def test_run():
three=binary_with_planet(
m1=0.6897 | units.MSun,m2=0.20255 | units.MSun,m_planet=0.333 | units.MJupiter,
r1=0.6489 | units.RSun,r2=0.22623 | units.RSun,r_planet=0.754 | units.RJupiter,
ecc_binary=0.15944,P_binary=41.08| units.day,ecc_planet=0.00685,a_planet=.7048 | units.AU,
pangle_planet=0.)
convert=nbody_system.nbody_to_si(1|units.MSun,1|units.AU)
code=Huayno(convert)
code.parameters.inttype_parameter=code.inttypes.SHARED4
code.parameters.timestep_parameter=0.1
# tend=100. | units.yr
tend=100. | units.day
snapfreq=1
dt=10. | units.day
# dt=convert.to_si( 1. | nbody_system.time).in_(units.day)
code.particles.add_particles(three)
x = AdaptingVectorQuantity()
y = AdaptingVectorQuantity()
z = AdaptingVectorQuantity()
vx = AdaptingVectorQuantity()
vy = AdaptingVectorQuantity()
vz = AdaptingVectorQuantity()
x.append(code.particles.x)
y.append(code.particles.y)
z.append(code.particles.z)
vx.append(code.particles.vx)
vy.append(code.particles.vy)
vz.append(code.particles.vz)
ts=[0.]
E0=code.potential_energy+code.kinetic_energy
dE=[1.e-14]
t=0. | units.day
i=0
while(t < tend-dt/2):
i+=1
t=t+dt
if i%snapfreq==0:
print t
ts.append(t.value_in(units.day))
code.evolve_model(t)
x.append(code.particles.x)
y.append(code.particles.y)
z.append(code.particles.z)
vx.append(code.particles.vx)
vy.append(code.particles.vy)
vz.append(code.particles.vz)
E=code.potential_energy+code.kinetic_energy
dE.append(abs(((E-E0)/E0)))
code.stop()
a,eps,pangle=elements(three.total_mass(),
x[:,2],
y[:,2],
z[:,2],
vx[:,2],
vy[:,2],
vz[:,2])
x=x.value_in(units.AU)
y=y.value_in(units.AU)
a=a.value_in(units.AU)
eps=eps
print a[-1],eps[-1],pangle[-1]
f=pyplot.figure(figsize=(8,8))
pyplot.plot(x[:,0],y[:,0],'r.')
pyplot.plot(x[:,1],y[:,1],'g.')
pyplot.plot(x[:,2],y[:,2],'b.')
pyplot.xlim(-3,3)
pyplot.ylim(-3,3)
pyplot.xlabel('AU')
pyplot.savefig('three_16b.eps')
f=pyplot.figure(figsize=(8,8))
pyplot.semilogy(ts,dE,'g.')
pyplot.xlabel('time (day)')
pyplot.ylabel('dE/E0')
pyplot.savefig('three_16b_eerr.eps')
f=pyplot.figure(figsize=(8,8))
pyplot.plot(ts,a,'g.')
pyplot.xlabel('time (day)')
pyplot.ylabel('a (AU)')
pyplot.savefig('three_16b_a.eps')
f=pyplot.figure(figsize=(8,8))
pyplot.plot(ts,eps,'g.')
pyplot.xlabel('time (day)')
pyplot.ylabel('eccentricity')
pyplot.savefig('three_16b_ecc.eps')
f=pyplot.figure(figsize=(8,8))
pyplot.plot(ts,pangle,'g.')
pyplot.xlabel('time (day)')
pyplot.ylabel('long. of periapsis')
pyplot.savefig('three_16b_pangle.eps')
def main():
dots = Dots()
line = Line()
f = h5py.File(args.filename, 'r')
fig = plt.figure()
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212)
for intr in f.values():
etas = []
final_smas_p0_vq = AdaptingVectorQuantity()
final_smas_p1_vq = AdaptingVectorQuantity()
for sim in intr.values():
etas.append(sim['eta'][0])
final_smas_p0_vq.append(sim['p0/sma'][-1]| retrieve_unit(sim['p0/sma']))
final_smas_p1_vq.append(sim['p1/sma'][-1]| retrieve_unit(sim['p1/sma']))
final_smas_p0 = final_smas_p0_vq.value_in(units.AU)
final_smas_p1 = final_smas_p1_vq.value_in(units.AU)
ax1.plot(etas, final_smas_p0, marker='o', label=intr.name, picker=5)
ax2.plot(etas, final_smas_p1, marker='o', label=intr.name, picker=5)
ax1.set_xlabel('eta')
ax2.set_xlabel('eta')
ax1.set_ylabel('final sma inner [AU]')
ax2.set_ylabel('final sma outer [AU]')
dset = f.values()[0].values()[0] #first dset available
time = quantify_dset(dset['time']).value_in(units.yr)
mass = quantify_dset(dset['mass']).value_in(units.MSun)
inner_sma0 = quantify_dset(dset['p0/sma']).value_in(units.AU)
outer_sma0 = quantify_dset(dset['p1/sma']).value_in(units.AU)
inner_sma_final_adiabatic_approx = sma_analytical(inner_sma0[0], 1.244e-5, time[-1], mass[0].sum() )
outer_sma_final_adiabatic_approx = sma_analytical(outer_sma0[0], 1.244e-5, time[-1], mass[0].sum() )
ax1.axhline(inner_sma_final_adiabatic_approx, xmin=0, xmax=1, c='m', label='adiabatic approx')
ax1.legend(loc='best')
ax2.axhline(outer_sma_final_adiabatic_approx, xmin=0, xmax=1, c='m', label='adiabatic approx')
ax2.legend(loc='best')
if args.logscale:
ax1.set_xscale('log')
ax2.set_xscale('log')
if args.ylim1:
ax1.set_ylim(args.ylim1)
if args.ylim2:
ax2.set_ylim(args.ylim2)
if args.xlim1:
ax1.set_xlim(args.xlim1)
if args.xlim2:
ax2.set_xlim(args.xlim2)
def onpick(event):
print("artist:{} ind:{}".format(event.artist, event.ind))
print("button: {}".format(event.mouseevent.button))
intr = f[event.artist.get_label()]
sim = intr.values()[event.ind]
time_vq = quantify_dset(sim['time'])
time = time_vq.value_in(units.yr)
position_vq = quantify_dset(sim['position'])
position = position_vq.value_in(units.AU)
CM_position_vq = quantify_dset(sim['CM_position'])
CM_position = CM_position_vq.value_in(units.AU)
x, y = 0, 1
central_x, central_y = position[:, 0, x] - CM_position[:,x], position[:, 0, y] - CM_position[:,y]
inner_x, inner_y = position[:, 1, x] - CM_position[:,x], position[:, 1, y] - CM_position[:,y]
outer_x, outer_y = position[:, 2, x] - CM_position[:,x], position[:, 2, y] - CM_position[:,y]
mass_vq = quantify_dset(sim['mass'])
mass = mass_vq[:,0].value_in(units.MSun)
if event.mouseevent.button == 1:
mu0 = quantify_dset(sim['mass'])[0].sum()
period_vq = quantify_dset(sim['p0/period'])
period = period_vq.value_in(units.yr)
true_anomaly = sim['p0/true_anomaly'].value
argument_of_periapsis = sim['p0/argument_of_periapsis'].value
sma_vq = quantify_dset(sim['p0/sma'])
sma = sma_vq.value_in(units.AU)
sma_an_vq = sma_analytical(sma_vq[0], 1.244e-5|(units.MSun/units.yr), time_vq, mu0)
sma_an = sma_an_vq.value_in(units.AU)
eccentricity = sim['p0/eccentricity'].value
newfig = plt.figure()
newax1 = newfig.add_subplot(511)
newax2 = newfig.add_subplot(512)
newax3 = newfig.add_subplot(513)
newax4 = newfig.add_subplot(514)
newax5 = newfig.add_subplot(515)
newax1.plot(time, sma, label='numerical')
newax1.plot(time, sma_an, label='analytical_adiabatic')
newax1.set_xlabel('time [yr]')
newax1.set_ylabel('sma [AU]')
newax1.legend(loc='best')
newax2.plot(time, eccentricity)
newax2.set_xlabel('time [yr]')
newax2.set_ylabel('eccentricity ')
newax3.plot(time, true_anomaly)
newax3.set_xlabel('time [yr]')
newax3.set_ylabel('true anomaly [degrees] ')
newax4.plot(time, argument_of_periapsis)
newax4.set_xlabel('time [yr]')
newax4.set_ylabel('argument of periapsis [degrees] ')
newax5.plot(time, period)
newax5.set_xlabel('time [yr]')
newax5.set_ylabel('period')
else:
newfig = plt.figure()
newax1 = newfig.add_subplot(321)
newax2 = newfig.add_subplot(322)
newax3 = newfig.add_subplot(323)
newax4 = newfig.add_subplot(324)
newax5 = newfig.add_subplot(325)
newax6 = newfig.add_subplot(326)
mass_vq = quantify_dset(sim['mass'])
mass = mass_vq.value_in(units.MSun)
kinetic_energy = sim['kinetic_energy'].value
potential_energy = sim['potential_energy'].value
total_energy = sim['total_energy'].value
CM_velocity_vq = quantify_dset(sim['CM_velocity'])
CM_velocity_mod = CM_velocity_vq.lengths().value_in(units.km/units.hour)
walltime = sim['walltime'].value - sim['walltime'][0]
newax1.plot(time, mass)
newax1.set_ylabel('central mass [MSun]')
newax1.set_xlabel('time [yr]')
newax2.plot(time, kinetic_energy, label='kinetic', **line.red)
newax2.plot(time, potential_energy,label='potential', **line.orange)
newax2.plot(time, total_energy, label='total', **line.white)
newax2.set_xlabel('time [yr]')
newax2.legend()
newax3.plot(central_x, central_y, **dots.white)
newax3.plot(inner_x, inner_y, **dots.red)
newax3.plot(outer_x, outer_y, **dots.yellow)
newax3.set_xlabel('x [AU]')
newax3.set_ylabel('y [AU]')
newax4.plot(CM_position[:, x], CM_position[:, y] )
newax4.set_xlim(-5, 5)
newax4.set_xlabel('CM position x [AU]')
newax4.set_ylabel('CM position y [AU]')
newax5.plot(time, CM_velocity_mod)
newax5.set_ylim(20, 30)
newax5.set_xlabel('time [yr]')
newax5.set_ylabel('CM velocity [km/hour]')
newax6.plot(time, walltime)
newax6.set_xlabel('time [yr]')
newax6.set_ylabel('walltime [s]')
newfig.show()
fig.canvas.mpl_connect('pick_event', onpick)
plt.show()
def main():
dots = Dots()
line = Line()
f = h5py.File(args.filename, 'r')
fig = plt.figure()
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212)
for intr in f.values():
timesteps_vq = AdaptingVectorQuantity()
final_smas_p0_vq = AdaptingVectorQuantity()
final_smas_p1_vq = AdaptingVectorQuantity()
for sim in intr.values():
timesteps_vq.append(sim['timestep'][0] | retrieve_unit(sim['timestep']))
final_smas_p0_vq.append(sim['p0/sma'][-1]| retrieve_unit(sim['p0/sma']))
final_smas_p1_vq.append(sim['p1/sma'][-1]| retrieve_unit(sim['p1/sma']))
timesteps = timesteps_vq.value_in(units.yr)
final_smas_p0 = final_smas_p0_vq.value_in(units.AU)
final_smas_p1 = final_smas_p1_vq.value_in(units.AU)
ax1.plot(timesteps, final_smas_p0, marker='o', label=intr.name, picker=5)
ax2.plot(timesteps, final_smas_p1, marker='o', label=intr.name, picker=5)
ax1.set_xlabel('Mass update interval [yr]')
ax2.set_xlabel('Mass update interval [yr]')
ax1.set_ylabel('final sma inner [AU]')
ax2.set_ylabel('final sma outer [AU]')
dset = f.values()[0].values()[0] #first dset available
time = quantify_dset(dset['time']).value_in(units.yr)
mass = quantify_dset(dset['mass']).value_in(units.MSun)
inner_sma0 = quantify_dset(dset['p0/sma']).value_in(units.AU)
outer_sma0 = quantify_dset(dset['p1/sma']).value_in(units.AU)
inner_sma_final_adiabatic_approx = sma_analytical(inner_sma0[0], 1.244e-5, time[-1], mass[0].sum() )
outer_sma_final_adiabatic_approx = sma_analytical(outer_sma0[0], 1.244e-5, time[-1], mass[0].sum() )
ax1.axhline(inner_sma_final_adiabatic_approx, xmin=0, xmax=1, c='m', label='adiabatic approx')
ax1.legend(loc='best')
ax2.axhline(outer_sma_final_adiabatic_approx, xmin=0, xmax=1, c='m', label='adiabatic approx')
ax2.legend(loc='best')
if args.logscale:
ax1.set_xscale('log')
ax2.set_xscale('log')
if args.ylim1:
ax1.set_ylim(args.ylim1)
if args.ylim2:
ax2.set_ylim(args.ylim2)
if args.xlim1:
ax1.set_xlim(args.xlim1)
if args.xlim2:
ax2.set_xlim(args.xlim2)
def onpick(event):
print("artist:{} ind:{}".format(event.artist, event.ind))
print("button: {}".format(event.mouseevent.button))
intr = f[event.artist.get_label()]
sim = intr.values()[event.ind]
time_vq = quantify_dset(sim['time'])
time = time_vq.value_in(units.yr)
position_vq = quantify_dset(sim['position'])
position = position_vq.value_in(units.AU)
CM_position_vq = quantify_dset(sim['CM_position'])
CM_position = CM_position_vq.value_in(units.AU)
x, y = 0, 1
central_x, central_y = position[:, 0, x] - CM_position[:,x], position[:, 0, y] - CM_position[:,y]
inner_x, inner_y = position[:, 1, x] - CM_position[:,x], position[:, 1, y] - CM_position[:,y]
outer_x, outer_y = position[:, 2, x] - CM_position[:,x], position[:, 2, y] - CM_position[:,y]
mass_vq = quantify_dset(sim['mass'])
mass = mass_vq[:,0].value_in(units.MSun)
if event.mouseevent.button == 1:
mu0 = quantify_dset(sim['mass'])[0].sum()
period_vq = quantify_dset(sim['p0/period'])
period = period_vq.value_in(units.yr)
true_anomaly = sim['p0/true_anomaly'].value
argument_of_periapsis = sim['p0/argument_of_periapsis'].value
sma_vq = quantify_dset(sim['p0/sma'])
sma = sma_vq.value_in(units.AU)
sma_an_vq = sma_analytical(sma_vq[0], 1.244e-5|(units.MSun/units.yr), time_vq, mu0)
sma_an = sma_an_vq.value_in(units.AU)
eccentricity = sim['p0/eccentricity'].value
newfig = plt.figure()
newax1 = newfig.add_subplot(511)
newax2 = newfig.add_subplot(512)
newax3 = newfig.add_subplot(513)
newax4 = newfig.add_subplot(514)
newax5 = newfig.add_subplot(515)
newax1.plot(time, sma, label='numerical')
newax1.plot(time, sma_an, label='analytical_adiabatic')
newax1.set_xlabel('time [yr]')
newax1.set_ylabel('sma [AU]')
newax1.legend(loc='best')
newax2.plot(time, eccentricity)
newax2.set_xlabel('time [yr]')
newax2.set_ylabel('eccentricity ')
newax3.plot(time, true_anomaly)
newax3.set_xlabel('time [yr]')
newax3.set_ylabel('true anomaly [degrees] ')
newax4.plot(time, argument_of_periapsis)
newax4.set_xlabel('time [yr]')
newax4.set_ylabel('argument of periapsis [degrees] ')
newax5.plot(time, period)
newax5.set_xlabel('time [yr]')
newax5.set_ylabel('period')
else:
newfig = plt.figure()
newax1 = newfig.add_subplot(321)
newax2 = newfig.add_subplot(322)
newax3 = newfig.add_subplot(323)
newax4 = newfig.add_subplot(324)
newax5 = newfig.add_subplot(325)
newax6 = newfig.add_subplot(326)
mass_vq = quantify_dset(sim['mass'])
mass = mass_vq.value_in(units.MSun)
kinetic_energy = sim['kinetic_energy'].value
potential_energy = sim['potential_energy'].value
total_energy = sim['total_energy'].value
CM_velocity_vq = quantify_dset(sim['CM_velocity'])
CM_velocity_mod = CM_velocity_vq.lengths().value_in(units.km/units.hour)
walltime = sim['walltime'].value - sim['walltime'][0]
newax1.plot(time, mass)
newax1.set_ylabel('central mass [MSun]')
newax1.set_xlabel('time [yr]')
newax2.plot(time, kinetic_energy, label='kinetic', **line.red)
newax2.plot(time, potential_energy,label='potential', **line.orange)
newax2.plot(time, total_energy, label='total', **line.white)
newax2.set_xlabel('time [yr]')
newax2.legend()
newax3.plot(central_x, central_y, **dots.white)
newax3.plot(inner_x, inner_y, **dots.red)
newax3.plot(outer_x, outer_y, **dots.yellow)
newax3.set_xlabel('x [AU]')
newax3.set_ylabel('y [AU]')
newax4.plot(CM_position[:, x], CM_position[:, y] )
newax4.set_xlim(-5, 5)
newax4.set_xlabel('CM position x [AU]')
newax4.set_ylabel('CM position y [AU]')
newax5.plot(time, CM_velocity_mod)
newax5.set_ylim(20, 30)
newax5.set_xlabel('time [yr]')
newax5.set_ylabel('CM velocity [km/hour]')
newax6.plot(time, walltime)
newax6.set_xlabel('time [yr]')
newax6.set_ylabel('walltime [s]')
newfig.show()
fig.canvas.mpl_connect('pick_event', onpick)
plt.show()
def test_run():
three = binary_with_planet(m1=0.6897 | units.MSun,
m2=0.20255 | units.MSun,
m_planet=0.333 | units.MJupiter,
r1=0.6489 | units.RSun,
r2=0.22623 | units.RSun,
r_planet=0.754 | units.RJupiter,
ecc_binary=0.15944,
P_binary=41.08 | units.day,
ecc_planet=0.00685,
a_planet=.7048 | units.AU,
pangle_planet=0.)
convert = nbody_system.nbody_to_si(1 | units.MSun, 1 | units.AU)
code = Huayno(convert)
code.parameters.inttype_parameter = code.inttypes.SHARED4
code.parameters.timestep_parameter = 0.1
# tend=100. | units.yr
tend = 100. | units.day
snapfreq = 1
dt = 10. | units.day
# dt=convert.to_si( 1. | nbody_system.time).in_(units.day)
code.particles.add_particles(three)
x = AdaptingVectorQuantity()
y = AdaptingVectorQuantity()
z = AdaptingVectorQuantity()
vx = AdaptingVectorQuantity()
vy = AdaptingVectorQuantity()
vz = AdaptingVectorQuantity()
x.append(code.particles.x)
y.append(code.particles.y)
z.append(code.particles.z)
vx.append(code.particles.vx)
vy.append(code.particles.vy)
vz.append(code.particles.vz)
ts = [0.]
E0 = code.potential_energy + code.kinetic_energy
dE = [1.e-14]
t = 0. | units.day
i = 0
while (t < tend - dt / 2):
i += 1
t = t + dt
if i % snapfreq == 0:
print t
ts.append(t.value_in(units.day))
code.evolve_model(t)
x.append(code.particles.x)
y.append(code.particles.y)
z.append(code.particles.z)
vx.append(code.particles.vx)
vy.append(code.particles.vy)
vz.append(code.particles.vz)
E = code.potential_energy + code.kinetic_energy
dE.append(abs(((E - E0) / E0)))
code.stop()
a, eps, pangle = elements(three.total_mass(), x[:, 2], y[:, 2], z[:, 2],
vx[:, 2], vy[:, 2], vz[:, 2])
x = x.value_in(units.AU)
y = y.value_in(units.AU)
a = a.value_in(units.AU)
eps = eps
print a[-1], eps[-1], pangle[-1]
f = pyplot.figure(figsize=(8, 8))
pyplot.plot(x[:, 0], y[:, 0], 'r.')
pyplot.plot(x[:, 1], y[:, 1], 'g.')
pyplot.plot(x[:, 2], y[:, 2], 'b.')
pyplot.xlim(-3, 3)
pyplot.ylim(-3, 3)
pyplot.xlabel('AU')
pyplot.savefig('three_16b.eps')
f = pyplot.figure(figsize=(8, 8))
pyplot.semilogy(ts, dE, 'g.')
pyplot.xlabel('time (day)')
pyplot.ylabel('dE/E0')
pyplot.savefig('three_16b_eerr.eps')
f = pyplot.figure(figsize=(8, 8))
pyplot.plot(ts, a, 'g.')
pyplot.xlabel('time (day)')
pyplot.ylabel('a (AU)')
pyplot.savefig('three_16b_a.eps')
f = pyplot.figure(figsize=(8, 8))
pyplot.plot(ts, eps, 'g.')
pyplot.xlabel('time (day)')
pyplot.ylabel('eccentricity')
pyplot.savefig('three_16b_ecc.eps')
f = pyplot.figure(figsize=(8, 8))
pyplot.plot(ts, pangle, 'g.')
pyplot.xlabel('time (day)')
pyplot.ylabel('long. of periapsis')
pyplot.savefig('three_16b_pangle.eps')
