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(0.1, age, dt) ## forward time
lbt = age - tt
zz = [ct.cosmoz(x) for x in lbt]
sfh = rz.sfr_behroozi_12(tt)
dtd = step_dtd(tt, *p)
norm_dtd = sum(step_dtd(tt, *p)) * dt
dtd = dtd / norm_dtd
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_xlabel('Redshift')
ax.set_ylabel(r'SN Ia Rate')
ax.set_ylabel(r'$10^{-4}$ SNe Ia per year per Mpc$^3$')
ax2.set_ylabel(r'SF Rate')
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]))
pn = (-20, 8.8, 11)
ax3.plot(tt, rz.dtdfunc(tt, *pn), 'b--', label='Best model')
ax.set_title(r' $k=%.4f$ M$_{\odot}^{-1}$, $f=%2.1f\%%$' %
(scale_k, frac * 100))
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)
savefig(plotname)
return ()
def plot_one(rates, plotname, *p, frac=0.05, age=13.6):
brates = u.gimme_rebinned_data(rates,
splits=arange(0, 1.125, 0.125).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(dt, age, dt)
lbt = age - tt
zz = [ct.cosmoz(x, ho=70) for x in lbt]
par_model = [0.0134, 2.55, 3.3, 6.1]
sfh = rz.csfh_time(tt, *par_model)
dtd = rz.dtdfunc(tt, *p)
pwrl = (-1.0, 1.0)
dud = rz.powerdtd(tt, *pwrl)
tmp = convolve(
sfh, dtd,
'full') * dt * frac * scale ## now convolved result in forward time
rate_fn = tmp[:len(dtd)]
jdud = convolve(sfh, dud, 'full') * dt * scale * 0.065
jdud = jdud[:len(dtd)]
clf()
ax = subplot(111)
ax2 = ax.twinx()
ax2.plot(zz, sfh, 'r-', label='CSFH')
ax.plot(zz, rate_fn, 'k-', label='Fit', zorder=3)
ax.plot(zz, jdud, 'k:', label=r'$t^{%.1f}$' % (pwrl[0]))
ax.errorbar(rates[:, 0],
rates[:, 1],
yerr=[rates[:, 3], rates[:, 2]],
fmt='o',
color='0.6',
alpha=0.4)
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, 2.6)
ax.set_ylim(0, 1.8)
ax2.set_ylim(0, 0.16)
ax3 = axes([0.65, 0.62, 0.23, 0.2])
## ax3 = axes([0.55, 0.6, 0.33, 0.25])
ax3.plot(tt, log10(dtd), 'b-',
label='Fit') #label='Norm = %1.1f' %(simps(dtd,x=time)))
ax3.plot(tt,
log10(rz.powerdtd(tt, *pwrl)),
'b:',
label=r'$t^{%.1f}$' % (pwrl[0]))
ax3.set_ylabel('$\log(\Phi)$')
ax3.set_xlabel('Delay Time (Gyr)')
ax3.set_xlim(0, 12)
ax3.set_ylim(-3, 0.5)
##ax3.set_ylim(0,1)
## ax3.legend(loc=1,ncol=1,frameon=False, fontsize=8)
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))
ax.set_title(r'$k=%.4f\,M_{\odot}^{-1},\,\,\varepsilon=%2.1f\%%$' %
(scale_k, frac * 100))
ax.legend(loc=2, frameon=False)
ax2.legend(loc=4, frameon=False)
savefig(plotname)
return ()
ax2.plot(tt,
log10(rz.powerdtd(tt, *pwrl)),
'b:',
label=r'$t^{%.1f}$' % (pwrl[0]))
frac = 0.065
rates = loadtxt('SNeIa_rates.txt')
rates[:, 1:] = rates[:, 1:] #*1.0e-4 ## put on the right scale
rates = rates[:, :4]
brates = u.gimme_rebinned_data(rates,
splits=arange(0, 1.125, 0.125).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...
lbt = age - tta
zz = [ct.cosmoz(x, ho=70) for x in lbt]
ax.errorbar(rates[:, 0],
rates[:, 1],
yerr=[rates[:, 3], rates[:, 2]],
fmt='o',
color='0.6',
alpha=0.4)
ax.errorbar(brates[:, 0],
brates[:, 1],
yerr=[brates[:, 3], brates[:, 2]],
xerr=[brates[:, 5], brates[:, 4]],
fmt='o',
color='0.0',
zorder=10)
def plot_one(rates, plotname, *p, frac=0.05, age=13.6):
brates = u.gimme_rebinned_data(rates,
splits=arange(0, 1.125, 0.125).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(dt, age, dt) ## forward time
lbt = age - tt
zz = [ct.cosmoz(x) for x in lbt]
par_model = [0.0134, 2.55, 3.3, 6.1]
sfh = rz.csfh_time(tt, *par_model)
dtd = rz.dtdfunc(tt, *p)
pwrl = (-1.0, 1.0)
dud = rz.powerdtd(tt, *pwrl)
tmp = convolve(
sfh, dtd,
'full') * dt * frac * scale ## now convolved result in forward time
rate_fn = tmp[:len(dtd)]
jdud = convolve(sfh, dud, 'full') * dt * scale * 0.065
jdud = jdud[:len(dtd)]
clf()
ax = subplot(111)
ax2 = ax.twinx()
ax2.plot(zz, sfh, 'r-')
ax.plot(zz, rate_fn, 'k-')
ax.plot(zz, jdud, '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)
ax.set_xlim(0, 2.5)
ax.set_ylim(0, 1.8)
ax.set_xlabel('Redshift')
ax.set_ylabel(r'SN Ia Rate')
ax.set_ylabel(r'$10^{-4}$ SNe Ia per year per Mpc$^3$')
ax2.set_ylabel(r'SF Rate')
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, dud, '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)
savefig(plotname)
return ()