def main(filename="stellar.hdf5"):
Tmax = 100000 #| untis.K
Tmin = 1000 #| untis.K
Lmax = 1.e+6 #| untis.LSun
Lmin = 0.01 #| untis.LSun
pyplot.ion()
filename = "nbody.hdf5"
stars = read_set_from_file(filename, 'hdf5')
m = 1 + 3.0 * stars.mass / min(stars.mass)
lim = 2 * max(
max(stars.x).value_in(stars.x.unit),
stars.center_of_mass().length().value_in(stars.x.unit))
for si in reversed(list(stars.iter_history())):
c = si.temperature.value_in(units.K)
scatter(si.temperature.value_in(units.K),
si.luminosity.value_in(units.LSun),
s=m,
c=c,
cmap=pyplot.cm.hsv)
pyplot.xlabel("T [K]")
pyplot.ylabel("L [$L_\odot$]")
pyplot.xlim(Tmax, Tmin)
pyplot.ylim(Lmin, Lmax)
pyplot.loglog()
pyplot.draw()
pyplot.cla()
pyplot.show()
def make_movie(input_filename, output_filename):
cluster, gas = read_set_from_file(input_filename, 'amuse',
names=("cluster", "gas"))
lim = abs(next(cluster.history).position).max()
fig = pyplot.figure(figsize=[10,10])
artists = []
for cl, gas in zip(cluster.history, gas.history):
print "Creating frame at time", cl.get_timestamp()
points = aplot.scatter(cl.x, cl.y, c='black')
gas_points = aplot.scatter(gas.x, gas.y, s=100, c='b',
edgecolors='none', alpha=0.3, rasterized=True)
time_label = "t = {:.2f} Myr".format(
cl.get_timestamp().value_in(units.Myr))
text = aplot.text(-4./5.*lim, 4./5.*lim, time_label)
aplot.xlabel("X")
aplot.ylabel("Y")
pyplot.axis('equal')
aplot.xlim(-lim, lim)
aplot.ylim(-lim, lim)
pyplot.savefig("plots/test."+str(cl.get_timestamp().value_in(units.Myr))+".png")
pyplot.close()
artists.append((points, gas_points, text))
def test_wind_creation_constant(self):
numpy.random.seed(123457)
star = self.create_star()
star.initial_wind_velocity = star.terminal_wind_velocity
star_wind = stellar_wind.new_stellar_wind(
3e-11 | units.MSun,
mode="accelerate",
acceleration_function="constant_velocity",
)
star_wind.particles.add_particles(star)
star_wind.evolve_model(10 | units.day)
wind = star_wind.create_wind_particles()
self.assertEqual(len(wind), 912)
radii = (wind.position - star.position).lengths()
velocities = (wind.velocity - star.velocity).lengths()
self.assertGreaterEqual(radii.min(), star.radius)
self.assertLessEqual(radii.max(), 8.212 | units.RSun)
self.assertAlmostEqual(radii.mean(), 2.32897955194 | units.RSun)
self.assertAlmostEqual(velocities.min(), star.initial_wind_velocity)
self.assertAlmostEqual(velocities.max(), star.initial_wind_velocity)
return
from matplotlib import pyplot
from amuse import plot as aplot
import pylab
n, bins, patches = pylab.hist(radii.value_in(units.RSun), 10)
pylab.show()
pyplot.clf()
aplot.scatter(radii, velocities)
pyplot.show()
def test_wind_creation_constant(self):
numpy.random.seed(123457)
star = self.create_star()
star.initial_wind_velocity = star.terminal_wind_velocity
star_wind = stellar_wind.new_stellar_wind(
3e-11 | units.MSun,
mode="accelerate",
acceleration_function="constant_velocity",
)
star_wind.particles.add_particles(star)
star_wind.evolve_model(10 | units.day)
wind = star_wind.create_wind_particles()
self.assertEqual(len(wind), 912)
radii = (wind.position - star.position).lengths()
velocities = (wind.velocity - star.velocity).lengths()
self.assertGreaterEqual(radii.min(), star.radius)
self.assertLessEqual(radii.max(), 8.212 | units.RSun)
self.assertAlmostEqual(radii.mean(), 2.32897955194 | units.RSun)
self.assertAlmostEqual(velocities.min(), star.initial_wind_velocity)
self.assertAlmostEqual(velocities.max(), star.initial_wind_velocity)
return
from matplotlib import pyplot
from amuse import plot as aplot
import pylab
n, bins, patches = pylab.hist(radii.value_in(units.RSun), 10)
pylab.show()
pyplot.clf()
aplot.scatter(radii, velocities)
pyplot.show()
def main(N=10,
t_end=10 | units.Myr,
dt=1 | units.Myr,
filename="stellar.hdf5",
Mmin=1.0 | units.MSun,
Mmax=100 | units.MSun,
z=0.02,
C="SeBa"):
if C.find("SeBa") >= 0:
print "SeBa"
stellar = SeBa()
elif C.find("SSE") >= 0:
print "SSE"
stellar = SSE()
elif C.find("MESA") >= 0:
stellar = MESA()
elif C.find("EVtwin") >= 0:
stellar = EVtwin()
else:
print "No stellar model specified."
return
stellar.parameters.metallicity = z
mZAMS = new_salpeter_mass_distribution(N, Mmin, Mmax)
mZAMS = mZAMS.sorted()
bodies = Particles(mass=mZAMS)
stellar.particles.add_particles(bodies)
stellar.commit_particles()
write_set_to_file(stellar.particles, filename, 'hdf5')
Mtot_init = stellar.particles.mass.sum()
time = 0.0 | t_end.unit
while time < t_end:
time += dt
stellar.evolve_model(time)
write_set_to_file(stellar.particles, filename, 'hdf5')
Mtot = stellar.particles.mass.sum()
print "T=", time,
print "M=", Mtot, "dM[SE]=", Mtot / Mtot_init #, stellar.particles[0].stellar_type
T = []
L = []
r = []
import math
for si in stellar.particles:
T.append(math.log10(si.temperature.value_in(units.K)))
L.append(math.log10(si.luminosity.value_in(units.LSun)))
r.append(1.0 + 10 * si.radius.value_in(units.RSun))
stellar.stop()
pyplot.xlim(4.5, 3.3)
pyplot.ylim(-4.0, 3.0)
scatter(T, L, s=r)
pyplot.xlabel("$log_{10}(T/K)$")
pyplot.ylabel("$log_{10}(L/L_\odot)$")
pyplot.show()
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)
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 main(**options):
print "calculating shock using exact solution"
exact = CalculateExactSolutionIn1D()
xpositions, rho, p, u = exact.get_solution_at_time(0.12 | time)
print "calculating shock using code"
model = CalculateSolutionIn3D(**options)
grids = model.get_solution_at_time(0.12 | time)
print "sampling grid"
if model.name_of_the_code in model.sph_hydro_codes:
samples = grids
else:
samplepoints = [(x, 0.5, 0.5) | length for x in numpy.linspace(0.0, 1.0, 2000)]
print len(grids)
samples = SamplePointsOnMultipleGrids(grids, samplepoints, SamplePointOnCellCenter)
print len(samples)
samples.filterout_duplicate_indices()
print len(samples)
print "saving data"
store_attributes(xpositions,rho,u,p,filename="exact_riemann_shock_tube_problem.csv")
store_attributes(samples.x,samples.rho,samples.rhovx,samples.energy,filename="riemann_shock_tube_problem.csv")
if IS_PLOT_AVAILABLE:
print "plotting solution"
plot.plot(xpositions,rho)
plot.scatter(samples.x, samples.rho)
pyplot.xlim(0.3,0.7)
pyplot.ylim(0.5,4.5)
pyplot.savefig("riemann_shock_tube_rho_"+model.name_of_the_code+".png")
pyplot.show()
def get_mass_via_number_density(cluster):
# progressbar
import sys
pbwidth = 42
# TODO: find different method to calculate M(<r)
print "Counting particles for which radii < r to obtain M(<r)"
radii = VectorQuantity.arange(units.kpc(1), units.kpc(10000),
units.parsec(1000))
M_gas_below_r = numpy.zeros(len(radii))
M_dm_below_r = numpy.zeros(len(radii))
N = len(radii)
for i, r in enumerate(radii):
M_gas_below_r[i] = ((numpy.where(cluster.gas.r < r)[0]).size)
M_dm_below_r[i] = ((numpy.where(cluster.dm.r < r)[0]).size)
if i % 1000 == 0:
# update the bar
progress = float(i + 1000) / N
block = int(round(pbwidth * progress))
text = "\rProgress: [{0}] {1:.1f}%".format(
"#" * block + "-" * (pbwidth - block), progress * 100)
sys.stdout.write(text)
sys.stdout.flush()
sys.stdout.write("\n")
print "Done counting particles :-)... TODO: improve this method?!"
M_gas_below_r *= cluster.M_gas / cluster.raw_data.Ngas
M_dm_below_r *= cluster.M_dm / cluster.raw_data.Ndm
amuse_plot.scatter(radii, M_gas_below_r, c="g", label="Gas")
amuse_plot.scatter(radii, M_dm_below_r, c="b", label="DM")
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 main(filename):
pyplot.figure(figsize=(12, 12))
particles = read_set_from_file(filename, "amuse")
for si in particles.history:
scatter(si.x, si.y, s=100)
xlabel("x")
ylabel("y")
pyplot.show()
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 plot_individual_cluster_mass(numerical, analytical, poster_style=False):
pyplot.figure(figsize=(16, 12))
if poster_style:
pyplot.style.use(["dark_background"])
pyplot.rcParams.update({"font.weight": "bold"})
fit_colour = "white"
else:
fit_colour = "k"
# magenta, dark blue, orange, green, light blue (?)
data_colour = [(255. / 255, 64. / 255, 255. / 255),
(0. / 255, 1. / 255, 178. / 255),
(255. / 255, 59. / 255, 29. / 255),
(45. / 255, 131. / 255, 18. / 255),
(41. / 255, 239. / 255, 239. / 255)]
amuse_plot.scatter(numerical.gas_radii,
numerical.M_gas_below_r,
c=data_colour[0],
edgecolor="face",
s=4 if poster_style else 1,
label="Gas, sampled")
amuse_plot.scatter(numerical.dm_radii,
numerical.M_dm_below_r,
c=data_colour[2],
edgecolor="face",
s=4 if poster_style else 1,
label="DM, sampled")
pyplot.gca().set_xscale("log")
pyplot.gca().set_yscale("log")
# Analytical solutions. Sample radii and plug into analytical expression.
# Plot analytical Hernquist model for the DM mass M(<r)
amuse_plot.plot(analytical.radius,
analytical.dm_cummulative_mass(),
ls="solid",
c=fit_colour,
lw=5 if poster_style else 1,
label="DM: Hernquist")
# Plot analytical cut-off beta model (Donnert 2014) for the gas mass M(<r)
amuse_plot.plot(analytical.radius,
analytical.gas_mass(),
ls="dotted",
c=fit_colour,
lw=5 if poster_style else 1,
label="Gas: " + analytical.modelname)
pyplot.xlabel(r"Radius [kpc]")
pyplot.ylabel(r"Cummulative Mass [MSun]")
pyplot.gca().set_xscale("log")
pyplot.gca().set_yscale("log")
pyplot.gca().set_xlim(xmin=1, xmax=1e4)
pyplot.legend(loc=4)
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 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 main(**options):
print "calculating shock using exact solution"
exact = CalculateExactSolutionIn1D()
xpositions, rho, p, u = exact.get_solution_at_time(0.12 | time)
print "calculating shock using code"
model = CalculateSolutionIn3D(**options)
grids = model.get_solution_at_time(0.12 | time)
print "sampling grid"
if model.name_of_the_code in model.sph_hydro_codes:
samples = grids
else:
samplepoints = [(x, 0.5, 0.5) | length
for x in numpy.linspace(0.0, 1.0, 2000)]
print len(grids)
samples = SamplePointsOnMultipleGrids(grids, samplepoints,
SamplePointOnCellCenter)
print len(samples)
samples.filterout_duplicate_indices()
print len(samples)
print "saving data"
filename = "riemann_shock_tube_rho_" + model.name_of_the_code
store_attributes(xpositions, rho, u, p, filename=filename + "_exact.csv")
store_attributes(samples.x,
samples.rho,
samples.rhovx,
samples.energy,
filename=filename + ".csv")
if IS_PLOT_AVAILABLE:
print "plotting solution"
from prepare_figure import *
from distinct_colours import get_distinct
x_label = "[length]"
y_label = "[mass/length$^3$]"
figure = single_frame(x_label,
y_label,
logx=False,
logy=True,
xsize=14,
ysize=10)
color = get_distinct(2)
plot.plot(xpositions, rho, c=color[0])
plot.scatter(samples.x, samples.rho, c=color[1], s=100)
# pyplot.xlim(0.3,0.7)
# pyplot.ylim(0.5,4.5)
pyplot.xlim(0.0, 1.0)
# pyplot.ylim(0.5,4.5)
pyplot.xlabel("[length]")
pyplot.ylabel("[mass/length$^3$]")
# pyplot.savefig("riemann_shock_tube_rho_"+model.name_of_the_code+".png")
pyplot.savefig(filename)
pyplot.show()
def group_plot(groups, no_group, figname="group_plot.png"):
colors = ["r", "g", "b", "y", "k", "w"] * 100
for group, color in zip(groups, colors):
scatter(group.x, group.y, c=color)
if len(no_group):
scatter(no_group.x, no_group.y, c="m", marker="s")
native_plot.gca().set_aspect("equal", adjustable="datalim")
native_plot.savefig(figname)
native_plot.clf()
def group_plot(groups, no_group, figname="group_plot.png"):
colors = ["r", "g", "b", "y", "k", "w"]*100
for group, color in zip(groups, colors):
scatter(group.x, group.y, c=color)
if len(no_group):
scatter(no_group.x, no_group.y, c="m", marker="s")
native_plot.gca().set_aspect("equal", adjustable = "datalim")
native_plot.savefig(figname)
native_plot.clf()
def main(N=10, t_end=10|units.Myr, dt=1|units.Myr, filename="stellar.hdf5",
Mmin=1.0|units.MSun, Mmax= 100|units.MSun, z=0.02, C="SeBa"):
if C.find("SeBa")>=0:
print "SeBa"
stellar = SeBa()
elif C.find("SSE")>=0:
print "SSE"
stellar = SSE()
elif C.find("MESA")>=0:
stellar = MESA()
elif C.find("EVtwin")>=0:
stellar = EVtwin()
else:
print "No stellar model specified."
return
stellar.parameters.metallicity = z
mZAMS = new_salpeter_mass_distribution(N, Mmin, Mmax)
mZAMS = mZAMS.sorted()
bodies = Particles(mass=mZAMS)
stellar.particles.add_particles(bodies)
stellar.commit_particles()
write_set_to_file(stellar.particles, filename, 'hdf5')
Mtot_init = stellar.particles.mass.sum()
time = 0.0 | t_end.unit
while time < t_end:
time += dt
stellar.evolve_model(time)
write_set_to_file(stellar.particles, filename, 'hdf5')
Mtot = stellar.particles.mass.sum()
print "T=", time,
print "M=", Mtot, "dM[SE]=", Mtot/Mtot_init #, stellar.particles[0].stellar_type
T = []
L = []
r = []
import math
for si in stellar.particles:
T.append(math.log10(si.temperature.value_in(units.K)))
L.append(math.log10(si.luminosity.value_in(units.LSun)))
r.append(1.0+10*si.radius.value_in(units.RSun))
stellar.stop()
pyplot.xlim(4.5, 3.3)
pyplot.ylim(-4.0, 3.0)
scatter(T, L, s=r)
pyplot.xlabel("$log_{10}(T/K)$")
pyplot.ylabel("$log_{10}(L/L_\odot)$")
pyplot.show()
def density_plot(coupled_system, i_step):
if not HAS_PYNBODY:
return
figname = os.path.join("plots", "hydro_giant{0:=04}.png".format(i_step))
print " - Hydroplot saved to: ", figname
pynbody_column_density_plot(coupled_system.gas_particles,
width=3 | units.AU,
vmin=26,
vmax=33)
scatter(coupled_system.dm_particles.x,
coupled_system.dm_particles.y,
c="w")
pyplot.savefig(figname)
pyplot.close()
def main(**options):
print "calculating shock using exact solution"
exact = CalculateExactSolutionIn1D()
xpositions, rho, p, u = exact.get_solution_at_time(0.12 | time)
print "calculating shock using code {0}".format(
options["name_of_the_code"])
model = CalculateSolutionIn3D(**options)
grids = model.get_solution_at_time(0.12 | time)
print "sampling grid"
if model.name_of_the_code in model.sph_hydro_codes:
samples = grids
else:
samplepoints = [(x, 0.5, 0.5) | length
for x in numpy.linspace(0.0, 1.0, 2000)]
print len(grids)
samples = SamplePointsOnMultipleGrids(grids, samplepoints,
SamplePointOnCellCenter)
print len(samples)
samples.filterout_duplicate_indices()
print len(samples)
print "saving data"
store_attributes(
xpositions,
rho,
u,
p,
filename="exact_riemann_shock_tube_problem_{0}.csv".format(
model.name_of_the_code))
store_attributes(samples.x,
samples.rho,
samples.rhovx,
samples.energy,
filename="riemann_shock_tube_problem_{0}.csv".format(
model.name_of_the_code))
if IS_PLOT_AVAILABLE:
print "plotting solution"
plot.plot(xpositions, rho)
plot.scatter(samples.x, samples.rho)
pyplot.xlim(0.3, 0.7)
pyplot.ylim(0.5, 4.5)
pyplot.savefig("riemann_shock_tube_rho_" + model.name_of_the_code +
".png")
pyplot.show()
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_cluster(stars, filename, t, rcl, V):
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) + "\nvelocity " + str(V) + "\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 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 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 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 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 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 evolve_system(system, t_end, n_steps):
times = (t_end * range(1, n_steps+1) / n_steps).as_quantity_in(units.day)
for i_step, time in enumerate(times):
system.evolve_model(time)
print " Evolved to:", time,
figname = os.path.join("plots", "hydro_giant_{0:=04}.png".format(i_step))
print " - Hydroplot saved to: ", figname
pynbody_column_density_plot(system.gas_particles, width=30|units.AU, vmin=26, vmax=33)
scatter(system.dm_particles.x, system.dm_particles.y, c="w")
pyplot.savefig(figname)
pyplot.close()
file_base_name = os.path.join("snapshots", "hydro_giant_{0:=04}_".format(i_step))
write_set_to_file(system.gas_particles, file_base_name+"gas.amuse", format='amuse')
write_set_to_file(system.dm_particles, file_base_name+"dm.amuse", format='amuse')
system.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 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 main(filename = "stellar.hdf5"):
Tmax = 100000 #| untis.K
Tmin = 1000 #| untis.K
Lmax = 1.e+6 #| untis.LSun
Lmin = 0.01 #| untis.LSun
pyplot.ion()
filename="nbody.hdf5"
stars = read_set_from_file(filename, 'hdf5')
m = 1 + 3.0*stars.mass/min(stars.mass)
lim = 2*max(max(stars.x).value_in(stars.x.unit), stars.center_of_mass().length().value_in(stars.x.unit))
for si in reversed(list(stars.iter_history())):
c = si.temperature.value_in(units.K)
scatter(si.temperature.value_in(units.K),
si.luminosity.value_in(units.LSun), s=m, c=c, cmap=pyplot.cm.hsv)
pyplot.xlabel("T [K]")
pyplot.ylabel("L [$L_\odot$]")
pyplot.xlim(Tmax, Tmin)
pyplot.ylim(Lmin, Lmax)
pyplot.loglog()
pyplot.draw()
pyplot.cla()
pyplot.show()
def dm_rvir_gas_sph_subplot(self):
fig = pyplot.figure(figsize=(20, 10))
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
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)
scatter(x, y, c='red', edgecolor='red', label=r'$r \geq r_{\rm vir}$')
x = innersphere.x.as_quantity_in(units.kpc)
y = innersphere.y.as_quantity_in(units.kpc)
scatter(x, y, c='green', edgecolor='green', label=r'$r < r_{\rm vir}$')
xlabel(r'$x$')
ylabel(r'$y$')
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)
scatter(x, y, color=colors, s=sizes, edgecolors="none", alpha=alpha)
xlabel(r'$x$')
ylabel(r'$y$')
xlim(-2.*virial_radius, 2*virial_radius)
ylim(-2.*virial_radius, 2*virial_radius)
pyplot.tight_layout()
pyplot.show()
from amuse.units import units, quantities
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()
def evolve_coupled_system(binary_system, giant_system, t_end, n_steps,
do_energy_evolution_plot, previous_data=None):
directsum = CalculateFieldForParticles(particles=giant_system.particles, gravity_constant=constants.G)
directsum.smoothing_length_squared = giant_system.parameters.gas_epsilon**2
coupled_system = Bridge(timestep=(t_end / (2 * n_steps)), verbose=False, use_threading=True)
coupled_system.add_system(binary_system, (directsum,), False)
coupled_system.add_system(giant_system, (binary_system,), False)
times = (t_end * range(1, n_steps+1) / n_steps).as_quantity_in(units.day)
if previous_data:
with open(previous_data, 'rb') as file:
(all_times, potential_energies, kinetic_energies, thermal_energies,
giant_center_of_mass, ms1_position, ms2_position,
giant_center_of_mass_velocity, ms1_velocity, ms2_velocity) = pickle.load(file)
all_times.extend(times + all_times[-1])
else:
all_times = times
if do_energy_evolution_plot:
potential_energies = coupled_system.particles.potential_energy().as_vector_with_length(1).as_quantity_in(units.erg)
kinetic_energies = coupled_system.particles.kinetic_energy().as_vector_with_length(1).as_quantity_in(units.erg)
thermal_energies = coupled_system.gas_particles.thermal_energy().as_vector_with_length(1).as_quantity_in(units.erg)
else:
potential_energies = kinetic_energies = thermal_energies = None
giant_center_of_mass = [] | units.RSun
ms1_position = [] | units.RSun
ms2_position = [] | units.RSun
giant_center_of_mass_velocity = [] | units.km / units.s
ms1_velocity = [] | units.km / units.s
ms2_velocity = [] | units.km / units.s
i_offset = len(giant_center_of_mass)
giant_total_mass = giant_system.particles.total_mass()
ms1_mass = binary_system.particles[0].mass
ms2_mass = binary_system.particles[1].mass
print " Evolving for", t_end
for i_step, time in enumerate(times):
coupled_system.evolve_model(time)
print " Evolved to:", time,
if do_energy_evolution_plot:
potential_energies.append(coupled_system.particles.potential_energy())
kinetic_energies.append(coupled_system.particles.kinetic_energy())
thermal_energies.append(coupled_system.gas_particles.thermal_energy())
giant_center_of_mass.append(giant_system.particles.center_of_mass())
ms1_position.append(binary_system.particles[0].position)
ms2_position.append(binary_system.particles[1].position)
giant_center_of_mass_velocity.append(giant_system.particles.center_of_mass_velocity())
ms1_velocity.append(binary_system.particles[0].velocity)
ms2_velocity.append(binary_system.particles[1].velocity)
a_giant, e_giant = calculate_orbital_elements(ms1_mass, ms2_mass,
ms1_position, ms2_position,
ms1_velocity, ms2_velocity,
giant_total_mass,
giant_center_of_mass,
giant_center_of_mass_velocity)
print "Outer Orbit:", time.in_(units.day), a_giant[-1].in_(units.AU), e_giant[-1], ms1_mass.in_(units.MSun), ms2_mass.in_(units.MSun), giant_total_mass.in_(units.MSun)
if i_step % 10 == 9:
snapshotfile = os.path.join("snapshots", "hydro_triple_{0:=04}_gas.amuse".format(i_step + i_offset))
write_set_to_file(giant_system.gas_particles, snapshotfile, format='amuse')
snapshotfile = os.path.join("snapshots", "hydro_triple_{0:=04}_core.amuse".format(i_step + i_offset))
write_set_to_file(giant_system.dm_particles, snapshotfile, format='amuse')
snapshotfile = os.path.join("snapshots", "hydro_triple_{0:=04}_binary.amuse".format(i_step + i_offset))
write_set_to_file(binary_system.particles, snapshotfile, format='amuse')
datafile = os.path.join("snapshots", "hydro_triple_{0:=04}_info.amuse".format(i_step + i_offset))
with open(datafile, 'wb') as outfile:
pickle.dump((all_times[:len(giant_center_of_mass)], potential_energies,
kinetic_energies, thermal_energies, giant_center_of_mass,
ms1_position, ms2_position, giant_center_of_mass_velocity,
ms1_velocity, ms2_velocity), outfile)
figname1 = os.path.join("plots", "hydro_triple_small{0:=04}.png".format(i_step + i_offset))
figname2 = os.path.join("plots", "hydro_triple_large{0:=04}.png".format(i_step + i_offset))
print " - Hydroplots are saved to: ", figname1, "and", figname2
for plot_range, plot_name in [(8|units.AU, figname1), (40|units.AU, figname2)]:
if HAS_PYNBODY:
pynbody_column_density_plot(coupled_system.gas_particles, width=plot_range, vmin=26, vmax=32)
scatter(coupled_system.dm_particles.x, coupled_system.dm_particles.y, c="w")
else:
pyplot.figure(figsize = [16, 16])
sph_particles_plot(coupled_system.gas_particles, gd_particles=coupled_system.dm_particles,
view=plot_range*[-0.5, 0.5, -0.5, 0.5])
pyplot.savefig(plot_name)
pyplot.close()
coupled_system.stop()
make_movie()
if do_energy_evolution_plot:
energy_evolution_plot(all_times[:len(kinetic_energies)-1], kinetic_energies,
potential_energies, thermal_energies)
print " Calculating semimajor axis and eccentricity evolution for inner binary"
# Some temporary variables to calculate semimajor_axis and eccentricity evolution
total_mass = ms1_mass + ms2_mass
rel_position = ms1_position - ms2_position
rel_velocity = ms1_velocity - ms2_velocity
separation_in = rel_position.lengths()
speed_squared_in = rel_velocity.lengths_squared()
# Now calculate the important quantities:
semimajor_axis_binary = (constants.G * total_mass * separation_in /
(2 * constants.G * total_mass - separation_in * speed_squared_in)).as_quantity_in(units.AU)
eccentricity_binary = numpy.sqrt(1.0 - (rel_position.cross(rel_velocity)**2).sum(axis=1) /
(constants.G * total_mass * semimajor_axis_binary))
print " Calculating semimajor axis and eccentricity evolution of the giant's orbit"
# Some temporary variables to calculate semimajor_axis and eccentricity evolution
rel_position = ((ms1_mass * ms1_position + ms2_mass * ms2_position)/total_mass -
giant_center_of_mass)
rel_velocity = ((ms1_mass * ms1_velocity + ms2_mass * ms2_velocity)/total_mass -
giant_center_of_mass_velocity)
total_mass += giant_total_mass
separation = rel_position.lengths()
speed_squared = rel_velocity.lengths_squared()
# Now calculate the important quantities:
semimajor_axis_giant = (constants.G * total_mass * separation /
(2 * constants.G * total_mass - separation * speed_squared)).as_quantity_in(units.AU)
eccentricity_giant = numpy.sqrt(1.0 - (rel_position.cross(rel_velocity)**2).sum(axis=1) /
(constants.G * total_mass * semimajor_axis_giant))
orbit_parameters_plot(semimajor_axis_binary, semimajor_axis_giant, all_times[:len(semimajor_axis_binary)])
orbit_ecc_plot(eccentricity_binary, eccentricity_giant, all_times[:len(eccentricity_binary)])
orbit_parameters_plot(separation_in.as_quantity_in(units.AU),
separation.as_quantity_in(units.AU),
all_times[:len(eccentricity_binary)],
par_symbol="r", par_name="separation")
orbit_parameters_plot(speed_squared_in.as_quantity_in(units.km**2 / units.s**2),
speed_squared.as_quantity_in(units.km**2 / units.s**2),
all_times[:len(eccentricity_binary)],
par_symbol="v^2", par_name="speed_squared")
def evolve_coupled_system(binary_system, giant_system, t_end, n_steps,
do_energy_evolution_plot, previous_data=None):
directsum = CalculateFieldForParticles(particles=giant_system.particles, gravity_constant=constants.G)
directsum.smoothing_length_squared = giant_system.parameters.gas_epsilon**2
coupled_system = Bridge(timestep=(t_end / (2 * n_steps)), verbose=False, use_threading=True)
coupled_system.add_system(binary_system, (directsum,), False)
coupled_system.add_system(giant_system, (binary_system,), False)
times = (t_end * list(range(1, n_steps+1)) / n_steps).as_quantity_in(units.day)
if previous_data:
with open(previous_data, 'rb') as file:
(all_times, potential_energies, kinetic_energies, thermal_energies,
giant_center_of_mass, ms1_position, ms2_position,
giant_center_of_mass_velocity, ms1_velocity, ms2_velocity) = pickle.load(file)
all_times.extend(times + all_times[-1])
else:
all_times = times
if do_energy_evolution_plot:
potential_energies = coupled_system.particles.potential_energy().as_vector_with_length(1).as_quantity_in(units.erg)
kinetic_energies = coupled_system.particles.kinetic_energy().as_vector_with_length(1).as_quantity_in(units.erg)
thermal_energies = coupled_system.gas_particles.thermal_energy().as_vector_with_length(1).as_quantity_in(units.erg)
else:
potential_energies = kinetic_energies = thermal_energies = None
giant_center_of_mass = [] | units.RSun
ms1_position = [] | units.RSun
ms2_position = [] | units.RSun
giant_center_of_mass_velocity = [] | units.km / units.s
ms1_velocity = [] | units.km / units.s
ms2_velocity = [] | units.km / units.s
i_offset = len(giant_center_of_mass)
giant_total_mass = giant_system.particles.total_mass()
ms1_mass = binary_system.particles[0].mass
ms2_mass = binary_system.particles[1].mass
print(" Evolving for", t_end)
for i_step, time in enumerate(times):
coupled_system.evolve_model(time)
print(" Evolved to:", time, end=' ')
if do_energy_evolution_plot:
potential_energies.append(coupled_system.particles.potential_energy())
kinetic_energies.append(coupled_system.particles.kinetic_energy())
thermal_energies.append(coupled_system.gas_particles.thermal_energy())
giant_center_of_mass.append(giant_system.particles.center_of_mass())
ms1_position.append(binary_system.particles[0].position)
ms2_position.append(binary_system.particles[1].position)
giant_center_of_mass_velocity.append(giant_system.particles.center_of_mass_velocity())
ms1_velocity.append(binary_system.particles[0].velocity)
ms2_velocity.append(binary_system.particles[1].velocity)
a_giant, e_giant = calculate_orbital_elements(ms1_mass, ms2_mass,
ms1_position, ms2_position,
ms1_velocity, ms2_velocity,
giant_total_mass,
giant_center_of_mass,
giant_center_of_mass_velocity)
print("Outer Orbit:", time.in_(units.day), a_giant[-1].in_(units.AU), e_giant[-1], ms1_mass.in_(units.MSun), ms2_mass.in_(units.MSun), giant_total_mass.in_(units.MSun))
if i_step % 10 == 9:
snapshotfile = os.path.join("snapshots", "hydro_triple_{0:=04}_gas.amuse".format(i_step + i_offset))
write_set_to_file(giant_system.gas_particles, snapshotfile, format='amuse')
snapshotfile = os.path.join("snapshots", "hydro_triple_{0:=04}_core.amuse".format(i_step + i_offset))
write_set_to_file(giant_system.dm_particles, snapshotfile, format='amuse')
snapshotfile = os.path.join("snapshots", "hydro_triple_{0:=04}_binary.amuse".format(i_step + i_offset))
write_set_to_file(binary_system.particles, snapshotfile, format='amuse')
datafile = os.path.join("snapshots", "hydro_triple_{0:=04}_info.amuse".format(i_step + i_offset))
with open(datafile, 'wb') as outfile:
pickle.dump((all_times[:len(giant_center_of_mass)], potential_energies,
kinetic_energies, thermal_energies, giant_center_of_mass,
ms1_position, ms2_position, giant_center_of_mass_velocity,
ms1_velocity, ms2_velocity), outfile)
figname1 = os.path.join("plots", "hydro_triple_small{0:=04}.png".format(i_step + i_offset))
figname2 = os.path.join("plots", "hydro_triple_large{0:=04}.png".format(i_step + i_offset))
print(" - Hydroplots are saved to: ", figname1, "and", figname2)
for plot_range, plot_name in [(8|units.AU, figname1), (40|units.AU, figname2)]:
if HAS_PYNBODY:
pynbody_column_density_plot(coupled_system.gas_particles, width=plot_range, vmin=26, vmax=32)
scatter(coupled_system.dm_particles.x, coupled_system.dm_particles.y, c="w")
else:
pyplot.figure(figsize = [16, 16])
sph_particles_plot(coupled_system.gas_particles, gd_particles=coupled_system.dm_particles,
view=plot_range*[-0.5, 0.5, -0.5, 0.5])
pyplot.savefig(plot_name)
pyplot.close()
coupled_system.stop()
make_movie()
if do_energy_evolution_plot:
energy_evolution_plot(all_times[:len(kinetic_energies)-1], kinetic_energies,
potential_energies, thermal_energies)
print(" Calculating semimajor axis and eccentricity evolution for inner binary")
# Some temporary variables to calculate semimajor_axis and eccentricity evolution
total_mass = ms1_mass + ms2_mass
rel_position = ms1_position - ms2_position
rel_velocity = ms1_velocity - ms2_velocity
separation_in = rel_position.lengths()
speed_squared_in = rel_velocity.lengths_squared()
# Now calculate the important quantities:
semimajor_axis_binary = (constants.G * total_mass * separation_in /
(2 * constants.G * total_mass - separation_in * speed_squared_in)).as_quantity_in(units.AU)
eccentricity_binary = numpy.sqrt(1.0 - (rel_position.cross(rel_velocity)**2).sum(axis=1) /
(constants.G * total_mass * semimajor_axis_binary))
print(" Calculating semimajor axis and eccentricity evolution of the giant's orbit")
# Some temporary variables to calculate semimajor_axis and eccentricity evolution
rel_position = ((ms1_mass * ms1_position + ms2_mass * ms2_position)/total_mass -
giant_center_of_mass)
rel_velocity = ((ms1_mass * ms1_velocity + ms2_mass * ms2_velocity)/total_mass -
giant_center_of_mass_velocity)
total_mass += giant_total_mass
separation = rel_position.lengths()
speed_squared = rel_velocity.lengths_squared()
# Now calculate the important quantities:
semimajor_axis_giant = (constants.G * total_mass * separation /
(2 * constants.G * total_mass - separation * speed_squared)).as_quantity_in(units.AU)
eccentricity_giant = numpy.sqrt(1.0 - (rel_position.cross(rel_velocity)**2).sum(axis=1) /
(constants.G * total_mass * semimajor_axis_giant))
orbit_parameters_plot(semimajor_axis_binary, semimajor_axis_giant, all_times[:len(semimajor_axis_binary)])
orbit_ecc_plot(eccentricity_binary, eccentricity_giant, all_times[:len(eccentricity_binary)])
orbit_parameters_plot(separation_in.as_quantity_in(units.AU),
separation.as_quantity_in(units.AU),
all_times[:len(eccentricity_binary)],
par_symbol="r", par_name="separation")
orbit_parameters_plot(speed_squared_in.as_quantity_in(units.km**2 / units.s**2),
speed_squared.as_quantity_in(units.km**2 / units.s**2),
all_times[:len(eccentricity_binary)],
par_symbol="v^2", par_name="speed_squared")
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)
converter = nbody_system.nbody_to_si(1 | units.MSun, 1 | units.parsec)
G_si = converter.to_si(nbody_system.G)
milkyway_mass = 1.26e12 | units.MSun
M = milkyway_mass
def escape_velocity(impact_parameter):
return ((2 * G_si * M) / impact_parameter).sqrt()
def v_infinity(escape_v, v):
return (v**2 - escape_v**2).sqrt()
pyplot.title("d,v-plane")
for i in numpy.arange(0.01, 1, 0.01):
d = 100000 | units.parsec * i
esc_velocity = escape_velocity(d)
for j in numpy.arange(0.01, 1, 0.01):
v = 10000 * j | units.parsec / units.Myr
if v < esc_velocity:
scatter(d, v, color='blue', s=2)
else:
scatter(d, v, color='red', s=2)
xlabel("d")
ylabel("v")
pyplot.xlim(0, 10000000)
pyplot.ylim(0, 10000)
pyplot.savefig("plots/dv_plot")
def integrate_solar_system(particles, end_time):
convert_nbody = nbody_system.nbody_to_si(particles.mass.sum(), particles[1].position.length())
gravity = Huayno(convert_nbody)
gravity.particles.add_particles(particles)
SunEarth = Particles()
SunEarth.add_particle(particles[0])
SunEarth.add_particle(particles[3])
channel_from_to_SunEarth = gravity.particles.new_channel_to(SunEarth)
from matplotlib import pyplot, rc
x_label = "$a-a_0$ [AU]"
y_label = "eccentricty"
figure = single_frame(x_label, y_label, xsize=14, ysize=10)
from amuse.plot import scatter
from matplotlib import pyplot, rc
kep = Kepler(convert_nbody)
kep.initialize_from_particles(SunEarth)
a, e = kep.get_elements()
a0 = a
color = get_distinct(1)
scatter((a-a0).in_(units.AU), e, c=color[0], lw=0)
pyplot.text((a-a0).value_in(units.AU)-0.0003, e, "{0:.0f}".format(0.001*gravity.model_time.value_in(units.yr))+"kyr")
dt = 100 | units.yr
dt_dia = 10000 |units.yr
while gravity.model_time < end_time:
kep.initialize_from_particles(SunEarth)
a, e = kep.get_elements()
scatter((a-a0).in_(units.AU), e, c=color[0], lw=0)
if gravity.model_time> dt_dia:
dt_dia += 10000 |units.yr
if gravity.model_time<=(100|units.yr):
pyplot.text(-0.0006, e, "{0:.0f}".format(0.001*gravity.model_time.value_in(units.yr))+"kyr")
elif gravity.model_time<=(15000|units.yr):
pyplot.text(0.0003, e, "{0:.0f}".format(0.001*gravity.model_time.value_in(units.yr))+"kyr")
elif gravity.model_time<=(25000|units.yr):
pyplot.text(0.0017, e, "{0:.0f}".format(0.001*gravity.model_time.value_in(units.yr))+"kyr")
elif gravity.model_time<=(35000|units.yr):
pyplot.text(0.0031, e, "{0:.0f}".format(0.001*gravity.model_time.value_in(units.yr))+"kyr")
elif gravity.model_time<=(45000|units.yr):
pyplot.text(0.0028, e, "{0:.0f}".format(0.001*gravity.model_time.value_in(units.yr))+"kyr")
elif gravity.model_time<=(55000|units.yr):
pyplot.text(0.0021, e, "{0:.0f}".format(0.001*gravity.model_time.value_in(units.yr))+"kyr")
elif gravity.model_time<=(65000|units.yr):
pyplot.text(0.0017, e+0.002, "{0:.0f}".format(0.001*gravity.model_time.value_in(units.yr))+"kyr")
elif gravity.model_time<=(75000|units.yr):
pyplot.text(0.0014, e-0.002, "{0:.0f}".format(0.001*gravity.model_time.value_in(units.yr))+"kyr")
elif gravity.model_time<=(85000|units.yr):
pyplot.text(0.0014, e, "{0:.0f}".format(0.001*gravity.model_time.value_in(units.yr))+"kyr")
else:
pyplot.text((a-a0).value_in(units.AU), e, "{0:.0f}".format(0.001*gravity.model_time.value_in(units.yr))+"kyr")
gravity.evolve_model(gravity.model_time + dt)
channel_from_to_SunEarth.copy()
gravity.stop()
pyplot.xlabel(x_label)
pyplot.ylabel(y_label)
# pyplot.show()
pyplot.savefig("EarthOrbitVariation")
return
def integrate_solar_system(particles, end_time):
convert_nbody = nbody_system.nbody_to_si(particles.mass.sum(), particles[1].position.length())
gravity = Huayno(convert_nbody)
gravity.particles.add_particles(particles)
SunEarth = Particles()
SunEarth.add_particle(particles[0])
SunEarth.add_particle(particles[3])
channel_from_to_SunEarth = gravity.particles.new_channel_to(SunEarth)
from matplotlib import pyplot, rc
x_label = "$a-a_0$ [AU]"
y_label = "eccentricty"
figure = single_frame(x_label, y_label, xsize=14, ysize=10)
from amuse.plot import scatter
from matplotlib import pyplot, rc
kep = Kepler(convert_nbody)
kep.initialize_from_particles(SunEarth)
a, e = kep.get_elements()
a0 = a
color = get_distinct(1)
scatter((a-a0).in_(units.AU), e, c=color[0], lw=0)
pyplot.text((a-a0).value_in(units.AU)-0.0003, e, "{0:.0f}".format(0.001*gravity.model_time.value_in(units.yr))+"kyr")
dt = 100 | units.yr
dt_dia = 10000 |units.yr
while gravity.model_time < end_time:
kep.initialize_from_particles(SunEarth)
a, e = kep.get_elements()
scatter((a-a0).in_(units.AU), e, c=color[0], lw=0, s=80)
if gravity.model_time> dt_dia:
dt_dia += 10000 |units.yr
if gravity.model_time<=(100|units.yr):
pyplot.text(-0.0006, e, "{0:.0f}".format(0.001*gravity.model_time.value_in(units.yr))+"kyr")
elif gravity.model_time<=(15000|units.yr):
pyplot.text(0.0003, e, "{0:.0f}".format(0.001*gravity.model_time.value_in(units.yr))+"kyr")
elif gravity.model_time<=(25000|units.yr):
pyplot.text(0.0017, e, "{0:.0f}".format(0.001*gravity.model_time.value_in(units.yr))+"kyr")
elif gravity.model_time<=(35000|units.yr):
pyplot.text(0.0031, e, "{0:.0f}".format(0.001*gravity.model_time.value_in(units.yr))+"kyr")
elif gravity.model_time<=(45000|units.yr):
pyplot.text(0.0028, e, "{0:.0f}".format(0.001*gravity.model_time.value_in(units.yr))+"kyr")
elif gravity.model_time<=(55000|units.yr):
pyplot.text(0.0021, e, "{0:.0f}".format(0.001*gravity.model_time.value_in(units.yr))+"kyr")
elif gravity.model_time<=(65000|units.yr):
pyplot.text(0.0017, e+0.002, "{0:.0f}".format(0.001*gravity.model_time.value_in(units.yr))+"kyr")
elif gravity.model_time<=(75000|units.yr):
pyplot.text(0.0014, e-0.002, "{0:.0f}".format(0.001*gravity.model_time.value_in(units.yr))+"kyr")
#elif gravity.model_time<=(85000|units.yr):
# pyplot.text(0.0014, e, "{0:.0f}".format(0.001*gravity.model_time.value_in(units.yr))+"kyr")
else:
pass
# pyplot.text((a-a0).value_in(units.AU), e, "{0:.0f}".format(0.001*gravity.model_time.value_in(units.yr))+"kyr")
gravity.evolve_model(gravity.model_time + dt)
channel_from_to_SunEarth.copy()
gravity.stop()
pyplot.xlabel(x_label)
pyplot.ylabel(y_label)
pyplot.xlim(-0.001, 0.004)
# pyplot.show()
pyplot.savefig("EarthOrbitVariation")
return
def plot_individual_cluster_density(numerical,
analytical,
observed=None,
poster_style=False):
""" Plot the particles' density radial profile and compare to model """
pyplot.figure(figsize=(12, 9))
if poster_style:
pyplot.style.use(["dark_background"])
pyplot.rcParams.update({"font.weight": "bold"})
# magenta, dark blue, orange, green, light blue (?)
fit_colour = "white"
else:
fit_colour = "k"
data_colour = [(255. / 255, 64. / 255, 255. / 255),
(0. / 255, 1. / 255, 178. / 255),
(255. / 255, 59. / 255, 29. / 255),
(45. / 255, 131. / 255, 18. / 255),
(41. / 255, 239. / 255, 239. / 255)]
if observed:
obs_colour = "k" if observed.name == "cygA" else "k"
pyplot.errorbar(observed.radius + observed.binsize / 2,
observed.density,
xerr=observed.binsize / 2,
yerr=observed.density_std,
marker="o",
ms=7 if poster_style else 3,
alpha=1,
elinewidth=5 if poster_style else 1,
ls="",
c=obs_colour,
label=observed.name)
# Gas RHO and RHOm from Toycluster living in AMUSE datamodel
# cut = numpy.where(numerical.gas.r < numerical.R200)
amuse_plot.scatter(numerical.gas.r,
numerical.gas.rho,
c=data_colour[3],
edgecolor="face",
s=10 if poster_style else 4,
label=r"Gas, sampled")
# amuse_plot.scatter(numerical.gas.r,
# numerical.gas.rhom,
# c="r", edgecolor="face", s=1, label=r"Generated IC: gas $\rho_{\rm model}$")
# DM density obtained from mass profile (through number density)
amuse_plot.scatter(numerical.dm_radii,
numerical.rho_dm_below_r,
c=data_colour[1],
edgecolor="face",
s=10 if poster_style else 4,
label=r"DM, sampled")
# Analytical solutions.
# Plot analytical cut-off beta model (Donnert 2014) for the gas density
amuse_plot.plot(analytical.radius,
analytical.gas_density(),
c=fit_colour,
lw=5 if poster_style else 1,
ls="dashed",
label="Gas: " + analytical.modelname) #+"\n"+\
#r"$\rho_0$ = {0:.3e} g/cm$^3$; $rc = ${1:.2f} kpc"\
#.format(analytical.rho0.number, analytical.rc.number))
# Plot analytical Hernquist model for the DM density
amuse_plot.plot(
analytical.radius,
analytical.dm_density(),
c=fit_colour,
lw=5 if poster_style else 1,
ls="solid",
label=r"DM: Hernquist"
) # "\n" r"$M_{{\rm dm}}= ${0:.2e} MSun; $a = $ {1} kpc".format(
#analytical.M_dm.number, analytical.a.number))
amuse_plot.ylabel(r"Density [g/cm$^3$]")
amuse_plot.xlabel(r"Radius [kpc]")
pyplot.gca().set_xlim(xmin=1, xmax=1e4)
pyplot.gca().set_ylim(ymin=1e-32, ymax=9e-24)
pyplot.gca().set_xscale("log")
pyplot.gca().set_yscale("log")
pyplot.axvline(x=numerical.R200.value_in(units.kpc), lw=1, c=fit_colour)
pyplot.text(numerical.R200.value_in(units.kpc) + 100,
4e-24,
r"$r_{200}$",
ha="left",
fontsize=22)
pyplot.fill_between(numpy.arange(numerical.R200.value_in(units.kpc), 1e4,
0.01),
1e-32,
9e-24,
facecolor="grey",
edgecolor="grey",
alpha=0.1)
ymin = analytical.gas_density(numerical.rc.as_quantity_in(units.kpc))
pyplot.vlines(x=numerical.rc.value_in(units.kpc),
ymin=ymin.value_in(units.g / units.cm**3),
ymax=9e-24,
lw=1,
linestyles="dashed",
color=fit_colour)
pyplot.text(numerical.rc.value_in(units.kpc) + 25 if
(observed and observed.name == "cygB") else
numerical.rc.value_in(units.kpc),
4e-24,
r"$r_c$",
ha="left",
fontsize=22)
ymin = analytical.dm_density(numerical.a.as_quantity_in(units.kpc))
pyplot.vlines(x=numerical.a.value_in(units.kpc),
ymin=ymin.value_in(units.g / units.cm**3),
ymax=9e-24,
lw=1,
linestyles="solid",
color=fit_colour)
pyplot.text(numerical.a.value_in(units.kpc) - 25,
4e-24,
r"$a$",
ha="right",
fontsize=22)
# Maximum value in histogram of numerical.gas.h
ymin = analytical.gas_density(107.5 | units.kpc)
pyplot.vlines(x=107.5,
ymin=ymin.value_in(units.g / units.cm**3),
ymax=9e-24,
lw=4,
linestyles="dashed",
color=data_colour[3])
pyplot.text(107.5 - 10,
4e-24,
r"$2h_{\rm sml}$",
color=data_colour[3],
ha="right",
fontsize=22)
# pyplot.savefig("out/actuallyran.png")
# pyplot.legend(loc=3, fontsize=28)
pyplot.tight_layout()
G_si = converter.to_si(nbody_system.G)
milkyway_mass = 1.26e12 | units.MSun
M = milkyway_mass
def escape_velocity(impact_parameter):
return ((2 * G_si * M)/impact_parameter).sqrt()
def v_infinity(escape_v, v):
return (v**2 - escape_v**2).sqrt()
pyplot.title("d,v-plane")
for i in numpy.arange(0.01,1,0.01):
d = 100000|units.parsec * i
esc_velocity = escape_velocity(d)
for j in numpy.arange(0.01,1,0.01):
v = 10000*j|units.parsec/units.Myr
if v < esc_velocity:
scatter(d,v, color='blue', s=2)
else:
scatter(d,v, color='red', s=2)
xlabel("d")
ylabel("v")
pyplot.xlim(0,10000000)
pyplot.ylim(0,10000)
pyplot.savefig("plots/dv_plot")
def dm_gas_sph_subplot(self, time=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, time),
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 test4(self):
random.seed(1001)
print "Test 4: complicated density field."
# A separate group below peak_density_threshold -> should be dropped
particles0 = new_plummer_model(90,
convert_nbody=nbody_system.nbody_to_si(
0.9 | units.MSun, 1.0 | units.RSun))
# A nearby group below peak_density_threshold -> should be attached to proper group
particles1 = new_plummer_model(80,
convert_nbody=nbody_system.nbody_to_si(
0.8 | units.MSun, 1.0 | units.RSun))
particles1.x += 10 | units.RSun
# A proper group very nearby other proper group -> groups should merge
particles2a = new_plummer_model(200,
convert_nbody=nbody_system.nbody_to_si(
2.0 | units.MSun,
1.0 | units.RSun))
particles2b = new_plummer_model(300,
convert_nbody=nbody_system.nbody_to_si(
3.0 | units.MSun,
1.0 | units.RSun))
particles2a.x += 11.0 | units.RSun
particles2b.x += 11.2 | units.RSun
# A separate proper group other proper group -> groups should be preserved
particles3 = new_plummer_model(400,
convert_nbody=nbody_system.nbody_to_si(
4.0 | units.MSun, 1.0 | units.RSun))
particles3.x += 20 | units.RSun
hop = Hop(
unit_converter=nbody_system.nbody_to_si(10.7 | units.MSun, 1.0
| units.RSun))
hop.parameters.number_of_neighbors_for_local_density = 100
hop.parameters.saddle_density_threshold_factor = 0.5
hop.parameters.relative_saddle_density_threshold = True
hop.commit_parameters()
for set in [
particles0, particles1, particles2a, particles2b, particles3
]:
hop.particles.add_particles(set)
hop.calculate_densities()
hop.parameters.outer_density_threshold = 0.1 * hop.particles.density.mean(
)
hop.parameters.peak_density_threshold = hop.particles.density.amax(
) / 4.0
hop.recommit_parameters()
hop.do_hop()
groups = list(hop.groups())
self.assertEquals(len(hop.particles), 1070)
self.assertEquals(len(groups), 2)
self.assertEquals(
hop.particles.select(lambda x: x < 5 | units.RSun, "x").group_id,
-1)
self.assertEquals(hop.get_number_of_particles_outside_groups(), 299)
self.assertEquals(1070 - len(groups[0]) - len(groups[1]), 299)
expected_size = [
477, 294
] # Less than [580, 400], because particles below outer_density_threshold are excluded
expected_average_x = [11.0, 20] | units.RSun
for index, group in enumerate(groups):
self.assertEquals(group.id_of_group(), index)
self.assertAlmostEquals(group.center_of_mass()[0],
expected_average_x[index], 1)
self.assertEquals(len(group), expected_size[index])
if False: # Make a plot
original = hop.particles.copy()
from amuse.plot import scatter, native_plot
colors = ["r", "g", "b", "y", "k", "w"] * 100
for group, color in zip(hop.groups(), colors):
scatter(group.x, group.y, c=color)
original -= group
scatter(original.x, original.y, c="m", marker="s")
native_plot.show()
hop.stop()
def test4(self):
random.seed(1001)
print "Test 4: complicated density field."
# A separate group below peak_density_threshold -> should be dropped
particles0 = new_plummer_model(90, convert_nbody=nbody_system.nbody_to_si(0.9 | units.MSun, 1.0 | units.RSun))
# A nearby group below peak_density_threshold -> should be attached to proper group
particles1 = new_plummer_model(80, convert_nbody=nbody_system.nbody_to_si(0.8 | units.MSun, 1.0 | units.RSun))
particles1.x += 10 | units.RSun
# A proper group very nearby other proper group -> groups should merge
particles2a = new_plummer_model(200, convert_nbody=nbody_system.nbody_to_si(2.0 | units.MSun, 1.0 | units.RSun))
particles2b = new_plummer_model(300, convert_nbody=nbody_system.nbody_to_si(3.0 | units.MSun, 1.0 | units.RSun))
particles2a.x += 11.0 | units.RSun
particles2b.x += 11.2 | units.RSun
# A separate proper group other proper group -> groups should be preserved
particles3 = new_plummer_model(400, convert_nbody=nbody_system.nbody_to_si(4.0 | units.MSun, 1.0 | units.RSun))
particles3.x += 20 | units.RSun
hop = Hop(unit_converter=nbody_system.nbody_to_si(10.7 | units.MSun, 1.0 | units.RSun))
hop.parameters.number_of_neighbors_for_local_density = 100
hop.parameters.saddle_density_threshold_factor = 0.5
hop.parameters.relative_saddle_density_threshold = True
hop.commit_parameters()
for set in [particles0, particles1, particles2a, particles2b, particles3]:
hop.particles.add_particles(set)
hop.calculate_densities()
hop.parameters.outer_density_threshold = 0.1 * hop.particles.density.mean()
hop.parameters.peak_density_threshold = hop.particles.density.amax() / 4.0
hop.recommit_parameters()
hop.do_hop()
groups = list(hop.groups())
self.assertEquals(len(hop.particles), 1070)
self.assertEquals(len(groups), 2)
self.assertEquals(hop.particles.select(lambda x: x < 5|units.RSun, "x").group_id, -1)
self.assertEquals(hop.get_number_of_particles_outside_groups(), 299)
self.assertEquals(1070 - len(groups[0]) - len(groups[1]), 299)
expected_size = [477, 294] # Less than [580, 400], because particles below outer_density_threshold are excluded
expected_average_x = [11.0, 20] | units.RSun
for index, group in enumerate(groups):
self.assertEquals(group.id_of_group(), index)
self.assertAlmostEquals(group.center_of_mass()[0], expected_average_x[index], 1)
self.assertEquals(len(group), expected_size[index])
if False: # Make a plot
original = hop.particles.copy()
from amuse.plot import scatter, native_plot
colors = ["r", "g", "b", "y", "k", "w"]*100
for group, color in zip(hop.groups(), colors):
scatter(group.x, group.y, c=color)
original -= group
scatter(original.x, original.y, c="m", marker="s")
native_plot.show()
hop.stop()
#We calculate v_{i+1/2} over the x y and z (I tried adding directly but got mismatched units)
#and we calculate x_{i+1} with the velocity v_{i+1/2}
Tracker[
i].velocity = Tracker[i].velocity + Accelout * gravity.timestep
Tracker[i].position = Tracker[
i].position + Tracker[i].velocity * gravity.timestep
k += 1
gravity.stop()
# In[6]:
for i in range(100):
scatter(Tracker[i].position[0], Tracker[i].position[1])
for i in range(41):
scatter(Galaxies[i].x, Galaxies[i].y)
#Quick plot to see end result, need to make some actual data analysis functions and such which will come later
#The other cell's are mostly just me randomly testing stuff to make sure things work, they can be ignored
# In[7]:
print(Tracker.position)
# In[12]:
a = 8
b = 9
# print "file " + out_file + " already exists, skip plotting"
# continue
print "ouputing " + out_file
cluster, gascloud = read_set_from_file(data_file, 'amuse',
names=("cluster", "gas"))
lim = abs(next(cluster.history).position).max()
fig = pyplot.figure(figsize=[10, 10])
artists = []
cl = None
gas = None
for c, g in zip(cluster.history, gascloud.history):
cl = c
gas = g
points = aplot.scatter(cl.x, cl.y, c='black',
label='Stellar Cluster')
gas_points = aplot.scatter(gas.x, gas.y, s=100, c='b',
edgecolors='none', alpha=0.3,
rasterized=True,
label='Giant Molecular Cloud')
time_label = r'$t_{\rm end}$ ' + "= {:.2f} Myr".format(cl.get_timestamp().
value_in(units.Myr))
text = aplot.text(-4./5.*lim, 4./5.*lim, time_label)
aplot.xlabel("X")
aplot.ylabel("Y")
pyplot.axis('equal')
aplot.xlim(-lim, lim)
aplot.ylim(-lim, lim)
pyplot.legend()
pyplot.title(r'Cluster orbitting GMC with $d$={0}, $v_\infty$={1}'
