def plotMFIdistsBetaEps(bclust=2.2, kmaxFP=10 ** 9, nMax=200, pimrange=[0.0, 5.0, 1.0], beta=1.25, fignums=[0, 1]):
# def plotMFIdistsBetaEps(bclust=2.2, kmaxFP=10**9, nMax=200, pimlist=[0.0,1.0, 2.0, 3.0, 4.0, 4.9], beta=1.25, fignums=[0,1]):
# (this function is probably redundant. same as ...Beta(), but fix beta and range over pim.) we know we get nice PL
# when we vary the shape dimension. what about if we fix D (beta) and vary eps. with the fractal-footprint?
#
# fig 5 in PRE pub.
#
# pimrange=[0.0,4.9,1.]
# "beta" version, use footprint ~ n**beta 1<beta<2.0 (or maybe 2.25 or something).
# for now, fixed epsilon (pim), fixed rho.
# rhorange = [minrho, maxrho, dRho]
# note: bcluse is slope of b-distribution, beta is the exponent for shape area/raduis (n) scaling.
fsize = 20
thisfont = pltf.FontProperties(size=fsize)
thisfont2 = pltf.FontProperties(size=fsize - 2)
#
fpmodel = 2 # aka, MFI with fractal dimension treatment (as opposed to solid shapes, etc.)
rho = 1.0 # basically, this puts all the empty sites into the fractal/branching structure.
thisrho = rho
bofpim = []
for fignum in fignums:
plt.figure(fignum)
plt.clf()
plt.xlabel("$log_{10}(k)$", size=fsize + 2)
plt.ylabel("$log_{10}(N(k))$", size=fsize + 2)
#
# starts=[]
#
primeRhos = [0.1, 0.3, 0.5, 0.7, 0.9, 1.0]
# thisrho=rhorange[0]
icount = 0
# while thisrho<=rhorange[1]:
# for beta in betas:
pim = pimrange[0]
pim0 = pim
while pim0 <= pimrange[1]:
# for pim in pimlist:
# X=getMFIsquarePropModel(bclust, thisrho, pim, kmaxFP, nMax, fpmodel)
pim = pim0
if pim > 4.9:
pim = 4.9
print "prams: %s, %s, %s, %s, %s, %s, %s" % (bclust, thisrho, pim, kmaxFP, nMax, fpmodel, beta)
# pim=pimrange[0]+float(icount)*pimrange[2]
X = getMFIsquarePropModel(bclust, thisrho, pim, kmaxFP, nMax, fpmodel, beta)
print "beta=%f, len=%d" % (beta, len(X))
x = []
y1 = []
y2 = []
# xstart=25
xstart = 15
sxtop = 500
for rw in X:
# print rw
x += [math.log10(rw[2])]
# print rw[3]
y1 += [math.log10(rw[3])]
y2 += [math.log10(rw[4])]
lf1 = yp.linefit([x[xstart:], y1[xstart:]])
lf2 = yp.linefit([x[xstart:], y2[xstart:]])
lf1.doFit()
lf2.doFit()
# lf1.doFit(None, None, 3)
# lf2.doFit(None, None, 3)
#
dotsies = yp.integerSpacedPoints([x[1:], y1[1:]], 0.5)
dotsies[0] += [x[-1]]
dotsies[1] += [y1[-1]]
if fpmodel > 1:
bofpim += [[pim, beta, lf1.a, -lf1.b, lf1.meanVar(), lf2.a, -lf2.b, lf2.meanVar()]]
else:
bofpim += [[pim, lf1.a, -lf1.b, lf1.meanVar(), lf2.a, -lf2.b, lf2.meanVar()]]
plt.figure(fignums[0])
# plt.title('$\\beta=2.2$')
# plt.plot(x[1:],y1[1:],'%s.-' % yp.pycolor(icount), label='$\\rho = %s, \\epsilon=%s, D=%s, b=%s$' % (str(thisrho), str(pim), str(beta), str(-lf1.b)[0:5]), lw=2) # beta!=2
plt.plot(
dotsies[0],
dotsies[1],
"%s%s" % (yp.pycolor(icount), yp.fitMarkerShortList[icount % len(yp.fitMarkerShortList)]),
label="$\\epsilon=%s, b=%s$" % (str(pim), str(-lf1.b)[0:5]),
ms=10,
)
plt.plot(x[1:], y1[1:], "%s-" % yp.pycolor(icount), lw=2, ms=10) # beta!=2
plt.plot(
[x[0], x[xstart], x[-1]],
[lf1.a + lf1.b * x[0], lf1.a + lf1.b * x[xstart], lf1.a + lf1.b * x[-1]],
"%s|-" % yp.pycolor(icount),
ms=15,
)
plt.figure(fignums[1])
# plt.title('$\\beta=2.0$')
# plt.plot(x[1:], y2[1:], '%s.-' % yp.pycolor(icount), label='$\\rho = %s, \\epsilon=%s, D=%s, b=%s$' % (str(thisrho), str(pim), str(beta), str(-lf2.b)[0:5]), lw=2) # beta=2 approximation
plt.plot(
x[1:],
y2[1:],
"%s.-" % yp.pycolor(icount),
label="$\\epsilon=%s, b=%s$" % (str(pim), str(-lf2.b)[0:5]),
lw=2,
ms=10,
) # beta=2
plt.plot(
[x[0], x[xstart], x[-1]],
[lf2.a + lf2.b * x[0], lf2.a + lf2.b * x[xstart], lf2.a + lf2.b * x[-1]],
"%s|-" % yp.pycolor(icount),
ms=15,
)
icount += 1
pim0 += pimrange[2]
#
# thisrho+=rhorange[2]
#
for fignum in fignums:
plt.figure(fignum)
ax = plt.gca()
plt.subplots_adjust(bottom=0.15)
plt.setp(ax.get_xticklabels(), fontsize=fsize)
plt.setp(ax.get_yticklabels(), fontsize=fsize)
# lgd=plt.legend(loc='best', numpoints=1, title='$D=1.25$', prop=thisfont2, ncol=1)
lgd = plt.legend(loc="best", numpoints=1, prop=thisfont2, ncol=1)
lgd.set_title("$D=1.25$")
plt.setp(lgd.get_title(), fontsize=fsize)
if fignum > 0:
continue
plt.savefig(
"writeup/mfi-aps/mfi-PRE-letter/figs/mfi-fractalType-varEps-bc%s-D125.png" % str(bclust).replace(".", "")
)
plt.savefig(
"writeup/mfi-aps/mfi-PRE-letter/figs/mfi-fractalType-varEps-bc%s-D125.eps" % str(bclust).replace(".", "")
)
plt.show()
return bofpim
def plotMFIdistsBeta(
bclust=2.2,
kmaxFP=10 ** 9,
nMax=200,
pim=4.9,
betas=[0.9, 1.0, 1.25, 1.5, 1.75, 2.0, 2.5],
fignums=[0, 1],
dosave=False,
):
# "Fig-4" in PRE publication.
#
# "beta" version, use footprint ~ n**beta 1<beta<2.0 (or maybe 2.25 or something).
# for now, fixed epsilon (pim), fixed rho.
# rhorange = [minrho, maxrho, dRho]
# note: bcluse is slope of b-distribution, beta is the exponent for shape area/raduis (n) scaling.
fpmodel = 2
rho = 1.0 # basically, this puts all the empty sites into the fractal/branching structure.
thisrho = rho
#
fsize = 20
thisfont = pltf.FontProperties(size=fsize)
thisfont2 = pltf.FontProperties(size=fsize - 2)
#
bofpim = []
for fignum in fignums:
plt.figure(fignum)
plt.clf()
plt.xlabel("$log_{10}(k)$", size=fsize + 2)
plt.ylabel("$log_{10}(N(k))$", size=fsize + 2)
#
# starts=[]
#
primeRhos = [0.1, 0.3, 0.5, 0.7, 0.9, 1.0]
# thisrho=rhorange[0]
icount = 0
# while thisrho<=rhorange[1]:
fitMarkerShortList = ["o", "^", "s", "p", "*", "h", "+", "H", "D", "x"]
for beta in betas:
# X=getMFIsquarePropModel(bclust, thisrho, pim, kmaxFP, nMax, fpmodel)
print "prams: %s, %s, %s, %s, %s, %s, %s" % (bclust, thisrho, pim, kmaxFP, nMax, fpmodel, beta)
X = getMFIsquarePropModel(bclust, thisrho, pim, kmaxFP, nMax, fpmodel, beta)
print "beta=%f, len=%d" % (beta, len(X))
x = []
y1 = []
y2 = []
# xstart=25
xstart = 2
sxtop = 500
for rw in X:
# print rw
x += [math.log10(rw[2])]
# print rw[3]
y1 += [math.log10(rw[3])]
y2 += [math.log10(rw[4])]
lf1 = yp.linefit([x[xstart:], y1[xstart:]])
lf2 = yp.linefit([x[xstart:], y2[xstart:]])
lf1.doFit()
lf2.doFit()
# lf1.doFit(None, None, 3)
# lf2.doFit(None, None, 3)
#
if fpmodel > 1:
bofpim += [[pim, beta, lf1.a, -lf1.b, lf1.meanVar(), lf2.a, -lf2.b, lf2.meanVar()]]
else:
bofpim += [[pim, lf1.a, -lf1.b, lf1.meanVar(), lf2.a, -lf2.b, lf2.meanVar()]]
plt.figure(fignums[0])
# plt.title('$\\beta=2.2$')
# plt.plot(x[1:],y1[1:],'%s.-' % yp.pycolor(icount), label='$\\rho = %s, \\epsilon=%s, \\beta_s=%s, b=%s$' % (str(thisrho), str(pim), str(beta), str(-lf1.b)[0:5]), lw=2) # beta!=2
dotsies = yp.integerSpacedPoints([x[1:], y1[1:]], 0.5)
dotsies[0] += [x[-1]]
dotsies[1] += [y1[-1]] # add end-bits for completeness.
plt.plot(x[1:], y1[1:], "%s-" % yp.pycolor(icount), lw=2, ms=10) # beta!=2
plt.plot(
dotsies[0],
dotsies[1],
"%s%s" % (yp.pycolor(icount), fitMarkerShortList[icount % len(fitMarkerShortList)]),
label="$D=%s, b=%s$" % (str(beta), str(-lf1.b)[0:5]),
ms=10,
)
plt.plot(
[x[0], x[xstart], x[-1]],
[lf1.a + lf1.b * x[0], lf1.a + lf1.b * x[xstart], lf1.a + lf1.b * x[-1]],
"%s|-" % yp.pycolor(icount),
ms=20,
lw=2,
)
#
plt.figure(fignums[1])
# plt.title('$\\beta=2.0$')
# plt.plot(x[1:], y2[1:], '%s.-' % yp.pycolor(icount), label='$\\rho = %s, \\epsilon=%s, \\beta_s=%s, b=%s$' % (str(thisrho), str(pim), str(beta), str(-lf2.b)[0:5]), lw=2) # beta=2 approximation
plt.plot(
x[1:],
y2[1:],
"%s.-" % yp.pycolor(icount),
label="$D=%s, b=%s$" % (str(beta), str(-lf2.b)[0:5]),
lw=2,
ms=10,
) # beta=2 approximation
plt.plot(
[x[0], x[xstart], x[-1]],
[lf2.a + lf2.b * x[0], lf2.a + lf2.b * x[xstart], lf2.a + lf2.b * x[-1]],
"%s|-" % yp.pycolor(icount),
ms=15,
lw=2,
)
icount += 1
#
# thisrho+=rhorange[2]
#
for fignum in fignums:
plt.figure(fignum)
ax = plt.gca()
plt.subplots_adjust(bottom=0.15)
plt.setp(ax.get_xticklabels(), fontsize=fsize)
plt.setp(ax.get_yticklabels(), fontsize=fsize)
# plt.legend(loc='best', numpoints=1, prop=thisfont2, ncol=1, title='$\\epsilon=4.9$')
lgd = plt.legend(loc="best", numpoints=1, prop=thisfont2, ncol=1)
lgd.set_title("$\\epsilon=4.9$")
plt.setp(lgd.get_title(), fontsize=fsize)
# mfi-fractalType-varEps-bc22-D125
if fignum > 0:
continue
if dosave:
plt.savefig(
"writeup/mfi-aps/mfi-PRE-letter/figs/mfi-fractalType-eps49-bc%s-varD.png" % str(bclust).replace(".", "")
)
plt.savefig(
"writeup/mfi-aps/mfi-PRE-letter/figs/mfi-fractalType-eps49-bc%s-varD.eps" % str(bclust).replace(".", "")
)
plt.show()
return bofpim
