def main(N, t_end, z, C="SeBa", plot=False):
if "SeBa" not in C:
x_label = "T [K]"
y_label = "L [L$_\odot$]"
figure = single_frame(x_label, y_label, logx=True, logy=True, xsize=14, ysize=10)
color = get_distinct(4)
pyplot.xlim(1.e+5, 1.e+3)
pyplot.ylim(1.e-4, 1.e+4)
filename = "Stellar_"+"SeBa"+".h5"
plot_HRD(filename, color[0])
filename = "Stellar_"+C+".h5"
plot_HRD(filename, color[1])
pyplot.savefig("HRD_N3000at4500Myr")
elif not plot:
numpy.random.seed(1)
masses = new_salpeter_mass_distribution(N)
stars = Particles(mass=masses)
get_stellar_temperature_and_luminosity(stars, C=C, z=z, t_end=t_end, write=True)
else:
x_label = "T [K]"
y_label = "L [L$_\odot$]"
figure = single_frame(x_label, y_label, logx=True, logy=True, xsize=14, ysize=10)
color = get_distinct(4)
pyplot.xlim(1.e+5, 1.e+3)
pyplot.ylim(1.e-4, 1.e+4)
filename = "Stellar_"+C+".h5"
plot_HRD(filename, color[0])
# pyplot.show()
pyplot.savefig("HRD_N3000at4500Myr")
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_XiTau(filename, nj):
from matplotlib import pyplot
colors = get_distinct(4)
figure = pyplot.figure(figsize=(16, 12))
ax = pyplot.gca()
ax.minorticks_on() # switch on the minor ticks
ax.locator_params(nbins=3)
t, a_out, a_new, e_out = retrieve_semi_major_axis_evolution(filename, nj)
from matplotlib import pyplot
#pyplot.plot(t.value_in(units.yr), e_out, c=colors[0])
pyplot.plot(t.value_in(units.yr), a_out.value_in(units.AU), c=colors[0])
pyplot.plot(t.value_in(units.yr), a_new.value_in(units.AU), c=colors[1])
t, a_out, a_new, e_out = retrieve_semi_major_axis_evolution(filename, 4.0)
pyplot.plot(t.value_in(units.yr), a_new.value_in(units.AU), c=colors[2], lw=2)
t, a_out, a_new, e_out = retrieve_semi_major_axis_evolution(filename, 8.0)
pyplot.plot(t.value_in(units.yr), a_new.value_in(units.AU), c=colors[3], lw=2)
pyplot.xlim(0, 12)
pyplot.xlabel("t [year]")
pyplot.ylabel("$a_{outer}$ [AU]")
pyplot.savefig("fig_XiTau_orbital_separation")
pyplot.show()
def plot_galaxy_and_stars(galaxy, stars):
colors = get_distinct(3)
single_frame('X [pc]', 'Y [pc]')
xlim = 60
pyplot.xlim(-xlim, xlim)
pyplot.ylim(-xlim, xlim)
ax = pyplot.gca()
import numpy as np
import pandas as pd
from scipy import stats, integrate
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(color_codes=True)
p = galaxy.select(lambda x: x<60|units.parsec,["x"])
p = p.select(lambda x: x>-60|units.parsec,["x"])
p = p.select(lambda y: y<60|units.parsec,["y"])
p = p.select(lambda y: y>-60|units.parsec,["y"])
x = p.x.value_in(units.parsec)
y = p.y.value_in(units.parsec)
sns.kdeplot(x, y, ax=ax)
m = 100*numpy.sqrt(stars.mass/stars.mass.max())
pyplot.scatter(stars.x.value_in(units.parsec), stars.y.value_in(units.parsec), c=colors[0], s=m, lw=0)
# pyplot.show()
pyplot.savefig("Fujii_Comparison_Figure")
def main(M, z, output_filename):
numpy.random.seed(31415)
x_label = "$R$ [R$_\odot$]"
y_label = "$\\rho$ [g/cm$^{3}$]"
fig, ax = figure_frame(x_label, y_label, xsize=12, ysize=8)
cols = get_distinct(3)
r, rho = get_density_profile(EVtwin, M, z)
pyplot.plot(r.value_in(units.RSun),
rho.value_in(units.g/units.cm**3),
label="EVtwin", c=cols[0])
r, rho = get_density_profile(MESA, M, z)
pyplot.plot(r.value_in(units.RSun),
rho.value_in(units.g/units.cm**3),
label="MESA", c=cols[1])
# Run the merger code.
r, rho = merge_two_stars(0.5*M, 0.5*M, 1|units.yr)
pyplot.plot(r.value_in(units.RSun),
rho.value_in(units.g/units.cm**3),
label="MESA", c=cols[2])
pyplot.semilogy()
if output_filename is not None:
pyplot.savefig(output_filename)
print '\nSaved figure in file', output_filename,'\n'
else:
output_filename = 'merger_stellar_density_profile.png'
pyplot.savefig(output_filename)
print '\nSaved figure in file', output_filename,'\n'
pyplot.show()
def main():
x_label = "$log_{10}[(t_{end}-t)/t_{MS}]$"
y_label = "$\zeta$"
figure = single_frame(x_label, y_label, logx=False, logy=False, xsize=14, ysize=10)
color = get_distinct(12)
pyplot.text(-2.2, 0.8, "giant branch")
pyplot.text(-0.1, 0.8, "main sequence")
pyplot.xlim(0.5, -4.)
pyplot.ylim(-0.6, 1.2)
fii = 0
ti=0
for dmi in dmdt:
for fi in filenames:
f = open(fi)
for line in f:
t, z, tms = process_file(f, dmi)
#print t, z, tms
for i in range(len(t)):
if z[i]<0:
ti = i
break
for i in range(len(t)):
t[i] = numpy.log10((t[-1]-t[i])/tms)
# pyplot.scatter(t, z)
pyplot.plot(t[ti:], z[ti:], c=color[fii], ls=ls[fii], lw=lw[fii])
f.close()
fii += 1
pyplot.show()
def plot_galaxy_and_stars(galaxy, stars):
colors = get_distinct(3)
single_frame('X [kpc]', 'Y [kpc]')
xlim = 10
pyplot.xlim(-xlim, xlim)
pyplot.ylim(-xlim, xlim)
ax = pyplot.gca()
import numpy as np
import pandas as pd
from scipy import stats, integrate
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(color_codes=True)
lim = 10|units.kpc
p = galaxy.select(lambda x: x<lim,["x"])
p = p.select(lambda x: x>-lim,["x"])
p = p.select(lambda y: y<lim,["y"])
p = p.select(lambda y: y>-lim,["y"])
p = p.select(lambda r: r.length()>5|units.kpc,["position"])
x = p.x.value_in(units.kpc)
y = p.y.value_in(units.kpc)
sns.kdeplot(x, y, ax=ax, shade=True, n_levels=20, shade_lowest=False)
m = 100*numpy.sqrt(stars.mass/stars.mass.max())
pyplot.scatter(stars.x.value_in(units.kpc), stars.y.value_in(units.kpc), c=colors[0], s=m, lw=0)
pyplot.savefig("SolarSiblings_life_galaxy")
def plot_projected_density(model, xmin=-1, xmax=1, col=0):
pyplot.xlim(xmin, xmax)
pyplot.ylim(xmin, xmax)
cols = get_distinct(4)
pyplot.scatter(model.x.value_in(nbody_system.length),
model.z.value_in(nbody_system.length),
c=cols[col], s=80, lw=0)
def main(N, m, M, ximf):
numpy.random.seed(31415)
x_label = "M$_\odot$"
y_label = "N"
fig, ax = figure_frame(x_label, y_label, xsize=12, ysize=8)
cols = get_distinct(2)
masses = new_salpeter_mass_distribution(N, m, M, ximf)
masses = new_salpeter_mass_distribution(N, m, M, ximf)
lm = math.log10(0.5*m.value_in(units.MSun))
lM = math.log10(1.5*M.value_in(units.MSun))
bins = 10**numpy.linspace(lm, lM, 51)
Nbin, bin_edges= numpy.histogram(masses.value_in(units.MSun), bins=bins)
bin_sizes = bin_edges[1:] - bin_edges[:-1]
y = Nbin / bin_sizes
x = (bin_edges[1:] + bin_edges[:-1]) / 2.0
for i in range(len(y)):
y[i] = max(y[i], 1.e-10)
pyplot.scatter(x, y, s=100, c=cols[0], lw=0)
c = ((M.value_in(units.MSun)**(ximf+1)) - (m.value_in(units.MSun)**(ximf+1))) / (ximf+1)
pyplot.plot(x, N/ c * (x**ximf), c=cols[1])
pyplot.loglog()
pyplot.xlabel('$M [M_\odot]$')
pyplot.ylabel('N')
#pyplot.xlim(-3, 3)
# pyplot.show()
pyplot.savefig("salpeter")
def main(N, W0, t_end, Rvir, Mmin, Mmax):
bodies = generate_initial_conditions(N, W0, Rvir, Mmin, Mmax)
x_label = "t [Myr]"
y_label = "R [pc]"
fig = single_frame(x_label, y_label, logx=False, logy=False, xsize=14, ysize=12)
color = get_distinct(4)
time, Lr25, Lr50, Lr75 = run_only_gravity(bodies.copy(), t_end)
pyplot.plot(time.value_in(units.Myr), Lr25.value_in(units.parsec), c=color[0], label= 'without mass loss')
pyplot.plot(time.value_in(units.Myr), Lr50.value_in(units.parsec), c=color[0])
pyplot.plot(time.value_in(units.Myr), Lr75.value_in(units.parsec), c=color[0])
time, Lr25, Lr50, Lr75 = run_sequential_gravity_and_stellar(bodies.copy(), t_end)
pyplot.plot(time.value_in(units.Myr), Lr25.value_in(units.parsec), c=color[1], label= 'with mass loss')
pyplot.plot(time.value_in(units.Myr), Lr50.value_in(units.parsec), c=color[1])
pyplot.plot(time.value_in(units.Myr), Lr75.value_in(units.parsec), c=color[1])
time, Lr25, Lr50, Lr75 = run_event_driven_gravity_and_stellar(bodies.copy(), t_end)
pyplot.plot(time.value_in(units.Myr), Lr25.value_in(units.parsec), c=color[2], label= 'Event driven')
pyplot.plot(time.value_in(units.Myr), Lr50.value_in(units.parsec), c=color[2])
pyplot.plot(time.value_in(units.Myr), Lr75.value_in(units.parsec), c=color[2])
pyplot.legend(loc="upper left", ncol=1, shadow=False, fontsize=24)
#pyplot.show()
pyplot.savefig("gravity_stellar_comparison")
def plot_ionization(ism, N, which, rmax, t_end, rS, ip):
r, x, R, X = get_ionization_fraction1d(ism)
xx, yy, xi, hh, ll = get_ionization_fraction2d(ism)
w = 14
if ip > 1:
pyplot.close('all')
pyplot.clf()
pyplot.figure(figsize=(w,6))
pyplot.rcParams.update({'font.size': 22})
colors = get_distinct(3)
# rscolor = 'green'
ax1 = pyplot.subplot(1,2,1)
#ax1.scatter(r, x, c=colors[0], lw=0, s=10)
ax1.scatter(r, x, c=x, lw=0, s=20, alpha=0.5, cmap="jet")
ax1.plot(R[1:], X[1:], c=colors[1], lw=3)
ax1.plot([rS, rS], [-1,2], color=colors[2], linestyle='dashed', lw=3)
ax1.text(rS+0.08, 1.06, r'$R_s$', color=colors[2], fontsize=20)
ax1.set_xlim(0, rmax)
ax1.set_ylim(-0.04, 1.19)
ax1.set_xlabel("r [pc]", fontsize=20)
ax1.set_ylabel(r'$\xi_{\rm ion}$', fontsize=20)
ax2 = pyplot.subplot(1,2,2)
h = numpy.array(hh)
h *= 72*w/(6.*ll) # approximate scaling
ax2.set_aspect(1)
sc2 = ax2.scatter(xx, yy, c=xi, s=numpy.pi*h**2,
alpha=0.05, edgecolor=None, cmap="jet")
if ll < rmax: ll = rmax
ax2.set_xlim(-ll,ll)
ax2.set_ylim(-ll,ll)
ax2.set_xlabel("x [pc]", fontsize=20)
ax2.set_ylabel("y [pc]", fontsize=20)
#for axi in ax2.flat:
ax2.xaxis.set_major_locator(pyplot.MaxNLocator(6))
ax2.yaxis.set_major_locator(pyplot.MaxNLocator(6))
#pyplot.colorbar(sc2, ax=ax2, fraction=0.046, pad=0.04) # magic numbers!
circle = pyplot.Circle((0, 0), rS, color=colors[2],
fill=False, linestyle='dashed', lw=3)
ax2.add_artist(circle)
if which == 0:
id_string = 'Plummer_model'
else:
id_string = 'Homogeneous_sphere'
param_string = "_N=%d_t=%.3f_Myr" % (N, t_end)
#pyplot.suptitle(id_string+param_string, y=0.96, fontsize=16)
savefile = 'fig_ionization_of_GMC_'+id_string+param_string+'.png'
pyplot.savefig(savefile, dpi=300)
print 'Figure saved in file', savefile
if ip == 0: pyplot.show()
def get_color_based_on_stellar_type(istp):
color = get_distinct(5)
if istp.value_in(units.stellar_type) < 2:
c = color[0]
elif istp.value_in(units.stellar_type) < 6:
c = color[3]
else:
c = color[4]
return c
def plot_single_image(particles, lim):
# nullfmt = NullFormatter() # no labels
left, width = 0.1, 0.65
bottom, height = 0.1, 0.65
bottom_h = left_h = left+width+0.05
rect_scatter = [left, bottom, width, height]
rect_histx = [left, bottom_h, width, 0.2]
rect_histy = [left_h, bottom, 0.2, height]
colors = get_distinct(4)
# pyplot.rcParams.update({'font.size': 30})
fig = pyplot.figure(figsize=(12,12))
# ax = pyplot.gca()
# ax.minorticks_on() # switch on the minor ticks
# ax.locator_params(nbins=3)
time = particles.get_timestamp()
# pyplot.title("Cluster at t="+str(time.in_(units.Gyr)))
xy = pyplot.axes(rect_scatter)
xy.text(11,11, "Galaxy at t="+str(time.in_(units.Gyr)), ha='left', va='bottom')
xz = pyplot.axes(rect_histx)
yz = pyplot.axes(rect_histy)
xy.set_xlabel("X [kpc]")
xy.set_ylabel("Y [kpc]")
xz.set_ylabel("Z [kpc]")
yz.set_xlabel("Z [kpc]")
xy.scatter([0.0], [0.0], s=200, c=colors[1], marker='+')
xz.scatter([0.0], [0.0], s=200, c=colors[1], marker='+')
yz.scatter([0.0], [0.0], s=200, c=colors[1], marker='+')
xy.scatter([8.5], [0.0], s=100, c=colors[2], marker='o', lw=0)
xz.scatter([8.5], [0.0], s=100, c=colors[2], marker='o', lw=0)
yz.scatter([0.0], [0.0], s=100, c=colors[2], marker='o', lw=0)
# axHistx.xaxis.set_major_formatter(nullfmt)
# axHisty.yaxis.set_major_formatter(nullfmt)
positions = particles.position
x, y, z = positions.x.value_in(units.kpc), positions.y.value_in(units.kpc), positions.z.value_in(units.kpc)
xy.scatter(x, y, c=colors[0], lw=0)
xy.set_xlim( (-lim, lim) )
xy.set_ylim( (-lim, lim) )
xz.scatter(x, z, c=colors[0], lw=0)
yz.scatter(z, y, c=colors[0], lw=0)
xz.set_xlim( xy.get_xlim() )
# yz.set_xlim( (-0.2*lim, 0.2*lim) )
# yz.set_xlim( xy.get_xlim() )
yz.set_ylim( xy.get_xlim() )
yz.set_xlim( (-0.1*lim, 0.1*lim) )
xz.set_ylim( (-0.1*lim, 0.1*lim) )
# pyplot.show()
# fig.savefig('test.png')
fig.savefig('SolarSiblings_static_galaxy')
def main():
"""
Mb = (2. + 1.) | units.MSun
Mb = 2. | units.MSun
a0 = 10. | units.AU
# vs = 35. | units.kms
vs = 30. | units.kms
Mdot_donor = 0.11 |units.MSun/units.Myr
Mdot = Bondi_Hoyle_Littleton_accretion_rate(Mb, vs, a0, Mdot_donor)
print Mdot.in_(units.MEarth/units.yr)
t = numpy.arange(0, 3, 0.1) | units.yr
mdot = Mdot*t
t += 4 | units.yr
"""
Mb = (2. + 1.) | units.MSun
Mb = 2. | units.MSun
a0 = 10. | units.AU
#vs = 30. | units.kms
# vs = 18. | units.kms
vs = 17.26 | units.kms
vorb = numpy.sqrt(constants.G*Mb/a0)
print "vorb=", vorb.in_(units.kms)
k = vorb/vs
m1 = 1.924785833858|units.MSun
m2 = 1.|units.MSun
mu = m2/(m1+m2)
Mdot_donor = 0.11 |units.MSun/units.Myr
cvw = 0.
print "k and mu:", k, mu
Mdot = Mdot_donor * mu**2 * k**4/(1 + k**2 + cvw**2)**(3./2.)
# print "Mdot:", Mdot.in_(units.MEarth/units.yr)
print "Mdot:", Mdot.in_(units.MSun/units.Myr)
t = numpy.arange(0, 3, 0.1) | units.yr
mdot = Mdot*t
t += 4.2 | units.yr
c = get_distinct(3)
x_label = 't [yr]'
y_label = 'M [$10^{-9}$M$_{\odot}$]'
figure = single_frame(x_label, y_label, logy=False, xsize=14, ysize=10)
filename = "hydro_give_or_take.data"
print t, mdot.value_in(units.MSun)
mdot /= (1.e-9|units.MSun)
pyplot.plot(t.value_in(units.yr), mdot, c=c[2])
plot_accretion_from_wind(filename, c[0])
filename = "hydro_give_or_take_gravity_NoG.data"
plot_accretion_from_wind(filename, c[1])
pyplot.savefig("hydro_accretion_from_windy_star")
def plot_ionization_fraction(pos, xion):
r = [] | units.parsec
x = []
for pi, xi in zip(pos, xion):
r.append(pi.length())
x.append(xi)
r, x = zip(*sorted(zip(r.value_in(units.parsec), x)))
R, X = binned_mean_data(r, x)
from matplotlib import pyplot
x_label = "r [pc]"
y_label = r'$\xi_{\rm ion}$'
figure = single_frame(x_label, y_label, logx=False, logy=False,
xsize=14, ysize=8)
pyplot.scatter(r, x, c=get_distinct(1), lw=0, s=100)
pyplot.plot(R, X, c=get_distinct(2)[1], lw=2)
pyplot.xlim(0, 6)
pyplot.ylim(-0.04, 1.19)
pyplot.savefig("fig_ionization_of_GMC")
pyplot.show()
def plot_function(data):
print_times_Myr,print_smas_AU,print_rps_AU,print_parent_is_deg,canonical_rp_min_A_AU,canonical_rp_min_B_AU = data
N_binaries = len(print_smas_AU)
pyplot.rc('text',usetex=True)
pyplot.rc('legend',fancybox=True)
linewidth=4
dlinewidth=2
fig=pyplot.figure(figsize=(10,9))
plot1=fig.add_subplot(2,1,1,yscale="log")
plot2=fig.add_subplot(2,1,2)
from distinct_colours import get_distinct
colors = get_distinct(4)
labels = ["$A$","$B$","$C$"]
labels_i = ["$i_{AC}$","$i_{BC}$",None]
for index_binary in range(N_binaries):
label = labels[index_binary]
label_i = labels_i[index_binary]
color = colors[index_binary]
plot1.plot(print_times_Myr,print_smas_AU[index_binary],color=color,linestyle='dashed',linewidth=dlinewidth)
plot1.plot(print_times_Myr,print_rps_AU[index_binary],color=color,linewidth=linewidth,label=label)
plot2.plot(print_times_Myr,print_parent_is_deg[index_binary],color=color,linewidth=linewidth,label=label_i)
plot1.axhline(y = canonical_rp_min_A_AU, color= colors[0],linestyle='dotted',linewidth=dlinewidth)
plot1.axhline(y = canonical_rp_min_B_AU, color= colors[1],linestyle='dotted',linewidth=dlinewidth)
handles,labels = plot1.get_legend_handles_labels()
plot1.legend(handles,labels,loc="upper right",fontsize=12)
handles,labels = plot2.get_legend_handles_labels()
plot2.legend(handles,labels,loc="lower right",fontsize=12)
plot1.set_xlabel("t [Myr]",fontsize=18)
plot2.set_xlabel("t [Myr]",fontsize=18)
plot1.set_ylabel("$a_i [\mathrm{AU}]$",fontsize=18)
plot2.set_ylabel("$i_{kl} [\mathrm{deg}]$",fontsize=18)
plot1.set_xlim(0.0,print_times_Myr[-1])
plot2.set_xlim(0.0,print_times_Myr[-1])
plot1.tick_params(axis='both', which ='major', labelsize = 18)
plot2.tick_params(axis='both', which ='major', labelsize = 18)
fig.savefig("figure.eps")
pyplot.show()
def plot_temperature_line(radii, electron_temperatures, ci):
colors = get_distinct(4)
s = 100
if ci==1:
s = 50
pyplot.scatter(radii.value_in(units.parsec),
electron_temperatures.value_in(units.K),
c=colors[ci], lw=0, s=s)
pyplot.xlabel('R [pc]')
pyplot.ylabel('T [K]')
pyplot.xlim(0.8, 3.2)
def make_plot(disk1, disk2, filename):
x_label = "X [kpc]"
y_label = "Y [kpc]"
figure = single_frame(x_label, y_label, logy=False, xsize=14, ysize=14)
from distinct_colours import get_distinct
c = get_distinct(2)
pyplot.xlim(-300, 300)
pyplot.ylim(-300, 300)
pyplot.scatter(disk1.x.value_in(units.kpc), disk1.y.value_in(units.kpc),
c=c[0], alpha=1, s=1, lw=0)
pyplot.scatter(disk2.x.value_in(units.kpc), disk2.y.value_in(units.kpc),
c=c[1], alpha=1, s=1, lw=0)
pyplot.savefig(filename)
def plot_HD971331(filename):
t_end = 4000|units.day
t_start = 8000|units.day
from matplotlib import pyplot
colors = get_distinct(4)
figure = pyplot.figure(figsize=(16, 12))
ax = pyplot.gca()
ax.minorticks_on() # switch on the minor ticks
ax.locator_params(nbins=3)
t, a_out, e_out, M_out, m1, m2 = read_orbital_elements(filename)
pyplot.plot(t.value_in(units.yr), a_out.value_in(units.AU), c=colors[0])
"""
t, a_out, a_new = retrieve_semi_major_axis_evolution(filename, nj=4.0, t_end=8000|units.day)
pyplot.plot(t.value_in(units.yr), a_new.value_in(units.AU), c='r')
pyplot.scatter(t[-1].value_in(units.yr), a_new[-1].value_in(units.AU), s=50, c=colors[1])
"""
t, a_out, a_new = retrieve_semi_major_axis_evolution(filename, nj=6, t_end=2000|units.day)
pyplot.plot(t.value_in(units.yr), a_new.value_in(units.AU), c='r')
pyplot.scatter(t[-1].value_in(units.yr), a_new[-1].value_in(units.AU), s=50, c=colors[1])
t, a_out, a_new = retrieve_semi_major_axis_evolution(filename, nj=4, t_start=2000|units.day, t_end=5000|units.day)
pyplot.plot(t.value_in(units.yr), a_new.value_in(units.AU), c=colors[1])
#pyplot.scatter(t[0].value_in(units.yr), a_new[0].value_in(units.AU), s=50, c=colors[1])
#pyplot.scatter(t[-1].value_in(units.yr), a_new[-1].value_in(units.AU), s=50, c=colors[1])
t, a_out, a_new = retrieve_semi_major_axis_evolution(filename, nj=2, t_start=5000|units.day, t_end=8000|units.day)
print t.value_in(units.yr), a_new.value_in(units.AU)
#pyplot.plot(t.value_in(units.yr), a_new.value_in(units.AU), c=colors[1])
#pyplot.scatter(t[0].value_in(units.yr), a_new[0].value_in(units.AU), s=50, c=colors[1])
#pyplot.scatter(t[-1].value_in(units.yr), a_new[-1].value_in(units.AU), s=50, c=colors[1])
t, a_out, a_new = retrieve_semi_major_axis_evolution(filename, nj=-7, t_start=8000|units.day)
pyplot.plot(t.value_in(units.yr), a_new.value_in(units.AU), c=colors[1])
#pyplot.scatter(t[0].value_in(units.yr), a_new[0].value_in(units.AU), s=50, c=colors[1])
pyplot.xlabel("t [year]")
pyplot.ylabel("$a_{giant}$ [AU]")
pyplot.ylim(0.77, 0.8201)
pyplot.xlim(0.0, 50.0)
pyplot.show()
def plot_riemann_shock_tube_rho():
x_label = "[length]"
y_label = "[mass/length$^3$]"
figure = single_frame(x_label, y_label, logx=False, logy=False, xsize=14, ysize=10)
color = get_distinct(3)
x, rho = read_csv("riemann_shock_tube_problem_exact.csv")
pyplot.plot(x,rho, c=color[0])
x, rho = read_csv("riemann_shock_tube_problem_fiN5.csv")
pyplot.scatter(x, rho, c=color[1], s=100, marker="o", lw=0)
x, rho = read_csv("riemann_shock_tube_problem_athenaN2.csv")
pyplot.scatter(x, rho, c=color[2], s=100, marker="s", lw=0)
pyplot.xlim(0.0,1.0)
# pyplot.savefig("riemann_shock_tube_rho_"+model.name_of_the_code+".png")
pyplot.savefig("riemann_shock_tube_rho")
pyplot.show()
def energy_plot(time, E_kin, E_pot, E_therm, figname):
if not HAS_MATPLOTLIB:
return
x_label = 'Time [hour]'
y_label = 'Energy [foe]'
single_frame(x_label, y_label, logx=False, logy=False, xsize=14, ysize=10, ymin=-1, ymax=-1)
cols = get_distinct(4)
FOE = 1.e+51 | units.erg
hour = 1|units.hour
pyplot.plot(time/hour, E_kin/FOE, label='E_kin', c=cols[0])
pyplot.plot(time/hour, E_pot/FOE, label='E_pot', c=cols[1])
pyplot.plot(time/hour, E_therm/FOE, label='E_therm', c=cols[2])
pyplot.plot(time/hour, (E_kin+E_pot+E_therm)/FOE, label='E_total', c=cols[3])
pyplot.legend(loc=4)
pyplot.savefig(figname)
print "\nPlot of energy evolution was saved to: ", figname
pyplot.close()
def plot_single_image(groups_of_particles, lim=10):
left, width = 0.1, 0.4
bottom, height = 0.1, 0.4
bottom_h = left_h = left+width+0.05
rect_xy = [left, bottom, width, height]
rect_xz = [left, bottom_h, width, 0.4]
rect_yz = [left_h, bottom, 0.4, height]
from distinct_colours import get_distinct
colors = get_distinct(12)
fig = pyplot.figure(figsize=(10,10))
xy = pyplot.axes(rect_xy)
xz = pyplot.axes(rect_xz)
yz = pyplot.axes(rect_yz)
xy.set_xlabel("X [pc]")
xy.set_ylabel("Y [pc]")
xz.set_ylabel("Z [pc]")
yz.set_xlabel("Z [pc]")
i = 0
for group in groups_of_particles:
x = group.x.value_in(units.parsec)
y = group.y.value_in(units.parsec)
z = group.z.value_in(units.parsec)
xy.scatter(x, y, lw=0, c=colors[min(11, i)], s=8)
xz.scatter(x, z, lw=0, c=colors[min(11, i)], s=8)
yz.scatter(z, y, lw=0, c=colors[min(11, i)], s=8)
i += 1
xy.set_xlim((-lim, lim))
xy.set_ylim((-lim, lim))
xz.set_xlim(xy.get_xlim())
yz.set_ylim(xy.get_xlim())
yz.set_xlim(xy.get_xlim())
xz.set_ylim(xy.get_xlim())
save_file = 'FractalClusterHop.png'
pyplot.savefig(save_file)
print '\nSaved figure in file', save_file,'\n'
pyplot.show()
def main(M, z, output_filename):
numpy.random.seed(31415)
x_label = "$R$ [R$_\odot$]"
y_label = "$\\rho$ [g/cm$^{3}$]"
fig, ax = figure_frame(x_label, y_label, xsize=12, ysize=8)
cols = get_distinct(3)
r, rho = get_density_profile(EVtwin, M, z)
pyplot.plot(r.value_in(units.RSun), rho.value_in(units.g/units.cm**3), label="EVtwin", c=cols[0])
r, rho = get_density_profile(MESA, M, z)
pyplot.plot(r.value_in(units.RSun), rho.value_in(units.g/units.cm**3), label="MESA", c=cols[1])
# run merger
r, rho = merge_two_stars(0.5*M, 0.5*M, 1|units.yr)
pyplot.plot(r.value_in(units.RSun), rho.value_in(units.g/units.cm**3), label="MESA", c=cols[2])
pyplot.semilogy()
if output_filename:
pyplot.savefig(output_filename)
else:
pyplot.show()
def main(M1, M2, M3, Pora, Pin, ain, aout, ein, eout,
t_end, nsteps, scheme, dtse_fac):
from matplotlib import pyplot
x_label = "$a/a_{0}$"
y_label = "$e/e_{0}$"
fig = single_frame(x_label, y_label, logx=False, logy=False,
xsize=10, ysize=8)
color = get_distinct(4)
if scheme == 0:
srange = [1,2,3]
else:
srange = [scheme]
i = 0
for s in srange:
time, ai, ei, ao, eo = evolve_triple_with_wind(M1, M2, M3,
Pora, Pin,
ain, aout,
ein, eout,
t_end, nsteps,
s, dtse_fac)
if i == 0:
pyplot.plot(ai, ei, c=color[0], label='inner')
pyplot.plot(ao, eo, c=color[1], label='outer')
i = 1
else:
pyplot.plot(ai, ei, c=color[0])
pyplot.plot(ao, eo, c=color[1])
pyplot.legend(loc='best', ncol=1, shadow=False, fontsize=20)
#save_file = 'evolve_triple_with_wind.png'
save_file = 'evolve_triple_with_wind' \
+'_s={:d}_dtse={:.3f}'.format(scheme, dtse_fac) \
+'.png'
pyplot.savefig(save_file)
print '\nSaved figure in file', save_file,'\n'
pyplot.show()
def main(N, t_end):
t_end = t_end | nbody_system.time
Q_init = 0.2
particles = new_plummer_model(N)
codes = [ph4, Huayno, BHTree]
cols = get_distinct(3)
ci = 0
x_label = "time [N-body units]"
# y_label = "$Q [\equiv -E_{\rm kin}/E_{\rm pot}]$"
y_label = "virial ratio $Q$"
figure = single_frame(x_label, y_label, xsize=14, ysize=10)
ax1 = pyplot.gca()
ax1.set_xlim(0, t_end.value_in(t_end.unit))
ax1.set_ylim(0, 0.65)
pyplot.plot([0, t_end.value_in(t_end.unit)], [0.5, 0.5], lw=1, ls='--', c='k')
for code in codes:
time, Q = virial_ratio_evolution(code, particles, Q_init, t_end)
pyplot.plot(time.value_in(t_end.unit), Q, c=cols[ci])
ci+=1
# pyplot.show()
pyplot.savefig("gravity_to_virial")
def main(M1, M2, M3, ain, aout, ein, eout, t_end, nsteps):
from matplotlib import pyplot
x_label = "$a/a_{0}$"
# x_label = "$t [Myr]$"
y_label = "ecc, $a/a_{0}$"
fig = single_frame(x_label, y_label, logx=False, logy=False, xsize=14, ysize=12)
color = get_distinct(4)
time, ain, ein, aout, eout = evolve_triple_with_wind(M1, M2, M3, ain, aout, ein, eout, t_end, nsteps)
pyplot.plot(ain, ein, c=color[0], label="inner")
pyplot.plot(aout, eout, c=color[1], label="outer")
"""
pyplot.plot(time, ein, c=color[0], label= '$e_{inner}$')
pyplot.plot(time, ain, c=color[1], label= '$a_{inner}$')
pyplot.plot(time, eout, c=color[2], label= '$e_{outer}$')
pyplot.plot(time, aout, c=color[3], label= '$a_{outer}$')
"""
pyplot.legend(loc="upper left", ncol=1, shadow=False, fontsize=24)
# pyplot.show()
pyplot.savefig("evolve_triple_with_wind")
0.09890162549942522, 0.2971536318319011, 0.5047269929821525,
0.6955052866807527, 0.8934247174201266, 1.0960741121502846,
1.296349286674131
]
S_all_gas = [
1011.82389615108, 382.3950057709804, 262.3523868909657, 154.2500699106347,
107.5704842567941, 88.9640154049995, 68.72257895441606
]
pyplot.rcParams.update({'font.size': 30})
fig, ax = pyplot.subplots(figsize=[16, 10])
ax.minorticks_on() # switch on the minor ticks
ax.tick_params('both', length=15, width=2, which='major')
ax.tick_params('both', length=6, width=1, which='minor')
from distinct_colours import get_distinct
colors = get_distinct(10)
pyplot.scatter(R_young_stars,
S_young_stars,
s=100,
marker='s',
c=colors[6],
lw=0)
pyplot.scatter(R_all_stars, S_all_stars, s=100, marker='s', c=colors[0], lw=0)
#pyplot.scatter(R_dense_gas, S_dense_gas, s=100, marker='s', c=colors[7], lw=0)
pyplot.scatter(R_all_gas, S_all_gas, s=100, marker='s', c=colors[3], lw=0)
def plot_radial_distribution(gas, nbin, c, lw):
X = []
Ymean = []
help="Dimension-less depth of the King potential (W0) [7.0]")
result.add_option("-z", dest="z", type="float", default = 0.02,
help="metalicity [0.02]")
return result
if __name__ in ('__main__', '__plot__'):
o, arguments = new_option_parser().parse_args()
time, Lr_stars25, Lr_bodies25, Lr_stars50, Lr_bodies50, Lr_stars75, Lr_bodies75 = main(**o.__dict__)
from matplotlib import pyplot
from prepare_figure import single_frame
from distinct_colours import get_distinct
x_label = "t [Myr]"
y_label = "R [pc]"
fig = single_frame(x_label, y_label, logx=False, logy=False, xsize=14, ysize=12)
color = get_distinct(4)
# import numpy
# Lr_stars = numpy.matrix.transpose(numpy.matrix(Lr_stars))
# Lr_bodies = numpy.matrix.transpose(numpy.matrix(Lr_bodies))
# print Lr_stars[6], Lr_bodies[6]
pyplot.plot(time.value_in(units.Myr), Lr_stars25.value_in(units.parsec), c=color[0], label= 'with mass loss')
pyplot.plot(time.value_in(units.Myr), Lr_bodies25.value_in(units.parsec), c=color[1], label= 'without mass loss')
pyplot.plot(time.value_in(units.Myr), Lr_stars50.value_in(units.parsec), c=color[0])
pyplot.plot(time.value_in(units.Myr), Lr_bodies50.value_in(units.parsec), c=color[1])
pyplot.plot(time.value_in(units.Myr), Lr_stars75.value_in(units.parsec), c=color[0])
pyplot.plot(time.value_in(units.Myr), Lr_bodies75.value_in(units.parsec), c=color[1])
fig = plt.figure(figsize=(8.5, 9.))
jj = 111
ax = fig.add_subplot(jj)
x = np.array([0, 1, 2, 3])
elabels = ['Voids', 'Sheets', 'Filaments', 'Knots']
plt.xticks(x, elabels)
ax.tick_params(axis='x', which='minor', bottom='off')
ytit = "$log_{10}(\Phi/ \\rm{h^{3} Mpc^{-3}})$"
ax.set_ylabel(ytit)
ymin = -7.
ymax = -1.
ax.set_ylim(ymin, ymax)
cols = get_distinct(nfiles)
# Bins
emin = -0.5
emax = 3.5
dm = 1.
frac = 0.85
lbar = dm * frac / nfiles
ebins = np.arange(emin, emax, dm)
vol = 500.**3. # Simulation volume in (Mpc/h)^3
for ii, ending in enumerate(endings):
infile = epath + root[ii] + survey + ending
if (not os.path.isfile(infile)):
print('STOP: {} not found'.format(infile))
sys.exit()
if __name__ in ('__main__','__plot__'):
# High-level structure of merge_two_stars_and_evolve.py and
# merge_two_stars_sph_evolve.py are designed to be identical.
set_printing_strategy("custom", #nbody_converter = converter,
precision = 11, prefix = "",
separator = " [", suffix = "]")
o, arguments = new_option_parser().parse_args()
Mprim = o.Mprim
Msec = o.Msec
tend = o.tend
tcoll = o.tcoll
color = get_distinct(4) # 0 = cyan, 1 = red, 2 = mustard, 3 = green
x_label = "T [K]"
y_label = "L [$L_\odot$]"
figure = single_frame(x_label, y_label, logx=True, logy=True,
xsize=14, ysize=10)
pyplot.xlim(2.e+4, 3.e3)
pyplot.ylim(20., 2.e+3)
print "Evolve single star of mass", Mprim.in_(units.MSun)
time, stp, mass, radius, temperature, luminosity \
= evolve_single_star(Mprim, tend)
pyplot.plot(temperature.value_in(units.K),
luminosity.value_in(units.LSun),
c=color[1], lw=2, zorder=1)
pyplot.scatter(temperature[0].value_in(units.K),
def plot_single_image(planets, disk, lim, index):
#centered on the Sun
com = planets[0].position
planets.position -= com
disk.position -= com
left, width = 0.1, 0.65
bottom, height = 0.1, 0.65
bottom_h = left_h = left + width + 0.05
rect_scatter = [left, bottom, width, height]
rect_histx = [left, bottom_h, width, 0.2]
rect_histy = [left_h, bottom, 0.2, height]
fig = pyplot.figure(figsize=(12, 12))
time = disk.get_timestamp()
# pyplot.title("Cluster at t="+str(time.in_(units.Gyr)))
xy = pyplot.axes(rect_scatter)
xy.text(110,
110,
"protoplanetary disk (img#" + str(index) + ")",
ha='left',
va='bottom')
xz = pyplot.axes(rect_histx)
yz = pyplot.axes(rect_histy)
xy.set_xlabel("X [AU]")
xy.set_ylabel("Y [AU]")
xz.set_ylabel("Z [AU]")
yz.set_xlabel("Z [AU]")
positions = disk.position
x, y, z = positions.x.value_in(units.AU), positions.y.value_in(units.AU), \
positions.z.value_in(units.AU)
alpha = 0.01
us = disk.u
u_min, u_max = min(us), max(us)
print "u=", u_min, u_max
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))
ps = disk.rho
p_min, p_max = min(ps), max(ps)
log_p = numpy.log((ps / p_min)) / numpy.log((p_max / p_min))
clipped_log_p = numpy.minimum(
numpy.ones_like(log_p), numpy.maximum(numpy.zeros_like(log_p), log_p))
red = 1.0 - clipped_log_u
blue = clipped_log_u
green = clipped_log_p
colors = numpy.transpose(numpy.array([red, green, blue]))
sizes = 2000 * disk.h_smooth / disk.h_smooth.max()
xy.scatter(x, y, sizes, c=colors, edgecolors="none", alpha=alpha)
xy.set_xlim((-lim, lim))
xy.set_ylim((-lim, lim))
sizes = 100 * disk.h_smooth / disk.h_smooth.max()
xz.scatter(x, z, sizes, c=colors, edgecolors="none", alpha=alpha)
yz.scatter(z, y, sizes, c=colors, edgecolors="none", alpha=alpha)
xz.set_xlim(xy.get_xlim())
yz.set_ylim(xy.get_xlim())
yz.set_xlim((-0.05 * lim, 0.05 * lim))
xz.set_ylim((-0.05 * lim, 0.05 * lim))
from distinct_colours import get_distinct
c = get_distinct(len(planets))
# m = 1000 * planets.mass/planets.mass.max()
m = 100 * numpy.log10(1 + planets.mass / planets.mass.min())
#m[0] = min(10*m[1:].max(), 30)
xy.scatter(planets.x.value_in(units.AU),
planets.y.value_in(units.AU),
s=m,
c=c,
lw=0)
xz.scatter(planets.x.value_in(units.AU),
planets.z.value_in(units.AU),
s=m,
c=c,
lw=0)
yz.scatter(planets.z.value_in(units.AU),
planets.y.value_in(units.AU),
s=m,
c=c,
lw=0)
filename = "planetary_system_i{0:04}.png".format(index)
fig.savefig(filename)
dEi = energy_error_of_integrated_Nbody_system(code, particles, t_end,
pri)
dE.append(abs(dEi))
print "integrated with precision=", pri, "dE/E=", dEi
return dE
if __name__ in ('__main__', '__plot__'):
numpy.random.seed(31415)
particles = new_plummer_model(1000)
precision = 10.**numpy.linspace(0., -3., 10)
t_end = 1.0 | nbody_system.time
cols = get_distinct(2)
print 'ph4'
code = ph4
dE = get_dE(code, precision, t_end)
pyplot.scatter(precision, dE, c=cols[0], lw=0, s=50, marker='o')
print 'BHTree'
code = BHTree
dE = get_dE(code, precision, t_end)
pyplot.scatter(precision, dE, c=cols[1], lw=0, s=50, marker='^')
t0 = 0.8
t1 = 0.02
ep = 4.e-5
eb = 0.07
def showSurveyStatistics(simulatedSurvey,
pdfFile=None,
pngFile=None,
usekde=False):
"""
Produce a plot with the survey statistics.
Parameters
----------
simulatedSurvey : Object containing the simulated survey.
Keywords
--------
pdfFile : string
Name of optional PDF file in which to save the plot.
pngFile : string
Name of optional PNG file in which to save the plot.
usekde : boolean
If true use kernel density estimates to show the distribution of survey quantities instead of
histograms.
"""
try:
_ = simulatedSurvey.observedParallaxes.shape
except AttributeError:
stderr.write("You have not generated the observations yet!\n")
return
parLimitPlot = 50.0
plxSnrLim = 5.0
positiveParallaxes = (simulatedSurvey.observedParallaxes > 0.0)
goodParallaxes = (simulatedSurvey.observedParallaxes /
simulatedSurvey.parallaxErrors >= plxSnrLim)
estimatedAbsMags = (
simulatedSurvey.observedMagnitudes[positiveParallaxes] +
5.0 * np.log10(simulatedSurvey.observedParallaxes[positiveParallaxes])
- 10.0)
relParErr = (simulatedSurvey.parallaxErrors[positiveParallaxes] /
simulatedSurvey.observedParallaxes[positiveParallaxes])
deltaAbsMag = estimatedAbsMags - simulatedSurvey.absoluteMagnitudes[
positiveParallaxes]
useagab(usetex=False, fontfam='sans')
fig = plt.figure(figsize=(27, 12))
axA = fig.add_subplot(2, 3, 1)
apply_tufte(axA, withgrid=False)
axA.set_prop_cycle(cycler('color', get_distinct(3)))
minPMinThird = np.power(simulatedSurvey.minParallax, -3.0)
maxPMinThird = np.power(parLimitPlot, -3.0)
x = np.linspace(simulatedSurvey.minParallax,
np.min([parLimitPlot, simulatedSurvey.maxParallax]), 1001)
axA.plot(x,
3.0 * np.power(x, -4.0) / (minPMinThird - maxPMinThird),
'--',
label='model',
lw=3)
if usekde:
scatter = rse(simulatedSurvey.trueParallaxes)
bw = 1.06 * scatter * simulatedSurvey.numberOfStarsInSurvey**(-0.2)
kde = KernelDensity(bandwidth=bw)
kde.fit(simulatedSurvey.trueParallaxes[:, None])
samples = np.linspace(simulatedSurvey.trueParallaxes.min(),
simulatedSurvey.trueParallaxes.max(), 200)[:,
None]
logdens = kde.score_samples(samples)
axA.plot(samples, np.exp(logdens), '-', lw=3, label='true')
else:
axA.hist(simulatedSurvey.trueParallaxes,
bins='auto',
density=True,
histtype='step',
lw=3,
label='true')
if usekde:
scatter = rse(simulatedSurvey.observedParallaxes)
bw = 1.06 * scatter * simulatedSurvey.numberOfStarsInSurvey**(-0.2)
kde = KernelDensity(bandwidth=bw)
kde.fit(simulatedSurvey.observedParallaxes[:, None])
samples = np.linspace(simulatedSurvey.observedParallaxes.min(),
simulatedSurvey.observedParallaxes.max(),
200)[:, None]
logdens = kde.score_samples(samples)
axA.plot(samples, np.exp(logdens), '-', lw=3, label='observed')
else:
axA.hist(simulatedSurvey.observedParallaxes,
bins='auto',
density=True,
histtype='step',
lw=3,
label='observed')
axA.set_xlabel(r'$\varpi$, $\varpi_\mathrm{true}$ [mas]')
axA.set_ylabel(r'$p(\varpi)$, $p(\varpi_\mathrm{true})$')
leg = axA.legend(loc='best', handlelength=1.0)
for t in leg.get_texts():
t.set_fontsize(14)
axA.text(0.025,
0.9,
'a',
horizontalalignment='center',
verticalalignment='center',
transform=axA.transAxes,
weight='bold',
fontsize=30)
axB = fig.add_subplot(2, 3, 2)
apply_tufte(axB, withgrid=False)
axB.set_prop_cycle(cycler('color', get_distinct(3)))
m = np.linspace(simulatedSurvey.observedMagnitudes.min(),
simulatedSurvey.observedMagnitudes.max(), 1000)
axB.plot(m,
np.exp(simulatedSurvey.apparentMagnitude_lpdf(m)),
'--',
lw=3,
label='model')
if usekde:
scatter = rse(simulatedSurvey.apparentMagnitudes)
bw = 1.06 * scatter * simulatedSurvey.numberOfStarsInSurvey**(-0.2)
kde = KernelDensity(bandwidth=bw)
kde.fit(simulatedSurvey.apparentMagnitudes[:, None])
samples = np.linspace(simulatedSurvey.apparentMagnitudes.min(),
simulatedSurvey.apparentMagnitudes.max(),
200)[:, None]
logdens = kde.score_samples(samples)
axB.plot(samples, np.exp(logdens), '-', label='true', lw=3)
else:
axB.hist(simulatedSurvey.apparentMagnitudes,
bins='auto',
density=True,
histtype='step',
lw=3,
label='true')
if usekde:
scatter = rse(simulatedSurvey.observedMagnitudes)
bw = 1.06 * scatter * simulatedSurvey.numberOfStarsInSurvey**(-0.2)
kde = KernelDensity(bandwidth=bw)
kde.fit(simulatedSurvey.observedMagnitudes[:, None])
samples = np.linspace(simulatedSurvey.observedMagnitudes.min(),
simulatedSurvey.observedMagnitudes.max(),
200)[:, None]
logdens = kde.score_samples(samples)
axB.plot(samples, np.exp(logdens), '-', label='observed', lw=3)
else:
axB.hist(simulatedSurvey.observedMagnitudes,
bins='auto',
density=True,
histtype='step',
lw=3,
label='observed')
axB.set_xlabel("$m$, $m_\mathrm{true}$")
axB.set_ylabel("$p(m)$, $p(m_\mathrm{true})$")
leg = axB.legend(loc=(0.03, 0.55), handlelength=1.0)
for t in leg.get_texts():
t.set_fontsize(14)
axB.text(0.025,
0.9,
'b',
horizontalalignment='center',
verticalalignment='center',
transform=axB.transAxes,
weight='bold',
fontsize=30)
axC = fig.add_subplot(2, 3, 3)
apply_tufte(axC, withgrid=False)
axC.set_prop_cycle(cycler('color', get_distinct(3)))
x = np.linspace(simulatedSurvey.absoluteMagnitudes.min(),
simulatedSurvey.absoluteMagnitudes.max(), 300)
axC.plot(x,
norm.pdf(x,
loc=simulatedSurvey.meanAbsoluteMagnitude,
scale=simulatedSurvey.stddevAbsoluteMagnitude),
'--',
lw=3,
label='model')
if usekde:
scatter = rse(simulatedSurvey.absoluteMagnitudes)
bw = 1.06 * scatter * simulatedSurvey.numberOfStarsInSurvey**(-0.2)
kde = KernelDensity(bandwidth=bw)
kde.fit(simulatedSurvey.absoluteMagnitudes[:, None])
samples = np.linspace(simulatedSurvey.absoluteMagnitudes.min(),
simulatedSurvey.absoluteMagnitudes.max(),
200)[:, None]
logdens = kde.score_samples(samples)
axC.plot(samples, np.exp(logdens), '-', label='true', lw=3)
else:
axC.hist(simulatedSurvey.absoluteMagnitudes,
bins='auto',
density=True,
histtype='step',
lw=3,
label='true')
if (simulatedSurvey.absoluteMagnitudes[goodParallaxes].size >= 3):
if usekde:
scatter = rse(simulatedSurvey.absoluteMagnitudes[goodParallaxes])
bw = 1.06 * scatter * simulatedSurvey.absoluteMagnitudes[
goodParallaxes].size**(-0.2)
kde = KernelDensity(bandwidth=bw)
kde.fit(simulatedSurvey.absoluteMagnitudes[goodParallaxes][:,
None])
samples = np.linspace(
simulatedSurvey.absoluteMagnitudes[goodParallaxes].min(),
simulatedSurvey.absoluteMagnitudes[goodParallaxes].max(),
200)[:, None]
logdens = kde.score_samples(samples)
axC.plot(
samples,
np.exp(logdens),
'-',
label=r'$\varpi/\sigma_\varpi\geq{0:.1f}$'.format(plxSnrLim),
lw=3)
else:
axC.hist(
simulatedSurvey.absoluteMagnitudes[goodParallaxes],
bins='auto',
density=True,
histtype='step',
lw=3,
label=r'$\varpi/\sigma_\varpi\geq{0:.1f}$'.format(plxSnrLim))
axC.set_xlabel("$M$")
axC.set_ylabel("$p(M)$")
leg = axC.legend(loc=(0.03, 0.55), handlelength=1.0)
for t in leg.get_texts():
t.set_fontsize(14)
axC.text(0.025,
0.9,
'c',
horizontalalignment='center',
verticalalignment='center',
transform=axC.transAxes,
weight='bold',
fontsize=30)
axD = fig.add_subplot(2, 3, 4)
apply_tufte(axD, withgrid=False)
axD.set_prop_cycle(cycler('color', get_distinct(3)))
axD.plot(simulatedSurvey.trueParallaxesNoLim,
simulatedSurvey.observedParallaxesNoLim -
simulatedSurvey.trueParallaxesNoLim,
'k,',
label=r'$m_\mathrm{lim}=\infty$')
axD.plot(simulatedSurvey.trueParallaxes,
simulatedSurvey.observedParallaxes -
simulatedSurvey.trueParallaxes,
'.',
label=r'$m_\mathrm{{lim}}={0}$'.format(
simulatedSurvey.apparentMagnitudeLimit))
axD.plot(simulatedSurvey.trueParallaxes[positiveParallaxes],
simulatedSurvey.observedParallaxes[positiveParallaxes] -
simulatedSurvey.trueParallaxes[positiveParallaxes],
'.',
label=r'$\varpi>0$')
axD.plot(simulatedSurvey.trueParallaxes[goodParallaxes],
simulatedSurvey.observedParallaxes[goodParallaxes] -
simulatedSurvey.trueParallaxes[goodParallaxes],
'o',
label=r'$\varpi/\sigma_\varpi\geq{0:.1f}$'.format(plxSnrLim))
axD.set_xlabel(r"$\varpi_\mathrm{true}$ [mas]")
axD.set_ylabel("$\\varpi-\\varpi_\\mathrm{true}$ [mas]")
leg = axD.legend(loc='best', handlelength=0.5, ncol=2)
for t in leg.get_texts():
t.set_fontsize(14)
axD.text(0.025,
0.9,
'd',
horizontalalignment='center',
verticalalignment='center',
transform=axD.transAxes,
weight='bold',
fontsize=30)
axE = fig.add_subplot(2, 3, 5)
apply_tufte(axE, withgrid=False)
axE.set_prop_cycle(cycler('color', get_distinct(3)))
axE.plot(simulatedSurvey.trueParallaxesNoLim,
simulatedSurvey.absoluteMagnitudesNoLim,
'k,',
label=r'$m_\mathrm{lim}=\infty$')
axE.plot(simulatedSurvey.trueParallaxes,
simulatedSurvey.absoluteMagnitudes,
'.',
label=r'$m_\mathrm{{lim}}={0}$'.format(
simulatedSurvey.apparentMagnitudeLimit))
axE.plot(simulatedSurvey.trueParallaxes[positiveParallaxes],
simulatedSurvey.absoluteMagnitudes[positiveParallaxes],
'.',
label=r'$\varpi>0$')
axE.plot(simulatedSurvey.trueParallaxes[goodParallaxes],
simulatedSurvey.absoluteMagnitudes[goodParallaxes],
'o',
label=r'$\varpi/\sigma_\varpi\geq{0:.1f}$'.format(plxSnrLim))
axE.set_xlabel(r"$\varpi_\mathrm{true}$ [mas]")
axE.set_ylabel("$M_\\mathrm{true}$")
axE.axhline(y=simulatedSurvey.meanAbsoluteMagnitude)
leg = axE.legend(loc='best', handlelength=0.5, ncol=2)
for t in leg.get_texts():
t.set_fontsize(14)
axE.text(0.025,
0.9,
'e',
horizontalalignment='center',
verticalalignment='center',
transform=axE.transAxes,
weight='bold',
fontsize=30)
plt.suptitle(
"Simulated survey statistics: $N_\\mathrm{{stars}}={0}$, ".format(
simulatedSurvey.numberOfStars) +
"$m_\\mathrm{{lim}}={0}$, ".format(
simulatedSurvey.apparentMagnitudeLimit) +
"$N_\\mathrm{{survey}}={0}$, ".format(
simulatedSurvey.numberOfStarsInSurvey) +
"${0}\\leq\\varpi\\leq{1}$, ".format(simulatedSurvey.minParallax,
simulatedSurvey.maxParallax) +
"$\\mu_M={0}$, ".format(simulatedSurvey.meanAbsoluteMagnitude) +
"$\\sigma_M={0:.2f}$".format(simulatedSurvey.stddevAbsoluteMagnitude))
if pdfFile is not None:
plt.savefig(pdfFile)
if pngFile is not None:
plt.savefig(pngFile)
if (pdfFile is None and pngFile is None):
plt.show()
# merge_two_stars_sph_evolve.py are designed to be identical.
set_printing_strategy(
"custom", #nbody_converter = converter,
precision=11,
prefix="",
separator=" [",
suffix="]")
o, arguments = new_option_parser().parse_args()
Mprim = o.Mprim
Msec = o.Msec
tend = o.tend
tcoll = o.tcoll
color = get_distinct(4) # 0 = cyan, 1 = red, 2 = mustard, 3 = green
x_label = "T [K]"
y_label = "L [$L_\odot$]"
figure = single_frame(x_label,
y_label,
logx=True,
logy=True,
xsize=14,
ysize=10)
pyplot.xlim(2.e+4, 3.e+3)
pyplot.ylim(20., 2.e+3)
print("Evolve single star of mass", Mprim.in_(units.MSun))
time, stp, mass, radius, temperature, luminosity \
= evolve_single_star(Mprim, tend)
def main(M1, M2, M3, Pora, Pin, ain, aout, ein, eout,
t_end, nsteps, scheme, integrator,
t_stellar, dt_se, dtse_fac, interp, show):
two_frames = False
plot_ae = True
color = get_distinct(4)
if two_frames:
plt.figure(figsize=(10, 8))
else:
plt.figure(figsize=(12, 8))
if scheme == 5:
if integrator != 3:
print 'Warning: scheme = 5 forces integrator = 3'
integrator = 3
srange = [1,scheme] # 1 = no mass loss; other = mass loss
# assume scheme > 1
i = 0
lw = [1,2]
srange = [1, scheme]
for s in srange:
time, mtot, ai, ei, ao, eo \
= evolve_triple_with_wind(M1, M2, M3,
Pora, Pin, ain, aout,
ein, eout,
t_end, nsteps,
s, integrator,
t_stellar, dt_se,
dtse_fac, interp)
if i == 0:
if two_frames: plt.subplot(1,2,1)
plt.plot(time, ai, c=color[0], linewidth=lw[i],
label='inner, no mass loss')
plt.plot(time, ao, c=color[3], linewidth=lw[i],
label='outer, no mass loss')
plt.xlabel('time (yr)')
plt.ylabel('$a/a_0$')
if two_frames:
plt.subplot(1,2,2)
if plot_ae:
plt.plot(ai, ei, c=color[0], linewidth=lw[i])
plt.plot(ao, eo, c=color[3], linewidth=lw[i])
plt.xlabel('$a/a_0$')
plt.ylabel('$e/e_0$')
else:
plt.plot(time, mtot, c=color[0], linewidth=lw[i])
plt.xlabel('time (yr)')
plt.ylabel('M')
i = 1
else:
if two_frames: plt.subplot(1,2,1)
plt.plot(time, ai, c=color[1], linewidth=lw[i],
label='inner, mass loss')
plt.plot(time, ao, c=color[2], linewidth=lw[i],
label='outer, mass loss')
if two_frames:
plt.subplot(1,2,2)
if plot_ae:
plt.plot(ai, ei, c=color[1], linewidth=lw[i])
plt.plot(ao, eo, c=color[2], linewidth=lw[i])
else:
plt.plot(time, mtot, c=color[1], linewidth=lw[i])
if two_frames: plt.subplot(1,2,1)
plt.legend(loc='best')
integrators = ['hermite', 'smalln', 'huayno', 'symple']
label = integrators[integrator]
label += ' integrator, stellev scheme= {:d}'.format(scheme)
save_file \
= 'evolve_triple_with_wind_t={:.3f}'.format(t_end.value_in(units.Myr)) \
+'_i={:d}_s={:d}'.format(integrator, scheme)
if scheme < 5:
label += ', dtse_fac = {:.3f}'.format(dtse_fac)
save_file += '_dtsefac={:.3f}'.format(dtse_fac)
else:
label += ', dt_se = {:.1f}'.format(dt_se.value_in(units.yr))
save_file += '_dtse={:.1f}'.format(dt_se.value_in(units.yr))
save_file += '.png'
if two_frames:
plt.tight_layout()
plt.subplots_adjust(top=0.88)
#plt.suptitle(label, y=0.97, fontsize=15)
ax = plt.gca()
ax.minorticks_on() # switch on the minor ticks
ax.tick_params(axis='both', which='both', direction='in')
ax.locator_params(nbins=3)
ax.get_yaxis().get_major_formatter().set_useOffset(False)
plt.savefig(save_file, dpi=300)
print '\nSaved figure in file', save_file,'\n'
if show: plt.show()
def load_matplot_stylefiles(self, catname, ncolours=0):
color = {}
linestyle = {}
marker = {}
markercol = {}
colormap = {}
colorbar = {}
plottype = {}
alpha = {}
plotlegend = {}
add_xaxis = {}
add_yaxis = {}
myData = aD.ArangeData()
mypaths = {
'0': self.mypath_handler['MATPLOT_LINESTYLE'],
'dic0': linestyle,
'1': self.mypath_handler['MATPLOT_COL'],
'dic1': color,
'2': self.mypath_handler['MATPLOT_MARKERSTYLE'],
'dic2': marker,
'3': self.mypath_handler['MATPLOT_MARKERCOL'],
'dic3': markercol,
'4': self.mypath_handler['MATPLOT_COLORMAP'],
'dic4': colormap,
'5': self.mypath_handler['MATPLOT_COLORBAR'],
'dic5': colorbar,
'6': self.mypath_handler['MATPLOT_PLOTTYPE'],
'dic6': plottype,
'7': self.mypath_handler['MATPLOT_ALPHA'],
'dic7': alpha,
'8': self.mypath_handler['MATPLOT_PLOTLEGEND'],
'dic8': plotlegend,
'9': self.mypath_handler['MATPLOT_ADD_XAXIS'],
'dic9': add_xaxis,
'10': self.mypath_handler['MATPLOT_ADD_YAXIS'],
'dic10': add_yaxis
}
a = 0
while a <= 10:
data_array = myData.readAnyFormat(config=False,
mydtype=np.str_,
mypath=mypaths[str(a)],
data_shape='shaped',
comment='',
delim='= ')
#print 'mypath:', mypaths[str(a)]
#print 'data_array:', data_array
if data_array.size == 2:
data_array = np.expand_dims(data_array, axis=0)
i = 0
while i < data_array[:, 0].size:
mypaths['dic' + str(a)].update(
{data_array[i, 0]: data_array[i, 1]})
i += 1
a += 1
if ncolours != 0:
import distinct_colours as cb_col
#print 'use color-blind friendly colors!'
# print catname
cb_colours = cb_col.get_distinct(ncolours)
if ncolours == 1:
if catname.find('Galacticus') != -1:
#print 'Gal'
color['0'] = '#225588'
elif catname.find('SAG_') != -1 or str(catname).find(
'SAG_1Gpc_v2') != -1:
#print 'SAG'
color['0'] = '#CC6677'
elif catname.find('SAGE') != -1:
#print 'SAGE'
color['0'] = '#DDCC77'
else:
#print 'else'
color['0'] = cb_colours[0]
else:
i = 0
while i < ncolours:
color[str(i)] = cb_colours[i]
i += 1
return linestyle, color, marker, markercol, colormap, colorbar, plottype, alpha, plotlegend, add_xaxis, add_yaxis
#path = '/gpfs/data/violeta/Galform_Out/v2.6.0/aquarius_trees/' #model = 'MillGas/gp15newmg.anders/' #.anders 'MillGas/gp15newmg/' path = '/gpfs/data/violeta/Galform_Out/v2.7.0/stable/' #model = 'MillGas/gp17/' #model = 'MillGas/gp17.spin/' #model = 'MillGas/gp17.spin.ramt0.01/' #model = 'MillGas/gp17.spin.ramt0.01.griffinBH/' #model = 'MillGas/gp17.spin.ramt0.01.griffinBH.stb075/' model = 'MillGas/gp17.spin.ramt0.01.stabledisk0.75.ac085/' line = 'OII3727' lline = '[OII]' outdir = '/gpfs/data/violeta/lines/desi_hod_o2/' ntypes = len(inleg) cols = get_distinct(ntypes - 1) cols.insert(0, 'k') # Initialize GSMF mmin = 8. mmax = 15. dm = 0.1 mbins = np.arange(mmin, mmax, dm) mhist = mbins + dm * 0.5 # Initialize SSFR smin = 3. smax = 12. ds = 0.1 sbins = np.arange(smin, smax, ds) shist = sbins + ds * 0.5
def plot_function(data):
print_times_Myr, print_smas_AU, print_rps_AU, print_parent_is_deg, canonical_rp_min_A_AU, canonical_rp_min_B_AU = data
N_binaries = len(print_smas_AU)
pyplot.rc('text', usetex=True)
pyplot.rc('legend', fancybox=True)
linewidth = 4
dlinewidth = 2
fig = pyplot.figure(figsize=(10, 9))
plot1 = fig.add_subplot(2, 1, 1, yscale="log")
plot2 = fig.add_subplot(2, 1, 2)
from distinct_colours import get_distinct
colors = get_distinct(4)
labels = ["$A$", "$B$", "$C$"]
labels_i = ["$i_{AC}$", "$i_{BC}$", None]
for index_binary in range(N_binaries):
label = labels[index_binary]
label_i = labels_i[index_binary]
color = colors[index_binary]
plot1.plot(print_times_Myr,
print_smas_AU[index_binary],
color=color,
linestyle='dashed',
linewidth=dlinewidth)
plot1.plot(print_times_Myr,
print_rps_AU[index_binary],
color=color,
linewidth=linewidth,
label=label)
plot2.plot(print_times_Myr,
print_parent_is_deg[index_binary],
color=color,
linewidth=linewidth,
label=label_i)
plot1.axhline(y=canonical_rp_min_A_AU,
color=colors[0],
linestyle='dotted',
linewidth=dlinewidth)
plot1.axhline(y=canonical_rp_min_B_AU,
color=colors[1],
linestyle='dotted',
linewidth=dlinewidth)
handles, labels = plot1.get_legend_handles_labels()
plot1.legend(handles, labels, loc="upper right", fontsize=12)
handles, labels = plot2.get_legend_handles_labels()
plot2.legend(handles, labels, loc="lower right", fontsize=12)
plot1.set_xlabel("t [Myr]", fontsize=18)
plot2.set_xlabel("t [Myr]", fontsize=18)
plot1.set_ylabel("$a_i [\mathrm{AU}]$", fontsize=18)
plot2.set_ylabel("$i_{kl} [\mathrm{deg}]$", fontsize=18)
plot1.set_xlim(0.0, print_times_Myr[-1])
plot2.set_xlim(0.0, print_times_Myr[-1])
plot1.tick_params(axis='both', which='major', labelsize=18)
plot2.tick_params(axis='both', which='major', labelsize=18)
fig.savefig("figure.eps")
pyplot.show()
# Prep plot
path = '/cosma5/data/durham/violeta/lines/cosmicweb/fsat/' + model + '/'
outplot = path + prop + '.pdf'
ytit = ('% satellites (>x)')
fig = plt.figure(figsize=(8., 9.))
ax = plt.subplot()
ax.set_xlabel(xtit)
ax.set_ylabel(ytit)
ax.set_xlim(xmin, xmax)
ax.set_ylim(0., 30.)
cols = get_distinct(len(surveys))
#################
# Loop over the snapshots, reading the percenage of satellites
lstyle = ['-', '--']
for iz, sn in enumerate(snap_list):
#infile = path+'test.dat'
infile = path + prop + '_' + str(sn) + '.dat'
# Read data
data = np.loadtxt(infile)
xdata = data[:, 0]
for isy, survey in enumerate(surveys):
def plot_ionization(ism, N, which, rmax, t_end, rS, ip):
r, x, R, X = get_ionization_fraction1d(ism)
xx, yy, xi, hh, ll = get_ionization_fraction2d(ism)
w = 14
if ip > 1:
pyplot.close('all')
pyplot.clf()
pyplot.figure(figsize=(w, 6))
pyplot.rcParams.update({'font.size': 22})
colors = get_distinct(3)
# rscolor = 'green'
ax1 = pyplot.subplot(1, 2, 1)
#ax1.scatter(r, x, c=colors[0], lw=0, s=10)
ax1.scatter(r, x, c=x, lw=0, s=20, alpha=0.5, cmap="jet")
ax1.plot(R[1:], X[1:], c=colors[1], lw=3)
ax1.plot([rS, rS], [-1, 2], color=colors[2], linestyle='dashed', lw=3)
ax1.text(rS + 0.08, 1.06, r'$R_s$', color=colors[2], fontsize=20)
ax1.set_xlim(0, rmax)
ax1.set_ylim(-0.04, 1.19)
ax1.set_xlabel("r [pc]", fontsize=20)
ax1.set_ylabel(r'$\xi_{\rm ion}$', fontsize=20)
ax2 = pyplot.subplot(1, 2, 2)
h = numpy.array(hh)
h *= 72 * w / (6. * ll) # approximate scaling
ax2.set_aspect(1)
sc2 = ax2.scatter(xx,
yy,
c=xi,
s=numpy.pi * h**2,
alpha=0.05,
edgecolor=None,
cmap="jet")
if ll < rmax: ll = rmax
ax2.set_xlim(-ll, ll)
ax2.set_ylim(-ll, ll)
ax2.set_xlabel("x [pc]", fontsize=20)
ax2.set_ylabel("y [pc]", fontsize=20)
#for axi in ax2.flat:
ax2.xaxis.set_major_locator(pyplot.MaxNLocator(6))
ax2.yaxis.set_major_locator(pyplot.MaxNLocator(6))
#pyplot.colorbar(sc2, ax=ax2, fraction=0.046, pad=0.04) # magic numbers!
circle = pyplot.Circle((0, 0),
rS,
color=colors[2],
fill=False,
linestyle='dashed',
lw=3)
ax2.add_artist(circle)
if which == 0:
id_string = 'Plummer_model'
else:
id_string = 'Homogeneous_sphere'
param_string = "_N=%d_t=%.3f_Myr" % (N, t_end)
#pyplot.suptitle(id_string+param_string, y=0.96, fontsize=16)
savefile = 'fig_ionization_of_GMC_' + id_string + param_string + '.png'
pyplot.savefig(savefile, dpi=300)
print 'Figure saved in file', savefile
if ip == 0: pyplot.show()
def main(N, W0, t_end, n_steps, filename, Mtot, Rvir, rgc, vgc):
numpy.random.seed(111)
converter = nbody_system.nbody_to_si(Mtot, Rvir)
dt = t_end / float(n_steps)
sun = Particles(1)
sun.mass = 1 | units.MSun
sun.radius = 1 | units.RSun
sun.position = [-8400.0, 0.0, 17.0] | units.parsec
x_label = "X [kpc]"
y_label = "Y [kpc]"
fig = pyplot.figure(figsize=(8, 8))
pyplot.xlim(-10, 10)
pyplot.ylim(-10, 10)
pyplot.axis('equal')
pyplot.xlabel("X [kpc]")
pyplot.ylabel("Y [kpc]")
colors = get_distinct(6)
MWG = MilkyWay_galaxy()
vc = MWG.vel_circ(sun.position.length())
sun.velocity = [11.352, (12.24 + vc.value_in(units.kms)), 7.41] | units.kms
sun.velocity *= -1
print "Current Sun:"
print sun
print "\nFinding birth location of the Sun..."
x, y = integrate_single_particle_in_potential(sun, t_end, dt, converter)
pyplot.plot(x, y, lw=4, alpha=0.2, c=colors[1])
print "Initial Sun:"
print sun
sun.velocity *= -1
cluster = new_king_model(N, W0=3, convert_nbody=converter)
cluster.mass = new_salpeter_mass_distribution(len(cluster),
0.1 | units.MSun,
10.0 | units.MSun)
eps2 = 0.25 * (float(N)) ** (-0.666667) * Rvir ** 2
cluster.scale_to_standard(convert_nbody=converter,
smoothing_length_squared=eps2)
cluster.position += sun.position
cluster.velocity += sun.velocity
cluster.radius = 0 | units.parsec
pyplot.scatter(cluster.x.value_in(units.kpc),
cluster.y.value_in(units.kpc),
s=10, c=colors[3])
print '\nTracking', N, 'siblings'
x, y = integrate_single_particle_in_potential(cluster, t_end, dt,
converter)
size = cluster.mass / (0.1 | units.MSun)
pyplot.scatter(cluster.x.value_in(units.kpc),
cluster.y.value_in(units.kpc),
c=colors[0], alpha=1.0, lw=0, s=size)
pyplot.scatter(sun.x.value_in(units.kpc),
sun.y.value_in(units.kpc),
marker='+', s=100, c=colors[2])
pyplot.scatter([0], [0], marker="+", s=300, c='r')
pyplot.scatter([-8.4], [0], marker="o", s=100, c='g')
save_file = 'SolarClusterInPotential.png'
pyplot.savefig(save_file)
print '\nSaved figure in file', save_file, '\n'
pyplot.show()
ymax = -1.
xtit = "${{\\rm M_{AB}(" + iband + ")}\, -\, 5log{\\rm h}}$"
ytit = "$log(\Phi/ \\rm{h^{3} Mpc^{-3} mag^{-1}})$"
fig = plt.figure(figsize=(8.5, 9.))
jj = 111
ax = fig.add_subplot(jj)
ax.set_autoscale_on(False)
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
ax.set_xlabel(xtit)
ax.set_ylabel(ytit)
#ax.tick_params(labelsize=fs-2)
start, end = ax.get_xlim()
cols = get_distinct(len(models))
colors = cols
g = ['grey']
colors.extend(g)
# Observational data
dobs = '/cosma/home/violeta/Galform2/galform-2.6.0/Obs_Data2/'
fobs = 'lfk_z0_driver12.data'
# Plot observations
file = dobs + fobs
# Driver observations
mag, den, err, num = np.loadtxt(file, unpack=True)
ind = np.where(den > 0.)
x = mag[ind]
y = np.log10(den[ind] / 0.5) # Observations are per 0.5 mag
def Sun_and_M67_in_the_Galaxy():
bodies = Particles(2)
Sun = bodies[0]
v_LSR = (-10, 5.2, 7.2) | units.kms
Sun.mass = 1 | units.MSun
Sun.radius = 1 | units.RSun
Sun.position = (8.4, 0.0, 0.017) | units.kpc
Sun.velocity = (-11.4, 232, 7.41) | units.kms # SPZ2009
M67 = bodies[1]
M67.mass = 50000 | units.MSun
M67.radius = 3 | units.parsec
M67.position = Sun.position + ((0.766, 0.0, 0.49) | units.kpc)
M67.velocity = Sun.velocity + ((31.92, -21.66, -8.87) | units.kms)
bodies.velocity *= -1
simulation_time = 4600. | units.Myr
dt_bridge = 5 | units.Myr
OS = 20 | (units.kms / units.kpc)
OB = 40 | (units.kms / units.kpc)
A = 1300 | (units.kms**2 / units.kpc)
M = 1.4e10 | units.MSun
m = 2
phi_bar, phi_sp = -0.34906, -0.34906
inte = IntegrateOrbit(t_end=simulation_time,
dt_bridge=dt_bridge,
phase_bar=phi_bar,
phase_spiral=phi_sp,
omega_spiral=OS,
omega_bar=OB,
amplitude=A,
m=m,
mass_bar=M)
MW = inte.galaxy()
print(MW.parameters)
print(MW.get_phi21())
print("Backwards integration")
time, xf, yf, zf, vxf, vyf, vzf, bar_angle, spiral_angle, t1, d1 = inte.get_pos_vel_and_orbit(
bodies)
print("Birth position of the Sun:", xf, yf, zf, vxf, vyf, vzf)
print("---")
print('time after backward integration:', time)
colors = get_distinct(4)
figure = pyplot.figure(figsize=(16, 12))
ax = pyplot.gca()
ax.minorticks_on() # switch on the minor ticks
ax.locator_params(nbins=3)
x_label = "t [Gyr]"
y_label = "d [kpc]"
pyplot.xlabel(x_label)
pyplot.ylabel(y_label)
pyplot.plot(-t1.value_in(units.Gyr),
d1.value_in(units.kpc),
lw=3,
c=colors[0])
pyplot.ylim(0, 6)
pyplot.xlim(-5, 0)
pyplot.savefig("sun_and_M67")
pyplot.show()
"""
#snap_list = [44, 42, 40, 37, 34] #MillGas snap_list = [42, 40] nvol = 3 #64 #path = '/gpfs/data/violeta/Galform_Out/v2.6.0/aquarius_trees/' #model = 'MillGas/gp15newmg.anders/' #.anders 'MillGas/gp15newmg/' path = '/gpfs/data/violeta/Galform_Out/v2.7.0/stable/' #model = 'MillGas/gp17/' #model = 'MillGas/gp17.spin/' model = 'MillGas/gp17.spin.ramt0.01/' line = 'OII3727' ; lline = '[OII]' outdir = '/gpfs/data/violeta/lines/desi_hod_o2/' ntypes = len(inleg) cols = get_distinct(ntypes-1) ; cols.insert(0,'k') # Initialize GSMF mmin = 8. mmax = 15. dm = 0.1 mbins = np.arange(mmin,mmax,dm) mhist = mbins + dm*0.5 # Initialize SSFR smin = 3. smax = 12. ds = 0.1 sbins = np.arange(smin,smax,ds) shist = sbins + ds*0.5 nlevel = 10
from optparse import OptionParser
from prepare_figure import single_frame, figure_frame, set_tickmarks
from distinct_colours import get_distinct
black = '#000000'
green = '#00FF00'
red = '#FF0000'
blue = '#0000FF'
lbl = '#FF00FF'
magenta = '#00FFFF'
Integrator = ["Hermite", "MI6", "ph4", "Huayno", "ph4_GPU",
"Gadget2", "BHTree", "Fi_3", "Bonsai"]
color = ['r', 'g', 'k', 'b', 'k', lbl, 'm', 'r', 'g', 'b', 'k', 'm']
color = get_distinct(len(color))
lstyles = ['-.', '-.', '-.', '-.', '--', '-', '-', '-', '-']
lwidth = [2, 2, 4, 2, 4, 2, 2, 2, 4]
def read_file(filename, column, keyword):
x = []
fptr = open(filename)
lines = fptr.readlines()
for line in lines:
l = line.split()
if l[1]==keyword:
# print line
x.append(float(line.split()[column]))
fptr.close()
return x
def kira(tend, N, R, Nbin):
logging.basicConfig(level=logging.ERROR)
mass = new_salpeter_mass_distribution(N, mass_min=10 | units.MSun)
converter = nbody_system.nbody_to_si(mass.sum(), R)
code = Hermite(converter)
stars = new_plummer_model(N, convert_nbody=converter)
stars.mass = mass
stars.radius = 0.01 / len(stars) | R.unit
single_stars, binary_stars, singles_in_binaries \
= make_secondaries(stars, Nbin)
print(binary_stars)
stellar = SeBa()
stellar.particles.add_particles(single_stars)
stellar.particles.add_particles(singles_in_binaries)
stellar.binaries.add_particles(binary_stars)
channel_to_stars = stellar.particles.new_channel_to(stars)
encounter_code = encounters.HandleEncounter(
kepler_code=new_kepler(converter),
resolve_collision_code=new_smalln(converter),
interaction_over_code=None,
G=constants.G)
multiples_code = encounters.Multiples(gravity_code=code,
handle_encounter_code=encounter_code,
G=constants.G)
multiples_code.particles.add_particles((stars - binary_stars).copy())
multiples_code.singles_in_binaries.add_particles(singles_in_binaries)
multiples_code.binaries.add_particles(binary_stars)
multiples_code.commit_particles()
channel_from_stars_to_particles \
= stellar.particles.new_channel_to(multiples_code.particles)
stopping_condition \
= multiples_code.stopping_conditions.binaries_change_detection
stopping_condition.enable()
from matplotlib import pyplot
from distinct_colours import get_distinct
pyplot.rcParams.update({'font.size': 30})
figure = pyplot.figure(figsize=(12, 9))
ax = pyplot.gca()
ax.get_yaxis().get_major_formatter().set_useOffset(False)
ax.xaxis._autolabelpos = True
ax.yaxis._autolabelpos = True
color = get_distinct(2)
pyplot.scatter(numpy.log10(
stellar.binaries.semi_major_axis.value_in(units.AU)),
stellar.binaries.eccentricity,
c=color[0],
s=200,
lw=0)
t = quantities.linspace(0 * tend, tend, 11)
for ti in t:
print(("t, Energy=", ti, multiples_code.particles.mass.sum(), \
multiples_code.get_total_energy()))
multiples_code.evolve_model(ti)
print(("at t=", multiples_code.model_time, \
"Nmultiples:", len(multiples_code.multiples)))
if stopping_condition.is_set():
resolve_changed_binaries(stopping_condition, stellar, converter)
stellar.evolve_model(ti)
channel_from_stars_to_particles.copy_attributes(["mass", "radius"])
update_dynamical_binaries_from_stellar(stellar, multiples_code,
converter)
print(("Lagrangian radii:", \
multiples_code.all_singles.LagrangianRadii(converter)))
print(("MC.particles", multiples_code.particles))
print(("Lagrangian radii:", \
multiples_code.particles.LagrangianRadii(converter)))
print(("t, Energy=", ti, multiples_code.get_total_energy()))
pyplot.scatter(numpy.log10(
stellar.binaries.semi_major_axis.value_in(units.AU)),
stellar.binaries.eccentricity,
c=color[1],
lw=0,
s=50)
pyplot.xlabel("$\log_{10}(a/R_\odot)$")
pyplot.ylabel("eccentricity")
save_file = 'kira_a_vs_e.pdf'
pyplot.savefig(save_file)
print('\nSaved figure in file', save_file, '\n')
pyplot.show()
stellar.stop()
if __name__ == "__main__":
x_label = "L [LSun]"
y_label = "density [$g/cm^{3}$]"
figure = single_frame(x_label,
y_label,
logx=True,
logy=True,
xsize=12,
ysize=10)
mass = [1, 1, 5, 5, 10, 10] | units.MSun
age = [1, 10000, 1, 90, 1, 20] | units.Myr
c = get_distinct(3)
color = []
ls = []
for ci in c:
color.append(ci)
color.append(ci)
ls.append("-")
ls.append("--")
symbol = ['v', 'v', 'o', 'o', '^', '^']
i = 0
for imass in range(len(mass)):
dm = 0.2 * mass[imass]
time = age[imass]
m, L, rho = stellar_density_profile_at_time(mass[imass], time)
pyplot.plot(L.value_in(units.LSun),
rho.value_in(units.g / units.cm**3),
def plot_single_image(planets, debris, disk, shell, figfile=None):
if len(shell)>0:
disk.add_particles(shell)
lim = 150
"""
#centered on the Sun
if len(planets)>1:
print "Center on COM"
com = planets[0].position
vcom = planets[0].velocity
planets.position -= com
planets.velocity -= vcom
disk.position -= com
disk.velocity -= vcom
debris.position -= com
debris.velocity -= vcom
"""
left, width = 0.1, 0.65
bottom, height = 0.1, 0.65
bottom_h = left_h = left+width+0.05
rect_scatter = [left, bottom, width, height]
rect_histx = [left, bottom_h, width, 0.2]
rect_histy = [left_h, bottom, 0.2, height]
fig = pyplot.figure(figsize=(12,12))
time = disk.get_timestamp()
# pyplot.title("Cluster at t="+str(time.in_(units.Gyr)))
xy = pyplot.axes(rect_scatter)
#xy.text(110,110, "protoplanetary disk (img#"+str(index)+")", ha='left', va='bottom')
xz = pyplot.axes(rect_histx)
yz = pyplot.axes(rect_histy)
xy.set_xlabel("X [AU]")
xy.set_ylabel("Y [AU]")
xz.set_ylabel("Z [AU]")
yz.set_xlabel("Z [AU]")
positions = disk.position
x, y, z = positions.x.value_in(units.AU), positions.y.value_in(units.AU), positions.z.value_in(units.AU)
# sizes = 1000
# sizes = 1000*disk.rho/disk.rho.max()
alpha = 0.01
us = disk.u
if hasattr(disk, "xion"):
xs = disk.xion
else:
xs = numpy.zeros(len(disk))
u_min, u_max = min(us), max(us)
xion_min, xion_max = min(xs), max(xs)
# if xion_min<=0:
# xion_min = xion_max/100.
# xs += xion_min
# xion_max += xion_min
print "u=", u_min, u_max
Ts = mu() / constants.kB * disk.u
T_min = max(20|units.K, mu() / constants.kB * u_min)
T_max = min(1800|units.K, mu() / constants.kB * u_max)
print "T=", T_min, T_max
print "X=", xion_min, xion_max
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))
# log_x = numpy.log((xs / xion_min)) / numpy.log((xion_max / xion_min))
# clipped_log_x = numpy.minimum(numpy.ones_like(log_x), numpy.maximum(numpy.zeros_like(log_x), log_x))
# xrange = (xs/xion_min) / (xion_max/xion_min)
# print "xrange=", xrange.min(), xrange.max()
clipped_log_x = xs
log_T = numpy.log((Ts / T_min)) / numpy.log((T_max / T_min))
clipped_log_T = numpy.minimum(numpy.ones_like(log_T), numpy.maximum(numpy.zeros_like(log_T), log_T))
ps = disk.rho
p_min, p_max = min(ps), max(ps)
log_p = numpy.log((ps / p_min)) / numpy.log((p_max / p_min))
clipped_log_p = numpy.minimum(numpy.ones_like(log_p), numpy.maximum(numpy.zeros_like(log_p), log_p))
# red = 1 - clipped_log_u**(1./2.)
# blue = clipped_log_u**(1./2.)
# green = numpy.minimum(red, blue)
# red = 1.0 - clipped_log_T
# red = 1-clipped_log_x**(1./2.)
blue = clipped_log_T
red = 1-clipped_log_T
#green = numpy.minimum(red, blue)
green = clipped_log_p
colors = numpy.transpose(numpy.array([red, green, blue]))
sizes = 2*2000*disk.h_smooth/disk.h_smooth.max()
xy.scatter(x, y, sizes, c=colors, edgecolors = "none", alpha = alpha)
xy.set_xlim( (-lim, lim) )
xy.set_ylim( (-lim, lim) )
sizes = 2*200*disk.h_smooth/disk.h_smooth.max()
xz.scatter(x, z, sizes, c=colors, edgecolors = "none", alpha = alpha)
yz.scatter(z, y, sizes, c=colors, edgecolors = "none", alpha = alpha)
xz.set_xlim( xy.get_xlim() )
yz.set_ylim( xy.get_xlim() )
yz.set_xlim( (-0.1*lim, 0.1*lim) )
xz.set_ylim( (-0.1*lim, 0.1*lim) )
# yz.set_xlim( (-lim, lim) )
# xz.set_ylim( (-lim, lim) )
if len(debris)>0:
c = 'k'
m = 0.1
xy.scatter(debris.x.value_in(units.AU), debris.y.value_in(units.AU), s=m, c=c, lw=0)
xz.scatter(debris.x.value_in(units.AU), debris.z.value_in(units.AU), s=m, c=c, lw=0)
yz.scatter(debris.z.value_in(units.AU), debris.y.value_in(units.AU), s=m, c=c, lw=0)
if len(planets)>0:
from distinct_colours import get_distinct
c = get_distinct(len(planets))
m = 1000 * planets.mass/planets.mass.max()
m[0] = min(10*m[1:].max(), 30)
xy.scatter(planets.x.value_in(units.AU), planets.y.value_in(units.AU), s=m, c=c, lw=0)
xz.scatter(planets.x.value_in(units.AU), planets.z.value_in(units.AU), s=m, c=c, lw=0)
yz.scatter(planets.z.value_in(units.AU), planets.y.value_in(units.AU), s=m, c=c, lw=0)
filename = "planetary_system.png"
fig.savefig(filename)
result.add_option("-R", unit=units.parsec,
dest="Rcluster", type="int", default = 1000|units.AU,
help="Cluster size [%default]")
result.add_option("-n",
dest="n", type="int", default = 100,
help="Number of pebbels per star [%default]")
return result
if __name__ in ('__main__', '__plot__'):
o, arguments = new_option_parser().parse_args()
filename = "sunandM67.hdf5"
sun_and_planets = read_set_from_file(filename, "amuse")
colors = get_distinct(4)
figure = pyplot.figure(figsize=(16, 12))
ax = pyplot.gca()
ax.minorticks_on() # switch on the minor ticks
ax.locator_params(nbins=3)
x_label = "t [Gyr]"
y_label = "$Z [kpc]$"
pyplot.xlabel(x_label)
pyplot.ylabel(y_label)
# pyplot.ylim(0, 1)
R = []
Z = [] | units.kpc
d = [] | units.kpc
t = [] | units.Gyr
def main(M1, M2, M3, Pora, Pin, ain, aout, ein, eout, t_end, nsteps, scheme,
dtse_fac):
t_begin = ctime.time()
x_label = "$a/a_{0}$"
y_label = "$e/e_{0}$"
fig = single_frame(x_label,
y_label,
logx=False,
logy=False,
xsize=10,
ysize=8)
color = get_distinct(4)
if scheme == 0:
srange = [1, 2, 3]
else:
srange = [scheme]
i = 0
for s in srange:
time, ai, ei, ao, eo, inci, inco, time_framework = evolve_triple_with_wind(
M1, M2, M3, Pora, Pin, ain, aout, ein, eout, t_end, nsteps, s,
dtse_fac)
if i == 0:
pyplot.plot(ai, ei, c=color[0], label='inner')
pyplot.plot(ao, eo, c=color[1], label='outer')
i = 1
else:
pyplot.plot(ai, ei, c=color[0])
pyplot.plot(ao, eo, c=color[1])
pyplot.legend(loc='best', ncol=1, shadow=False, fontsize=20)
pyplot.title('Relative orbital parameters for t=0.1Myr')
pyplot.xlabel('$a/a_0$')
pyplot.ylabel('$e/e_0$')
#save_file = 'evolve_triple_with_wind.png'
save_file = 'evolve_triple_with_wind' \
+'_s={:d}_dtse={:.3f}'.format(scheme, dtse_fac) \
+'.png'
pyplot.savefig(save_file)
print '\nSaved figure in file', save_file, '\n'
#pyplot.show()
data = numpy.array(zip(ai, ei))
numpy.save('aiei0', data)
data = numpy.array(zip(inci, inco))
numpy.save('incoinci0', data)
data = numpy.array(zip(ao, eo))
numpy.save('aoeo0', data)
t_end = ctime.time()
total_time = t_end - t_begin
print 'Total time, ', total_time
print 'Total time in framework, ', time_framework
print time_framework / total_time * 100, '% spend in non-Amuse framework'
data = numpy.array(
[total_time, time_framework, time_framework / total_time * 100])
numpy.save('time0', data)
for pri in precision:
dEi = energy_error_of_integrated_Nbody_system(code, particles,
t_end, pri)
dE.append(abs(dEi))
print "integrated with precision=", pri, "dE/E=", dEi
return dE
if __name__ in ('__main__','__plot__'):
numpy.random.seed(31415)
particles = new_plummer_model(1000)
precision = 10.**numpy.linspace(0., -3., 10)
t_end = 1.0| nbody_system.time
cols = get_distinct(2)
print 'ph4'
code = ph4
dE = get_dE(code, precision, t_end)
pyplot.scatter(precision, dE, c=cols[0], lw=0, s=50, marker='o')
print 'BHTree'
code = BHTree
dE = get_dE(code, precision, t_end)
pyplot.scatter(precision, dE, c=cols[1], lw=0, s=50, marker='^')
t0 = 0.8
t1 = 0.02
ep = 4.e-5
eb = 0.07
help="Cluster size [%default]")
result.add_option("-n",
dest="n",
type="int",
default=100,
help="Number of pebbels per star [%default]")
return result
if __name__ in ('__main__', '__plot__'):
o, arguments = new_option_parser().parse_args()
filename = "sunandM67.hdf5"
sun_and_planets = read_set_from_file(filename, "amuse")
colors = get_distinct(4)
figure = pyplot.figure(figsize=(16, 12))
ax = pyplot.gca()
ax.minorticks_on() # switch on the minor ticks
ax.locator_params(nbins=3)
x_label = "t [Gyr]"
y_label = "$Z [kpc]$"
pyplot.xlabel(x_label)
pyplot.ylabel(y_label)
# pyplot.ylim(0, 1)
R = []
Z = [] | units.kpc
d = [] | units.kpc
t = [] | units.Gyr
matplotlib.use('Agg')
from matplotlib import pyplot as plt
from Cosmology import *
import scipy.ndimage
from distinct_colours import get_distinct
import mpl_style
from astropy.io import fits
plt.style.use(mpl_style.style1)
dir = '/cosma5/data/durham/violeta/Galform_Out/v2.7.0/stable/MillGas/'
nvol = 64
model = 'gp19/'
zlist = [61, 44, 41, 39, 34] #z=0.6, 0.83, 1., 1.5
cols = get_distinct(len(zlist))
#############################
line = 'OII3727'
lline = '[OII]'
outdir = '/cosma5/data/durham/violeta/lines/cosmicweb/plots/' + model
plotfile = outdir + line + '_decam.pdf'
fluxcut = 8. * 10.**-17.
#############################
# Prepare plots
fig = plt.figure(figsize=(8.5, 9.))
plt.xlabel("$(r-z)_{\\rm DECam}$")
plt.ylabel("$(g-r)_{\\rm DECam}$")
for i in range(len(mbins)):
if ntot[index, i] > 0.:
npas[index, i] = npas[index, i] / ntot[index, i]
nsp[index, i] = nsp[index, i] / ntot[index, i]
nsat[index, i] = nsat[index, i] / ntot[index, i]
else:
npas[index, i] = -999.
nsp[index, i] = -999.
nsat[index, i] = -999.
############
# Figure http://matplotlib.org/users/gridspec.html
fig = plt.figure(figsize=(8.5, 9.))
ax = plt.subplot()
cols = get_distinct(len(inleg) + 1)
colors = cols
g = ['grey'] #; colors.extend(g) ; colors.extend(g)
colors[len(colors) - 1] = 'grey'
colors.extend(g)
xtit = "$log(\\rm{M_{*}/M_{\odot} h^{-1}})$"
ytit = "Passive fraction"
ax.set_xlabel(xtit)
ax.set_ylabel(ytit)
ax.set_autoscale_on(False)
ax.minorticks_on()
ax.set_xlim(mmin, 12)
ax.set_ylim(0., 1.)
# Observations
help="metalicity [0.02]")
return result
if __name__ in ('__main__', '__plot__'):
o, arguments = new_option_parser().parse_args()
x_label = "T [$K$]"
y_label = "L [$L_\odot$]"
figure = single_frame(x_label,
y_label,
logx=False,
logy=True,
xsize=14,
ysize=10)
color = get_distinct(6)
L, T = single_star_evolution(M=1.4 | units.MSun,
z=0.04,
model_time=4 | units.Gyr)
pyplot.plot(T.value_in(units.K), L.value_in(units.LSun), c=color[0])
L, T = single_star_evolution(**o.__dict__)
pyplot.plot(T.value_in(units.K), L.value_in(units.LSun), c=color[4])
m67 = plot_M67Data.Cluster()
m67.read()
pyplot.scatter(m67.Teff, m67.L, c=color[1], lw=0, s=100)
pyplot.scatter(TBSS, LBSS, c=color[3], lw=0, s=250)
pyplot.scatter(TBSS[3], LBSS[3], c=color[2], lw=0, s=250)
snap_list = [42]
nvol = 3 #64
zleg = []
bands = ['DEIMOS-R', 'MegaCam-i-atmos', 'MegaCam-i-atmos']
mcuts = [24.1, 22.5, 24]
fcuts = [
2.7 * 10.**-17., 3.5 * 10.**-17., 1.9 * 10.**-17., 10.**-16.,
8. * 10.**-17.
]
inleg = [
'DEEP2 cuts', 'VVDS-Wide cuts', 'VVDS-Deep cuts', 'eBOSS cuts', 'DESI cuts'
]
cols = ['gray']
cols.extend(get_distinct(len(inleg)))
# Initialize histogram
lmin = 8.5
lmax = 16.
dl = 0.1
lbins = np.arange(lmin, lmax, dl)
lhist = lbins + dl * 0.5
############################################
# Initialize the parameters for the figures
ytit = "$log_{10} (\\rm M_{*}/M_{\odot}h^{-1})$"
xtit = "$log_{10} (\\rm M_{\\rm halo}/M_{\odot}h^{-1})$"
ymin = 8.
ymax = 11.5
xmin = 10.5
