def plot_lc_cocoon(t, umax, umin, Er, k, Mej, n0, p, jetType=3, seg='G'):
nu = np.empty(t.shape)
nu[:] = 1.0e14
Y = np.array(
[umax, umin, Er, k, Mej, 0, 0, 0, n0, p, 0.1, 1.0e-4, 1.0, 10])
FnuSA = grb.fluxDensity(t, nu, jetType, 0, *Y)
tc = t_coast(umax, umin, Er, k, Mej, n0, p)
te = t_end(umax, umin, Er, k, Mej, n0, p)
# ip = np.searchsorted(t, tp)
# F0 = FnuSA[ip]
# FnuSA /= F0
Fnu = pl_lc_cocoon(t, umax, umin, Er, k, Mej, n0, p, seg)
ir = np.searchsorted(t, 0.1 * te)
F0 = FnuSA[ir] / Fnu[ir]
FnuSA /= F0
Tc = np.sqrt(t[1:] * t[:-1])
dF = np.log(Fnu[1:] / Fnu[:-1]) / np.log(t[1:] / t[:-1])
dFSA = np.log(FnuSA[1:] / FnuSA[:-1]) / np.log(t[1:] / t[:-1])
fig, ax = plt.subplots(2, 1)
ax[0].axvline(tc, color='grey', ls=':')
ax[0].axvline(te, color='grey', ls='--')
ax[1].axvline(tc, color='grey', ls=':')
ax[1].axvline(te, color='grey', ls='--')
ax[0].plot(t, Fnu)
ax[0].plot(t, FnuSA)
ax[1].plot(Tc, dF)
ax[1].plot(Tc, dFSA)
ax[0].set_xscale("log")
ax[1].set_xscale("log")
ax[0].set_yscale("log")
ax[1].set_yscale("linear")
ax[1].set_xlabel(r"$t$ (s)")
ax[0].set_ylabel(r"$F_\nu/F_0$")
ax[1].set_ylabel(r"d$\log F_\nu$/d$\log t$")
ax[0].set_xlim(t.min(), t.max())
ax[1].set_xlim(t.min(), t.max())
fig2 = None
return fig, fig2
def makeNumAnaCompPlots(regime='G', jetModel=0):
fig, ax = plt.subplots(5, 2, figsize=(10, 15))
N = 100
NU, tN0, Y, beta = getRegimePars(regime)
nu = np.empty(N)
nu[:] = NU
# thVs = [0.1, 0.3, 0.5, 0.7, 0.1, 0.3, 0.5, 0.7, 0.1, 0.3, 0.5, 0.7]
# thCs = [0.1, 0.1, 0.1, 0.1, 0.05, 0.05, 0.05, 0.05, 0.2, 0.2, 0.2, 0.2]
# thWs = [0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4]
thVs = [0.2, 0.5, 0.8]
thCs = [0.05, 0.05, 0.05]
thWs = [0.4, 0.4, 0.4]
b = Y[4]
n0 = Y[8]
p = Y[9]
epse = Y[10]
epsB = Y[11]
dL = Y[13]
nufac = 1.1
ax[2, 0].axhline(beta, lw=2, color='grey', alpha=0.5)
ax[2, 1].axhline(beta, lw=2, color='grey', alpha=0.5)
for thV, thC, thW in zip(thVs, thCs, thWs):
chi = np.geomspace(max(2 * math.sin(0.5 * (thV - thW)), 1.0e-2),
2 * math.sin(0.5 * (thV)), N)
th = thV - 2 * np.arcsin(0.5 * chi)
f = get_f(jetModel, thC, thW, b)
tp = calc_tp(th, tN0, thV, f, regime, p)
Ip = calc_Ip(th, tN0, thV, f, nu, regime, n0, p, epse, epsB, dL)
dOm = calc_dOm(th, tN0, thV, f, regime, p)
Y[0] = thV
Y[2] = thC
Y[3] = thW
Fnu = grb.fluxDensity(tp, nu, jetModel, 0, *Y, latRes=10)
FnuR = grb.fluxDensity(tp, nu * nufac, jetModel, 0, *Y, latRes=10)
FnuL = grb.fluxDensity(tp, nu / nufac, jetModel, 0, *Y, latRes=10)
FnuA = Ip * dOm
alA = np.log(FnuA[1:] / FnuA[:-1]) / np.log(tp[1:] / tp[:-1])
al = np.log(Fnu[1:] / Fnu[:-1]) / np.log(tp[1:] / tp[:-1])
betaN = np.log(FnuR / FnuL) / np.log(nufac * nufac)
chiC = np.sqrt(chi[1:] * chi[:-1])
thCC = thV - 2 * np.arcsin(0.5 * chiC)
gA = calc_g(thCC, thV, f)
somA = 1.0 * np.ones(thCC.shape)
gN = calc_g_from_al_som(al, somA, regime, p)
somN = calc_som_from_al_g(al, gA, regime, p)
tpC = np.sqrt(tp[1:] * tp[:-1])
label = r'$\theta_V =$ {0:.1f}'.format(thV)
label += r', $\theta_C = $ {0:.1f}'.format(thC)
label += r', $\theta_W = $ {0:.1f}'.format(thW)
ax[0, 0].plot(chi, Fnu, label=label)
ax[0, 0].plot(chi, FnuA)
ax[1, 0].plot(chiC, al)
ax[1, 0].plot(chiC, alA)
ax[2, 0].plot(chi, betaN)
ax[3, 0].plot(chiC, gN)
ax[3, 0].plot(chiC, gA)
ax[4, 0].plot(chiC, somN)
ax[4, 0].plot(chiC, somA)
ax[0, 1].plot(tp, Fnu)
ax[0, 1].plot(tp, FnuA)
ax[1, 1].plot(tpC, al)
ax[1, 1].plot(tpC, alA)
ax[2, 1].plot(tp, betaN)
ax[3, 1].plot(tpC, gN)
ax[3, 1].plot(tpC, gA)
ax[4, 1].plot(tpC, somN)
ax[4, 1].plot(tpC, somA)
ax[0, 0].legend()
ax[0, 0].set_xscale('log')
ax[0, 0].set_yscale('log')
ax[1, 0].set_xscale('log')
ax[1, 0].set_ylim(-3.0, 3.0)
ax[2, 0].set_xscale('log')
ax[3, 0].set_xscale('log')
ax[3, 0].set_yscale('log')
ax[4, 0].set_xscale('log')
ax[0, 1].set_xscale('log')
ax[0, 1].set_yscale('log')
ax[1, 1].set_xscale('log')
ax[1, 1].set_ylim(-3.0, 3.0)
ax[2, 1].set_xscale('log')
ax[3, 1].set_xscale('log')
ax[3, 1].set_yscale('log')
ax[4, 1].set_xscale('log')
ax[0, 0].set_ylabel(r'$F_\nu$')
ax[1, 0].set_ylabel(r'$\alpha$')
ax[2, 0].set_ylabel(r'$\beta$')
ax[3, 0].set_ylabel(r'$g$')
ax[4, 0].set_ylabel(r'$s_{{\Omega}}$')
ax[4, 0].set_xlabel(r'$\chi$')
ax[4, 1].set_xlabel(r'$t_{{obs}}$')
fig.tight_layout()
plotname = 'numanacomp.pdf'
save(fig, plotname)
plt.close(fig)
def makeConvergencePlots():
lrs = [1, 3, 5, 10, 20, 40]
trs = [250, 500, 1000, 2000, 4000, 8000]
lrRef = 80
trRef = 16000
# lss = ['-', '--', '-.', ':', '-']
# cols = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple']
E0 = 1.0e52
thC = 0.05
thV = 0.3
thW = 0.5
b = 2
n0 = 1.0
p = 2.2
epse = 0.1
epsB = 0.01
xiN = 1.0
dL = 1.23e26
Y = np.array(
[thV, E0, thC, thW, b, 0.0, 0.0, 0.0, n0, p, epse, epsB, xiN, dL])
jetType = 0
t = np.geomspace(1.0e3, 1.0e8, 100)
nu = np.empty(t.shape)
nu[:] = 1.0e18
FnuRef = grb.fluxDensity(t, nu, jetType, 0, *Y, latRes=lrRef, tRes=trRef)
fig, ax = plt.subplots(1, 1, figsize=(4, 3))
ax.plot(t, FnuRef)
ax.set_xlabel(r'$t$ (s)')
ax.set_ylabel('')
ax.set_xscale('log')
ax.set_yscale('log')
fig.tight_layout()
save(fig, "conv_ref.pdf")
plt.close(fig)
fig, ax = plt.subplots(1, 1, figsize=(4, 3))
fig2, ax2 = plt.subplots(1, 1, figsize=(4, 3))
for i, lr in enumerate(lrs):
Fnu = grb.fluxDensity(t, nu, jetType, 0, *Y, latRes=lr, tRes=trRef)
err = np.fabs(Fnu - FnuRef) / FnuRef
ax.plot(t, err)
ax2.plot(t, Fnu)
ax2.plot(t, FnuRef, lw=0.5, color='k')
ax.set_xlabel(r'$t$ (s)')
ax.set_ylabel('Error')
ax.set_xscale('log')
ax.set_yscale('log')
fig.tight_layout()
save(fig, "conv_latRes.pdf")
plt.close(fig)
ax2.set_xlabel(r'$t$ (s)')
ax2.set_ylabel('Flux')
ax2.set_xscale('log')
ax2.set_yscale('log')
fig2.tight_layout()
save(fig2, "conv_flux_latRes.pdf")
plt.close(fig2)
fig, ax = plt.subplots(1, 1, figsize=(4, 3))
fig2, ax2 = plt.subplots(1, 1, figsize=(4, 3))
for i, tr in enumerate(trs):
Fnu = grb.fluxDensity(t, nu, jetType, 0, *Y, latRes=lrRef, tRes=tr)
err = np.fabs(Fnu - FnuRef) / FnuRef
ax.plot(t, err)
ax2.plot(t, Fnu)
ax.set_xlabel(r'$t$ (s)')
ax.set_ylabel('Error')
ax.set_xscale('log')
ax.set_yscale('log')
fig.tight_layout()
save(fig, "conv_tRes.pdf")
plt.close(fig)
ax2.set_xlabel(r'$t$ (s)')
ax2.set_ylabel('Flux')
ax2.set_xscale('log')
ax2.set_yscale('log')
fig2.tight_layout()
save(fig2, "conv_flux_tRes.pdf")
plt.close(fig2)
def makeThVPlots():
E0 = 1.0e52
thC = 0.05
thV = 0.6
thW = 0.5 * np.pi
b = 2
n0 = 1.0e-3
p = 2.2
epse = 0.1
epsB = 0.01
xiN = 1.0
dL = 1.23e26
Y = np.array(
[thV, E0, thC, thW, b, 0.0, 0.0, 0.0, n0, p, epse, epsB, xiN, dL])
parstr = r"$n_0$={0:.01f}x$10^{{-3}}cm^{{-3}}$".format(n0 / 1.0e-3)
parstr += r" $p$={0:.01f}".format(p)
parstr += r" $\epsilon_e$={0:g}".format(epse)
parstr += r" $\epsilon_B$={0:g}".format(epsB)
parstr += r" $d_L$={0:.02f}x$10^{{26}}$cm".format(dL / 1.0e26)
N = 300
nuO = (grb.c / 6.0e-5) * np.ones(N)
t = grb.day2sec * np.geomspace(0.1, 300, N)
thV = 0.0
Y[0] = thV
FnuA = grb.fluxDensity(t, nuO, 0, 0, *Y)
thV = 2 * thC
Y[0] = thV
FnuB = grb.fluxDensity(t, nuO, 0, 0, *Y)
thV = 4 * thC
Y[0] = thV
FnuC = grb.fluxDensity(t, nuO, 0, 0, *Y)
thV = 6 * thC
Y[0] = thV
FnuD = grb.fluxDensity(t, nuO, 0, 0, *Y)
fig, ax = plt.subplots(1, 1, figsize=(4, 3))
ax.plot(t * grb.sec2day, FnuA, label=r'$\theta_V = 0$', color='k')
ax.plot(t * grb.sec2day,
FnuB,
label=r'$\theta_V = 2\theta_C$',
color='tab:purple')
ax.plot(t * grb.sec2day,
FnuC,
label=r'$\theta_V = 4\theta_C$',
color='tab:blue')
ax.plot(t * grb.sec2day,
FnuD,
label=r'$\theta_V = 6\theta_C$',
color='tab:green')
ax.legend()
ax.set_xlabel(r'$t$ (d)')
ax.set_ylabel(r'$F_\nu$ (mJy)')
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_title(parstr)
save(fig, 'GaussianThV.pdf')
plt.close(fig)
thC = 0.05
thW = 0.3
Y[2] = thC
Y[3] = thW
thV = 0.0
Y[0] = thV
FnuA = grb.fluxDensity(t, nuO, 4, 0, *Y)
thV = 4 * thC
Y[0] = thV
FnuB = grb.fluxDensity(t, nuO, 4, 0, *Y)
thV = 8 * thC
Y[0] = thV
FnuC = grb.fluxDensity(t, nuO, 4, 0, *Y)
thV = 12 * thC
Y[0] = thV
FnuD = grb.fluxDensity(t, nuO, 4, 0, *Y)
fig, ax = plt.subplots(1, 1, figsize=(4, 3))
ax.plot(t * grb.sec2day, FnuA, label=r'$\theta_V = 0$', color='k')
ax.set_xscale('log')
ax.set_yscale('log')
ylim = ax.get_ylim()
ax.plot(t * grb.sec2day,
FnuB,
label=r'$\theta_V = 2\theta_C$',
color='tab:purple')
ax.plot(t * grb.sec2day,
FnuC,
label=r'$\theta_V = 4\theta_C$',
color='tab:blue')
ax.plot(t * grb.sec2day,
FnuD,
label=r'$\theta_V = 6\theta_C$',
color='tab:green')
ax.legend()
print(ylim)
ax.set_xlabel(r'$t$ (d)')
ax.set_ylabel(r'$F_\nu$ (mJy)')
ax.set_ylim(*ylim)
ax.set_title(parstr)
save(fig, 'PowerlawThV.pdf')
plt.close(fig)
def makeAnalyticTestPlots():
regimes = ['D', 'E', 'F', 'G', 'H']
# regimes = ['E', 'G', 'H']
# regimes = ['D']
# regimes = ['H']
jetModel = 4
spread = True
# thWs = [0.15, 0.25, 0.35, 0.45, 0.55, 0.65, 0.75, 0.85, 0.95]
thWs = [0.25, 0.45, 0.65, 0.85]
# thCs = [0.02, 0.04, 0.08, 0.12, 0.24, 0.48]
if jetModel is 4:
thCs = [0.02, 0.04, 0.08, 0.16]
else:
thCs = [0.08, 0.16, 0.24, 0.32]
# thVs = [0.15, 0.3, 0.45, 0.6, 0.75, 0.9]
thVs = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
NC = len(thCs)
NV = len(thVs)
NW = len(thWs)
N = 100
nufac = 1.2
tfac = 1.2
t = np.empty(N)
nu = np.empty(N)
alOffN = np.empty((NW, NV, NC))
# alPreN = np.empty((NW, NV, NC))
alStructN = np.empty((NW, NV, NC))
alStructN2 = np.empty((NW, NV, NC))
alStructNAveLin = np.empty((NW, NV, NC))
alStructNAveLog = np.empty((NW, NV, NC))
alPostN = np.empty((NW, NV, NC))
FbN = np.empty((NW, NV, NC))
for regime in regimes:
NU, tN, Y, betaE = getRegimePars(regime)
# ax.set_ylim(0, 10)
minTW = np.inf
maxTB = -np.inf
nu[:] = NU
for k, thW in enumerate(thWs):
for j, thC in enumerate(thCs):
for i, thV in enumerate(thVs):
Y[0] = thV
Y[2] = thC
Y[3] = thW
tW = calcTW(jetModel, Y, regime)
tb = calcTB(jetModel, Y, regime)
if tW > 0.0 and tW < minTW:
minTW = tW
if tb > maxTB:
maxTB = tb
t[:] = np.geomspace(0.03 * minTW, 10 * maxTB, num=N)
for k, thW in enumerate(thWs):
# fig, ax = plt.subplots(3*NV, NC, figsize=(2*NC, 6*NV))
panelWidth = 6.0
panelHeight = 9.0
fig = plt.figure(figsize=(panelWidth * NV, panelHeight * NC))
gs0 = fig.add_gridspec(NC, NV)
for i, thC in enumerate(thCs):
for j, thV in enumerate(thVs):
print(i + 1, j + 1)
Y[0] = thV
Y[2] = thC
Y[3] = thW
tW = calcTW(jetModel, Y, regime)
tb = calcTB(jetModel, Y, regime)
tbpm = calcTBpm(jetModel, Y, regime)
Fnu = grb.fluxDensity(t,
nu,
jetModel,
0,
*Y,
spread=spread)
Fnutr = grb.fluxDensity(t * tfac,
nu,
jetModel,
0,
*Y,
spread=spread)
Fnutl = grb.fluxDensity(t / tfac,
nu,
jetModel,
0,
*Y,
spread=spread)
Fnunr = grb.fluxDensity(t,
nu * nufac,
jetModel,
0,
*Y,
spread=spread)
Fnunl = grb.fluxDensity(t,
nu / nufac,
jetModel,
0,
*Y,
spread=spread)
al = np.log(Fnutr / Fnutl) / np.log(tfac * tfac)
al2 = np.log(Fnutr * Fnutl / (Fnu * Fnu)) / np.log(tfac)**2
al3 = np.zeros(al.shape)
al3[1:-1] = (al2[2:] - al2[:-2]) / np.log(tfac * tfac)
beta = np.log(Fnunr / Fnunl) / np.log(nufac * nufac)
if tW > 0.0:
t1 = tW
tStruct = math.sqrt(tW * tb)
else:
tStruct = max(0.01 * tb, math.sqrt(minTW * tb))
t1 = tStruct
t2 = tb
tPost = np.power(tb * tb * tb * tN, 1.0 / 4.0)
tOff = 0.1 * tW
nStruct = np.searchsorted(t, tStruct)
alStructN[k, j, i] = al[nStruct]
nPost = np.searchsorted(t, tPost)
alPostN[k, j, i] = al[nPost]
if tOff > 0.0:
nOff = np.searchsorted(t, tOff)
alOffN[k, j, i] = al[nOff]
else:
alOffN[k, j, i] = np.inf
nb = np.searchsorted(t, tb)
FbN[k, j, i] = Fnu[nb]
indS = (t > t1) & (t < t2)
alNlog = al[indS].mean()
dtS = t[indS][1:] - t[indS][:-1]
TS = t[indS][-1] - t[indS][0]
alNlin = (0.5 * (al[indS][1:] + al[indS][:-1]) * dtS /
TS).sum()
alStructNAveLin[k, j, i] = alNlin
alStructNAveLog[k, j, i] = alNlog
indStruct = np.argwhere((t[:-1] > tW) & (t[:-1] < tb)
& (al2[:-1] > 0)
& (al2[1:] < 0))
# indStruct = np.argwhere((t > tW) & (t < tb) & (al2 > 0))
if len(indStruct) > 0:
nStruct2 = indStruct[-1]
else:
# indStruct = np.argwhere((t > tW) & (t < tb)
# & (al3 > 0))
# nStruct2 = indStruct[-1]
indStruct1 = np.argwhere((t[:-1] > tW) & (t[:-1] < tb)
& (al3[:-1] > 0)
& (al3[1:] < 0))
indStruct2 = np.argwhere((t[:-1] > tW) & (t[:-1] < tb)
& (al3[:-1] < 0)
& (al3[1:] > 0))
if len(indStruct1) > 0:
nStruct2 = indStruct1[-1]
elif len(indStruct2) > 0:
nStruct2 = indStruct2[-1]
else:
nStruct2 = nStruct
tStruct2 = np.sqrt(t[nStruct2] * t[nStruct2 + 1])
alStructN2[k, j,
i] = 0.5 * (al[nStruct2] + al[nStruct2 + 1])
gs = gs0[i, j].subgridspec(6, 1, hspace=0.0)
axF = fig.add_subplot(gs[0:-4, 0])
axa = fig.add_subplot(gs[-4, 0])
axa2 = fig.add_subplot(gs[-3, 0])
axa3 = fig.add_subplot(gs[-2, 0])
axb = fig.add_subplot(gs[-1, 0])
if tW > 0.0:
axF.axvline(tW, ls='--', color='grey')
axa.axvline(tW, ls='--', color='grey')
axa2.axvline(tW, ls='--', color='grey')
axa3.axvline(tW, ls='--', color='grey')
axb.axvline(tW, ls='--', color='grey')
if tb > 0.0:
axF.axvline(tb, ls='-', color='grey')
axa.axvline(tb, ls='-', color='grey')
axa2.axvline(tb, ls='-', color='grey')
axa3.axvline(tb, ls='-', color='grey')
axb.axvline(tb, ls='-', color='grey')
if tbpm > 0.0:
axF.axvline(tbpm, ls=':', color='grey')
axa.axvline(tbpm, ls=':', color='grey')
axa2.axvline(tbpm, ls=':', color='grey')
axa3.axvline(tbpm, ls=':', color='grey')
axb.axvline(tbpm, ls=':', color='grey')
axF.axvline(tN, ls='-', lw=4, color='lightgrey')
axa.axvline(tN, ls='-', lw=4, color='lightgrey')
axa2.axvline(tN, ls='-', lw=4, color='lightgrey')
axa3.axvline(tN, ls='-', lw=4, color='lightgrey')
axb.axvline(tN, ls='-', lw=4, color='lightgrey')
axF.axvline(tStruct, ls=':', lw=4, color='lightgrey')
axa.axvline(tStruct, ls=':', lw=4, color='lightgrey')
axa2.axvline(tStruct, ls=':', lw=4, color='lightgrey')
axa3.axvline(tStruct, ls=':', lw=4, color='lightgrey')
axb.axvline(tStruct, ls=':', lw=4, color='lightgrey')
axF.axvline(tPost, ls=':', lw=4, color='lightgrey')
axa.axvline(tPost, ls=':', lw=4, color='lightgrey')
axa2.axvline(tPost, ls=':', lw=4, color='lightgrey')
axa3.axvline(tPost, ls=':', lw=4, color='lightgrey')
axb.axvline(tPost, ls=':', lw=4, color='lightgrey')
axF.axvline(tOff, ls=':', lw=4, color='lightgrey')
axa.axvline(tOff, ls=':', lw=4, color='lightgrey')
axa2.axvline(tOff, ls=':', lw=4, color='lightgrey')
axa3.axvline(tOff, ls=':', lw=4, color='lightgrey')
axb.axvline(tOff, ls=':', lw=4, color='lightgrey')
axF.axvline(tStruct2, ls='-.', lw=4, color='lightgrey')
axa.axvline(tStruct2, ls='-.', lw=4, color='lightgrey')
axa2.axvline(tStruct2, ls='-.', lw=4, color='lightgrey')
axa3.axvline(tStruct2, ls='-.', lw=4, color='lightgrey')
axb.axvline(tStruct2, ls='-.', lw=4, color='lightgrey')
alOff = calcSlopeOffaxis(jetModel, Y, regime)
alStruct = calcSlopeStruct(jetModel, Y, regime)
alPre = calcSlopePre(jetModel, Y, regime)
alPost = calcSlopePost(jetModel, Y, regime)
axa.axhline(alOff, color='grey', ls='--')
axa.axhline(alStruct, color='grey', ls='-')
axa.axhline(alPre, color='grey', ls='-.')
axa.axhline(alNlin, color='orange', ls='-')
axa.axhline(alNlog, color='orange', ls='--')
axa.axhline(alPost, color='grey', ls=':')
axb.axhline(betaE, color='grey')
axF.plot(t, Fnu)
axa.plot(t, al)
axa2.plot(t, al2)
axa3.plot(t, al3)
axb.plot(t, beta)
axF.set_xscale('log')
axF.set_yscale('log')
axa.set_xscale('log')
axa2.set_xscale('log')
axa3.set_xscale('log')
axb.set_xscale('log')
lim = axF.get_ylim()
if lim[0] < 1.0e-12 * lim[1]:
F0 = FbN[k, j, i]
if F0 / lim[0] < lim[1] / F0 and F0 / lim[0] < 1.0e12:
axF.set_ylim(lim[0], 1.0e12 * lim[0])
elif F0 / lim[0] >= lim[1] / F0 and lim[
1] / F0 < 1.0e12:
axF.set_ylim(1.0e-12 * lim[1], lim[1])
else:
axF.set_ylim(1.0e-6 * F0, 1.0e6 * F0)
if j == 0:
axF.set_ylabel(r'$F_\nu$')
axa.set_ylabel(r'$\alpha$')
axa2.set_ylabel(r"$\alpha'$")
axa3.set_ylabel(r"$\alpha''$")
axb.set_ylabel(r'$\beta$')
if i == NV - 1:
axb.set_xlabel(r'$t$')
fig.tight_layout()
plotname = "lcGrid_{0:s}_thW_{1:d}.png".format(regime, k)
save(fig, plotname)
plt.close(fig)
filename = 'slopes_{0:s}.h5'.format(regime)
print("Creating archive " + filename)
fi = h5.File(filename, "w")
fi.create_dataset("alOffN", data=alOffN)
fi.create_dataset("alStructN", data=alStructN)
fi.create_dataset("alStructN2", data=alStructN2)
fi.create_dataset("alStructNAveLin", data=alStructNAveLin)
fi.create_dataset("alStructNAveLog", data=alStructNAveLog)
fi.create_dataset("alPostN", data=alPostN)
fi.create_dataset("FbN", data=np.array(FbN))
fi.create_dataset("thW", data=np.array(thWs))
fi.create_dataset("thV", data=np.array(thVs))
fi.create_dataset("thC", data=np.array(thCs))
fi.create_dataset("Y", data=Y)
fi.close()
color = [
'tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple',
'tab:brown', 'tab:pink', 'tab:grey', 'tab:olive', 'tab:cyan'
]
ls = ['-', '--', '-.', ':']
fig, ax = plt.subplots(1, 1)
for k, thW in enumerate(thWs):
for i, thC in enumerate(thCs):
ax.plot(thVs, alOffN[k, :, i], color=color[k], ls=ls[i])
plotname = "alOff_{0:s}_thW.png".format(regime)
save(fig, plotname)
plt.close(fig)
fig, ax = plt.subplots(1, 1)
for k, thW in enumerate(thWs):
for i, thC in enumerate(thCs):
ax.plot(thVs, alStructN[k, :, i], color=color[k], ls=ls[i])
plotname = "alStruct_{0:s}_thW.png".format(regime)
save(fig, plotname)
plt.close(fig)
fig, ax = plt.subplots(1, 1)
for k, thW in enumerate(thWs):
for i, thC in enumerate(thCs):
ax.plot(thVs, alPostN[k, :, i], color=color[k], ls=ls[i])
plotname = "alPost_{0:s}_thW.png".format(regime)
save(fig, plotname)
plt.close(fig)
return 0
thW = 0.4
E0 = 1.0e52
n0 = 1.0e-2
p = 2.3
epse = 0.1
epsB = 0.001
dL = 1.0e27
t = np.logspace(3, 8, 200)
nu = np.empty(t.shape)
nu[:] = 1.0e14
Y = np.array([thV, E0, thC, thW, 0, 0, 0, n0, p, epse, epsB, 1.0, dL])
Fnu = grb.fluxDensity(t, nu, jetType, 0, *Y)
Nth = 10
FnuR = np.empty((Nth, t.shape[0]))
dth = thW/Nth
for i in range(Nth):
thout = thW - i*dth
thin = thW - (i+1)*dth
if thout < 0.0:
thin = 0.0
x = float(i)/(Nth-1)
th = x*thin+(1-x)*thout
E = E0*np.exp(-0.5*th*th/(thC*thC))
Y[1] = E
dL = 1.23e26
Y = np.array([thV, E0, thC, thW, b, 0, 0, 0, n0, p, epse, epsB, ksiN, dL])
# Time and Frequencies
ta = 1.0e-1 * grb.day2sec
tb = 1.0e3 * grb.day2sec
t = np.geomspace(ta, tb, num=100)
nuR = 6.0e9
nuO = 1.0e14
nuX = 1.0e18
# Calculate!
print("Calc Radio")
FnuR = grb.fluxDensity(t, nuR, jetType, specType, *Y)
print("Calc Optical")
FnuO = grb.fluxDensity(t, nuO, jetType, specType, *Y)
print("Calc X-ray")
FnuX = grb.fluxDensity(t, nuX, jetType, specType, *Y)
# Plot!
print("Plot")
tday = t * grb.sec2day
fig, ax = plt.subplots(1, 1)
ax.plot(tday, FnuR, ls='-', label=r'$\nu=6$ GHz')
ax.plot(tday, FnuO, ls='--', label=r'$\nu=10^{14}$ Hz')
ax.plot(tday, FnuX, ls='-.', label=r'$\nu=10^{18}$ Hz')
ax.set_xscale('log')
def makeCharEvolPlots():
nuD, tND, YD, betaD = getRegimePars('D')
nuE, tNE, YE, betaE = getRegimePars('E')
nuF, tNF, YF, betaF = getRegimePars('F')
nuG, tNG, YG, betaG = getRegimePars('G')
nuH, tNH, YH, betaH = getRegimePars('H')
N = 100
tN0 = tNG
t = np.geomspace(1.0e-5 * tN0, tN0, N)
nu = np.empty(N)
jetModel = 0
pD = betaD
pE = betaE
pF = betaF
pG = betaG
pHs = betaH
pHf = betaH
figs, axs = plt.subplots(2, 2)
figf, axf = plt.subplots(2, 2)
thVs = [0.0, 0.25, 0.5]
for thV in thVs:
YD[0] = thV
YE[0] = thV
YF[0] = thV
YG[0] = thV
YH[0] = thV
nu[:] = nuD
FD = grb.fluxDensity(t, nu, jetModel, 0, *YD)
nu[:] = nuE
FE = grb.fluxDensity(t, nu, jetModel, 0, *YE)
nu[:] = nuF
FF = grb.fluxDensity(t, nu, jetModel, 0, *YF)
nu[:] = nuG
FG = grb.fluxDensity(t, nu, jetModel, 0, *YG)
nu[:] = nuH
FHs = grb.fluxDensity(t, nu, jetModel, 0, *YH)
FHf = grb.fluxDensity(t, nu, jetModel, 0, *YF)
# Slow Cooling
nums = np.power(FG / FD * np.power(nuD, pD) * np.power(nuG, -pG),
1.0 / (pD - pG))
nucs = np.power(FHs / FG * np.power(nuG, pG) * np.power(nuH, -pHs),
1.0 / (pG - pHs))
Fms = np.power(
np.power(FD, pG) * np.power(FG, -pD) *
np.power(nuG / nuD, pD * pG), 1.0 / (pG - pD))
Fcs = np.power(
np.power(FG, pHs) * np.power(FHs, -pG) *
np.power(nuH / nuG, pG * pHs), 1.0 / (pHs - pG))
# Fast Cooling
nucf = np.power(FF / FE * np.power(nuE, pE) * np.power(nuF, -pF),
1.0 / (pE - pF))
numf = np.power(FHf / FF * np.power(nuF, pF) * np.power(nuH, -pHf),
1.0 / (pF - pHf))
Fcf = np.power(
np.power(FE, pF) * np.power(FF, -pE) *
np.power(nuF / nuE, pE * pF), 1.0 / (pF - pE))
Fmf = np.power(
np.power(FF, pHf) * np.power(FHf, -pF) *
np.power(nuH / nuF, pF * pHf), 1.0 / (pHf - pF))
axs[0, 0].plot(t / tN0,
Fms,
label=r'$\theta_V = {0:.2}$ rad'.format(thV))
axs[0, 1].plot(t / tN0, Fcs)
axs[1, 0].plot(t / tN0, nums)
axs[1, 1].plot(t / tN0, nucs)
axf[0, 0].plot(t / tN0,
Fmf,
label=r'$\theta_V = {0:.2}$ rad'.format(thV))
axf[0, 1].plot(t / tN0, Fcf)
axf[1, 0].plot(t / tN0, numf)
axf[1, 1].plot(t / tN0, nucf)
axs[0, 0].legend()
axs[0, 0].set_xscale('log')
axs[0, 0].set_yscale('log')
axs[0, 0].set_ylabel(r'$F_m$')
axs[0, 1].set_xscale('log')
axs[0, 1].set_yscale('log')
axs[0, 1].set_ylabel(r'$F_c$')
axs[1, 0].set_xscale('log')
axs[1, 0].set_yscale('log')
axs[1, 0].set_ylabel(r'$\nu_m$')
axs[1, 1].set_xscale('log')
axs[1, 1].set_yscale('log')
axs[1, 1].set_ylabel(r'$\nu_c$')
axs[1, 0].set_xlabel(r'$\tau$')
axs[1, 1].set_xlabel(r'$\tau$')
figs.tight_layout()
axf[0, 0].legend()
axf[0, 0].set_xscale('log')
axf[0, 0].set_yscale('log')
axf[0, 0].set_ylabel(r'$F_m$')
axf[0, 1].set_xscale('log')
axf[0, 1].set_yscale('log')
axf[0, 1].set_ylabel(r'$F_c$')
axf[1, 0].set_xscale('log')
axf[1, 0].set_yscale('log')
axf[1, 0].set_ylabel(r'$\nu_m$')
axf[1, 1].set_xscale('log')
axf[1, 1].set_yscale('log')
axf[1, 1].set_ylabel(r'$\nu_c$')
axf[1, 0].set_xlabel(r't')
axf[1, 1].set_xlabel(r't')
figf.tight_layout()
plotname = "charEvolSlow.pdf"
save(figs, plotname)
plt.close(figs)
plotname = "charEvolFast.pdf"
save(figf, plotname)
plt.close(figf)
Y1[0] = 0.0 Y2[0] = thW - 2 * thC Y3[0] = thW + 2 * thC tmin1 = 3.0e3 # t.min() tmax1 = t.max() tmin2 = 1.0e4 tmax2 = t.max() tmin3 = 1.0e4 tmax3 = t.max() Fmax = 3e-3 Fmin = 3e-13 Fnu1 = grb.fluxDensity(t, nu, jetType, 0, *Y1, **Z) Fnu2 = grb.fluxDensity(t, nu, jetType, 0, *Y2, **Z) Fnu3 = grb.fluxDensity(t, nu, jetType, 0, *Y3, **Z) Fnu1g = grb.fluxDensity(t, nu, jetType, 0, *Y1, **Zg) Fnu2g = grb.fluxDensity(t, nu, jetType, 0, *Y2, **Zg) Fnu3g = grb.fluxDensity(t, nu, jetType, 0, *Y3, **Zg) al_G_FOA = pp.calcSlopeOffaxis(jetType, Y1, 'G') al_G_PRE = pp.calcSlopePre(jetType, Y1, 'G') al_G_POS = pp.calcSlopePost(jetType, Y1, 'G') al_G_STR1 = pp.calcSlopeStructEff(jetType, Y1, 'G') al_G_STR2 = pp.calcSlopeStructEff(jetType, Y2, 'G') al_G_STR3 = pp.calcSlopeStructEff(jetType, Y3, 'G') be_G = 0.5 * (1 - p) al_G_POS2 = -p
nu = np.empty(t.shape)
nu[:] = 1.0e14
thC0 = 0.1
bs = [2, 4, 6, 8]
thVs = [0.0, 0.2, 0.4, 0.6]
cs = ['C1', 'C2', 'C3', 'C4']
ls = ['-', '--', '-.', ':']
spread = True
for j, thV in enumerate(thVs):
Y[0] = thV
Y[2] = thC0
Fnu = grb.fluxDensity(t, nu, -1, 0, *Y, spread=spread)
ax2.flat[j].plot(t, Fnu, color='k', ls=ls[j])
Y[2] = thC0 / np.sqrt(1.0)
Fnu = grb.fluxDensity(t, nu, 0, 0, *Y, spread=spread)
ax2.flat[j].plot(t, Fnu, color='C0', ls=ls[j])
for i, b in enumerate(bs):
Y[2] = thC0
Y[4] = b
# Fnu = grb.fluxDensity(t, nu, 4, 0, *Y, spread=False)
# ax2.plot(t, Fnu, color=cs[i])
Y[2] = thC0 * np.sqrt(b / 2)
# Y[2] = thC0 * np.sqrt(b)
# if b == 2:
# Y[2] = thC0 / np.sqrt(2.0)
Y[4] = b
def fluxDensity(t, nu, jetType, specType, *Y):
mJy = grbpy.fluxDensity(t, nu, jetType, specType, *Y)
return mJy
def plot_lc_jet(t, thV, thC, thW, E0, n0, p, jetType=0, seg='G'):
nu = np.empty(t.shape)
nu[:] = 1.0e14
Y = np.array([thV, E0, thC, thW, 0, 0, 0, n0, p, 0.1, 1.0e-4, 1.0, 10])
FnuSA = grb.fluxDensity(t, nu, jetType, 0, *Y)
to = t_on(thV, thC, thW, E0, n0, p, jetType)
tp = t_peak(thV, thC, thW, E0, n0, p)
# ip = np.searchsorted(t, tp)
# F0 = FnuSA[ip]
# FnuSA /= F0
if seg is not None:
Fnu = pl_lc_jet(t, thV, thC, thW, E0, n0, p, jetType, seg)
# else:
# FnuSAb = grb.fluxDensity(t, nu*2, jetType, 0, *Y)
# FnuSAa = grb.fluxDensity(t, nu*0.5, jetType, 0, *Y)
# beta = np.log(FnuSAb/FnuSAa) / np.log(2/0.5)
# bD = 1.0/3.0
# bE = 1.0/3.0
# bF = -0.5
# bG = 0.5*(1-p)
# bH = -0.5*p
# HHH = beta < 0.25-0.5*p
# FFF = (beta >= 0.25-0.5*p)
ir = np.searchsorted(t, 0.1 * tp)
F0 = FnuSA[ir] / Fnu[ir]
FnuSA /= F0
tc = np.sqrt(t[1:] * t[:-1])
dF = np.log(Fnu[1:] / Fnu[:-1]) / np.log(t[1:] / t[:-1])
dFSA = np.log(FnuSA[1:] / FnuSA[:-1]) / np.log(t[1:] / t[:-1])
psi = np.linspace(np.abs(thV - thW), thV, 100)
tpsi = t_psi(psi, thV, thC, E0, n0, jetType)
dFpsi = slope_struct_psi(psi, thV, thC, p, jetType, seg)
fig, ax = plt.subplots(2, 1)
ax[0].axvline(to, color='grey', ls=':')
ax[0].axvline(tp, color='grey', ls='--')
ax[1].axvline(to, color='grey', ls=':')
ax[1].axvline(tp, color='grey', ls='--')
ax[0].plot(t, Fnu)
ax[0].plot(t, FnuSA)
ax[1].plot(tc, dF)
ax[1].plot(tc, dFSA)
ax[1].plot(tpsi, dFpsi)
ax[0].set_xscale("log")
ax[1].set_xscale("log")
ax[0].set_yscale("log")
ax[1].set_yscale("linear")
ax[1].set_xlabel(r"$t$ (s)")
ax[0].set_ylabel(r"$F_\nu/F_0$")
ax[1].set_ylabel(r"d$\log F_\nu$/d$\log t$")
ax[0].set_xlim(t.min(), t.max())
ax[1].set_xlim(t.min(), t.max())
fig2 = None
if decompPlot:
nth = 10
fig2, ax2 = plt.subplots()
ax2.plot(t, FnuSA)
ths = np.linspace(0.0, thW, nth + 1)
Y = np.array([thV, E0, thC, thW, 0, 0, 0, n0, p, 0.1, 1.0e-4, 1.0, 10])
for i in range(nth):
Y[1] = E0 * f_struct(0.5 * (ths[i] + ths[i + 1]), thC, jetType)
Y[2] = ths[i]
Y[3] = ths[i + 1]
FnuR = grb.fluxDensity(t, nu, -2, 0, *Y) / F0
ax2.plot(t, FnuR, ls='--')
ax2.set_xscale('log')
ax2.set_yscale('log')
ax2.set_xlabel(r"$t$ (s)")
ax2.set_ylabel(r"$F_\nu / F_0$")
return fig, fig2
def findJetBreak(jetType,
Y,
Z,
regime,
NU,
printMode=None,
returnAll=False,
ax=None):
tN0 = pp.calcTN0(Y)
tb = 10 * tN0
thV = Y[0]
thC = Y[2]
thW = Y[3]
chip = 2 * math.sin(0.5 * (thC + thV))
tj_guess = 0.24 * tN0 * math.pow(chip, 8.0 / 3.0)
if jetType == -1:
if thV <= thC:
ta = 1.0e-3 * tj_guess
else:
tC = 2 * pp.calcTC(jetType, Y, regime)
ta = 2 * tC
else:
if thV <= thC and thV <= thW:
ta = 1.0e-3 * tj_guess
elif thV <= thW:
ta = 1.0e-3 * tj_guess
else:
tW = pp.calcTW(jetType, Y, regime)
ta = 2.0 * tW
if ta == 0.0:
print(ta, tb, thV, thC, thW)
t = np.geomspace(ta, tb, 200)
theta = np.zeros(t.shape)
phi = np.zeros(t.shape)
"""
_, _, u, _ = grb.jet.shockVals(theta, phi, t, jetType, *Y, **Z)
while u.max() < 1.0:
ta = ta / 10.0
print("Retrying with ta = {0:.3e} s".format(ta))
t = np.geomspace(ta, tb, 200)
_, _, u, _ = grb.jet.shockVals(theta, phi, t, jetType, *Y, **Z)
rel = (u >= 0.5) & (u < 1.0e5)
t = t[rel]
u = u[rel]
theta = theta[rel]
phi = phi[rel]
"""
ta = 5.0e-2 * tj_guess
tb = 2.0e1 * tj_guess
t = np.geomspace(ta, tb, 200)
theta = np.zeros(t.shape)
phi = np.zeros(t.shape)
_, _, u, _ = grb.jet.shockVals(theta, phi, t, jetType, *Y, **Z)
nu = np.empty(t.shape)
nu[:] = NU
nufac = 1.01
Fnu = grb.fluxDensity(t, nu, jetType, 0, *Y, **Z)
Fnua = grb.fluxDensity(t, nu / nufac, jetType, 0, *Y, **Z)
Fnub = grb.fluxDensity(t, nu * nufac, jetType, 0, *Y, **Z)
beta = np.log(Fnub / Fnua) / np.log(nufac * nufac)
right = (np.fabs(beta - betaRegime(regime, Y)) < 0.02)\
& (t > 3*ta) & (t < tb/3)
t_fit = t[right]
# nu_fit = nu[right]
# u_fit = u[right]
# theta_fit = theta[right]
# phi_fit = phi[right]
Fnu_fit = Fnu[right]
t_guess = 0.24 * tN0 * np.power(2 * np.sin(0.5 * (thC + thV)), 8.0 / 3.0)
i0s = [
1,
len(t_fit) // 4,
len(t_fit) // 2, 3 * len(t_fit) // 4,
len(t_fit) - 2
]
i0 = np.searchsorted(t_fit, t_guess)
if i0 > 1 and i0 < len(t_fit) - 2:
i0s.append(i0)
bmin = -10
bmax = 10
bounds = [(math.log10(t_fit.min()) - 1, math.log10(t_fit.max()) + 1),
(math.log10(Fnu_fit.min()) - 1, math.log10(Fnu_fit.max()) + 1),
(bmin, bmax), (bmin, bmax), (-1.5, 1.5)]
chi2min = np.inf
x_best = None
for i0 in i0s:
if printMode is 'all':
print("i0 = {0:d}".format(i0))
lt0 = math.log10(t_fit[i0])
lF0 = math.log10(Fnu_fit[i0])
ls = 1.0
b0 = math.log(Fnu_fit[i0] / Fnu_fit[0]) / math.log(
t_fit[i0] / t_fit[0])
b1 = math.log(Fnu_fit[-1] / Fnu_fit[i0]) / math.log(
t_fit[-1] / t_fit[i0])
if b0 < bmin:
b0 = bmin + 0.1
if b0 > bmax:
b0 = bmax - 0.1
if b1 < bmin:
b1 = bmin + 0.1
if b1 > bmax:
b1 = bmax - 0.1
x0 = [lt0, lF0, b0, b1, ls]
func = lf_spl2
if printMode is 'all':
print("chi2(x0) = {0:.3e}".format(chi2(x0, t_fit, Fnu_fit, func)))
res = opt.minimize(chi2,
x0, (t_fit, Fnu_fit, func),
bounds=bounds,
method='TNC',
options={'maxiter': 8000})
if printMode is 'all':
print("chi2(x1) = {0:.3e}".format(chi2(res.x, t_fit, Fnu_fit,
func)))
if printMode is 'all':
print(res)
elif printMode is 'summary':
print("Success: " + str(res.success) +
" chi2={0:.2e}".format(res.fun))
if res.fun < chi2min:
chi2min = res.fun
x_best = res.x
x = x_best
if x[3] - x[2] > -0.74:
print(" is that even a jetbreak? b0={0:.2f} b1={1:.2f} thV={2:.2f}".
format(x[2], x[3], thV))
if ax is not None:
ax.plot(t, Fnu)
ax.plot(t_fit, func(t_fit, *x0), lw=1, color='k', ls='--')
ax.plot(t_fit, func(t_fit, *x), lw=1, color='k')
# ax2 = ax.twinx()
# ax2.plot(t, u, color='tab:orange')
# ax2.set_yscale('log')
ax.set_xscale('log')
ax.set_yscale('log')
if returnAll:
return x
return math.pow(10.0, x[0])
def makeNormalizationPlots(regime='G', jetModel=0):
fig, ax = plt.subplots(4, 2, figsize=(12, 15))
NU, tN0, Y, beta = getRegimePars(regime)
N = 100
nu = np.empty(N)
nu[:] = NU
# thVs = [0.1, 0.3, 0.5, 0.7, 0.1, 0.3, 0.5, 0.7, 0.1, 0.3, 0.5, 0.7]
# thCs = [0.1, 0.1, 0.1, 0.1, 0.05, 0.05, 0.05, 0.05, 0.2, 0.2, 0.2, 0.2]
# thWs = [0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4]
thVs = [0.1, 0.3, 0.5, 0.7, 0.1, 0.3, 0.5, 0.7]
thCs = [0.05, 0.05, 0.05, 0.05, 0.2, 0.2, 0.2, 0.2]
thWs = [0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4]
b = Y[4]
n0 = Y[8]
p = Y[9]
epse = Y[10]
epsB = Y[11]
dL = Y[13]
nufac = 1.1
ax[3, 0].axhline(beta, lw=2, color='grey', alpha=0.5)
ax[3, 1].axhline(beta, lw=2, color='grey', alpha=0.5)
for thV, thC, thW in zip(thVs, thCs, thWs):
chi = np.geomspace(max(2 * math.sin(0.5 * (thV - thW)), 1.0e-2),
2 * math.sin(0.5 * (thV)), N)
th = thV - 2 * np.arcsin(0.5 * chi)
f = get_f(jetModel, thC, thW, b)
tp = calc_tp(th, tN0, thV, f, regime, p)
Ip = calc_Ip(th, tN0, thV, f, nu, regime, n0, p, epse, epsB, dL)
# dth = 1.0e-4
# fl = f(th-dth)
# fc = f(th)
# fr = f(th+dth)
# dlfdth = np.log(fr/fl)/(2*dth)
# d2lfdth2 = np.log(fr*fl/(fc*fc))/(dth*dth)
# dth1 = np.power(np.fabs(dlfdth), -1.0)
# dth2 = np.power(np.fabs(d2lfdth2), -0.5)
dth2 = thC # 0.1
Y[0] = thV
Y[2] = thC
Y[3] = thW
Fnu = grb.fluxDensity(tp, nu, jetModel, 0, *Y, latRes=20)
FnuR = grb.fluxDensity(tp, nu * nufac, jetModel, 0, *Y, latRes=10)
FnuL = grb.fluxDensity(tp, nu / nufac, jetModel, 0, *Y, latRes=10)
betaN = np.log(FnuR / FnuL) / np.log(nufac * nufac)
norm = Fnu / Ip
som = np.log(norm[1:] / norm[:-1]) / np.log(chi[1:] / chi[:-1])
chiC = np.sqrt(chi[1:] * chi[:-1])
tpC = np.sqrt(tp[1:] * tp[:-1])
label = r'$\theta_V =$ {0:.1f}'.format(thV)
label += r', $\theta_C = $ {0:.1f}'.format(thC)
label += r', $\theta_W = $ {0:.1f}'.format(thW)
linelist = ax[0, 0].plot(chi, Fnu, label=label)
ax[1, 0].plot(chi, norm)
# ax[1, 0].plot(chi, chi*dth1, color=linelist[0].get_color(), ls='--')
ax[1, 0].plot(chi, chi * dth2, color=linelist[0].get_color(), ls=':')
ax[2, 0].plot(chiC, som)
ax[3, 0].plot(chi, betaN)
ax[0, 1].plot(tp, Fnu)
ax[1, 1].plot(tp, norm)
ax[2, 1].plot(tpC, som)
ax[3, 1].plot(tp, betaN)
ax[0, 0].legend()
ax[0, 0].set_xscale('log')
ax[0, 0].set_yscale('log')
ax[1, 0].set_xscale('log')
ax[1, 0].set_yscale('log')
ax[1, 0].set_ylim(1.0e-6, 1.0)
ax[2, 0].set_xscale('log')
ax[2, 0].set_ylim(-2.0, 6.0)
ax[3, 0].set_xscale('log')
ax[0, 1].set_xscale('log')
ax[0, 1].set_yscale('log')
ax[1, 1].set_xscale('log')
ax[1, 1].set_yscale('log')
ax[2, 1].set_xscale('log')
ax[2, 1].set_ylim(-2.0, 6.0)
ax[3, 1].set_xscale('log')
ax[0, 0].set_ylabel(r'$F_\nu$')
ax[1, 0].set_ylabel(r'$\Delta \Omega = F_\nu / I_\nu^*$')
ax[2, 0].set_ylabel(r'$s_\Omega$')
ax[3, 0].set_ylabel(r'$\beta$')
ax[2, 0].set_xlabel(r'$\chi$')
ax[2, 1].set_xlabel(r'$t_{{obs}}$')
fig.tight_layout()
plotname = 'som.pdf'
save(fig, plotname)
plt.close(fig)
alPL = pp.calcSlopeStructEff(4, Y, 'G')
alPL2 = pp.calcSlopeStructEff(4, Y2, 'G')
alPL6 = pp.calcSlopeStructEff(4, Y6, 'G')
label = (r'$\theta_{{\mathrm{{obs}}}} / \theta_{{\mathrm{{c}}}}'
r' = {0:.0f}$'.format(thV / thC))
tb = 0.2 * np.power(9*E0/(16*np.pi*n0*grb.mp*grb.c**5), 1.0/3.0)\
* np.power(2*np.sin(0.5*(thC+thV)), 8.0/3.0)
t0 = tb / 5
i0 = np.searchsorted(t, t0)
subInd = t < 1.2 * tb
Fnu = grb.fluxDensity(t, nu, 0, 0, *Y)
axG.plot(t, Fnu, color=colors[i])
axG.plot(t[subInd],
Fnu[i0] * np.power(t / t0, alG)[subInd],
color=colors[i],
ls='--',
lw=3.0,
alpha=0.5)
axMG.plot(t, Fnu, color=colors[i])
axMG.plot(t[subInd],
Fnu[i0] * np.power(t / t0, alG)[subInd],
color=colors[i],
ls='--',
lw=3.0,
alpha=0.5)
def makeRegimePlots():
nuD, tND, YD, betaD = getRegimePars('D')
nuE, tNE, YE, betaE = getRegimePars('E')
nuF, tNF, YF, betaF = getRegimePars('F')
nuG, tNG, YG, betaG = getRegimePars('G')
nuH, tNH, YH, betaH = getRegimePars('H')
N = 100
tN0 = tNG
jetModel = 4
nu = np.geomspace(1.0e0, 1.0e20, N)
t = np.empty(N)
Ts = np.logspace(-5, 0, base=10.0, num=6) * tN0
fig, ax = plt.subplots(1, 1)
figF, axF = plt.subplots(1, 1)
figS, axS = plt.subplots(1, 1)
axF.axvline(nuE, color='grey')
axF.axvline(nuF, color='grey')
axF.axvline(nuH, color='grey')
axS.axvline(nuD, color='grey')
axS.axvline(nuG, color='grey')
axS.axvline(nuH, color='grey')
for T in Ts:
t[:] = T
FnuF = grb.fluxDensity(t, nu, jetModel, 0, *YE)
FnuS = grb.fluxDensity(t, nu, jetModel, 0, *YG)
axF.plot(nu, FnuF)
axS.plot(nu, FnuS)
axF.set_xscale('log')
axF.set_yscale('log')
axS.set_xscale('log')
axS.set_yscale('log')
plotname = 'specFast.pdf'
save(figF, plotname)
plt.close(figF)
plotname = 'specSlow.pdf'
save(figS, plotname)
plt.close(figS)
t = np.geomspace(1.0e-6 * tN0, 1.0 * tN0, N)
nu = np.empty(N)
nu[:] = nuD
FnuD = grb.fluxDensity(t, nu, jetModel, 0, *YD)
nu[:] = nuE
FnuE = grb.fluxDensity(t, nu, jetModel, 0, *YE)
nu[:] = nuF
FnuF = grb.fluxDensity(t, nu, jetModel, 0, *YF)
nu[:] = nuG
FnuG = grb.fluxDensity(t, nu, jetModel, 0, *YG)
nu[:] = nuH
FnuH = grb.fluxDensity(t, nu, jetModel, 0, *YH)
ax.plot(t, FnuD)
ax.plot(t, FnuE)
ax.plot(t, FnuF)
ax.plot(t, FnuG)
ax.plot(t, FnuH)
ax.set_xscale('log')
ax.set_yscale('log')
plotname = 'lc_regimes.pdf'
save(fig, plotname)
plt.close(fig)
p = 2.15
epse = 1.0e-1
epsB = 1.0e-2
ksiN = 1.0
dL = 1.23e26
Y = np.array([thV, E0, thC, thW, b, L0, q, ts, n0, p, epse, epsB, ksiN, dL])
nua = 1.0e6 # Frequencies in Hz
nub = 1.0e20 # Frequencies in Hz
t = 1.0 * grb.day2sec # spectrum at 1 day
nu = np.geomspace(nua, nub, num=100)
print("Calculating")
Fnu = grb.fluxDensity(t, nu, jetType, specType, *Y)
print("Writing spec.txt")
f = open("spec.txt", 'w')
f.write("# t " + str(t) + ' (s)\n')
f.write("# jetType " + str(jetType) + " specType " + str(specType) + "\n")
f.write("# " + " ".join([str(y) for y in Y]) + "\n")
for i in range(len(nu)):
f.write("{0:.6e} {1:.6e}\n".format(nu[i], Fnu[i]))
f.close()
print("Plotting")
fig, ax = plt.subplots(1, 1)
ax.plot(nu, Fnu)
'd_L': 1.36e26, # Luminosity distance in cm
'z': 0.01
} # redshift
# Time and Frequencies
ta = 1.0e-1 * grb.day2sec
tb = 1.0e3 * grb.day2sec
t = np.geomspace(ta, tb, num=100)
nuR = 6.0e9
nuO = 1.0e14
nuX = 1.0e18
# Calculate!
print("Calc Radio")
FnuR = grb.fluxDensity(t, nuR, **Z)
print("Calc Optical")
FnuO = grb.fluxDensity(t, nuO, **Z)
print("Calc X-ray")
FnuX = grb.fluxDensity(t, nuX, **Z)
# Plot!
print("Plot")
tday = t * grb.sec2day
fig, ax = plt.subplots(1, 1)
ax.plot(tday, FnuR, ls='-', label=r'$\nu=6$ GHz')
ax.plot(tday, FnuO, ls='--', label=r'$\nu=10^{14}$ Hz')
ax.plot(tday, FnuX, ls='-.', label=r'$\nu=10^{18}$ Hz')
ax.set_xscale('log')
def makeCentralPlots(modelPlots=True, poster=True):
thC = 0.1
thW = 0.4
b = 2
E0 = 1.0e52
n0 = 1.0e-2
p = 2.2
epse = 0.1
epsB = 0.001
xiN = 1.0
dL = 1.23e26
Y = np.array(
[0.0, E0, thC, thW, b, 0.0, 0.0, 0.0, n0, p, epse, epsB, xiN, dL])
latResTH = 10
latResG = 10
latResPL = 10
N = 1000
tday = np.geomspace(0.1, 1000, N)
t = grb.day2sec * tday
nu = 1.0e14 * np.ones(N)
Flim = (1.0e-6, 1.0e1)
if poster:
labelsize = posterLabelsize
ticksize = posterTicksize
legendsize = posterLegendsize
else:
labelsize = None
ticksize = None
legendsize = None
if modelPlots:
thV = 0.5 * thC
Y[0] = thV
FnuTH = grb.fluxDensity(t, nu, -1, 0, *Y, latRes=latResTH)
FnuG = grb.fluxDensity(t, nu, 0, 0, *Y, latRes=latResG)
FnuPL = grb.fluxDensity(t, nu, 4, 0, *Y, latRes=latResPL)
fig, ax = plt.subplots(1, 1, figsize=(4, 3))
ax.plot(tday, FnuTH, label='Top-Hat', lw=2)
ax.plot(tday, FnuG, label='Gaussian', lw=2)
ax.plot(tday, FnuPL, label='Power-Law', lw=2)
ax.set_xscale("log")
ax.set_yscale("log")
ax.set_ylim(*Flim)
ax.set_xlabel(r'$t$ (days)', fontsize=labelsize)
ax.set_ylabel(r'$F_\nu$ (mJy)', fontsize=labelsize)
ax.tick_params(labelsize=ticksize)
ax.legend(fontsize=legendsize)
fig.tight_layout()
figname = "lc_on.pdf"
save(fig, figname)
plt.close(fig)
thV = 2 * thC
Y[0] = thV
FnuTH = grb.fluxDensity(t, nu, -1, 0, *Y, latRes=latResTH)
FnuG = grb.fluxDensity(t, nu, 0, 0, *Y, latRes=latResG)
FnuPL = grb.fluxDensity(t, nu, 4, 0, *Y, latRes=latResPL)
fig, ax = plt.subplots(1, 1, figsize=(4, 3))
ax.plot(tday, FnuTH, label='Top-Hat', lw=2)
ax.plot(tday, FnuG, label='Gaussian', lw=2)
ax.plot(tday, FnuPL, label='Power-Law', lw=2)
ax.set_xscale("log")
ax.set_yscale("log")
ax.set_ylim(*Flim)
ax.set_xlabel(r'$t$ (days)', fontsize=labelsize)
ax.set_ylabel(r'$F_\nu$ (mJy)', fontsize=labelsize)
ax.tick_params(labelsize=ticksize)
ax.legend(fontsize=legendsize, loc='lower left')
fig.tight_layout()
figname = "lc_off1.pdf"
save(fig, figname)
plt.close(fig)
thV = 6 * thC
Y[0] = thV
FnuTH = grb.fluxDensity(t, nu, -1, 0, *Y, latRes=latResTH)
FnuG = grb.fluxDensity(t, nu, 0, 0, *Y, latRes=latResG)
FnuPL = grb.fluxDensity(t, nu, 4, 0, *Y, latRes=latResPL)
fig, ax = plt.subplots(1, 1, figsize=(4, 3))
ax.plot(tday, FnuTH, label='Top-Hat', lw=2)
ax.plot(tday, FnuG, label='Gaussian', lw=2)
ax.plot(tday, FnuPL, label='Power-Law', lw=2)
ax.set_xscale("log")
ax.set_yscale("log")
ax.set_ylim(*Flim)
ax.set_xlabel(r'$t$ (days)', fontsize=labelsize)
ax.set_ylabel(r'$F_\nu$ (mJy)', fontsize=labelsize)
ax.tick_params(labelsize=ticksize)
ax.legend(fontsize=legendsize)
fig.tight_layout()
figname = "lc_off2.pdf"
save(fig, figname)
plt.close(fig)
fig = plt.figure(figsize=(4, 4))
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
nsegs = 12
width = 0.05
cmap = cm.inferno
thWd = 180.0 * thW / np.pi
dth = thWd / nsegs
shockFront = []
shockBody = []
cols = []
for i in range(nsegs):
th1 = i * dth
th2 = (i + 1) * dth
th = 0.5 * (th1 + th2)
# col = np.exp(-0.5*th*th / (thCd*thCd))
col = 1.0 - th * th / (thWd * thWd)
p1 = patches.Wedge((0., 0.), 1., 90 - th2, 90 - th1, width=width)
p2 = patches.Wedge((0., 0.), 1., 90 + th1, 90 + th2, width=width)
shockFront.append(p1)
shockFront.append(p2)
p1 = patches.Wedge((0., 0.), 1. - width, 90 - th2, 90 - th1)
p2 = patches.Wedge((0., 0.), 1. - width, 90 + th1, 90 + th2)
shockBody.append(p1)
shockBody.append(p2)
cols.append(col)
cols.append(col)
p = collections.PatchCollection(shockFront, cmap=cmap)
p.set_array(np.array(cols))
ax.add_collection(p)
p = collections.PatchCollection(shockBody, alpha=0.3, cmap=cmap)
p.set_array(np.array(cols))
ax.add_collection(p)
ax.set_xlim(-0.5, 0.5)
ax.set_ylim(0, 1)
ax.set_aspect('equal')
figname = "jet.pdf"
save(fig, figname)
plt.close(fig)
fig = plt.figure()
lbuff = 0.16
bbuff = 0.16
rbuff = 0.15
tbuff = 0.05
space = 0.01
width2 = 0.03
width1 = 1.0 - lbuff - rbuff - space - width2
height = 1.0 - bbuff - tbuff
ax = fig.add_axes([lbuff, bbuff, width1, height])
ax2 = fig.add_axes([lbuff + width1 + space, bbuff, width2, height])
tday = np.geomspace(0.01, 1000, N)
t = grb.day2sec * tday
thV = 1.25 * thW
Y[0] = thV
dth = thW / nsegs
colList = []
for i in range(nsegs):
th1 = i * dth
th2 = (i + 1) * dth
x = float(i) / (nsegs - 1.0)
th = x * th2 + (1 - x) * th1
th_h = 0.5 * (th1 + th2)
E = E0 * np.exp(-0.5 * th * th / (thC * thC))
col = 1.0 - th_h * th_h / (thW * thW)
Y[1] = E
Y[2] = th1
Y[3] = th2
FnuC = grb.fluxDensity(t, nu, -2, 0, *Y)
ax.plot(tday, FnuC, color=cmap(col))
colList.append(cmap(col))
# myCmap = cmap.from_list('Custom cmap', colList, nsegs)
myCmap = mpl.colors.ListedColormap(colList)
thBounds = dth * np.arange(nsegs + 1)
norm = mpl.colors.BoundaryNorm(thBounds, nsegs)
cb = mpl.colorbar.ColorbarBase(ax2,
cmap=myCmap,
norm=norm,
spacing='proportional',
ticks=thBounds,
format='%.2f')
cb.set_label(r'$\theta$ (rad)', fontsize=labelsize)
Y[1] = E0
Y[2] = thC
Y[3] = thW
FnuG = grb.fluxDensity(t, nu, 0, 0, *Y)
ax.plot(tday, FnuG, color='grey', lw=2)
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_xlim(3.0e-2, 1.0e3)
ax.set_ylim(1.0e-7, 1.0e-2)
ax.set_xlabel(r'$t$ (days)', fontsize=labelsize)
ax.set_ylabel(r'$F_\nu$ (mJy)', fontsize=labelsize)
ax.tick_params(labelsize=ticksize)
ax2.tick_params(labelsize=ticksize)
# fig.tight_layout()
figname = "lc_decomp.pdf"
save(fig, figname)
plt.close(fig)
'epsilon_B': 0.01, # epsilon_B
'xi_N': 1.0, # Fraction of electrons accelerated
'd_L': 1.0e28, # Luminosity distance in cm
'z': 0.55
} # redshift
# Space time points geometrically, from 10^3 s to 10^7 s
t = np.geomspace(1.0e3, 1.0e7, 300)
# Calculate flux in a single X-ray band (all times have same frequency)
nu = np.empty(t.shape)
nu[:] = 1.0e18
# Calculate!
Fnu = grb.fluxDensity(t, nu, **Z)
# Write to a file
print("Writing lc.txt")
with open("lc.txt", 'w') as f:
f.write("# nu " + str(nu) + '\n')
f.write("# t(s) Fnu(mJy)\n")
for i in range(len(t)):
f.write("{0:.6e} {1:.6e}\n".format(t[i], Fnu[i]))
# Plot!
print("Plotting")
fig, ax = plt.subplots(1, 1)
p = 2.15
epse = 1.0e-1
epsB = 1.0e-3
ksiN = 1.0
dL = 1.23e26
Y = np.array([thV, E0, thC, thW, b, L0, q, ts, n0, p, epse, epsB, ksiN, dL])
ta = 1.0e-1 * grb.day2sec
tb = 1.0e3 * grb.day2sec
t = np.geomspace(ta, tb, num=100)
nu = 2.0e17
print("Calculating")
Fnu = grb.fluxDensity(t, nu, jetType, specType, *Y, spread=False, latRes=5)
print("Writing lc.txt")
f = open("lc.txt", 'w')
f.write("# nu " + str(nu) + '\n')
f.write("# jetType " + str(jetType) + " specType " + str(specType) + "\n")
f.write("# " + " ".join([str(y) for y in Y]) + "\n")
for i in range(len(t)):
f.write("{0:.6e} {1:.6e}\n".format(t[i], Fnu[i]))
f.close()
print("Plotting")
fig, ax = plt.subplots(1, 1)
ax.plot(t / grb.day2sec, Fnu)
