def main():
f = h5py.File(args.filename, 'r')
linecycler_1 = cycle(["-"])
linecycler_2 = cycle(["-"])
cmap = cm.jet
for intr in f.values():
fig = plt.figure()
ax1 = fig.add_subplot(131)
ax2 = fig.add_subplot(132)
ax3 = fig.add_subplot(133)
ax1.set_color_cycle(cmap(numpy.linspace(0, 0.95, 10)))
ax2.set_color_cycle(cmap(numpy.linspace(0, 0.95, 10)))
ax3.set_color_cycle(cmap(numpy.linspace(0, 0.95, 10)))
for sim in intr.values():
time = (quantify_dset(sim['time'])).value_in(units.yr)
eccentricity = sim['p0/eccentricity'].value
sma = (quantify_dset(sim['p0/sma'])).value_in(units.AU)
ml_index = sim['p0/massloss_index'].value
sma_clipped = sma[numpy.where(sma > 0)]
time_clipped = time[numpy.where(sma > 0)]
ax1.plot(time,
eccentricity,
lw=1,
ls=next(linecycler_1),
label="e0= " + str(round(eccentricity[0], 1)))
ax2.plot(time_clipped,
sma_clipped,
lw=1,
ls=next(linecycler_2),
label="e0= " + str(round(eccentricity[0], 1)))
ax3.plot(time,
ml_index,
lw=1,
ls=next(linecycler_2),
label="e0= " + str(round(eccentricity[0], 1)))
adiabat = sma_analytical(sma[0], 5e-5, time_clipped, 1)
ax2.plot(time_clipped, adiabat, lw=2, c='k', label='adiabat')
ax1.set_xlabel('time [yr]')
ax1.set_ylabel('eccentricity ')
ax1.set_xlim(0, time[-1])
ax1.set_ylim(0, 1.3)
ax1.grid(True)
ax1.legend(loc='upper left', ncol=2)
ax2.set_xlabel('time [yr]')
ax2.set_ylabel('semi-major-axis [AU]')
ax2.set_xlim(0, time[-1])
ax2.grid(True)
ax3.set_xlabel('time [yr]')
ax3.set_ylabel('massloss_index')
ax3.set_xlim(0, time[-1])
ax3.set_yscale('log')
ax3.grid(True)
if args.ylim2:
ax2.set_ylim(args.ylim[0], args.ylim[1])
else:
ax2.set_ylim(sma_clipped.min(), sma_clipped.max())
ax2.legend(loc='upper left', ncol=1)
plt.show()
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()
def main():
f = h5py.File(args.filename, 'r')
linecycler_1 = cycle(["-"])
linecycler_2 = cycle(["-"])
cmap = cm.jet
for intr in f.values():
fig = plt.figure()
ax1 = fig.add_subplot(131)
ax2 = fig.add_subplot(132)
ax3 = fig.add_subplot(133)
ax1.set_color_cycle(cmap(numpy.linspace(0, 0.95, 10)))
ax2.set_color_cycle(cmap(numpy.linspace(0, 0.95, 10)))
ax3.set_color_cycle(cmap(numpy.linspace(0, 0.95, 10)))
for sim in intr.values():
time = (quantify_dset(sim['time'])).value_in(units.yr)
eccentricity = sim['p0/eccentricity'].value
sma = (quantify_dset(sim['p0/sma'])).value_in(units.AU)
ml_index = sim['p0/massloss_index'].value
sma_clipped = sma[numpy.where(sma>0)]
time_clipped = time[numpy.where(sma>0)]
ax1.plot(time, eccentricity, lw=1, ls=next(linecycler_1),
label="e0= "+str(round(eccentricity[0], 1)))
ax2.plot(time_clipped, sma_clipped, lw=1, ls=next(linecycler_2),
label="e0= "+str(round(eccentricity[0], 1)))
ax3.plot(time, ml_index, lw=1, ls=next(linecycler_2),
label="e0= "+str(round(eccentricity[0], 1)))
adiabat = sma_analytical(sma[0], 5e-5, time_clipped, 1)
ax2.plot(time_clipped, adiabat, lw=2, c='k', label='adiabat')
ax1.set_xlabel('time [yr]')
ax1.set_ylabel('eccentricity ')
ax1.set_xlim(0, time[-1])
ax1.set_ylim(0, 1.3)
ax1.grid(True)
ax1.legend(loc='upper left', ncol=2)
ax2.set_xlabel('time [yr]')
ax2.set_ylabel('semi-major-axis [AU]')
ax2.set_xlim(0, time[-1])
ax2.grid(True)
ax3.set_xlabel('time [yr]')
ax3.set_ylabel('massloss_index')
ax3.set_xlim(0, time[-1])
ax3.set_yscale('log')
ax3.grid(True)
if args.ylim2:
ax2.set_ylim(args.ylim[0], args.ylim[1])
else:
ax2.set_ylim(sma_clipped.min(), sma_clipped.max())
ax2.legend(loc='upper left', ncol=1)
plt.show()
def binaries(simdata, **kwargs):
f = simdata.hdf5file
figdir = simdata.figdir
targetdir = directory+figdir
if not os.path.exists(targetdir):
os.makedirs(targetdir)
if 'integrator' in kwargs:
integrator = kwargs['integrator']
else:
integrator = simdata.available_integrators()[0]
centralmass = float(simdata.parameters['M'].split()[0])
interval = float(simdata.parameters['interval'].split()[0])
datapoints = int(simdata.parameters['datapoints'])
mdot = float(simdata.parameters['mdot'].split()[0])
sma0 = float(simdata.parameters['sma'].split()[0])
print("sma0 {} AU".format(sma0))
firstavailablesim = f[integrator].values()[0]
time = quantify_dset(firstavailablesim['time']).value_in(units.yr)
totalmass = quantify_dset(firstavailablesim['mass']).lengths().value_in(units.MSun)
smas = []
eccentricities = []
ecc_labels = []
smas_ad = []
ml_indices = []
arguments_of_periapsis = []
angular_momenta = []
true_anomalies = []
periods = []
for sim in f[integrator].values():
sma = quantify_dset(sim['p0/sma']).value_in(units.AU)
smas.append(sma)
smas_ad.append(sma_analytical(sma0, mdot, time, centralmass))
ml_indices.append(sim['p0/massloss_index'].value)
eccentricities.append(sim['p0/eccentricity'].value)
ecc_labels.append("e0= "+str(round(sim['p0/eccentricity'][0], 1)) )
arguments_of_periapsis.append(sim['p0/argument_of_periapsis'].value)
periods.append(quantify_dset(sim['p0/period']).value_in(units.yr))
angular_momenta.append(quantify_dset(sim['angular_momentum']).number)
true_anomalies.append(sim['p0/true_anomaly'].value)
cmap = cm.jet
savefigargs = dict(bbox_inches='tight', dpi=150)
def sma_vs_time():
imgname = 'sma_vs_time.png'
fig = plt.figure(figsize=(32,8))
ax = fig.add_subplot(111)
ax.set_color_cycle(cmap(numpy.linspace(0, 0.95, 10)))
for sma, lbl in zip(smas, ecc_labels):
ax.plot(time, sma, lw=1, ls='-', label=lbl)
ax.set_xlim(time[0], time[-1])
ax.set_xlabel('time [yr]')
ax.set_ylabel('sma [AU]')
plt.savefig(targetdir+imgname, **savefigargs )
plt.close()
def sma_vs_adiabaticity():
imgname = 'sma_vs_adiabaticity.png'
fig = plt.figure(figsize=(32,8))
ax = fig.add_subplot(111)
ax.set_color_cycle(cmap(numpy.linspace(0, 0.95, 10)))
for sma, ml_index, lbl in zip(smas, ml_indices, ecc_labels):
ax.plot(ml_index, sma, lw=1, ls='-', label=lbl)
ax.set_xlim(ml_index[0], ml_index[-1])
ax.set_xlabel('massloss-index')
ax.set_ylabel('sma [AU]')
plt.savefig(targetdir+imgname, **savefigargs)
plt.close()
def sma_error_vs_time():
imgname = 'sma_error_vs_time.png'
fig = plt.figure(figsize=(32,8))
ax = fig.add_subplot(111)
ax.set_color_cycle(cmap(numpy.linspace(0, 0.95, 10)))
for sma, sma_ad, lbl in zip(smas, smas_ad, ecc_labels):
ax.plot(time, (sma-sma_ad)/sma_ad, lw=1, ls='-', label=lbl)
ax.set_xlim(time[0], time[-1])
ax.set_xlabel('time [yr]')
ax.set_ylabel('(sma-sma_ad)/sma_ad ')
plt.savefig(targetdir+imgname, **savefigargs)
plt.close()
def sma_error_vs_adiabaticity():
imgname = 'sma_error_vs_adiabaticity.png'
fig = plt.figure(figsize=(32,8))
ax = fig.add_subplot(111)
ax.set_color_cycle(cmap(numpy.linspace(0, 0.95, 10)))
for ml_index, sma_ad, lbl in zip(ml_indices, smas_ad, ecc_labels):
ax.plot(ml_index, (sma-sma_ad)/sma_ad, lw=1, ls='-', label=lbl)
ax.set_xlim(ml_index[0], ml_index[-1])
ax.set_xlabel('massloss-index')
ax.set_ylabel('(sma-sma_ad)/sma_ad ')
plt.savefig(targetdir+imgname, **savefigargs)
plt.close()
def eccentricity_vs_time():
imgname = 'eccentricity_vs_time.png'
fig = plt.figure(figsize=(32,16))
ax = fig.add_subplot(111)
ax.set_color_cycle(cmap(numpy.linspace(0, 0.95, 10)))
for ecc, lbl in zip(eccentricities, ecc_labels):
ax.plot(time, ecc, lw=2, ls='-', label=lbl)
ax.set_xlim(time[0], time[-1])
ax.set_xlabel('time [yr]')
ax.set_ylabel('eccentricity')
plt.savefig(targetdir+imgname, **savefigargs)
plt.close()
def eccentricity_vs_adiabaticity():
imgname = 'eccentricity_vs_adiabaticity.png'
fig = plt.figure(figsize=(32,16))
ax = fig.add_subplot(111)
ax.set_color_cycle(cmap(numpy.linspace(0, 0.95, 10)))
for ecc, ml_index, lbl in zip(eccentricities, ml_indices, ecc_labels):
ax.plot(ml_index, ecc, lw=2, ls='-', label=lbl)
ax.set_xlim(ml_index[0], ml_index[-1])
ax.set_xlabel('massloss-index')
ax.set_ylabel('eccentricity')
plt.savefig(targetdir+imgname, **savefigargs)
plt.close()
def sma_error_vs_eccentricity_error():
imgname = 'sma_error_vs_eccentricity_error.png'
fig = plt.figure(figsize=(32,8))
ax = fig.add_subplot(111)
ax.set_color_cycle(cmap(numpy.linspace(0, 0.95, 10)))
for sma, sma_ad, ecc, lbl in zip(smas, smas_ad, eccentricities, ecc_labels):
ax.plot((ecc-ecc[0])/ecc[0], (sma-sma_ad)/sma_ad, lw=1, ls='-', label=lbl)
ax.set_xlabel('(ecc-ecc0)/ecc0')
ax.set_ylabel('(sma-sma_ad)/sma_ad ')
plt.savefig(targetdir+imgname, **savefigargs)
plt.close()
def true_anomaly_vs_time():
imgname = 'true_anomaly_vs_time.png'
fig = plt.figure(figsize=(30, 10))
colorcycle = cycle(cmap(numpy.linspace(0, 0.95, 10)))
position_generator = axis_position(10, 1)
totalsims = len(f[integrator].values())
for i, true_anomaly, lbl in zip(range(totalsims), true_anomalies, ecc_labels):
ax = fig.add_subplot(*next(position_generator))
ax.plot(time, true_anomaly, lw=2, ls='-', c=next(colorcycle), label=lbl)
if i+1 != totalsims:
ax.set_xticklabels([])
else:
ax.set_xticks(numpy.arange(1000, 15000, 1000))
ax.set_xlim(time[0], time[-1])
ax.set_ylim(0, 360)
ax.set_yticks([90, 180, 270])
ax.set_xlabel('time [yr]')
ax.set_ylabel('f [degrees]')
plt.subplots_adjust(hspace=0.001)
plt.savefig(targetdir+imgname, **savefigargs)
plt.close()
def argument_of_periapsis_vs_time():
imgname = 'argument_of_periapsis_vs_time.png'
fig = plt.figure(figsize=(20, 20))
colorcycle = cycle(cmap(numpy.linspace(0, 0.95, 10)))
position_generator = axis_position(3, 4)
for w, lbl in zip(arguments_of_periapsis, ecc_labels):
ax = fig.add_subplot(*next(position_generator), polar=True)
ax.plot(w, time, lw=1, ls='-', c=next(colorcycle), label=lbl)
ax.set_rgrids(numpy.array([5000,10000,15000]), angle=270)
plt.savefig(targetdir+imgname, **savefigargs)
plt.close()
def adiabaticity_vs_time():
imgname = 'adiabaticity_vs_time.png'
fig = plt.figure(figsize=(8, 16))
ax1 = fig.add_subplot(311)
ax2 = fig.add_subplot(312)
ax3 = fig.add_subplot(313)
colorcycle1 = cycle(cmap(numpy.linspace(0, 0.95, 10)))
colorcycle2 = cycle(cmap(numpy.linspace(0, 0.95, 10)))
colorcycle3 = cycle(cmap(numpy.linspace(0, 0.95, 10)))
for ml_index, period, lbl in zip(ml_indices, periods, ecc_labels):
ax1.plot(time, mdot/totalmass, lw=1, ls='-', c=next(colorcycle1), label=lbl)
ax2.plot(time, period, lw=1, ls='-', c=next(colorcycle2), label=lbl)
ax3.plot(time, ml_index, lw=1, ls='-', c=next(colorcycle3), label=lbl)
ax3.set_yscale('log')
ax1.set_xlabel('time [yr]')
ax2.set_xlabel('time [yr]')
ax3.set_xlabel('time [yr]')
plt.subplots_adjust(hspace=0.001)
plt.savefig(targetdir+imgname, **savefigargs)
plt.close()
def angular_momentum_error_vs_time():
imgname = 'angular_momentum_error_vs_time.png'
fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(111)
colorcycle = cycle(cmap(numpy.linspace(0, 0.95, 10)))
for h, lbl in zip(angular_momenta, ecc_labels):
h_error = (h[0] - h)/h[0]
ax.plot(time, h_error, lw=1, ls='-', c=next(colorcycle), label=lbl)
ax.set_xlim(time[0], time[-1])
plt.savefig(targetdir+imgname, **savefigargs)
plt.close()
def angular_momentum_error_vs_adiabaticity():
imgname = 'angular_momentum_error_vs_adiabaticity.png'
fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(111)
colorcycle = cycle(cmap(numpy.linspace(0, 0.95, 10)))
for h, ml_index, lbl in zip(angular_momenta, ml_indices, ecc_labels):
h_error = (h[0] - h)/h[0]
ax.plot(ml_index, h_error, lw=1, ls='-', c=next(colorcycle), label=lbl)
ax.set_xlim(ml_index[0], ml_index[-1])
plt.savefig(targetdir+imgname, **savefigargs)
plt.close()
sma_vs_time()
sma_vs_adiabaticity()
sma_error_vs_time()
sma_error_vs_adiabaticity()
eccentricity_vs_time()
eccentricity_vs_adiabaticity()
sma_error_vs_eccentricity_error()
true_anomaly_vs_time()
argument_of_periapsis_vs_time()
adiabaticity_vs_time()
angular_momentum_error_vs_time()
angular_momentum_error_vs_adiabaticity()
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 create_mpeg(sim):
time_vq = quantify_dset(sim['time'])
time = time_vq.value_in(units.yr)
if args.frames is None:
frames = range(1, len(time), 5)
else:
frames = range(args.frames[0], args.frames[1], args.frames[2])
pbar = pb.ProgressBar(widgets=drawwidget(args.sim),
maxval=frames[-1]).start()
position_vq = quantify_dset(sim['position'])
position = position_vq.value_in(units.AU)
seperation_vq = (position_vq[:, 1, :] - position_vq[:, 2, :]).lengths()
seperation = seperation_vq.value_in(units.AU)
CM_position_vq = quantify_dset(sim['CM_position'])
CM_position = CM_position_vq.value_in(units.AU)
period_inner_vq = quantify_dset(sim['p0/period'])
period_inner = period_inner_vq.value_in(units.yr)
mass_vq = quantify_dset(sim['mass'])
mass = mass_vq[:,0].value_in(units.MSun)
kinetic_energy = sim['kinetic_energy'].value
potential_energy = sim['potential_energy'].value
total_energy = sim['total_energy'].value
mu0 = quantify_dset(sim['mass'])[0].sum()
sma_inner_vq = quantify_dset(sim['p0/sma'])
sma_inner = sma_inner_vq.value_in(units.AU)
sma_outer_vq = quantify_dset(sim['p1/sma'])
sma_outer = sma_outer_vq.value_in(units.AU)
eccentricity_inner = sim['p0/eccentricity'].value
eccentricity_outer = sim['p1/eccentricity'].value
true_anomaly_inner = sim['p0/true_anomaly'].value
true_anomaly_outer = sim['p1/true_anomaly'].value
sma_inner_analytical_vq = sma_analytical(sma_inner_vq[0],
1.244e-5|(units.MSun/units.yr), time_vq, mu0)
sma_inner_analytical = sma_inner_analytical_vq.value_in(units.AU)
sma_outer_analytical_vq = sma_analytical(sma_outer_vq[0],
1.244e-5|(units.MSun/units.yr), time_vq, mu0)
sma_outer_analytical = sma_outer_analytical_vq.value_in(units.AU)
x, y = 0, 1
central_x = position[:, 0, x] - CM_position[:,x]
central_y = position[:, 0, y] - CM_position[:,y]
inner_x = position[:, 1, x] - CM_position[:,x]
inner_y = position[:, 1, y] - CM_position[:,y]
outer_x = position[:, 2, x] - CM_position[:,x]
outer_y = position[:, 2, y] - CM_position[:,y]
nr_datapoints = len(time)
yrs_per_datapoint = time[-1]/nr_datapoints
fig = plt.figure(figsize=(22, 12))
gs = gridspec.GridSpec(4, 4)
ax1 = fig.add_subplot(gs[:2, :2])
ax2 = fig.add_subplot(gs[2,0])
ax3 = fig.add_subplot(gs[3,0])
ax4 = fig.add_subplot(gs[2,1])
ax5 = fig.add_subplot(gs[3,1])
ax6 = fig.add_subplot(gs[0,2:])
ax7 = fig.add_subplot(gs[1,2:])
ax8 = fig.add_subplot(gs[2,2:])
ax9 = fig.add_subplot(gs[3,2:])
orange = '#FF6500'
green = '#07D100'
central_loc, = ax1.plot([], [], c='y', ls="o", mfc="y", mec="y", marker='o',
ms=6)
inner_loc, = ax1.plot([], [], c='w', ls="o", mfc="w", mec="w", marker='o',
ms=4)
outer_loc, = ax1.plot([], [], c='w', ls="o", mfc="w", mec="w", marker='o',
ms=4)
inner_artist, = ax1.plot([], [], c=green, ls="-", lw=2, alpha=0.8)
outer_artist, = ax1.plot([], [], c='r', ls="-", lw=2, alpha=0.8)
sma0_ana_artist, = ax2.plot([], [], c='w', ls="-", lw=2)
sma1_ana_artist, = ax3.plot([], [], c='w', ls="-", lw=2)
sma0_artist, = ax2.plot([], [], c=green, ls="-", lw=2)
sma1_artist, = ax3.plot([], [], c='r', ls="-", lw=2)
ecc0_artist, = ax4.plot([], [], c=green, ls="-", lw=2)
ecc1_artist, = ax5.plot([], [], c='r', ls="-", lw=2)
ecc0_ana_artist, = ax4.plot([], [], c='w', ls="-", lw=2)
ecc1_ana_artist, = ax5.plot([], [], c='w', ls="-", lw=2)
true_anomaly0_artist, = ax6.plot([], [], c=green, ls="-", lw=2)
true_anomaly1_artist, = ax6.plot([], [], c='r', ls="-", lw=2)
seperation_artist, = ax7.plot([], [], c=orange, ls="-", lw=2)
K_artist, = ax8.plot([], [], c='r', ls="-", lw=2)
U_artist, = ax8.plot([], [], c=green, ls="-", lw=2)
E_artist, = ax8.plot([], [], c=orange, ls="-", lw=2)
mass_artist, = ax9.plot([], [], c=orange, ls="-", lw=2)
ax2.set_xlabel('time [yr]')
ax2.set_ylabel('sma [AU]')
ax3.set_xlabel('time [yr]')
ax3.set_ylabel('sma [AU]')
ax4.set_xlabel('time [yr]')
ax4.set_ylabel('eccentricity ')
ax5.set_xlabel('time [yr]')
ax5.set_ylabel('eccentricity ')
ax6.set_xlabel('time [yr]')
ax6.set_ylabel('true anomaly [degrees] ')
ax7.set_xlabel('time [yr]')
ax7.set_ylabel('seperation [AU]')
ax8.set_xlabel('time [yr]')
ax8.set_ylabel('energies')
ax9.set_ylabel('central mass [MSun]')
ax9.set_xlabel('time [yr]')
ax1.set_xlim(-200, 150)
ax1.set_ylim(-250, 100)
def update_fig(i):
t = time[i]
lag = int(20*period_inner[i]/(yrs_per_datapoint))
orbitlag = int(0.9*period_inner[i]/(yrs_per_datapoint))
if i <= lag :
tbegin = time[0]
time_range = time[0:i]
inner_artist.set_data(inner_x[0:i], inner_y[0:i])
outer_artist.set_data(outer_x[0:i], outer_y[0:i])
sma0_artist.set_data(time[0:i], sma_inner[0:i])
sma1_artist.set_data(time[0:i], sma_outer[0:i])
sma0_ana_artist.set_data(time[0:i], sma_inner_analytical[0:i])
sma1_ana_artist.set_data(time[0:i], sma_outer_analytical[0:i])
ecc0_artist.set_data(time[0:i], eccentricity_inner[0:i])
ecc1_artist.set_data(time[0:i], eccentricity_outer[0:i])
true_anomaly0_artist.set_data(time[0:i], true_anomaly_inner[0:i])
true_anomaly1_artist.set_data(time[0:i], true_anomaly_outer[0:i])
K_artist.set_data(time[0:i], kinetic_energy[0:i])
U_artist.set_data(time[0:i], potential_energy[0:i])
E_artist.set_data(time[0:i], total_energy[0:i])
mass_artist.set_data(time[0:i], mass[0:i])
seperation_artist.set_data(time[0:i], seperation[0:i])
else:
tbegin = time[i-lag]
time_range = time[i-lag:i]
inner_artist.set_data(inner_x[i-orbitlag:i], inner_y[i-orbitlag:i])
outer_artist.set_data(outer_x[i-orbitlag:i], outer_y[i-orbitlag:i])
sma0_artist.set_data(time_range, sma_inner[i-lag:i])
sma1_artist.set_data(time_range, sma_outer[i-lag:i])
sma0_ana_artist.set_data(time_range, sma_inner_analytical[i-lag:i])
sma1_ana_artist.set_data(time_range, sma_outer_analytical[i-lag:i])
ecc0_artist.set_data(time_range, eccentricity_inner[i-lag:i])
ecc1_artist.set_data(time_range, eccentricity_outer[i-lag:i])
true_anomaly0_artist.set_data(time_range, true_anomaly_inner[i-lag:i])
true_anomaly1_artist.set_data(time_range, true_anomaly_outer[i-lag:i])
K_artist.set_data(time_range, kinetic_energy[i-lag:i])
U_artist.set_data(time_range, potential_energy[i-lag:i])
E_artist.set_data(time_range, total_energy[i-lag:i])
mass_artist.set_data(time_range, mass[i-lag:i])
seperation_artist.set_data(time_range, seperation[i-lag:i])
try:
central_loc.set_data(central_x[i-1], central_y[i-1])
inner_loc.set_data(inner_x[i-1], inner_y[i-1])
outer_loc.set_data(outer_x[i-1], outer_y[i-1])
except IndexError:
pass
ax2.set_ylim(sma_inner_analytical[i]*0.99, sma_inner_analytical[i]*1.01)
ax3.set_ylim(sma_outer_analytical[i]*0.99, sma_outer_analytical[i]*1.01)
ax4.set_ylim(eccentricity_inner[i]- 0.01, eccentricity_inner[i] + 0.01)
ax5.set_ylim(eccentricity_outer[i]- 0.01, eccentricity_outer[i] + 0.01)
ax6.set_ylim(0, 360)
ax7.set_ylim(5, 250)
ax8.set_ylim(-2.5e36, 2e36)
ax9.set_ylim(mass[i]*0.99, mass[i]*1.01)
ax2.set_xlim(tbegin, t)
ax3.set_xlim(tbegin, t)
ax4.set_xlim(tbegin, t)
ax5.set_xlim(tbegin, t)
ax6.set_xlim(tbegin, t)
ax7.set_xlim(tbegin, t)
ax8.set_xlim(tbegin, t)
ax9.set_xlim(tbegin, t)
pbar.update(i)
ffmpeg = animation.FFMpegWriter(fps=30, codec='mpeg4',
extra_args=['-vcodec','libx264'])
anim = animation.FuncAnimation(fig, update_fig, frames=frames, interval=50)
if args.animate:
plt.show()
elif args.output:
anim.save(args.output, writer=ffmpeg, dpi=args.dpi)
def binaries(simdata, **kwargs):
f = simdata.hdf5file
figdir = simdata.figdir
targetdir = directory + figdir
if not os.path.exists(targetdir):
os.makedirs(targetdir)
if 'integrator' in kwargs:
integrator = kwargs['integrator']
else:
integrator = simdata.available_integrators()[0]
centralmass = float(simdata.parameters['M'].split()[0])
interval = float(simdata.parameters['interval'].split()[0])
datapoints = int(simdata.parameters['datapoints'])
mdot = float(simdata.parameters['mdot'].split()[0])
sma0 = float(simdata.parameters['sma'].split()[0])
print("sma0 {} AU".format(sma0))
firstavailablesim = f[integrator].values()[0]
time = quantify_dset(firstavailablesim['time']).value_in(units.yr)
totalmass = quantify_dset(
firstavailablesim['mass']).lengths().value_in(units.MSun)
smas = []
eccentricities = []
ecc_labels = []
smas_ad = []
ml_indices = []
arguments_of_periapsis = []
angular_momenta = []
true_anomalies = []
periods = []
for sim in f[integrator].values():
sma = quantify_dset(sim['p0/sma']).value_in(units.AU)
smas.append(sma)
smas_ad.append(sma_analytical(sma0, mdot, time, centralmass))
ml_indices.append(sim['p0/massloss_index'].value)
eccentricities.append(sim['p0/eccentricity'].value)
ecc_labels.append("e0= " +
str(round(sim['p0/eccentricity'][0], 1)))
arguments_of_periapsis.append(
sim['p0/argument_of_periapsis'].value)
periods.append(quantify_dset(sim['p0/period']).value_in(units.yr))
angular_momenta.append(
quantify_dset(sim['angular_momentum']).number)
true_anomalies.append(sim['p0/true_anomaly'].value)
cmap = cm.jet
savefigargs = dict(bbox_inches='tight', dpi=150)
def sma_vs_time():
imgname = 'sma_vs_time.png'
fig = plt.figure(figsize=(32, 8))
ax = fig.add_subplot(111)
ax.set_color_cycle(cmap(numpy.linspace(0, 0.95, 10)))
for sma, lbl in zip(smas, ecc_labels):
ax.plot(time, sma, lw=1, ls='-', label=lbl)
ax.set_xlim(time[0], time[-1])
ax.set_xlabel('time [yr]')
ax.set_ylabel('sma [AU]')
plt.savefig(targetdir + imgname, **savefigargs)
plt.close()
def sma_vs_adiabaticity():
imgname = 'sma_vs_adiabaticity.png'
fig = plt.figure(figsize=(32, 8))
ax = fig.add_subplot(111)
ax.set_color_cycle(cmap(numpy.linspace(0, 0.95, 10)))
for sma, ml_index, lbl in zip(smas, ml_indices, ecc_labels):
ax.plot(ml_index, sma, lw=1, ls='-', label=lbl)
ax.set_xlim(ml_index[0], ml_index[-1])
ax.set_xlabel('massloss-index')
ax.set_ylabel('sma [AU]')
plt.savefig(targetdir + imgname, **savefigargs)
plt.close()
def sma_error_vs_time():
imgname = 'sma_error_vs_time.png'
fig = plt.figure(figsize=(32, 8))
ax = fig.add_subplot(111)
ax.set_color_cycle(cmap(numpy.linspace(0, 0.95, 10)))
for sma, sma_ad, lbl in zip(smas, smas_ad, ecc_labels):
ax.plot(time, (sma - sma_ad) / sma_ad, lw=1, ls='-', label=lbl)
ax.set_xlim(time[0], time[-1])
ax.set_xlabel('time [yr]')
ax.set_ylabel('(sma-sma_ad)/sma_ad ')
plt.savefig(targetdir + imgname, **savefigargs)
plt.close()
def sma_error_vs_adiabaticity():
imgname = 'sma_error_vs_adiabaticity.png'
fig = plt.figure(figsize=(32, 8))
ax = fig.add_subplot(111)
ax.set_color_cycle(cmap(numpy.linspace(0, 0.95, 10)))
for ml_index, sma_ad, lbl in zip(ml_indices, smas_ad, ecc_labels):
ax.plot(ml_index, (sma - sma_ad) / sma_ad,
lw=1,
ls='-',
label=lbl)
ax.set_xlim(ml_index[0], ml_index[-1])
ax.set_xlabel('massloss-index')
ax.set_ylabel('(sma-sma_ad)/sma_ad ')
plt.savefig(targetdir + imgname, **savefigargs)
plt.close()
def eccentricity_vs_time():
imgname = 'eccentricity_vs_time.png'
fig = plt.figure(figsize=(32, 16))
ax = fig.add_subplot(111)
ax.set_color_cycle(cmap(numpy.linspace(0, 0.95, 10)))
for ecc, lbl in zip(eccentricities, ecc_labels):
ax.plot(time, ecc, lw=2, ls='-', label=lbl)
ax.set_xlim(time[0], time[-1])
ax.set_xlabel('time [yr]')
ax.set_ylabel('eccentricity')
plt.savefig(targetdir + imgname, **savefigargs)
plt.close()
def eccentricity_vs_adiabaticity():
imgname = 'eccentricity_vs_adiabaticity.png'
fig = plt.figure(figsize=(32, 16))
ax = fig.add_subplot(111)
ax.set_color_cycle(cmap(numpy.linspace(0, 0.95, 10)))
for ecc, ml_index, lbl in zip(eccentricities, ml_indices,
ecc_labels):
ax.plot(ml_index, ecc, lw=2, ls='-', label=lbl)
ax.set_xlim(ml_index[0], ml_index[-1])
ax.set_xlabel('massloss-index')
ax.set_ylabel('eccentricity')
plt.savefig(targetdir + imgname, **savefigargs)
plt.close()
def sma_error_vs_eccentricity_error():
imgname = 'sma_error_vs_eccentricity_error.png'
fig = plt.figure(figsize=(32, 8))
ax = fig.add_subplot(111)
ax.set_color_cycle(cmap(numpy.linspace(0, 0.95, 10)))
for sma, sma_ad, ecc, lbl in zip(smas, smas_ad, eccentricities,
ecc_labels):
ax.plot((ecc - ecc[0]) / ecc[0], (sma - sma_ad) / sma_ad,
lw=1,
ls='-',
label=lbl)
ax.set_xlabel('(ecc-ecc0)/ecc0')
ax.set_ylabel('(sma-sma_ad)/sma_ad ')
plt.savefig(targetdir + imgname, **savefigargs)
plt.close()
def true_anomaly_vs_time():
imgname = 'true_anomaly_vs_time.png'
fig = plt.figure(figsize=(30, 10))
colorcycle = cycle(cmap(numpy.linspace(0, 0.95, 10)))
position_generator = axis_position(10, 1)
totalsims = len(f[integrator].values())
for i, true_anomaly, lbl in zip(range(totalsims), true_anomalies,
ecc_labels):
ax = fig.add_subplot(*next(position_generator))
ax.plot(time,
true_anomaly,
lw=2,
ls='-',
c=next(colorcycle),
label=lbl)
if i + 1 != totalsims:
ax.set_xticklabels([])
else:
ax.set_xticks(numpy.arange(1000, 15000, 1000))
ax.set_xlim(time[0], time[-1])
ax.set_ylim(0, 360)
ax.set_yticks([90, 180, 270])
ax.set_xlabel('time [yr]')
ax.set_ylabel('f [degrees]')
plt.subplots_adjust(hspace=0.001)
plt.savefig(targetdir + imgname, **savefigargs)
plt.close()
def argument_of_periapsis_vs_time():
imgname = 'argument_of_periapsis_vs_time.png'
fig = plt.figure(figsize=(20, 20))
colorcycle = cycle(cmap(numpy.linspace(0, 0.95, 10)))
position_generator = axis_position(3, 4)
for w, lbl in zip(arguments_of_periapsis, ecc_labels):
ax = fig.add_subplot(*next(position_generator), polar=True)
ax.plot(w, time, lw=1, ls='-', c=next(colorcycle), label=lbl)
ax.set_rgrids(numpy.array([5000, 10000, 15000]), angle=270)
plt.savefig(targetdir + imgname, **savefigargs)
plt.close()
def adiabaticity_vs_time():
imgname = 'adiabaticity_vs_time.png'
fig = plt.figure(figsize=(8, 16))
ax1 = fig.add_subplot(311)
ax2 = fig.add_subplot(312)
ax3 = fig.add_subplot(313)
colorcycle1 = cycle(cmap(numpy.linspace(0, 0.95, 10)))
colorcycle2 = cycle(cmap(numpy.linspace(0, 0.95, 10)))
colorcycle3 = cycle(cmap(numpy.linspace(0, 0.95, 10)))
for ml_index, period, lbl in zip(ml_indices, periods, ecc_labels):
ax1.plot(time,
mdot / totalmass,
lw=1,
ls='-',
c=next(colorcycle1),
label=lbl)
ax2.plot(time,
period,
lw=1,
ls='-',
c=next(colorcycle2),
label=lbl)
ax3.plot(time,
ml_index,
lw=1,
ls='-',
c=next(colorcycle3),
label=lbl)
ax3.set_yscale('log')
ax1.set_xlabel('time [yr]')
ax2.set_xlabel('time [yr]')
ax3.set_xlabel('time [yr]')
plt.subplots_adjust(hspace=0.001)
plt.savefig(targetdir + imgname, **savefigargs)
plt.close()
def angular_momentum_error_vs_time():
imgname = 'angular_momentum_error_vs_time.png'
fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(111)
colorcycle = cycle(cmap(numpy.linspace(0, 0.95, 10)))
for h, lbl in zip(angular_momenta, ecc_labels):
h_error = (h[0] - h) / h[0]
ax.plot(time,
h_error,
lw=1,
ls='-',
c=next(colorcycle),
label=lbl)
ax.set_xlim(time[0], time[-1])
plt.savefig(targetdir + imgname, **savefigargs)
plt.close()
def angular_momentum_error_vs_adiabaticity():
imgname = 'angular_momentum_error_vs_adiabaticity.png'
fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(111)
colorcycle = cycle(cmap(numpy.linspace(0, 0.95, 10)))
for h, ml_index, lbl in zip(angular_momenta, ml_indices,
ecc_labels):
h_error = (h[0] - h) / h[0]
ax.plot(ml_index,
h_error,
lw=1,
ls='-',
c=next(colorcycle),
label=lbl)
ax.set_xlim(ml_index[0], ml_index[-1])
plt.savefig(targetdir + imgname, **savefigargs)
plt.close()
sma_vs_time()
sma_vs_adiabaticity()
sma_error_vs_time()
sma_error_vs_adiabaticity()
eccentricity_vs_time()
eccentricity_vs_adiabaticity()
sma_error_vs_eccentricity_error()
true_anomaly_vs_time()
argument_of_periapsis_vs_time()
adiabaticity_vs_time()
angular_momentum_error_vs_time()
angular_momentum_error_vs_adiabaticity()
def create_mpeg(sim):
time_vq = quantify_dset(sim['time'])
time = time_vq.value_in(units.yr)
if args.frames is None:
frames = range(1, len(time), 5)
else:
frames = range(args.frames[0], args.frames[1], args.frames[2])
pbar = pb.ProgressBar(widgets=drawwidget(args.sim),
maxval=frames[-1]).start()
position_vq = quantify_dset(sim['position'])
position = position_vq.value_in(units.AU)
seperation_vq = (position_vq[:, 1, :] - position_vq[:, 2, :]).lengths()
seperation = seperation_vq.value_in(units.AU)
CM_position_vq = quantify_dset(sim['CM_position'])
CM_position = CM_position_vq.value_in(units.AU)
period_inner_vq = quantify_dset(sim['p0/period'])
period_inner = period_inner_vq.value_in(units.yr)
mass_vq = quantify_dset(sim['mass'])
mass = mass_vq[:, 0].value_in(units.MSun)
kinetic_energy = sim['kinetic_energy'].value
potential_energy = sim['potential_energy'].value
total_energy = sim['total_energy'].value
mu0 = quantify_dset(sim['mass'])[0].sum()
sma_inner_vq = quantify_dset(sim['p0/sma'])
sma_inner = sma_inner_vq.value_in(units.AU)
sma_outer_vq = quantify_dset(sim['p1/sma'])
sma_outer = sma_outer_vq.value_in(units.AU)
eccentricity_inner = sim['p0/eccentricity'].value
eccentricity_outer = sim['p1/eccentricity'].value
true_anomaly_inner = sim['p0/true_anomaly'].value
true_anomaly_outer = sim['p1/true_anomaly'].value
sma_inner_analytical_vq = sma_analytical(
sma_inner_vq[0], 1.244e-5 | (units.MSun / units.yr), time_vq, mu0)
sma_inner_analytical = sma_inner_analytical_vq.value_in(units.AU)
sma_outer_analytical_vq = sma_analytical(
sma_outer_vq[0], 1.244e-5 | (units.MSun / units.yr), time_vq, mu0)
sma_outer_analytical = sma_outer_analytical_vq.value_in(units.AU)
x, y = 0, 1
central_x = position[:, 0, x] - CM_position[:, x]
central_y = position[:, 0, y] - CM_position[:, y]
inner_x = position[:, 1, x] - CM_position[:, x]
inner_y = position[:, 1, y] - CM_position[:, y]
outer_x = position[:, 2, x] - CM_position[:, x]
outer_y = position[:, 2, y] - CM_position[:, y]
nr_datapoints = len(time)
yrs_per_datapoint = time[-1] / nr_datapoints
fig = plt.figure(figsize=(22, 12))
gs = gridspec.GridSpec(4, 4)
ax1 = fig.add_subplot(gs[:2, :2])
ax2 = fig.add_subplot(gs[2, 0])
ax3 = fig.add_subplot(gs[3, 0])
ax4 = fig.add_subplot(gs[2, 1])
ax5 = fig.add_subplot(gs[3, 1])
ax6 = fig.add_subplot(gs[0, 2:])
ax7 = fig.add_subplot(gs[1, 2:])
ax8 = fig.add_subplot(gs[2, 2:])
ax9 = fig.add_subplot(gs[3, 2:])
orange = '#FF6500'
green = '#07D100'
central_loc, = ax1.plot([], [],
c='y',
ls="o",
mfc="y",
mec="y",
marker='o',
ms=6)
inner_loc, = ax1.plot([], [],
c='w',
ls="o",
mfc="w",
mec="w",
marker='o',
ms=4)
outer_loc, = ax1.plot([], [],
c='w',
ls="o",
mfc="w",
mec="w",
marker='o',
ms=4)
inner_artist, = ax1.plot([], [], c=green, ls="-", lw=2, alpha=0.8)
outer_artist, = ax1.plot([], [], c='r', ls="-", lw=2, alpha=0.8)
sma0_ana_artist, = ax2.plot([], [], c='w', ls="-", lw=2)
sma1_ana_artist, = ax3.plot([], [], c='w', ls="-", lw=2)
sma0_artist, = ax2.plot([], [], c=green, ls="-", lw=2)
sma1_artist, = ax3.plot([], [], c='r', ls="-", lw=2)
ecc0_artist, = ax4.plot([], [], c=green, ls="-", lw=2)
ecc1_artist, = ax5.plot([], [], c='r', ls="-", lw=2)
ecc0_ana_artist, = ax4.plot([], [], c='w', ls="-", lw=2)
ecc1_ana_artist, = ax5.plot([], [], c='w', ls="-", lw=2)
true_anomaly0_artist, = ax6.plot([], [], c=green, ls="-", lw=2)
true_anomaly1_artist, = ax6.plot([], [], c='r', ls="-", lw=2)
seperation_artist, = ax7.plot([], [], c=orange, ls="-", lw=2)
K_artist, = ax8.plot([], [], c='r', ls="-", lw=2)
U_artist, = ax8.plot([], [], c=green, ls="-", lw=2)
E_artist, = ax8.plot([], [], c=orange, ls="-", lw=2)
mass_artist, = ax9.plot([], [], c=orange, ls="-", lw=2)
ax2.set_xlabel('time [yr]')
ax2.set_ylabel('sma [AU]')
ax3.set_xlabel('time [yr]')
ax3.set_ylabel('sma [AU]')
ax4.set_xlabel('time [yr]')
ax4.set_ylabel('eccentricity ')
ax5.set_xlabel('time [yr]')
ax5.set_ylabel('eccentricity ')
ax6.set_xlabel('time [yr]')
ax6.set_ylabel('true anomaly [degrees] ')
ax7.set_xlabel('time [yr]')
ax7.set_ylabel('seperation [AU]')
ax8.set_xlabel('time [yr]')
ax8.set_ylabel('energies')
ax9.set_ylabel('central mass [MSun]')
ax9.set_xlabel('time [yr]')
ax1.set_xlim(-200, 150)
ax1.set_ylim(-250, 100)
def update_fig(i):
t = time[i]
lag = int(20 * period_inner[i] / (yrs_per_datapoint))
orbitlag = int(0.9 * period_inner[i] / (yrs_per_datapoint))
if i <= lag:
tbegin = time[0]
time_range = time[0:i]
inner_artist.set_data(inner_x[0:i], inner_y[0:i])
outer_artist.set_data(outer_x[0:i], outer_y[0:i])
sma0_artist.set_data(time[0:i], sma_inner[0:i])
sma1_artist.set_data(time[0:i], sma_outer[0:i])
sma0_ana_artist.set_data(time[0:i], sma_inner_analytical[0:i])
sma1_ana_artist.set_data(time[0:i], sma_outer_analytical[0:i])
ecc0_artist.set_data(time[0:i], eccentricity_inner[0:i])
ecc1_artist.set_data(time[0:i], eccentricity_outer[0:i])
true_anomaly0_artist.set_data(time[0:i], true_anomaly_inner[0:i])
true_anomaly1_artist.set_data(time[0:i], true_anomaly_outer[0:i])
K_artist.set_data(time[0:i], kinetic_energy[0:i])
U_artist.set_data(time[0:i], potential_energy[0:i])
E_artist.set_data(time[0:i], total_energy[0:i])
mass_artist.set_data(time[0:i], mass[0:i])
seperation_artist.set_data(time[0:i], seperation[0:i])
else:
tbegin = time[i - lag]
time_range = time[i - lag:i]
inner_artist.set_data(inner_x[i - orbitlag:i],
inner_y[i - orbitlag:i])
outer_artist.set_data(outer_x[i - orbitlag:i],
outer_y[i - orbitlag:i])
sma0_artist.set_data(time_range, sma_inner[i - lag:i])
sma1_artist.set_data(time_range, sma_outer[i - lag:i])
sma0_ana_artist.set_data(time_range,
sma_inner_analytical[i - lag:i])
sma1_ana_artist.set_data(time_range,
sma_outer_analytical[i - lag:i])
ecc0_artist.set_data(time_range, eccentricity_inner[i - lag:i])
ecc1_artist.set_data(time_range, eccentricity_outer[i - lag:i])
true_anomaly0_artist.set_data(time_range,
true_anomaly_inner[i - lag:i])
true_anomaly1_artist.set_data(time_range,
true_anomaly_outer[i - lag:i])
K_artist.set_data(time_range, kinetic_energy[i - lag:i])
U_artist.set_data(time_range, potential_energy[i - lag:i])
E_artist.set_data(time_range, total_energy[i - lag:i])
mass_artist.set_data(time_range, mass[i - lag:i])
seperation_artist.set_data(time_range, seperation[i - lag:i])
try:
central_loc.set_data(central_x[i - 1], central_y[i - 1])
inner_loc.set_data(inner_x[i - 1], inner_y[i - 1])
outer_loc.set_data(outer_x[i - 1], outer_y[i - 1])
except IndexError:
pass
ax2.set_ylim(sma_inner_analytical[i] * 0.99,
sma_inner_analytical[i] * 1.01)
ax3.set_ylim(sma_outer_analytical[i] * 0.99,
sma_outer_analytical[i] * 1.01)
ax4.set_ylim(eccentricity_inner[i] - 0.01,
eccentricity_inner[i] + 0.01)
ax5.set_ylim(eccentricity_outer[i] - 0.01,
eccentricity_outer[i] + 0.01)
ax6.set_ylim(0, 360)
ax7.set_ylim(5, 250)
ax8.set_ylim(-2.5e36, 2e36)
ax9.set_ylim(mass[i] * 0.99, mass[i] * 1.01)
ax2.set_xlim(tbegin, t)
ax3.set_xlim(tbegin, t)
ax4.set_xlim(tbegin, t)
ax5.set_xlim(tbegin, t)
ax6.set_xlim(tbegin, t)
ax7.set_xlim(tbegin, t)
ax8.set_xlim(tbegin, t)
ax9.set_xlim(tbegin, t)
pbar.update(i)
ffmpeg = animation.FFMpegWriter(fps=30,
codec='mpeg4',
extra_args=['-vcodec', 'libx264'])
anim = animation.FuncAnimation(fig, update_fig, frames=frames, interval=50)
if args.animate:
plt.show()
elif args.output:
anim.save(args.output, writer=ffmpeg, dpi=args.dpi)
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()
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()