def diffusivityDistance(binned, fig=0, ax=0, i=-1):
p = inf.getInputParameters()
if binned:
d = inf.readBinnedAverages()
else:
d = inf.readAverages()
cove = d.getRecomputedCoverage() / p.sizI / p.sizJ
ratios = p.getRatios()
Na = cove * p.sizI * p.sizJ
fig = plt.figure(num=33, figsize=(6, 5))
ax = plt.gca()
x = cove
cm = plt.get_cmap("Accent")
alpha = 0.5
handles = []
hops = fun.timeDerivative(d.hops, d.time) / (4 * Na)
lg, = ax.loglog(
x,
hops,
label=r"$\frac{l^2}{2dN_a} \; \frac{d\langle N_h\rangle}{dt}$",
marker="+",
ls="",
mew=1,
markeredgecolor=cm(7 / 8),
ms=7,
alpha=alpha)
handles.append(lg)
Malpha = inf.readPossibleFromList()
for l in range(0, 4):
Malpha[l] = fun.timeDerivative(Malpha[l], d.time)
individualHopsCalc = []
individualHopsCalc.append((Malpha[0] * ratios[0]) / (4 * Na))
individualHopsCalc.append((Malpha[1] * ratios[8]) / (4 * Na))
individualHopsCalc.append((Malpha[2] * ratios[15]) / (4 * Na))
individualHopsCalc.append((Malpha[3] * ratios[24]) / (4 * Na))
hopsCalc = np.sum(individualHopsCalc, axis=0)
lgC, = plt.loglog(x, hopsCalc, "-", label="hops calc")
# marker="*", ls="", mew=mew, markerfacecolor=cm(5/8), ms=5, alpha=alpha)
lg, = plt.loglog(x, individualHopsCalc[0], "-", label="hops calc0")
handles.append(lg)
lg, = plt.loglog(x, individualHopsCalc[1], "-", label="hops calc1") #,
handles.append(lg)
lg, = plt.loglog(x, individualHopsCalc[2], "-", label="hops calc2") #
handles.append(lg)
lg, = plt.loglog(x, individualHopsCalc[3], "-", label="hops calc3") #
handles.append(lg)
handles.append(lgC)
ax.grid()
ax.set_xlabel(r"$\theta$", size=16)
#ax.set_ylim([1e-7,1e13])
ax.set_xlim([1e-5, 1e0])
ax.legend(loc="best", prop={'size': 6})
#ax.legend(handles=handles, loc=(0.46,0.3), numpoints=1, prop={'size':15}, markerscale=2)
fig.savefig("../../../plot" + str(p.flux) + str(p.temp) + ".png")
plt.close(33)
def diffusivityDistance(binned, fig=0, ax=0, i=-1):
p = inf.getInputParameters()
if binned:
d = inf.readBinnedAverages()
else:
d = inf.readAverages()
cove = d.getRecomputedCoverage()/p.sizI/p.sizJ
ratios = p.getRatios()
Na = cove * p.sizI * p.sizJ
fig = plt.figure(num=33, figsize=(6,5))
ax = plt.gca()
x = list(range(0,len(d.time)))
x = cove
cm = plt.get_cmap("Accent")
alpha = 0.5
mew = 0
diff = fun.timeDerivative(d.diff, d.time)/(4*Na)
handles = []
#lg, = ax.loglog(x, d.diff/d.time/(4*Na), label=r"$\frac{1}{2dN_a} \; \frac{\langle R^2\rangle}{t}$", ls="-", color=cm(3/8), lw=2); handles.append(lg)
lg, = ax.loglog(x, d.hops/d.time/(4*Na), label=r"$\frac{l^2}{2dN_a} \; \frac{\langle N_h\rangle}{t}$",
marker="x", color=cm(4.1/8), ls="", solid_capstyle="round",lw=5); handles.append(lg)
Malpha = inf.readPossibleFromList()#/d.time
MalphaP = inf.readInstantaneous(False)
for k in range(0,4):
Malpha[k] = Malpha[k]/d.time
label = r"$\theta_{"+str(k)+"}$"
lg, = ax.loglog(x, fun.timeAverage(d.negs[k]/p.sizI/p.sizJ, d.time), label=label, ms=1, lw=2, ls="-", color=cm(k/8)); handles.append(lg)
#lg, = plt.loglog(x, MalphaP[k]/d.negs[k], label=r"$m_"+str(k)+"$"); handles.append(lg) # Around 6, 2, 2, 0.1
individualHopsCalc = []
individualHopsCalc.append((Malpha[0]*ratios[0])/(4*Na))
individualHopsCalc.append((Malpha[1]*ratios[8])/(4*Na))
individualHopsCalc.append((Malpha[2]*ratios[15])/(4*Na))
individualHopsCalc.append((Malpha[3]*ratios[24])/(4*Na))
lg, = plt.loglog(x, individualHopsCalc[0], label="hops calc0")
handles.append(lg)
lg, = plt.loglog(x, individualHopsCalc[1], label="hops calc1")#,
handles.append(lg)
lg, = plt.loglog(x, individualHopsCalc[2], label="hops calc2")#
handles.append(lg)
lg, = plt.loglog(x, individualHopsCalc[3], label="hops calc3")#
handles.append(lg)
hopsCalc = np.sum(individualHopsCalc, axis=0)
lgC, = plt.loglog(x, hopsCalc, label="hops calc")
# marker="*", ls="", mew=mew, markerfacecolor=cm(5/8), ms=5, alpha=alpha)
handles.append(lgC)
ax.grid()
ax.set_xlabel(r"$\theta$", size=16)
#ax.set_ylim([1e-7,1e13])
ax.set_xlim([1e-5,1e0])
ax.legend(loc="best", prop={'size':6})
#ax.legend(handles=handles, loc=(0.46,0.3), numpoints=1, prop={'size':15}, markerscale=2)
fig.savefig("../../../plot"+str(p.flux)+str(p.temp)+".png")
plt.close(33)
def diffusivityDistance(smooth, binned, fig=0, ax=0, i=-1):
p = inf.getInputParameters()
if binned:
d = inf.readBinnedAverages()
else:
d = inf.readAverages()
cove = d.getRecomputedCoverage() / p.sizI / p.sizJ
ratios = p.getRatios()
Na = cove * p.sizI * p.sizJ
innerFig = fig == 0 and ax == 0
if innerFig:
fig = plt.figure(num=33, figsize=(5, 7))
ax = plt.gca()
x = list(range(0, len(d.time)))
x = cove
cm = plt.get_cmap("Accent")
alpha = 0.5
mew = 0
handles = []
lgE, = ax.loglog(x,
d.diff / d.hops * 1e9,
label="$f_T\cdot10^9$",
marker="o",
ms=1,
ls="",
mec=cm(3 / 20),
mfc=cm(3 / 20),
lw=2)
lgR3, = ax.loglog(x,
d.diff / d.time / (4 * Na),
label=r"$g \; \frac{\langle R^2\rangle}{t}$",
ls="-",
color=cm(3 / 8),
lw=5)
lgN3, = ax.loglog(x,
d.hops / d.time / (4 * Na),
label=r"$gl^2 \; \frac{\langle N_h\rangle}{t}$",
ls=":",
color=cm(4.1 / 8),
lw=4)
lg, = ax.loglog(x, (np.sum(d.negs[0:7], axis=0)) / p.sizI / p.sizJ * 1e5,
ms=1,
lw=1,
ls="-.",
color="black",
label=r"$\theta \cdot 10^5$") #, marker=markers[4])
markers = ["o", "s", "D", "^", "d", "h", "p", "o"]
cm1 = plt.get_cmap("Set1")
bbox_props = dict(boxstyle="round", fc="w", ec="1", alpha=0.7, pad=0.1)
for k in range(0, 4):
label = r"$\theta_" + str(k) + r"\cdot 10^4$"
ax.loglog(x,
d.negs[k] / p.sizI / p.sizJ * 1e5,
label=label,
ms=3,
lw=1,
ls="-",
color=cm1(k / 8)) # marker=markers[k])
#ax.text(x[1],1e1,"text")
index = np.where(d.negs[k] > 0)[0][2]
ax.text(x[index],
d.negs[k][index] / p.sizI / p.sizJ * 1e5,
r"$\theta_{" + str(k) + r"}$",
color=cm1(k / 8),
bbox=bbox_props)
index = np.where(d.negs[4] > 0)[0][2]
ax.text(x[index],
d.negs[4][index] / p.sizI / p.sizJ * 1e5,
r"$\theta_{4+}$",
color=cm1(7 / 8),
bbox=bbox_props)
ax.loglog(x, (d.negs[4] + d.negs[5] + d.negs[6]) / p.sizI / p.sizJ * 1e5,
label=label,
ms=1,
lw=1,
ls="-",
color=cm1(7 / 8)) #, marker=markers[4])
if smooth:
ySmooth = np.exp(savgol_filter(np.log(d.negs[k]), 9, 1))
ax.loglog(x, ySmooth / p.sizI / p.sizJ, lw=2)
d.negs[k] = ySmooth
handles.append(lg)
isld = d.isld
lg, = ax.loglog(x,
isld / p.sizI / p.sizJ * 1e5,
ls="--",
lw=2,
color=cm(6 / 8),
label=r"$N_{isl}\cdot 10^5$",
markerfacecolor="None")
handles.append(lg)
handles = [lgR3, lgN3, lgE] + handles
#ax.grid()
ax.set_xlabel(r"$\theta$", size=16)
ax.set_ylim([1e-2, 1e13])
ax.set_xlim([1e-5, 1e0])
ax.yaxis.set_major_locator(LogLocator(100, [1e-2]))
addFreeDiffusivity(ax, x, p)
if innerFig:
ax.legend(handles=handles,
loc=(0.46, 0.3),
numpoints=1,
prop={'size': 15},
markerscale=2)
#addSurface(p.temp)
fig.savefig("../../../plot" + str(p.flux) + str(p.temp) + ".png")
plt.close(33)
else:
addSurface(p.temp, ax)
if i == 2:
thetas = mlines.Line2D([], [],
color='black',
markersize=15,
label=r"$\theta_{i} \cdot 10^5$" + "\n" +
r"$i = 0,1,2,3,4+$")
handles.append(thetas)
ax.legend(handles=handles,
loc=(1.05, .15),
numpoints=4,
prop={'size': 12},
markerscale=1,
labelspacing=1,
ncol=1,
columnspacing=.7,
borderpad=0.3)
# -.15/1.03
if i > 2:
xlim = (7e-1, 1)
if i == 1:
rect = Rectangle((7e-1, 1e1),
30,
1e6,
facecolor="white",
edgecolor=cm(8 / 8))
ax.add_patch(rect)
if i == 2:
rect = Rectangle((7e-1, 1e3),
30,
1e7,
facecolor="white",
edgecolor=cm(8 / 8))
ax.add_patch(rect)
ax.annotate("",
xy=(3e-2, 1e5),
xytext=(6e-1, 1e5),
arrowprops=dict(arrowstyle="->",
connectionstyle="angle3",
color=cm(8 / 8)))
position = [0.1, 0.3, 0.06, 0.3]
position[0:2] += ax.get_position().get_points().reshape(4)[0:2]
newax = plt.gcf().add_axes(position, zorder=+100)
newax.loglog(x,
diff,
label=r"$g \; \frac{d(R^2)}{dt}$",
marker="s",
ls="",
mew=mew,
markerfacecolor=cm(0 / 8),
ms=8,
alpha=alpha)
newax.loglog(x,
hops,
label=r"$gl^2 \; \frac{d(N_h)}{dt}$",
marker="+",
ls="",
mew=1,
markeredgecolor=cm(7 / 8),
ms=7,
alpha=alpha)
newax.loglog(x,
d.diff / d.time / (4 * Na),
label=r"new",
color=cm(3 / 8),
lw=2)
newax.loglog(x,
d.hops / d.time / (4 * Na),
"--",
label=r"new",
color=cm(4.1 / 8),
lw=1.8)
newax.xaxis.set_major_formatter(plticker.NullFormatter())
newax.yaxis.set_major_formatter(plticker.NullFormatter())
arrow = dict(arrowstyle="-",
connectionstyle="arc3",
color=cm(8 / 8))
x1Big = 7e-1
if i == 1:
ax.annotate("",
xy=(x1Big, 1e1),
arrowprops=arrow,
xytext=(3e-3, 1.5e-1))
ax.annotate("",
xy=(1, 1e1),
arrowprops=arrow,
xytext=(3.8e-1, 1.3e-1))
#ax.annotate("",xy=(1,1e6), arrowprops=arrow,
# xytext=(3.8e-1,2e4))
ax.annotate("",
xy=(x1Big, 1e6),
arrowprops=arrow,
xytext=(2.8e-3, 2e4))
newax.set_xlim(x1Big, 1)
newax.set_ylim(1, 1e6)
if i == 2:
#ax.annotate("",xy=(x1Big,1e3), arrowprops=arrow,
# xytext=(3e-3,1.5e-1))
#ax.annotate("",xy=(1,1e3), arrowprops=arrow,
# xytext=(3.8e-1,1.3e-1))
#ax.annotate("",xy=(x1Big,1e7), arrowprops=arrow,
# xytext=(2.8e-3,2e4))
newax.set_xlim(x1Big, 1)
newax.set_ylim(1e3, 3e7)
def diffusivityDistance(debug, smooth, smoothCalc, binned, readMultiplicities):
p = inf.getInputParameters()
if binned:
d = inf.readBinnedAverages()
else:
d = inf.readAverages()
cove = d.getRecomputedCoverage() / p.sizI / p.sizJ
ratios = p.getRatios()
Na = cove * p.sizI * p.sizJ
plt.clf()
x = list(range(0, len(d.time)))
x = cove
cm = plt.get_cmap("Accent")
alpha = 0.5
mew = 0
diff = fun.timeDerivative(d.diff, d.time) / (4 * Na)
handles = []
if readMultiplicities:
Malpha = inf.readInstantaneous()
lg, = plt.loglog(x, Malpha[0] / d.negs[0], label=r"$\frac{m_0}{n_0}$")
handles.append(lg) # Around 6
lg, = plt.loglog(x, Malpha[1] / d.negs[1], label=r"$\frac{m_1}{n_1}$")
handles.append(lg) # Around 2
lg, = plt.loglog(x, Malpha[2] / d.negs[2], label=r"$\frac{m_2}{n_2}$")
handles.append(lg) # Around 2
lg, = plt.loglog(x, Malpha[3] / d.negs[3], label=r"$\frac{m_3}{n_3}$")
handles.append(lg) # Around 0.1
lgR, = plt.loglog(x,
diff,
label=r"$\frac{1}{2dN_a} \; \frac{d(R^2)}{dt}$",
marker="s",
ls="",
mew=mew,
markerfacecolor=cm(0 / 8),
ms=8,
alpha=alpha)
hops = fun.timeDerivative(d.hops, d.time) / (4 * Na)
lgN, = plt.loglog(x,
hops,
label=r"$\frac{l^2}{2dN_a} \; \frac{d(N_h)}{dt}$",
marker="p",
ls="",
mew=mew,
markerfacecolor=cm(7 / 8),
ms=7,
alpha=alpha)
lgRh, = plt.loglog(x,
d.prob / (4 * Na),
label=r"$\frac{l^2}{2dN_a} R_{h} $",
marker="o",
ls="",
mew=mew,
markerfacecolor=cm(1 / 8),
ms=5.5,
alpha=alpha)
#coverages
cm1 = plt.get_cmap("Set1")
lg, = plt.loglog(x, cove, label=r"$\theta$", color=cm1(1 / 9))
handles.append(lg)
for k in range(0, 7):
if k < 4:
label = r"${n_" + str(k) + "}$"
lg, = plt.loglog(x,
d.negs[k] / p.sizI / p.sizJ,
label=label,
ms=1,
marker=".",
color=cm1((k + 2) / 9))
if smooth:
ySmooth = np.exp(savgol_filter(np.log(d.negs[k]), 9, 1))
plt.loglog(x, ySmooth / p.sizI / p.sizJ, lw=2)
d.negs[k] = ySmooth
handles.append(lg)
hopsCalc0 = (6 * d.negs[0] * ratios[0]) / (4 * Na)
lgCAll = []
if debug:
lgC0, = plt.loglog(x, hopsCalc0, "p-", label="hops calc0")
lgCAll.append(lgC0)
hopsCalc1 = (2 * d.negs[1] * ratios[8]) / (4 * Na)
lgC1, = plt.loglog(x, hopsCalc1, "x-", label="hops calc1") #,
lgCAll.append(lgC1)
hopsCalc2 = (2 * d.negs[2] * ratios[15]) / (4 * Na)
lgC2, = plt.loglog(x, hopsCalc2, "o-", label="hops calc2") #
lgCAll.append(lgC2)
hopsCalc3 = (0.1 * d.negs[3] * ratios[24]) / (4 * Na)
lgC3, = plt.loglog(x, hopsCalc3, "*-", label="hops calc3") #
lgCAll.append(lgC3)
else:
lgC0, = plt.loglog(x,
hopsCalc0,
label="hops calc0",
marker="",
ls=":",
mew=mew,
color=cm(8 / 8),
ms=4,
alpha=1)
lgCAll.append(lgC0)
if p.calc == "AgUc":
hopsCalc = (6 * d.negs[0] * ratios[0] + 2 * d.negs[1] * ratios[8] +
2 * d.negs[2] * ratios[15] +
0.1 * d.negs[3] * ratios[24]) / (4 * Na)
if smoothCalc:
hopsCalc = np.exp(savgol_filter(np.log(hopsCalc), 9, 1))
lgC, = plt.loglog(x,
hopsCalc,
label="hops calc",
marker="*",
ls="",
mew=mew,
markerfacecolor=cm(5 / 8),
ms=5,
alpha=alpha)
handles = [lgR, lgN, lgRh, lgC] + lgCAll + handles
else:
handles = [lgR, lgN, lgRh, lgC0] + handles
plt.subplots_adjust(left=0.12,
bottom=0.1,
right=0.7,
top=0.9,
wspace=0.2,
hspace=0.2)
plt.legend(handles=handles,
numpoints=1,
prop={'size': 8},
bbox_to_anchor=(1.05, 1),
loc=2,
borderaxespad=0.)
plt.grid()
plt.title("flux: {:.1e} temperature: {:d} size {:d}x{:d} \n {}".format(
p.flux, int(p.temp), p.sizI, p.sizJ, os.getcwd()),
fontsize=10)
plt.savefig("../../../plot" + str(p.flux) + str(p.temp) + ".png")
def diffusivityDistance(smooth, binned, fig=0, ax=0, i=-1):
p = inf.getInputParameters()
if binned:
d = inf.readBinnedAverages()
else:
d = inf.readAverages()
cove = d.getRecomputedCoverage() / p.sizI / p.sizJ
ratios = p.getRatios()
Na = cove * p.sizI * p.sizJ
innerFig = fig == 0 and ax == 0
if innerFig:
fig = plt.figure(num=33, figsize=(5, 7))
ax = plt.gca()
x = list(range(0, len(d.time)))
x = cove
cm = plt.get_cmap("Accent")
alpha = 0.5
mew = 0
handles = []
lgE, = ax.loglog(x,
d.diff / d.hops * 1e7,
label="$f_T\cdot10^7$",
marker="o",
ms=1,
ls="",
mec=cm(3 / 20),
mfc=cm(3 / 20),
lw=2)
lgR3, = ax.loglog(x,
d.diff / d.time / (4 * Na),
label=r"$g \; \frac{\langle R^2\rangle}{t}$",
ls="-",
color=cm(3 / 8),
lw=5)
lgN3, = ax.loglog(x,
d.hops / d.time / (4 * Na),
label=r"$gl^2 \; \frac{\langle N_h\rangle}{t}$",
ls=":",
color=cm(4.1 / 8),
lw=4)
up = 1e4
lg, = ax.loglog(x, (np.sum(d.negs[0:7], axis=0)) / p.sizI / p.sizJ * up,
ms=1,
lw=1,
ls="-.",
color="black",
label=r"$\theta \cdot 10^5$") #, marker=markers[4])
markers = ["o", "s", "D", "^", "d", "h", "p", "o"]
cm1 = plt.get_cmap("Set1")
bbox_props = dict(boxstyle="round", fc="w", ec="1", alpha=0.7, pad=0.1)
for k in range(0, 4):
label = r"$\theta_" + str(k) + r"\cdot 10^4$"
ax.loglog(x,
d.negs[k] / p.sizI / p.sizJ * up,
label=label,
ms=3,
lw=1,
ls="-",
color=cm1(k / 8)) # marker=markers[k])
#ax.text(x[1],1e1,"text")
index = np.where(d.negs[k] > 0)[0][2]
ax.text(x[index],
d.negs[k][index] / p.sizI / p.sizJ * up,
r"$\theta_{" + str(k) + r"}$",
color=cm1(k / 8),
bbox=bbox_props)
if smooth:
ySmooth = np.exp(savgol_filter(np.log(d.negs[k]), 9, 1))
ax.loglog(x, ySmooth / p.sizI / p.sizJ, lw=2)
d.negs[k] = ySmooth
handles.append(lg)
isld = d.isld
lg, = ax.loglog(x,
isld / p.sizI / p.sizJ * up,
ls="--",
lw=2,
color=cm(6 / 8),
label=r"$N_{isl}\cdot 10^4$",
markerfacecolor="None")
handles.append(lg)
handles = [lgR3, lgN3, lgE] + handles
#ax.grid()
ax.set_xlabel(r"$\theta$", size=16)
ax.set_ylim([1e-3, 5e10])
ax.set_xlim([1e-5, 1e0])
ax.yaxis.set_major_locator(LogLocator(100, [1e-2]))
addFreeDiffusivity(ax, x, p)
if innerFig:
ax.legend(handles=handles,
loc=(0.46, 0.3),
numpoints=1,
prop={'size': 15},
markerscale=2)
fig.savefig("../../../plot" + str(p.flux) + str(p.temp) + ".png")
plt.close(33)
else:
addSurface(p.temp, ax)
if i == 2:
thetas = mlines.Line2D([], [],
color='black',
markersize=15,
label=r"$\theta_{i} \cdot 10^5$" + "\n" +
r"$i = 0,1,2,3-4$")
handles.append(thetas)
ax.legend(handles=handles,
loc=(1.05, .15),
numpoints=4,
prop={'size': 12},
markerscale=1,
labelspacing=1,
ncol=1,
columnspacing=.7,
borderpad=0.3)