def plot_one(rates, plotname, *p, frac=0.05, age=13.6):
brates = u.gimme_rebinned_data(rates,
splits=arange(0, 1.167, 0.167).tolist())
scale_k = quad(imf.salpeter, 3, 8)[0] / quad(imf.salpeter1, 0.1, 125)[0]
scale = scale_k * 0.7**2. * 1e4 ## factors of h...
dt = 0.05
tt = arange(0.0, age, dt)
lbt = age - tt
zz = [ct.cosmoz(x, ho=70) for x in lbt]
par_model = [0.013, 2.6, 3.2, 6.1]
sfh = rz.csfh_time(tt, *par_model)
dtd = rz.dtdfunc(tt, *p)
tmp = convolve(
sfh, dtd,
'full') * dt * frac * scale ## now convolved result in forward time
rate_fn = tmp[:len(dtd)]
clf()
ax = subplot(111)
ax2 = ax.twinx()
ax2.plot(zz, sfh, 'r-')
ax.plot(zz, rate_fn, 'k-')
ax.errorbar(rates[:, 0],
rates[:, 1],
yerr=[rates[:, 3], rates[:, 2]],
fmt='o',
color='0.6')
ax.errorbar(brates[:, 0],
brates[:, 1],
yerr=[brates[:, 3], brates[:, 2]],
xerr=[brates[:, 5], brates[:, 4]],
fmt='o',
color='0.0',
zorder=10)
pwrl = (-1.0, 1.0)
ax.set_xlim(0, 2.5)
ax.set_ylim(0, 1.8)
ax2.set_ylim(0, 0.16)
ax3 = axes([0.65, 0.6, 0.23, 0.2])
ax3.plot(tt, dtd, 'b-',
label='Fit') #label='Norm = %1.1f' %(simps(dtd,x=time)))
ax3.plot(tt, rz.powerdtd(tt, *pwrl), 'b:', label=r'$t^{%.1f}$' % (pwrl[0]))
ax3.set_ylabel('$\Phi$')
ax3.set_xlabel('Delay Time (Gyr)')
ax3.set_xlim(0, 12)
ax3.set_ylim(0, 1.3)
ax3.legend(frameon=False)
ax.set_xlabel('Redshift')
ax.set_ylabel(r'$10^{-4}$ SNe Ia per year per Mpc$^3$')
ax2.set_ylabel(r'M$_{\odot}$ per year per Mpc$^3$')
ax.set_title(r'$k=%.4f\,M_{\odot}^{-1},\,\,f=%2.1f\%%$' %
(scale_k, frac * 100))
savefig(plotname)
return ()
def run_model(tt, *p):
ddt = average(diff(tt))
sfh = rz.csfh_time(tt, *csfh_model)
dtd = rz.dtdfunc(tt, *p[1:])
tmp = convolve(sfh, dtd, 'full') * ddt * np.exp(
p[0]) #*frac*scale ## now convolved result in forward time
rate_fn = tmp[:len(dtd)]
return (rate_fn)
def run_model(tt, *p):
ddt = average(diff(tt))
sfh = rz.csfh_time(tt, *csfh_model)
dtd = rz.dtdfunc(tt, *p[1:])
tmp = convolve(sfh, dtd, 'full')*ddt*p[0]#*frac*scale ## now convolved result in forward time
rate_fn=tmp[:len(dtd)]
## print(p,sum(rate_fn))
## if sum(rate_fn)==0.:
## pdb.set_trace()
return(rate_fn)
def dtdfit(time, *p):
ff, aa, bb, cc = p
scale = quad(imf.salpeter, 3, 8)[0] / quad(imf.salpeter1, 0.1, 125)[0]
scale = scale * 0.7**2. * 1e4
par_model = [0.0134, 2.55, 3.3, 6.1]
sfh = rz.csfh_time(time, *par_model)
dt = sum(diff(time)) / (len(time) - 1)
p1 = (aa, bb, cc)
res = rz.dtdfunc(time, *p1, norm=True)
tmp = convolve(sfh, res, 'full')
return (ff * tmp[:len(time)] * dt * scale)
def dtdfit(time, *p):
ff, m, w, k = p
scale = quad(imf.salpeter, 3, 8)[0] / quad(imf.salpeter1, 0.1, 125)[0]
scale = scale * 0.7**2. * 1e4
par_model = [0.0134, 2.55, 3.3, 6.1] ## Compendium
## par_model = [0.013, 2.6, 3.2, 6.1] ## MD14
## par_model = [0.009, 2.7, 2.5, 4.1] ## Driver18
sfh = rz.csfh_time(time, *par_model)
dt = sum(diff(time)) / (len(time) - 1)
p1 = (m, w, k)
res = rz.dtdfunc(time, *p1, norm=True)
tmp = convolve(sfh, res, 'full')
return (np.exp(ff) * tmp[:len(time)] * dt * scale)
color='0.0',
zorder=10)
ysample = asarray([run_model(tta, *pi[:-1]) for pi in draws]) * scale
ax.plot(zz, run_model(tta, *p_val) * scale, 'b-', label='Fit')
lower = percentile(ysample, 15.9, axis=0)
upper = percentile(ysample, 84.1, axis=0)
ax.fill_between(zz, upper, lower, color='green', alpha=0.2)
lower = percentile(ysample, 2.5, axis=0)
upper = percentile(ysample, 97.5, axis=0)
ax.fill_between(zz, upper, lower, color='green', alpha=0.2)
pwrl = (-1.0, 1.0)
dud = rz.powerdtd(tta, *pwrl)
jdud = convolve(rz.csfh_time(tta, *csfh_model), dud, 'full') * average(
diff(tta)) * frac * scale
jdud = jdud[:len(dud)]
ax.plot(zz, jdud, 'b:', label=r'$t^{%.1f}$' % (pwrl[0]))
## pdb.set_trace()
ax.set_xlim(0, 2.5)
ax.set_ylim(0, 1.8)
ax2.set_xlim(0, 12)
ax2.set_ylim(-3, 0.5)
ax.set_xlabel('Redshift')
ax.set_ylabel(r'$10^{-4}$ SNe Ia per year per Mpc$^3$')
ax2.set_ylabel('$\log(\Phi)$')
ax2.set_xlabel('Delay Time (Gyr)')
ax.set_title(r'$k=%.4f\,M_{\odot}^{-1},\,\,\varepsilon=%2.1f\%%$' %
idx = where(ysample[:,2]>0.)
ysample = ysample[idx]
ax.plot(zz, run_model(tta, *p_val)*scale, 'b-')
lower = percentile(ysample, 15.9, axis=0)
upper = percentile(ysample, 84.1, axis=0)
ax.fill_between(zz, upper, lower, color='green', alpha=0.2)
lower = percentile(ysample, 2.5, axis=0)
upper = percentile(ysample, 97.5, axis=0)
ax.fill_between(zz, upper, lower, color='green', alpha=0.2)
pwrl = (-1.0,1.0)
dud = rz.powerdtd(tta, *pwrl)
jdud = convolve(rz.csfh_time(tta, *csfh_model), dud, 'full')*average(diff(tta))*frac*scale
jdud = jdud[:len(dud)]
ax.plot(zz, jdud, 'b:')
## pdb.set_trace()
ax.set_xlim(0,2.5)
ax.set_xlabel('Redshift')
ax.set_ylabel(r'$10^{-4}$ SNe Ia per year per Mpc$^3$')
ax2.set_ylabel('$\Phi$')
ax2.set_xlabel('Delay Time (Gyr)')
ax.set_title(r'$k=%.4f\,M_{\odot}^{-1},\,\,\varepsilon=%2.1f\%%$' %(scale_k, p_val[0]*100))
savefig('figure_fit_demo_werr.png', transparent=True)
