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)