k = 1.0
# plt1.add_patch(matplotlib.patches.Rectangle((avgbind2*100*10**(-k),0.),avgbind2*(10**(k)-10**(-k)),maxperc,facecolor=cmap(.4),alpha=.15))
# plt1.add_patch(matplotlib.patches.Rectangle((0.01,0.),0.99,maxperc,facecolor=cmap(.4),alpha=.15))
plt1.set_xlabel(r'$\tau_{off}$ [ms]', fontsize=15) #,x=.76)
else:
plt1.set_xlabel(ur'$\tau_{off}$ [µs]', fontsize=15) #,x=.76)
if plotlin:
plt2 = plt.subplot(gs[1, 1])
for k in range(lendata):
if ood[k] == 0:
type1 = plt2.scatter([t[k]], [a[k]], color=color2, s=8)
else:
type0 = plt2.scatter([t[k]], [a[k]], color=color3, s=8)
plt2.invert_yaxis()
plt2.set_ylim([maxperc, minperc])
plt2.set_xlim([-2e-2 * max(t), max(t) * (1. + 2e-2)])
plt2.axes.get_yaxis().set_visible(False)
plt2.axes.get_xaxis().major.locator.set_params(nbins=6)
plt3 = plt.subplot(gs[1, 2])
else:
plt3 = plt.subplot(gs[1, 1])
n, bins, patches = plt3.hist(np.array(a),
15,
normed=1,
orientation='horizontal',
color=color1,
alpha=.5)
plt3.invert_yaxis()
plt3.set_xlim([0., max(n) * 1.2])
plt3.set_ylim([maxperc, minperc])
plt3.axes.get_xaxis().set_visible(False)
plt3.axes.get_yaxis().set_visible(False)
if plotlin:
plt4 = plt.subplot(gs[0, 1])
n, bins, patches = plt4.hist(np.array(t),
20,
normed=1,
color=color1,
alpha=.5)
plt4.set_xlim([-2e-2 * max(t), max(t) * (1. + 2e-2)])
plt4.axes.get_xaxis().set_visible(False)
plt4.axes.get_yaxis().set_visible(False)
else:
plt4 = plt.subplot(gs[0, 0])
n, bins, patches = plt4.hist(map(lambda x: math.log10(x), np.array(t)),
25,
normed=1,
color=color1,
alpha=.5,
align='mid')
plt4.set_xlim([math.log10(.2 * min(t)), math.log10(max(t) * 5.)])
plt4.axes.get_xaxis().set_visible(False)
plt4.axes.get_yaxis().set_visible(False)
plt.legend([type1, type0],
['successful translocation', 'did not translocate'],
scatterpoints=4,
loc=(.6, 0.50))
plt.tight_layout()
nano.savefigs(name=figname, DIR='/home/bstadlbau/plots/', pdf=True)
print 'savefig:'
print figname
return f([x, z])
else:
return f([-x, z])
R, Htop, Hbot = 7, 2, 12
fig, ax = plt.subplots(figsize=(8, 6), num="pot")
tr, vertex_values = mesh2triang(mesh)
zz = 1000*vertex_values(f)
minmax = max(abs(zz))
#plt.triplot(tr)
#pc = plt.tripcolor(tr, zz, cmap=cm.bwr)
pc = plt.tripcolor(tr, zz, cmap=cm.bwr, vmin=-minmax, vmax=minmax)
#pc = plt.pcolor(X, Y, U, cmap=cm.coolwarm_r) #, vmin=0, vmax=1)
cb = plt.colorbar(pc)
cb.ax.set_ylabel("Electric potential [mV]")
#plot_polygon(ax, pugh.polygon(diamPore=6., rmem=13))
plt.xlim(-R, R)
plt.ylim(-Hbot, Htop)
p = get_pore()
plot_polygon(p.protein)
plot_polygon(p.membrane)
from folders import FIGDIR
from nanopores import savefigs
savefigs("potential", FIGDIR + "/ahem", (6, 4.5))
#dolfin.plot(v, backend="matplotlib", cmap=cm.viridis)
plt.show()
plt.plot(dxx, fplot(cdx_exp, dxx), "-", label="Wei et al.")
for i in range(5):
plt.plot(dxx,
np.ones(len(dxx)) * cdxall_exp[i],
"-",
color="C0",
alpha=0.5)
#plt.plot(dxx, fplot(cdx_exp - vdx_exp, dxx), "-", color="C1")
#plt.plot(dxx, fplot(cdx_exp + vdx_exp, dxx), "-", color="C1")
#plt.fill_between(dxx, fplot(cdx_exp - vdx_exp, dxx),
# fplot(cdx_exp + vdx_exp, dxx), alpha=0.5)
#plt.plot(dx, fplot(cdx, dx), "o", label="Simulation")
plt.plot(dx, fplot(cdx, dx), "o", label="Simulations", color="C1")
for i in (0, 1, 2):
plt.plot(dx, fplot(cdxall[:, i], dx), "o", color="C1", alpha=0.5)
#plt.fill_between(dx, fplot(cdx - cdxstd, dx), fplot(cdx + cdxstd, dx), alpha=0.5)
plt.annotate(r"$\delta$=5.5nm", (5.5, cdxall[4, 0] - 1.),
xytext=(5.5 - .79, cdxall[4, 0] - 8.),
color="C1",
arrowprops=dict(arrowstyle="->", color="C1"))
plt.xlabel(r"Bond rupture length $\delta$ [nm]")
plt.ylabel(r"$\alpha$ [1/V]")
plt.legend(loc="upper left", frameon=False)
import folders
nanopores.savefigs("tau_off2", folders.FIGDIR_HOWORKA + "/wei", ending=".pdf")
#nanopores.savefigs("tau_off2", folders.FIGDIR + "/wei", ending=".eps")
# if ood[k]==0:
# type1 = plt2.scatter([t[k]],[a[k]],color=color2,s=8)
# else:
# type0 = plt2.scatter([t[k]],[a[k]],color=color3,s=8)
#plt2.invert_yaxis()
#plt2.set_ylim([maxperc,minperc])
#plt2.set_xlim([-2e-2*max(t),max(t)*(1.+2e-2)])
#plt2.axes.get_yaxis().set_visible(False)
#plt2.axes.get_xaxis().major.locator.set_params(nbins=6)
#
#plt3=plt.subplot(gs[1,2])
#n, bins, patches = plt3.hist(np.array(a),15,normed=1,orientation='horizontal',color=color1,alpha=.5)
#plt3.invert_yaxis()
#plt3.set_xlim([0.,max(n)*1.2])
#plt3.set_ylim([maxperc,minperc])
#plt3.axes.get_xaxis().set_visible(False)
#plt3.axes.get_yaxis().set_visible(False)
#
#
#
#plt4=plt.subplot(gs[0,1])
#n, bins, patches = plt4.hist(np.array(t),20,normed=1,color=color1,alpha=.5)
#plt4.set_xlim([-2e-2*max(t),max(t)*(1.+2e-2)])
#plt4.axes.get_xaxis().set_visible(False)
#plt4.axes.get_yaxis().set_visible(False)
#plt.tight_layout()
#plt.show()
#plt.savefig('events_both_compare.pdf')
nanopores.savefigs("rw/events", FIGDIR, ending=".pdf")
R, Htop, Hbot = 7, 2, 12
fig = plt.figure(figsize=(2.6, 2.15))
ax = plt.axes(xlim=(-R, R), ylim=(-Hbot, Htop))
tr, vertex_values = mesh2triang(mesh)
zz = 1000 * vertex_values(f)
minmax = max(abs(zz))
#plt.triplot(tr)
#pc = plt.tripcolor(tr, zz, cmap=cm.bwr)
pc = plt.tripcolor(tr, zz, cmap=cm.bwr, vmin=-minmax, vmax=minmax)
#pc = plt.pcolor(X, Y, U, cmap=cm.coolwarm_r) #, vmin=0, vmax=1)
cb = plt.colorbar(pc)
cb.ax.set_ylabel("Electric potential [mV]")
#plot_polygon(ax, pugh.polygon(diamPore=6., rmem=13))
#plt.xlim(-R, R)
#plt.ylim(-Hbot, Htop)
p = get_pore()
plot_polygon(p.protein, lw=.5)
plot_polygon(p.membrane, lw=.5)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
from folders import FIGDIR_HOWORKA
from nanopores import savefigs
savefigs("potential", FIGDIR_HOWORKA + "/ahem", ending=".pdf")
#dolfin.plot(v, backend="matplotlib", cmap=cm.viridis)
plt.show()
return D3D([-x, z]) / D0
#D = uCross(u=D3D, axis=1, degree=1, dim=2)
# obtain 2D mesh where we will evaluate field
rx, ry = pugh.pughpore.params["R"], 0.5 * pugh.pughpore.params["H"]
rx, ry = 13, 28
Nx, Ny = 201, 401
#mesh2D = RectangleMesh([-R,-H/2.], [R, H/2.], int(4*R), int(2*H))
Y, X = np.mgrid[-ry:ry:Ny * 1j, -rx:rx:Nx * 1j]
U = np.zeros((Ny, Nx))
for y in range(Ny):
for x in range(Nx):
U[y][x] = F(X[y][x], Y[y][x])
fig, ax = plt.subplots(figsize=(6.5, 6.5), num="r0.11")
pc = plt.pcolor(X, Y, U, cmap=cm.inferno, vmin=0, vmax=1)
plt.colorbar(pc)
plot_polygon(ax, pugh.polygon(diamPore=6., rmem=13))
plt.xlim(-13, 13)
plt.ylim(-25, 28)
#plt.xlabel("x [nm]")
#plt.ylabel("z [nm]")
fig.axes[1].set_ylabel(r"$D_{zz} / D_0$")
#cb = fig.colorbar(CS, cax=cax, extend="both", orientation="vertical", format=formt)
import folders
nanopores.savefigs("pugh_Dfield_protein", folders.FIGDIR)
plt.show()
# with constant D = 0.3*D0 in pore
dd = None
params = dict(default, diffusivity_data=dd)
plt.figure("rD03", figsize=(2.6, 2.))
get_IV(calc, **params)
# with 'simple' D interpolation (only analytical near-plane model)
dd = ddsimplefine
params = dict(default, diffusivity_data=dd)
plt.figure("pdDsimple")
get_IV(calc, **params)
# with analytical r-dependence of D multiplied by z profile
dd = ddcoupled
params = dict(default, diffusivity_data=dd)
plt.figure("pdDcoupled")
get_IV(calc, **params)
# comparison
#plt.figure("finevscoarse")
#fine_vs_coarse(False, **default)
plt.figure("compareD", figsize=(2.8, 2.1)) #, dpi=300)
compare_D_models(calc, **default)
plt.figure("compareD_simple", figsize=(1.7, 1.6))
compare_D_models_simple(calc, **default)
from nanopores import savefigs
savefigs("IV", folders.FIGDIR_HOWORKA + "/ahem", ending=".pdf")
#plt.show()
plt.legend(frameon=False)
# big plot
plt.figure("p0_fit")
plt.plot(P2, data2.p0, ".", label="Simulated (N=20000)", zorder=100)
PP = np.linspace(0, 1, 500)
plt.plot(PP, 1. - np.exp(-a * PP), label="Poisson (a = 0.3)")
plt.plot(PP, pmod * (1. - np.exp(-a1 * PP)), label="Mod. Poisson (a = 2.2)")
print "pmod", pmod
plt.xlabel(r"Binding probability $p$")
plt.ylabel(r"$p_0$") #probability of >= 1 binding")
plt.gca().add_patch(Rectangle((-0.01, -0.002), 0.07, 0.02, fc="none", ec="k"))
plt.legend(frameon=False)
import folders
nanopores.savefigs("binding_prob", folders.FIGDIR + "/wei", (4, 3))
print "binding prob. inferred from simulations: p = %.6f" % p
ap = -np.log(1 - p0)
p1 = ap / a
print "binding prob. inferred from assumed Poisson distribution: p = %.6f" % p1
# plot mean, std, log of time
plt.figure("time_stdmean")
mu = np.array(data4.mean_time)
sigma = np.array(data4.std_time)
log = np.array(data4.mean_log_time)
plt.plot(P4, (mu / sigma)**2, "o", label="Simulated (N=100000)")
def f(x):
import nanopores
import matplotlib.pyplot as plt
from mysolve import load_estimators as load
figsize = 5 * 0.8, 4 * 0.8
est2D = load("anaPNPS_2D")
est3D = load("anaPNPS_3D")
fig = plt.figure("refinement", figsize=figsize)
est2D["Jvol"].name = r"$|J_h - J|/J$ (2D)"
est3D["Jvol"].name = r"$|J_h - J|/J$ (3D)"
est2D["Jvol"].plot(rate=-1., fig=False)
est3D["Jvol"].plot(rate=-2. / 3, fig=False)
ax = plt.gca()
ax.lines[1].set_label("")
ax.lines[3].set_label("")
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
from folders import FIGDIR
nanopores.savefigs("anaPNPS", FIGDIR)
plt.ylim(300, 985)
if rw.success[i]:
plt.scatter([t[i0]], [J(z[i0])], s=dotsize, c=stopcolor)
scalebar = AnchoredSizeBar(ax.transData,
10,
"10ns",
2,
pad=0.4,
color="#666666",
frameon=False,
size_vertical=25,
fontproperties=fm.FontProperties(size=8)
#fontproperties=fontprops
)
ax.add_artist(scalebar)
plt.xlabel("Time")
plt.ylabel("Current")
plt.tick_params(axis="both",
which='both',
left="off",
labelleft="off",
bottom='off',
labelbottom='off')
#plt.gcf().tight_layout()
nanopores.savefigs("exittime",
FIGDIR + "/ahem",
ending=".pdf",
bbox_inches="tight")
plt.show()
if r>=0.25: continue
names = {0.152: r"$\rm{K}^{+}$", 0.167: r"$\rm{Cl}^{-}$"}
Dmax = max(D)
D0 = [d/Dmax for d in D]
ax = ax_small
ax.plot(Z, D0, ".-", label=names[r])
ax.set_xlabel("z position of molecule [nm]")
ax.set_ylabel("D/D0")
ax.set_title("rel. diffusivity (2D model)")
# coarser calculation for remaining radii
for r in [0.5, 1., 1.5]:
N = 2e4
data = f.get_fields("pugh_diffusivity2D", rMolecule=r, h=4., Nmax=N)
Z, D = zsorted(data, "D")
# plot
ax = ax_big
ax.plot(Z, D, ".-", label="r=%.3f, N=20k" %r)
ax.set_xlabel("z position of molecule [nm]")
ax.set_ylabel("D/D0")
ax.set_title("rel. diffusivity (2D model)")
ax_big.legend(bbox_to_anchor=(1.05, 1.), loc="upper left", borderaxespad=0.,)
ax_small.legend(bbox_to_anchor=(1.05, 1.), loc="upper left", borderaxespad=0.,)
ax_small.legend(loc="lower right")
nanopores.savefigs("pugh_diffusivity", folders.FIGDIR)
plt.show()
U = np.zeros((Ny, Nx))
for y in range(Ny):
for x in range(Nx):
U[y][x] = F(X[y][x], Y[y][x])
fig, ax = plt.subplots(figsize=(1.73, 1.9)) #, dpi=300)
pc = plt.pcolor(X, Y, U, cmap=plt.get_cmap("bone"), vmin=0, vmax=1)
plot_polygon(ax, pughpolygon(diamPore=6., rmem=15), linewidth=0.2)
plt.xlim(-15, 15)
plt.ylim(-25, 28)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
bbox = ax.get_position()
l, b, h = bbox.x0, bbox.y0, bbox.height
w = 0.05
cbaxes = fig.add_axes([l - w - 0.07, b, w, h])
cb = plt.colorbar(pc, cax=cbaxes, ax=ax)
cbaxes.set_ylabel("Rel. diffusivity") # (r"$D_{zz} / D_0$")
cb.set_ticks([0., 1.])
cb.set_ticklabels([0, 1])
cbaxes.yaxis.set_ticks_position('left')
cbaxes.yaxis.set_label_position('left')
cbaxes.yaxis.labelpad = -3.
import os
HOME = os.path.expanduser("~")
FIGDIR = os.path.join(HOME, "Dropbox", "Paper Howorka", "figures")
nanopores.savefigs("pugh/Dfield", FIGDIR)
plt.show()
)
# one binding
fig, ax = plt.subplots(num="bindzones_1", figsize=figsize2)
configure_ax(ax)
rainbow_text(
-11.5,
texty,
"Long binding".split(),
[clong, "k"],
)
plot_seq(ax, blong1, clong, 5 * lw)
# two bindings
fig, ax = plt.subplots(num="bindzones_2", figsize=figsize2)
configure_ax(ax)
rainbow_text(-13.5, texty + .5 * line, "Long and short".split(),
[clong, "k", cshort])
rainbow_text(
-6.5,
texty - .5 * line,
["binding"],
["k"],
)
plot_seq(ax, bshort, cshort, lw2)
plot_seq(ax, blong2, clong, lw2)
#figdir = nanopores.dirnames.DROPBOX_FIGDIR
figdir = nanopores.dirnames.HOME + "/Dropbox/Paper Howorka/figures/"
nanopores.savefigs("rw/trace", figdir, ending=".pdf", pad_inches=0)
R = r0 - rMol - eps
Z = np.linspace(-R, R, 21)
X = [[z,0.,0.] for z in Z]
#X = [[0.,0.,0.]]
print velocities(X, nproc=7, name="howorka_velo3D_2", **params)
do_plot = False
redo_plot = False
if do_plot:
imax = user_params(imax=15)["imax"]
if redo_plot:
x = [0.2, 0., 0.]
#x = [0., 0., 0.]
setup = Howorka.Setup(x0=x, **params)
f, v, dv = velocity_iteration(setup, imax)
nanopores.save_stuff("velocity_iteration", f.tolist(), v.tolist(), dv.tolist())
f, v, dv = nanopores.load_stuff("velocity_iteration")
dim = params["dim"]
plt.semilogy(range(1, imax+1), 1e12*np.sqrt(np.sum(np.array(f)**2, 1)),
"s-", label="net force on molecule")
plt.ylabel("force [pN]")
plt.xlabel("# iterations")
plt.xlim(xmin=1, xmax=imax)
plt.xticks(range(1,imax+1))
plt.legend(loc="best")
fig = plt.gcf()
fig.set_size_inches((4,3))
nanopores.savefigs("howorka_velocity_3D_z0", FIGDIR)
# scale times
scale = 1e-6 # microseconds
times = [t * 1e-3 / scale for t in times]
from matplotlib import pyplot as plt
t = linspace(1e-9 / scale, 8e-6 / scale, 500)
hist = plt.hist(times,
bins=30,
color="#aaaaff",
linewidth=0.5,
weights=[1. / 500.] * len(times),
label="BD simulations")
pt = pp(t, scale) * integrate_hist(hist) * scale
plt.plot(t, pt, "-", color="g", linewidth=3, label="FPT model")
plt.legend(loc="upper right", frameon=False)
plt.xlabel(u"dwell time [µs]")
plt.ylabel(u"rel. frequency")
print "integral", integrate_hist(hist), "==", integrate(t, pt)
#plt.figure()
#plt.hist(data["Fzavg"], bins=30, color="#aaaaff", linewidth=0.5)
#
#plt.figure()
#plt.hist(data["Dzavg"], bins=30, color="#aaaaff", linewidth=0.5)
from folders import FIGDIR
savefigs("current-nobind-hist", FIGDIR + "/rw")
def Dfit(z, rion=0.11):
rpore = dist([0., z])
beta = rion / rpore
return 1. / (A + B * np.exp(beta / C) + D * np.exp(beta / E))
def diff_profile_fit(a=-10.3, b=0.05, N=20):
Z = np.linspace(a, b, N)
return Z, [Dfit(z) for z in Z]
z, D = diff_profile_fit(a=-12, b=2, N=100)
plt.plot(z, D, "-b", label="Tabulated (infinite cylinder)")
data = diff_profile_z_ahem(a=-12, b=2, N=100, **params)
z = [x0[2] for x0 in data["x"]]
Dz = data["D"]
plt.plot(z, Dz, "og", label="Full hydrodynamic model")
plt.ylabel("Rel. diffusivity")
plt.xlabel("z [nm]")
plt.xlim(-10, 0)
ax = plt.gca()
#ax.yaxis.tick_right()
#ax.yaxis.set_label_position("right")
plt.legend(loc="upper left", frameon=False)
from nanopores import savefigs
from folders import FIGDIR
savefigs("Dz", FIGDIR + "/ahem", (6, 4.5))
#print results
offset = ScaledTranslation(18, 27, ax.transScale)
tform = offset + ax.transLimits + ax.transAxes
ell = mpatch.Ellipse((0, 0), 1.8, 10, angle=0.0, transform=tform,
fc="None", ec=long_bind_color)
ax.add_patch(ell)
plt.text(15, 21, "Assumed\n long bindings", color=long_bind_color,
horizontalalignment="center")
########## simulations with one binding ##########
plt.figure("1bind", figsize=figsize)
a, t, ood = fields.get("eventsnew_onlyone_2_", "a", "t", "ood")
a, t, success = np.array(a), np.array(t), np.array(ood) == 0
plt.scatter(t[~success], a[~success], **s_fail)
plt.scatter(t[success], a[success], **s_success)
plt.scatter(t_exp, a_exp, **s_experiment)
format_ticks(plt.gca())
########## simulations with two bindings ##########
plt.figure("2bind", figsize=figsize)
a, t, ood = fields.get("eventsnew_both_1_", "a", "t", "ood")
a, t, success = np.array(a), np.array(t), np.array(ood) == 0
plt.scatter(t[~success], a[~success], **s_fail)
plt.scatter(t[success], a[success], **s_success)
plt.scatter(t_exp, a_exp, **s_experiment)
format_ticks(plt.gca())
figdir = nanopores.dirnames.HOME + "/Dropbox/Paper Howorka/figures/"
nanopores.savefigs("rw/events", figdir, ending=".pdf")
label=r"$v_r$ (exact)")
plt.plot(z, [x[0] for x in data["v1"]],
".--",
color="#0099ff",
label=r"$v_r$ (approx.)")
plt.xlabel("radial position of molecule [nm]") ## modify
plt.ylabel(r"velocity [m/s]")
plt.legend(loc="best") #(bbox_to_anchor=(0.5, 1.), loc="upper left")
#plt.plot(z, np.abs(np.array(v0)-np.array(v1)))
fig = plt.gcf()
fig.set_size_inches((4, 3))
plt.figure()
plt.plot(z, [x[2] for x in data["v0"]],
"v-",
color="#009900",
label=r"$v_z$ (exact)")
plt.plot(z, [x[2] for x in data["v1"]],
".--",
color="#99ff00",
label=r"$v_z$ (approx.)")
plt.xlabel("radial position of molecule [nm]") ## modify
plt.ylabel("velocity [m/s]")
plt.legend(loc="best") #bbox_to_anchor=(0., 1.), loc="upper left")
#plt.plot(z, np.abs(np.array(v0)-np.array(v1)))
fig = plt.gcf()
fig.set_size_inches((4, 3))
nanopores.savefigs("velo", FIGDIR)
#plt.show()
Dzz1 = max(Dzz)
xlin = linspace(r + 1e-3, 3., 100)
dn = [Dn_plane(t, r, N=20) for t in xlin]
dn = [d * Dxx1 / dn[-1] for d in dn]
plt.plot(xlin, dn, "-b")
dt = [Dt_plane(t, r) for t in xlin]
dt = [d * Dzz1 / dt[-1] for d in dt]
plt.plot(xlin, dt, "-g")
plt.xlim(0., 1.5)
plt.xticks([0, 0.5, 1., 1.5])
plt.plot(x, Dxx, "ob", label=r"$D_{xx}$")
plt.plot(x, Dyy, "sg", label=r"$D_{yy}$")
plt.plot(x, Dzz, ".r", label=r"$D_{zz}$")
#plt.plot(x, [D2D]*len(x), "--k", label="2D cyl.")
plt.xlabel("x distance from pore wall [nm]")
plt.ylabel("Rel. diffusivity")
plt.ylim(0, 1)
plt.axvline(x=0.11, linestyle="--", color="#666666")
plt.annotate("Ion radius", (0.11, 0.94),
xytext=(0.25, 0.94 - 0.002),
color="#666666",
arrowprops=dict(arrowstyle="->", color="#666666"))
plt.yticks([i / 10. for i in range(0, 11, 2)])
plt.legend(loc="lower right"
) #bbox_to_anchor=(1.05, 1.), loc="upper left", borderaxespad=0.,)
plt.gcf().set_size_inches(3.2, 3.2)
nanopores.savefigs("pugh_Dions", FIGDIR)
plt.show()
color="red",
rwidth=0.9,
label="Did not translocate")
#total = rw.integrate_hist(hist, cutoff)
#tmean = times[times > cutoff].mean()
#T = np.logspace(a-3, b, 1000)
#fT = np.exp(-T/tmean)*T/tmean
#fT *= total/integrate_values(T, fT, cutoff)
#plt.plot(T, fT, label="exp. fit, mean = %.2f ms" % (tmean,),
# color="dark" + color, **params)
#plt.xlim(10**a, 10**b)
#fake0 = fake[fake > bins[1]]
plt.hist(fake,
bins=bins,
histtype="step",
color="orange",
label="Wei et al. 2012")
plt.xscale("log")
plt.yscale("log")
plt.ylabel("Count")
plt.xlabel(r"$\tau$ off [s]")
plt.ylim(ymin=1., ymax=1e5)
plt.legend()
#plt.show()
import folders
nanopores.savefigs("tau_off", folders.FIGDIR + "/wei", (4, 3))
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")
calc = True
default = dict(geoname="alphahem", dim=2, h=1., Nmax=2e4, rDPore=0.3)
# with constant D = 0.3*D0 in pore
dd = None
params = dict(default, diffusivity_data=dd)
plt.figure("rD03")
get_IV(calc, **params)
# with 'simple' D interpolation (only analytical near-plane model)
dd = ddsimplefine
params = dict(default, diffusivity_data=dd)
plt.figure("pdDsimple")
get_IV(calc, **params)
# with analytical r-dependence of D multiplied by z profile
dd = ddcoupled
params = dict(default, diffusivity_data=dd)
plt.figure("pdDcoupled")
get_IV(calc, **params)
# comparison
#plt.figure("finevscoarse")
#fine_vs_coarse(False, **default)
plt.figure("compareD")
compare_D_models(calc, **default)
from nanopores import savefigs
savefigs("IV", folders.FIGDIR + "/ahem", size=(5, 3.7))
#plt.show()
