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 ()
def func(x, *p):
aa = p
return (aa * rz.dtdfunc(x, *p0) * scale)
p_t = [1.0]
color = '#4BADFF'
## pwrl = (-1.0,1.0)
## ax.plot(time,rz.powerdtd(time, *pwrl, normed=False)*(1.3e-3), 'b--', label=r'$t^{%.1f}$'%(pwrl[0]))
pwrl = (-1.10, 1.0)
perr = (0.09, 0.0)
cov = diag(array(perr)**2)
ps = np.random.multivariate_normal(pwrl, cov, 100)
ysample = asarray(
[rz.powerdtd(time, *pi, normed=False) * (1.6e-3) for pi in ps])
lower = percentile(ysample, 15.9, axis=0)
upper = percentile(ysample, 84.1, axis=0)
ax.fill_between(time, upper, lower, color=color, alpha=0.2)
ax.plot(time,
rz.powerdtd(time, *pwrl, normed=False) * (1.6e-3),
'--',
color=color,
label=r'Field ($\beta={%.2f}^{+0.08}_{-0.07}$)' % (pwrl[0]))
color = '#F0CBC8'
pwrl = (-1.39, 1.0)
perr = (0.32, 0.0)
cov = diag(array(perr)**2)
ps = np.random.multivariate_normal(pwrl, cov, 100)
ysample = asarray(
print('%d total samples' % len(temp))
samples = temp
ndraws = 100 #21 is actually pretty good
draws = samples[np.random.randint(0, len(samples), ndraws), :]
ysample = asarray([rz.dtdfunc(tta, *pi[1:-1]) for pi in draws])
lower = percentile(ysample, 15.9, axis=0)
upper = percentile(ysample, 84.1, axis=0)
ax2.fill_between(tta, log10(upper), log10(lower), color='green', alpha=0.2)
lower = percentile(ysample, 2.5, axis=0)
upper = percentile(ysample, 97.5, axis=0)
ax2.fill_between(tta, log10(upper), log10(lower), color='green', alpha=0.2)
pwrl = (-1., 1.)
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]
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 ()
pickle.dump(ysample, open(outfile,'wb'))
print ('have ysample')
##idx = where((sum(ysample, axis=1)==0.)&(ysample[:,2]<=0.))
idx = where(ysample[:,2]>0.)
ysample = ysample[idx]
lower = percentile(ysample, 15.9, axis=0)
upper = percentile(ysample, 84.1, axis=0)
ax2.fill_between(tta, upper, lower, color='green', alpha=0.2)
lower = percentile(ysample, 2.5, axis=0)
upper = percentile(ysample, 97.5, axis=0)
ax2.fill_between(tta, upper, lower, color='green', alpha=0.2)
pwrl=(-1.,1.)
ax2.plot(tt,rz.powerdtd(tt, *pwrl), 'b:', label=r'$t^{%.1f}$'%(pwrl[0]))
ax2.set_xlim(0,12)
ax2.set_ylim(0,0.8)
frac = 0.062
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(0,1.167,0.167).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...
lbt = age - tta
zz = [ct.cosmoz(x, ho=70) for x in lbt]
def rate_per_galaxy(
sfh,
lbu=13.65,
lbl=0.05,
p0=None,
frac_ia=0.05,
plotit=True,
title=None,
testing=False,
):
scale = quad(imf.salpeter, 3, 8)[0] / quad(imf.salpeter1, 0.1, 125)[0]
scale = scale * 0.70**2.
if not p0:
p0 = (-1.4, 3.5, -1.0)
sfh_data = loadtxt(sfh)
ii = where((sfh_data[:, 0] > lbl) &
(sfh_data[:, 0] <= lbu)) # cuts the data range
sfh_data = sfh_data[ii]
sfh_data[:, 0] = lbu - sfh_data[:, 0][::-1]
sfh_data[:, 1] = sfh_data[:, 1][::-1] ## now in forward time
if testing:
## reframes everything in a more continuous time.
n_out = IUS(sfh_data[:, 0], sfh_data[:, 1])
time = arange(0, lbu + 0.1, 0.1)
sfh_data = zeros((len(time), 2), )
sfh_data[:, 0] = time
sfh_data[:, 1] = n_out(
time) ## now continuous, evenly spaced, not sure that's important.
if testing:
## just makes a SFH box
box_ll = 4.
ii = where((sfh_data[:, 0] > box_ll) & (sfh_data[:, 0] < box_ll + 2.))
sfh_data[:, 1] = 0.0
sfh_data[:, 1][ii] = 10.
#warnings.simplefilter('ignore',RuntimeWarning)
dtd = rz.dtdfunc(sfh_data[:, 0], *p0)
if testing:
## makes the dtd function gaussian
p1 = (1., 4.0, 0.01)
dtd = u.gauss(sfh_data[:, 0], *p1)
dt = sum(diff(sfh_data[:, 0])) / (len(sfh_data[:, 0]) - 1)
rate_fn = zeros((len(sfh_data), 2), )
tmp = convolve(
sfh_data[:, 1], dtd,
'full') * dt * scale * frac_ia ## now convolved result in forward time
rate_fn[:, 1] = tmp[:len(
dtd)] #*concatenate((array([0.]),diff(sfh_data[:,0])),)
rate_fn[:, 0] = sfh_data[:, 0]
rate_fn[:, 1] = rate_fn[:, 1]
rate = rate_fn[-1, 1]
if testing:
## a check of the shift in the effective time on each
tmp1 = sum((sfh_data[:, 0]) * sfh_data[:, 1]) / sum(sfh_data[:, 1])
tmp2 = sum(rate_fn[:, 1] * (rate_fn[:, 0])) / sum(rate_fn[:, 1])
print(tmp1, tmp2, tmp2 - tmp1)
if plotit:
clf()
ax1 = subplot(111)
ax1.plot(lbu - sfh_data[:, 0], sfh_data[:, 1], '--',
color='0.25') #,alpha=0.3)
ax1.set_xlabel('Lookback Time (Gyr)')
ax1.set_ylabel('$\psi(M_{\odot}\,yr^{-1})$')
if title: ax1.set_title(title)
## ax1.axvline(x=0, color='r')
ax3 = ax1.twinx()
ax3.plot(lbu - rate_fn[:, 0],
rate_fn[:, 1] * 1.e3,
'k-',
label='$R_{Ia}(0)=%2.2f\, (1000\, yr)^{-1}$' % (rate * 1.e3),
alpha=0.3)
ax3.set_ylabel('$R_{Ia}[\#\,(1000\, yr)^{-1}]$')
ax3.legend(frameon=False)
ttt, ddd = rz.greggio()
ttt = array(ttt)
ddd = array(ddd)
ii = where(ttt > 0)
ttt = ttt[ii]
ddd = ddd[ii]
time = arange(0.1, 15, 0.1)
dtd = rz.dtdfunc(time, *p0)
pwrl = (-1.0, 1.0)
ax2 = axes([0.6, 0.55, 0.25, 0.25])
ax2.plot(time, dtd, 'r-',
label='Best Fit') #label='Norm = %1.1f' %(simps(dtd,x=time)))
ax2.plot(time,
rz.powerdtd(time, *pwrl),
'b--',
label=r'$t^{%.1f}$' % (pwrl[0]))
## ax2.plot(ttt,ddd,'b--', label='Greggio')
ax2.set_ylabel('$\Phi$')
ax2.set_xlabel('Delay Time (Gyr)')
ax2.set_xlim(0, 12)
ax2.set_ylim(0, 1.3)
ax2.legend(frameon=False)
#tight_layout()
ax4 = axes([0.6, 0.2, 0.25, 0.25])
img = mpimg.imread('107.jpg') ## Thoth
ax4.imshow(img[75:135, 50:140])
#img = mpimg.imread('206.jpg') ## Borg
#ax4.imshow(img[75:135,70:160])
ax4.set_yticks([])
ax4.set_xticks([])
if title:
savefig(title + '.png')
else:
savefig('figure_sfh_demo.png')
return (rate)