def plot_tracks(temperatures_original, luminosities_original,
temperatures_helium, luminosities_helium):
x_label = "T [K]"
y_label = "L [$L_\odot$]"
figure = single_frame(x_label, y_label, logx=True, logy=True,
xsize=14, ysize=10)
colors = get_distinct(2)
loglog(temperatures_original, luminosities_original, label='progenitor',
c=colors[0])
loglog(temperatures_helium, luminosities_helium, label='helium star',
c=colors[1])
scatter(temperatures_helium[-1], luminosities_helium[-1], marker='*',
s=400, c=colors[1])
xlabel('Effective Temperature')
ylabel('Luminosity')
pyplot.xlim(1.0e5, 4000)
pyplot.ylim(1.0,1.0e5)
pyplot.legend(loc=4, fontsize=24)
save_file = 'HertzsprungRussel_HeliumStar.png'
pyplot.savefig(save_file)
print('\nSaved figure in file', save_file,'\n')
pyplot.show()
def plot_results(stars, time):
mass_loss = stars.zams_mass - stars.mass
x = stars.x.in_(units.parsec)
y = stars.y.in_(units.parsec)
pyplot.figure(figsize=(8,8))
aplot.plot(x, y, "*")
for x, y, mass_loss in zip(x.number, y.number, mass_loss):
pyplot.annotate("%0.2f"%abs(mass_loss.number), xy=(x,y+2),
horizontalalignment='center', verticalalignment='bottom')
pyplot.axis('equal')
pyplot.xlim([-60, 60])
pyplot.ylim([-60, 60])
aplot.xlabel("x")
aplot.ylabel("y")
pyplot.title("time = " + str(time))
if not os.path.exists("plots"):
os.mkdir("plots")
name = "plots/plot_{0:=05}.png".format(int(time.value_in(units.Myr)))
print("creating", name)
pyplot.savefig(name)
pyplot.close()
def temperature_density_plot(data, mass, age):
figure = pyplot.figure(figsize = (8, 10))
pyplot.subplot(2, 1, 1)
ax = pyplot.gca()
plotT = semilogy(data["radius"], data["temperature"], 'r-', label = r'$T(r)$')
xlabel('Radius')
ylabel('Temperature')
ax.twinx()
plotrho = semilogy(data["radius"], data["density"], 'g-', label = r'$\rho(r)$')
plots = plotT + plotrho
labels = [one_plot.get_label() for one_plot in plots]
ax.legend(plots, labels, loc=3)
ylabel('Density')
pyplot.subplot(2, 1, 2)
semilogy(data["radius"], data["composition"][0], label = data["species_names"][0])
semilogy(data["radius"], data["composition"][1], label = data["species_names"][1])
semilogy(data["radius"], data["composition"][2], label = data["species_names"][2])
semilogy(data["radius"], data["composition"][3], label = data["species_names"][3])
semilogy(data["radius"], data["composition"][4], label = data["species_names"][4])
pyplot.ylim(0.0, 1.0)
xlabel('Radius')
ylabel('Mass fraction')
pyplot.legend()
pyplot.suptitle('Structure of a {0} star at {1}'.format(mass, age))
pyplot.show()
def make_effective_iso_potential_plot(gravity_code):
omega = (constants.G * gravity_code.particles.total_mass() / (1.0|units.AU**3)).sqrt()
center_of_mass = gravity_code.particles.center_of_mass()[:2]
pyplot.rcParams.update({'font.size': 30})
figure = pyplot.figure(figsize = (12, 12))
ax = pyplot.gca()
ax.get_yaxis().get_major_formatter().set_useOffset(False)
ax.minorticks_on()
current_axes = pyplot.subplot(1, 1, 1)
current_axes.set_aspect("equal", adjustable = "box")
lim = 1.7
effective_iso_potential_plot(gravity_code,
omega,
xlim = [-lim, lim] | units.AU,
ylim = [-lim, lim] | units.AU,
center_of_rotation = center_of_mass,
fraction_screen_filled=0.8)
xlabel('x')
ylabel('y')
pyplot.text(0.6, -0.06, "$L_1$")
pyplot.text(1.35, -0.06, "$L_2$")
pyplot.text(-0.99, -0.06, "$L_3$")
pyplot.text(0.40, 0.82, "$L_4$")
pyplot.text(0.40, -0.92, "$L_5$")
# pyplot.show()
pyplot.savefig("lagrange_points")
def main(filename):
pyplot.figure(figsize=(12,12))
particles = read_set_from_file(filename, "hdf5")
for si in particles.history:
scatter(si.x, si.y, s=100)
xlabel("x")
ylabel("y")
pyplot.show()
def make_plot(radius_profile, brunt_profile, mass, age):
figure = pyplot.figure()
semilogy(radius_profile, -brunt_profile, 'g-', label = r'convective, $N^2$ < 0')
semilogy(radius_profile, brunt_profile, 'r-', label = r'radiative, $N^2$ > 0')
xlabel('Radius')
ylabel(r'$\|N^2\|$')
pyplot.title('Brunt-Vaisala frequency squared of a {0} star at {1}'.format(mass, age))
pyplot.legend(loc=3)
pyplot.show()
def main(N=10):
figure(figsize=(5,5))
bodies = new_plummer_model(N)
scatter(bodies.x, bodies.y)
xlim(-1, 1)
ylim(-1, 1)
xlabel("X")
ylabel("Y")
show()
def plot_track(temperature_at_time, luminosity_at_time):
pyplot.figure(figsize=(8, 6))
pyplot.title('Hertzsprung-Russell diagram', fontsize=12)
loglog(temperature_at_time, luminosity_at_time)
xlabel('Effective Temperature')
ylabel('Luminosity')
pyplot.xlim(pyplot.xlim()[::-1])
pyplot.ylim(.1, 1.e4)
pyplot.show()
def thermal_energy_plot(time, E_therm, figname):
if not HAS_MATPLOTLIB:
return
pyplot.figure(figsize = (5, 5))
plot(time, E_therm.as_quantity_in(units.erg), label='E_therm')
xlabel('Time')
ylabel('Energy')
pyplot.legend(loc=3)
pyplot.savefig(figname)
print "\nPlot of thermal energy evolution was saved to: ", figname
pyplot.close()
def plot_tracks(temperature1, luminosity1, temperature2, luminosity2):
from matplotlib import pyplot
from amuse.plot import loglog, xlabel, ylabel
pyplot.figure(figsize = (8, 6))
pyplot.title('Hertzsprung-Russell diagram', fontsize=12)
loglog(temperature1, luminosity1)
loglog(temperature2, luminosity2)
xlabel('Effective Temperature')
ylabel('Luminosity')
pyplot.xlim(pyplot.xlim()[::-1])
pyplot.show()
def plot_tracks(temperatures_original, luminosities_original, temperatures_helium, luminosities_helium):
pyplot.figure(figsize = (8, 6))
pyplot.title('Hertzsprung-Russell diagram', fontsize=12)
loglog(temperatures_original, luminosities_original, label='progenitor')
loglog(temperatures_helium, luminosities_helium, label='helium star')
xlabel('Effective Temperature')
ylabel('Luminosity')
pyplot.xlim(pyplot.xlim()[::-1])
pyplot.ylim(1.0,1.0e5)
pyplot.legend(loc=3)
pyplot.show()
def energy_evolution_plot(time, kinetic, potential, thermal, figname = "energy_evolution.png"):
time.prepend(0.0 | units.day)
pyplot.figure(figsize = (5, 5))
plot(time, kinetic, label='K')
plot(time, potential, label='U')
plot(time, thermal, label='Q')
plot(time, kinetic + potential + thermal, label='E')
xlabel('Time')
ylabel('Energy')
pyplot.legend(prop={'size':"x-small"}, loc=4)
pyplot.savefig(figname)
pyplot.close()
def orbit_plot(distance, mass, time):
figure = pyplot.figure(figsize=(6, 10), dpi=100)
subplot = figure.add_subplot(2, 1, 1)
plot(time, distance)
xlabel('t')
ylabel('separation')
pyplot.margins(0.05)
subplot = figure.add_subplot(2, 1, 2)
plot(time, ((mass - mass[0]) / mass[0]) * 100.0)
xlabel('t')
ylabel('mass')
pyplot.margins(0.05)
pyplot.show()
def composition_comparison_plot(radii_SE, comp_SE, radii_SPH, comp_SPH, figname):
if not HAS_MATPLOTLIB:
return
pyplot.figure(figsize = (7, 5))
plot(radii_SE.as_quantity_in(units.RSun), comp_SE,
label='stellar evolution model')
plot(radii_SPH, comp_SPH, 'go', label='SPH model')
xlabel('radius')
ylabel('mass fraction')
pyplot.legend()
pyplot.savefig(figname)
print "\nPlot of composition profiles was saved to: ", figname
pyplot.close()
def internal_energy_comparison_plot(radii_SE, u_SE, radii_SPH, u_SPH, figname):
if not HAS_MATPLOTLIB:
return
pyplot.figure(figsize = (7, 5))
semilogy(radii_SE.as_quantity_in(units.RSun), u_SE,
label='stellar evolution model')
semilogy(radii_SPH, u_SPH, 'go', label='SPH model')
xlabel('radius')
ylabel('internal energy')
pyplot.legend()
pyplot.savefig(figname)
print "\nPlot of internal energy profiles was saved to: ", figname
pyplot.close()
def energy_evolution_plot(time, kinetic, potential, thermal, figname="energy_evolution.png"):
native_plot.subplot(211)
plot(time, kinetic, label='K')
plot(time, potential, label='U')
plot(time, thermal, label='Q')
plot(time, kinetic + potential + thermal, label='E')
xlabel('Time')
ylabel('Energy')
native_plot.legend(prop={'size':"x-small"}, loc=4)
native_plot.subplot(212)
plot(time, thermal, label='Q')
native_plot.savefig(figname)
native_plot.clf()
def make_plots(Ncl, Rcl, t_end):
""" Not finished """
try:
import matplotlib
matplotlib.use("Agg")
from matplotlib import pyplot
except ImportError:
print "Unable to produce plots: couldn't find matplotlib"
else:
fig = pyplot.figure(figsize=[10, 10])
for data in ["hybrid", "tree", "direct"]
print "Generating plot data of {0} run".format(data)
data_file = "CA_Exam_TLRH_{0}.amuse".format(data)
stars_below_cut, stars_above_cut =\
read_set_from_file(data_file, 'amuse',
names=("stars_below_cut", "stars_below_cut"))
lim = abs(next(stars_below_cut.history).position).max()
if data == "hybrid":
pass
# dashed curve
# points = aplot.scatter(dE, time, c='orange',
# label='Hybrid')
if data == "direct":
pass
# dotted curve
# points = aplot.scatter(dE, time, c='red',
# label='Direct')
if data == "tree":
pass
# solid curve
# points = aplot.scatter(dE, time, c='green',
# label='Tree')
aplot.xlabel("Time")
aplot.ylabel("Relative Energy Error")
pyplot.axis('equal')
aplot.xlim(-lim, lim)
aplot.ylim(-lim, lim)
pyplot.legend()
pyplot.title(r'Cluster with $N=${0}, $r=${1}'
.format(Ncl, Rcl ) +
r', evolved until $t_{\rm end}$={0}'
.format(t_end))
pyplot.savefig("out.png")
pyplot.close()
def energy_plot(time, E_kin, E_pot, E_therm, figname):
if not HAS_MATPLOTLIB:
return
pyplot.figure(figsize=(5, 5))
plot(time, E_kin.as_quantity_in(units.erg), label="E_kin")
plot(time, E_pot, label="E_pot")
plot(time, E_therm, label="E_therm")
plot(time, E_kin + E_pot + E_therm, label="E_total")
xlabel("Time")
ylabel("Energy")
pyplot.legend(loc=3)
pyplot.savefig(figname)
print "\nPlot of energy evolution was saved to: ", figname
pyplot.close()
def orbit_parameters_plot(semi_major_in,semi_major_out, time, par_symbol="a", par_name="semimajor_axis"):
figure = pyplot.figure(figsize = (10, 6), dpi = 100)
subplot = figure.add_subplot(2, 1, 1)
plot(time,semi_major_in )
xlabel('t')
ylabel('$'+par_symbol+'_\mathrm{binary}$')
subplot = figure.add_subplot(2, 1, 2)
plot(time,semi_major_out )
xlabel('t')
ylabel('$'+par_symbol+'_\mathrm{giant}$')
pyplot.minorticks_on()
pyplot.savefig(par_name+"_evolution.png")
pyplot.close()
def orbit_ecc_plot(eccentricity_in,eccentricity_out,time):
figure = pyplot.figure(figsize = (10, 6), dpi = 100)
subplot = figure.add_subplot(2, 1, 1)
plot(time,eccentricity_in)
xlabel('t')
ylabel('e$_\mathrm{binary}$')
subplot = figure.add_subplot(2, 1, 2)
plot(time,eccentricity_out )
xlabel('t')
ylabel('e$_\mathrm{giant}$')
pyplot.minorticks_on()
pyplot.savefig("eccentricity_evolution.png")
pyplot.close()
def energy_plot(time, E_kin_list, E_pot_list, E_therm_list, figname):
if not HAS_MATPLOTLIB:
return
pyplot.figure(figsize = (5, 5))
for i, (E_kin, E_pot, E_therm) in enumerate(zip(E_kin_list, E_pot_list, E_therm_list)):
plot(time, E_kin.as_quantity_in(units.erg), label=labels[i][0])
plot(time, E_pot, label=labels[i][1])
plot(time, E_therm, label=labels[i][2])
plot(time, E_kin+E_pot+E_therm, label=labels[i][3])
xlabel('Time')
ylabel('Energy')
pyplot.legend(prop={'size':"x-small"}, loc=3)
pyplot.savefig(figname)
print "\nPlot of energy evolution was saved to: ", figname
pyplot.close()
def plot_cluster(stars, filename, t, rcl):
filename += ".png"
#lim = 10*stars.center_of_mass().length().value_in(stars.x.unit)
lim = (R * 2).value_in(units.m)
m = 1 + 3.0*stars.mass/min(stars.mass)
pyplot.title("Cluster at distance "+str(R)+" with cluster radius "+
str(rcl)+"\nat t="+str(t))
scatter(stars.x, stars.y) # s size (in point^2)
xlabel("X")
ylabel("Y")
pyplot.xlim(-lim, lim)
pyplot.ylim(-lim, lim)
pyplot.savefig(filename)
pyplot.clf()
def plot(x, y):
pyplot.figure(figsize=(8,8))
colormap = ['yellow', 'green', 'blue'] # specific to a 3-body plot
size = [40, 20, 20]
edgecolor = ['orange', 'green', 'blue']
for si in particles.history:
scatter(si.x, si.y, c=colormap, s=size, edgecolor=edgecolor)
xlabel("x")
ylabel("y")
save_file = 'plot_gravity.png'
pyplot.savefig(save_file)
print '\nSaved figure in file', save_file,'\n'
pyplot.show()
def test2(self):
""" Test a basic plot with and without units and labels"""
if not HAS_MATPLOTLIB:
return self.skip()
pyplot.clf()
x = numpy.linspace(0, 100, 100) | units.yr
y = numpy.linspace(0, 200, 100)
aplot.plot(x, y)
self.assertEquals("[yr]", self.xaxis().get_label_text())
self.assertEquals("", self.yaxis().get_label_text())
aplot.xlabel("time")
aplot.ylabel("radius")
self.assertEquals("time [yr]", self.xaxis().get_label_text())
self.assertEquals("radius ", self.yaxis().get_label_text())
def main(filename, lim):
pyplot.ion()
particles = read_set_from_file(filename, "hdf5")
if lim<=zero:
lim = max(particles.x).value_in(lim.unit)
time = 0
for si in particles.history:
pyplot.title("Cluster at t="+str(time))
scatter(si.x.as_quantity_in(lim.unit), si.y.as_quantity_in(lim.unit))
xlabel("X")
ylabel("Y")
if lim>zero:
pyplot.xlim(-lim.value_in(lim.unit), lim.value_in(lim.unit))
pyplot.ylim(-lim.value_in(lim.unit), lim.value_in(lim.unit))
pyplot.draw()
pyplot.cla()
pyplot.show()
def __init__(self):
self.merger = ClusterMerger()
timesteps = VectorQuantity.arange(0 | units.Myr, 1 | units.Gyr, 50 | units.Myr)
tot = len(timesteps)
end_time = timesteps[-1]
print "Starting Simulation :-)"
print "Generating plots on the fly :-)"
gasA_vel_list = [] | (units.km/units.s)
dmA_vel_list = [] | (units.km/units.s)
gasB_vel_list = [] | (units.km/units.s)
dmB_vel_list = [] | (units.km/units.s)
time_list = [] | units.Gyr
for i, time in enumerate(timesteps):
print_progressbar(i, tot)
self.merger.code.evolve_model(time)
self.merger.dm_gas_sph_subplot(i)
gasA_vel_list.append(self.merger.gasA.center_of_mass_velocity())
dmA_vel_list.append(self.merger.dmA.center_of_mass_velocity())
gasB_vel_list.append(self.merger.gasB.center_of_mass_velocity())
dmB_vel_list.append(self.merger.dmB.center_of_mass_velocity())
time_list.append(time)
write_set_to_file(self.merger.code.particles,
'out/{0}/data/cluster_{1}.amuse'.format(self.merger.timestamp, i),
"amuse")
print "Plotting velocity as function of time"
fig = pyplot.figure(figsize=(12, 10), dpi=50)
plot(time_list.number, gasA_vel_list.number, label="gasA", c='r', ls='solid')
plot(time_list.number, dmA_vel_list.number, label="dmA", c='r', ls='dashed')
plot(time_list.number, gasB_vel_list.number, label="gasB", c='g', ls='solid')
plot(time_list.number, dmB_vel_list.number, label="dmB", c='g', ls='dashed')
xlabel("Time")
ylabel("Velocity")
pyplot.legend()
pyplot.show()
print "Generating gif :-)"
self.merger.create_gif()
print "Stopping the code. End of pipeline :-)"
self.merger.code.stop()
def main(filename = "nbody.hdf5", lim=3):
pyplot.ion()
storage = store.StoreHDF(filename,"r")
stars = storage.load()
# lim = 4*stars.center_of_mass().length().value_in(stars.x.unit)
lim = 2*max(max(stars.x).value_in(stars.x.unit), stars.center_of_mass().length().value_in(stars.x.unit))
m = 1 + 3.0*stars.mass/min(stars.mass)
for si in stars.history:
time = si.get_timestamp()
pyplot.title("Cluster at t="+str(time))
print "time = ", time
scatter(si.x, si.y, s=m)
xlabel("X")
ylabel("Y")
pyplot.xlim(-lim, lim)
pyplot.ylim(-lim, lim)
pyplot.draw()
pyplot.cla()
pyplot.show()
def subplot(potential, orbits, codes, fit_orbit, labels):
hor, vert = labels
X = numpy.linspace(-160, 160, 100) | units.parsec
Y = numpy.linspace(-160, 160, 100) | units.parsec
X, Y = quantities.meshgrid(X, Y)
if labels == 'xy':
pot_args = [X, Y, 0 | units.parsec]
fit_horizontal = fit_orbit[0]
fit_vertical = fit_orbit[1]
elif labels == 'xz':
pot_args = [X, 0 | units.parsec, Y]
fit_horizontal = fit_orbit[0]
fit_vertical = fit_orbit[2]
elif labels == 'yz':
pot_args = [0 | units.parsec, X, Y]
fit_horizontal = fit_orbit[1]
fit_vertical = fit_orbit[2]
phi = potential.get_potential_at_point(None, *pot_args)
aplot.imshow_color_plot(X, Y, phi, cmap="Blues")
del aplot.UnitlessArgs.arg_units[2]
aplot.scatter(0 | units.parsec, 0 | units.parsec, c='black')
aplot.plot(fit_horizontal, fit_vertical, c="green", ls="--",
label="Kruijssen et al. 2014")
colors = cycle(['red', 'black', 'yellow', 'grey', 'green'])
for full_orbit, code in zip(orbits, codes):
horizontal = full_orbit.x if hor == 'x' else full_orbit.y
vertical = full_orbit.y if vert == 'y' else full_orbit.z
color = next(colors)
aplot.plot(horizontal, vertical, c=color, label=code)
aplot.scatter(horizontal[-1], vertical[-1], c=color, edgecolor=color)
pyplot.axis('equal')
aplot.xlim([-150, 150] | units.parsec)
aplot.ylim([-150, 150] | units.parsec)
aplot.xlabel(hor)
aplot.ylabel(vert)
def make_effective_iso_potential_plot(gravity_code):
omega = (constants.G * gravity_code.particles.total_mass() /
(1.0 | units.AU**3)).sqrt()
center_of_mass = gravity_code.particles.center_of_mass()[:2]
figure = pyplot.figure(figsize=(9, 8))
current_axes = pyplot.subplot(2, 2, 1)
current_axes.set_aspect("equal", adjustable="box")
effective_iso_potential_plot(gravity_code,
omega,
center_of_rotation=center_of_mass,
fraction_screen_filled=0.7)
xlabel('x')
ylabel('y')
current_axes = pyplot.subplot(2, 2, 3)
current_axes.set_aspect("equal", adjustable="box")
effective_iso_potential_plot(gravity_code,
omega,
center_of_rotation=center_of_mass,
xlim=[0.9, 1.1] | units.AU,
ylim=[-0.1, 0.1] | units.AU,
number_of_contours=20)
xlabel('x')
ylabel('y')
gravity_code.particles[1].mass *= 10000
omega = (constants.G * gravity_code.particles.total_mass() /
(1.0 | units.AU**3)).sqrt()
center_of_mass = gravity_code.particles.center_of_mass()[:2]
current_axes = pyplot.subplot(2, 2, 2)
current_axes.set_aspect("equal", adjustable="box")
effective_iso_potential_plot(gravity_code,
omega,
center_of_rotation=center_of_mass,
number_of_contours=20,
fraction_screen_filled=0.7)
xlabel('x')
ylabel('y')
current_axes = pyplot.subplot(2, 2, 4)
current_axes.set_aspect("equal", adjustable="box")
effective_iso_potential_plot(gravity_code,
omega,
center_of_rotation=center_of_mass,
xlim=[0.6, 1.4] | units.AU,
ylim=[-0.4, 0.4] | units.AU,
number_of_contours=20,
fraction_screen_filled=0.9)
xlabel('x')
ylabel('y')
pyplot.show()
native_plot, plot, scatter, xlabel, ylabel, hist
)
import numpy as np
if __name__ == "__main__":
# latex_support()
x = np.pi / 20.0 * (list(range(-10, 10)) | units.m)
y1 = units.MSun.new_quantity(np.sin(x.number))
y2 = units.MSun.new_quantity(x.number)
native_plot.subplot(2, 2, 1)
plot(x, y2, label='model')
scatter(x, y1, label='data')
xlabel('x')
ylabel('mass [$M_\odot$]') # overrides auto unit!
native_plot.legend(loc=2)
x = list(range(50)) | units.Myr
y1 = quantities.new_quantity(
np.sin(np.arange(0, 1.5, 0.03)), 1e50 * units.erg)
y2 = -(1e43 | units.J) - y1
native_plot.subplot(2, 2, 2)
plot(x, y1, label='$E_\mathrm{kin}$')
plot(x, y2, label='$E_\mathrm{pot}$')
xlabel('t')
ylabel('E')
native_plot.legend()
x = list(range(7)) | units.day
def dm_gas_sph_subplot(self, i=0):
fig = pyplot.figure(figsize=(20, 12))
gs = gridspec.GridSpec(2, 2, height_ratios=[1, 4])
# gs.update(left=0.05, right=0.48, wspace=0.05)
ax_text = pyplot.subplot(gs[0, :])
ax_text.axis('off')
time_text = ax_text.text(0.02, 1.0, '', transform=ax_text.transAxes, fontsize=42)
time_text.set_text('Time: {0:.1f} Myr'.format(self.code.model_time.value_in(units.Myr)))
energy_text = ax_text.text(0.02, -0.2, '', transform=ax_text.transAxes, fontsize=42)
Ekin = self.code.kinetic_energy.value_in(units.erg)
Epot = self.code.potential_energy.value_in(units.erg)
Eth = self.code.thermal_energy.value_in(units.erg)
energy_text.set_text('Ekin: {0:.3e} erg\nEpot: {1:.3e} erg\nEth: {2:.3e} erg'
.format(Ekin, Epot, Eth))
# lim = max(abs((self.dmA.center_of_mass() - self.dmB.center_of_mass()).value_in(units.Mpc)))
lim = 2.5
ax_dm = pyplot.subplot(gs[1, 0], xlim=(-4*lim, 4*lim), ylim=(-4*lim, 4*lim))
ax_gas = pyplot.subplot(gs[1, 1], aspect='equal',
sharex=ax_dm, sharey=ax_dm, xlim=(-4*lim, 4*lim), ylim=(-4*lim, 4*lim))
# ax_dm = fig.add_subplot(121, aspect='equal')
# ax_gas = fig.add_subplot(122, aspect='equal',
# sharex=ax_dm, sharey=ax_dm)
# plot dark matter
pyplot.gcf().sca(ax_dm)
x = self.dmA.x.as_quantity_in(units.Mpc)
y = self.dmA.y.as_quantity_in(units.Mpc)
scatter(x, y, c='red', edgecolor='red', label=str(self.subClusterA.name))
x = self.dmB.x.as_quantity_in(units.Mpc)
y = self.dmB.y.as_quantity_in(units.Mpc)
scatter(x, y, c='green', edgecolor='green', label=str(self.subClusterB.name))
xlabel(r'$x$')
ylabel(r'$y$')
pyplot.legend()
# plot gas as sph plot
def plot_sph(gas):
# Adjusted code from amuse.plot.sph_particles_plot
pyplot.gcf().sca(ax_gas)
min_size = 100
max_size = 10000
alpha = 0.1
x = gas.x
y = gas.y
z = gas.z
z, x, y, us, h_smooths = z.sorted_with(x, y, gas.u, gas.h_smooth)
u_min, u_max = min(us), max(us)
log_u = numpy.log((us / u_min)) / numpy.log((u_max / u_min))
clipped_log_u = numpy.minimum(numpy.ones_like(log_u), numpy.maximum(numpy.zeros_like(log_u), log_u))
red = 1.0 - clipped_log_u**4
blue = clipped_log_u**4
green = numpy.minimum(red, blue)
colors = numpy.transpose(numpy.array([red, green, blue]))
n_pixels = pyplot.gcf().get_dpi() * pyplot.gcf().get_size_inches()
ax_gas.set_axis_bgcolor('#101010')
ax_gas.set_aspect("equal", adjustable="datalim")
length_unit = smart_length_units_for_vector_quantity(x)
phys_to_pix2 = n_pixels[0]*n_pixels[1] / ((max(x)-min(x))**2 + (max(y)-min(y))**2)
sizes = numpy.minimum(numpy.maximum((h_smooths**2 * phys_to_pix2), min_size), max_size)
scatter(x.as_quantity_in(length_unit), y.as_quantity_in(length_unit),
color=colors, s=sizes, edgecolors="none", alpha=alpha)
plot_sph(self.gasA)
plot_sph(self.gasB)
xlabel(r'$x$')
ylabel(r'$y$')
pyplot.tight_layout()
pyplot.savefig('out/{0}/plots/dm_gas_sph_subplot_{1}.png'
.format(self.timestamp, i), dpi=50)
# pyplot.show()
pyplot.close()
def plot_individual_cluster_density(cluster):
""" Plot the particles' density radial profile and compare to model """
pyplot.figure(figsize=(24, 18))
# AMUSE datamodel particles. Gas has RHO and RHOm; dm rho from model.
amuse_plot.scatter(cluster.gas.r,
cluster.gas.rho,
c="g",
edgecolor="face",
s=1,
label=r"Generated IC: gas $\rho$")
# amuse_plot.scatter(cluster.gas.r,
# cluster.gas.rhom,
# c="r", edgecolor="face", s=1, label=r"Generated IC: gas $\rho_{\rm model}$")
amuse_plot.scatter(cluster.dm.r,
cluster.dm.rho,
c="b",
edgecolor="none",
label=r"Generated IC: DM $\rho$")
# Analytical solutions. Sample radii and plug into analytical expression.
r = VectorQuantity.arange(units.kpc(1), units.kpc(10000),
units.parsec(100))
# Plot analytical beta model (Donnert 2014) for the gas density
amuse_plot.plot(r,
cluster.gas_density_double_beta(r),
c="k",
ls="dashed",
label=r"Analytical, $\beta$-model:"
"\n"
r"$\rho_0$ = {0} g/cm$^3$; $rc = ${1} kpc".format(
cluster.rho0gas.number, cluster.rc.number))
# Plot analytical double beta model (Donnert et al. 2016, in prep) for gas
amuse_plot.plot(
r,
cluster.gas_density_beta(r),
c="k",
ls="dotted",
label=r"Analytical, double $\beta$-model:"
"\n"
r"$\rho_0$ = {0} g/cm$^3$; $rc =$ {1} kpc; $r_{{\rm cut}}$ = {2} kpc".
format(cluster.rho0gas.number, cluster.rc.number, cluster.rcut.number))
# Plot analytical Hernquist model for the DM density
amuse_plot.plot(r,
cluster.dm_density(r),
c="k",
ls="solid",
label=r"Analytical, Hernquist-model"
"\n"
r"$M_{{\rm dm}}= ${0:.2e} MSun; $a = $ {1} kpc".format(
cluster.M_dm.number, cluster.a.number))
pyplot.legend(loc=3)
amuse_plot.xlabel(r"$r$")
amuse_plot.ylabel(r"$\rho$")
pyplot.gca().set_xlim(xmin=10, xmax=1e4)
pyplot.gca().set_ylim(ymin=1e-30, ymax=9e-24)
pyplot.gca().set_xscale("log")
pyplot.gca().set_yscale("log")
pyplot.axvline(x=cluster.R200.value_in(units.kpc), lw=1, c="grey")
pyplot.text(cluster.R200.value_in(units.kpc), 5e-24,
r"$r_{{cut}} =$ {0}".format(cluster.rcut))
pyplot.axvline(x=cluster.rc.value_in(units.kpc), lw=1, c="grey")
pyplot.text(cluster.rc.value_in(units.kpc), 5e-24,
r"$rc =$ {0}".format(cluster.rc))
pyplot.axvline(x=cluster.a.value_in(units.kpc), lw=1, c="grey")
pyplot.text(cluster.a.value_in(units.kpc), 1e-24,
r"$a =$ {0}".format(cluster.a))
def dm_rvir_gas_sph_3dsubplot(self):
fig = pyplot.figure(figsize=(20, 10))
ax_dm = fig.add_subplot(121, aspect='equal', projection='3d')
ax_gas = fig.add_subplot(122, aspect='equal', projection='3d',
sharex=ax_dm, sharey=ax_dm)
# plot dark matter
center_of_mass = self.dm.center_of_mass()
virial_radius = self.dm.virial_radius().as_quantity_in(units.kpc)
innersphere = self.dm.select(lambda r: (center_of_mass-r).length()<virial_radius,["position"])
outersphere = self.dm.select(lambda r: (center_of_mass-r).length()>= virial_radius,["position"])
pyplot.gcf().sca(ax_dm)
x = outersphere.x.as_quantity_in(units.kpc)
y = outersphere.y.as_quantity_in(units.kpc)
z = outersphere.z.as_quantity_in(units.kpc)
plot(x, y, z, 'o', c='red', label=r'$r \geq r_{\rm vir}$')
x = innersphere.x.as_quantity_in(units.kpc)
y = innersphere.y.as_quantity_in(units.kpc)
z = innersphere.z.as_quantity_in(units.kpc)
plot(x, y, z, 'o', c='green', label=r'$r < r_{\rm vir}$')
xlabel(r'$x$')
ylabel(r'$y$')
ax_dm.set_zlabel(r'$z$ [{0}]'.format(virial_radius.unit))
pyplot.legend()
# plot gas as sph plot
# Adjusted code from amuse.plot.sph_particles_plot
pyplot.gcf().sca(ax_gas)
min_size = 100
max_size = 10000
alpha = 0.1
x = self.gas.x.as_quantity_in(units.kpc)
y = self.gas.y.as_quantity_in(units.kpc)
z = self.gas.z.as_quantity_in(units.kpc)
z, x, y, us, h_smooths = z.sorted_with(x, y, self.gas.u, self.gas.h_smooth)
u_min, u_max = min(us), max(us)
log_u = numpy.log((us / u_min)) / numpy.log((u_max / u_min))
clipped_log_u = numpy.minimum(numpy.ones_like(log_u), numpy.maximum(numpy.zeros_like(log_u), log_u))
red = 1.0 - clipped_log_u**4
blue = clipped_log_u**4
green = numpy.minimum(red, blue)
colors = numpy.transpose(numpy.array([red, green, blue]))
n_pixels = pyplot.gcf().get_dpi() * pyplot.gcf().get_size_inches()
ax_gas.set_axis_bgcolor('#101010')
ax_gas.set_aspect("equal", adjustable = "datalim")
phys_to_pix2 = n_pixels[0]*n_pixels[1] / ((max(x)-min(x))**2 + (max(y)-min(y))**2)
sizes = numpy.minimum(numpy.maximum((h_smooths**2 * phys_to_pix2), min_size), max_size)
ax_gas.scatter(x.number, y.number, z.number, color=colors, s=sizes, edgecolors="none", alpha=alpha)
xlabel(r'$x$')
ylabel(r'$y$')
ax_gas.set_zlabel(r'$z$ [{0}]'.format(virial_radius.unit))
xlim(-2.*virial_radius, 2*virial_radius)
ylim(-2.*virial_radius, 2*virial_radius)
ax_dm.set_zlim(-2.*virial_radius.number, 2*virial_radius.number)
ax_gas.set_zlim(-2.*virial_radius.number, 2*virial_radius.number)
pyplot.tight_layout()
pyplot.show()
from amuse.plot import (
native_plot, plot, scatter, xlabel, ylabel, hist
)
import numpy as np
if __name__ == "__main__":
# latex_support()
x = np.pi/20.0 * (range(-10, 10) | units.m)
y1 = units.MSun.new_quantity(np.sin(x.number))
y2 = units.MSun.new_quantity(x.number)
native_plot.subplot(2, 2, 1)
plot(x, y2, label='model')
scatter(x, y1, label='data')
xlabel('x')
ylabel('mass [$M_\odot$]') # overrides auto unit!
native_plot.legend(loc=2)
x = range(50) | units.Myr
y1 = quantities.new_quantity(
np.sin(np.arange(0, 1.5, 0.03)), 1e50*units.erg)
y2 = -(1e43 | units.J) - y1
native_plot.subplot(2, 2, 2)
plot(x, y1, label='$E_\mathrm{kin}$')
plot(x, y2, label='$E_\mathrm{pot}$')
xlabel('t')
ylabel('E')
native_plot.legend()
x = range(7) | units.day
from matplotlib import pyplot
import pickle
from amuse.plot import scatter, xlabel, ylabel
if __name__ in '__main__':
datapoints = pickle.load(open("bound_mass.dat", "rb"))
filename = "plots/bound_mass_at_6000_pc"
pyplot.title("bound mass fraction of cluster orbiting at 6kpc")
for datapoint in datapoints:
scatter(datapoint["radius"], datapoint["hop_mass_fraction"])
xlabel("cluster radius")
ylabel("bound fraction of cluster mass")
pyplot.savefig(filename)
def __init__(self):
self.merger = ClusterMerger()
timesteps = VectorQuantity.arange(0 | units.Myr, 1 | units.Gyr,
50 | units.Myr)
tot = len(timesteps)
end_time = timesteps[-1]
print "Starting Simulation :-)"
print "Generating plots on the fly :-)"
gasA_vel_list = [] | (units.km / units.s)
dmA_vel_list = [] | (units.km / units.s)
gasB_vel_list = [] | (units.km / units.s)
dmB_vel_list = [] | (units.km / units.s)
time_list = [] | units.Gyr
for i, time in enumerate(timesteps):
print_progressbar(i, tot)
self.merger.code.evolve_model(time)
self.merger.dm_gas_sph_subplot(i)
gasA_vel_list.append(self.merger.gasA.center_of_mass_velocity())
dmA_vel_list.append(self.merger.dmA.center_of_mass_velocity())
gasB_vel_list.append(self.merger.gasB.center_of_mass_velocity())
dmB_vel_list.append(self.merger.dmB.center_of_mass_velocity())
time_list.append(time)
write_set_to_file(
self.merger.code.particles,
'out/{0}/data/cluster_{1}.amuse'.format(
self.merger.timestamp, i), "amuse")
print "Plotting velocity as function of time"
fig = pyplot.figure(figsize=(12, 10), dpi=50)
plot(time_list.number,
gasA_vel_list.number,
label="gasA",
c='r',
ls='solid')
plot(time_list.number,
dmA_vel_list.number,
label="dmA",
c='r',
ls='dashed')
plot(time_list.number,
gasB_vel_list.number,
label="gasB",
c='g',
ls='solid')
plot(time_list.number,
dmB_vel_list.number,
label="dmB",
c='g',
ls='dashed')
xlabel("Time")
ylabel("Velocity")
pyplot.legend()
pyplot.show()
print "Generating gif :-)"
self.merger.create_gif()
print "Stopping the code. End of pipeline :-)"
self.merger.code.stop()