Yael-II/MSc1-Internship-Stellar-Cluster
1
0
Fork 0
mirror of https://codeberg.org/Yael-II/MSc1-Internship-Stellar-Cluster.git synced 2026-03-15 11:16:27 +01:00
Code Issues Packages Projects Releases Wiki Activity
Files
80c6d54f008d97f513646526f50916674aa00a76
MSc1-Internship-Stellar-Clu…/COSMIC/old/COSMIC-VIS_v2.py
Yaël M e5bb7b3ebd Add files via upload
2024-06-19 15:45:57 +02:00

386 lines
16 KiB
Python
Raw Blame History

"""
* COSMIC-VIS : Cluster Orbital SysteM Integration Code - Visualisation
* Version 2 - 2024-02-27
@ Yaël Moussouni
@ Unistra, P&E, MSc1-MoFP
@ Observatory of Strasbourg (Intern)
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import os
# * Parameters
if "YII" in plt.style.available: plt.style.use("YII")
if "YII_light" in plt.style.available: light = "YII_light"
else: light = "default"
# * Variables
in_directory = "./COSMIC_output/"
out_directory = "../Figs/COSMIC_figs/"
cluster_suffix = "_cluster.csv"
stars_suffix = "_stars.csv"
YES = ["Y", "YES", "1", "OUI"]
MARKER = ["o", "s", "D", "v", "^", "<", ">"]
Tmin = 2000 # K - Temperature truncation
Tmax = 10000 # K - Temperature truncation
# * Functions
def density_hist(X,Y,M,T,bins,i):
x_cm = np.average(X[i], weights=M[i])
y_cm = np.average(Y[i], weights=M[i])
Radius = np.sqrt((X[i]-x_cm)**2 + (Y[i]-y_cm)**2)
Rho = np.zeros(len(bins)-1)
for k in range(len(bins)-1):
r1 = bins[k]
r2 = bins[k+1]
w = np.where((Radius > r1) & (Radius < r2))
Rho[k] = np.sum(M[i,w])/(4*np.pi*r2**3/3 - 4*np.pi*r1**3/3)
return Rho
# * Listing files for selection
filelist = np.sort(os.listdir(in_directory))
filelist = [f for f in filelist if f[0] != "."]
dates = []
for f in filelist:
if len(f) == len("YYYY-MM-DD_HH:MM_cluster.csv") or len(f) == len("YYYY-MM-DD_HH:MM_stars.csv"):
date = f[0:16]
if not date in dates:
dates.append(date)
# * File selection
print("\033[36m"+"Available dates:"+"\033[0m")
for i in range(len(dates)):
print("\033[36m"+"\t{}: ".format(i+1) + "\033[0m" + "{}".format(dates[i].replace("h", ":").replace("_", " ")))
j = int(input("\033[32m"+"Select a date number: "+"\033[0m"))-1
print("")
date_prefix = dates[j]
# * Data collection
cluster = np.loadtxt(in_directory+date_prefix+cluster_suffix, skiprows=1, comments="#", delimiter=",")
stars = np.loadtxt(in_directory+date_prefix+stars_suffix, skiprows=1, comments="#", delimiter=",")
T = cluster[:,1]
E = cluster[:,2]
DE = cluster[:,3]
L = cluster[:,4]
DL = cluster[:,5]
M = np.zeros(np.int32(stars[-1,0:2])+1)
X = np.zeros(np.int32(stars[-1,0:2])+1)
Y = np.zeros(np.int32(stars[-1,0:2])+1)
Z = np.zeros(np.int32(stars[-1,0:2])+1)
VX = np.zeros(np.int32(stars[-1,0:2])+1)
VY = np.zeros(np.int32(stars[-1,0:2])+1)
VZ = np.zeros(np.int32(stars[-1,0:2])+1)
Lumi = np.zeros(np.int32(stars[-1,0:2])+1)
Radi = np.zeros(np.int32(stars[-1,0:2])+1)
Temp = np.zeros(np.int32(stars[-1,0:2])+1)
for star in stars:
i = np.int32(star[0])
j = np.int32(star[1])
M[i,j] = star[2]
X[i,j] = star[3]
Y[i,j] = star[4]
Z[i,j] = star[5]
VX[i,j] = star[6]
VY[i,j] = star[7]
VZ[i,j] = star[8]
Lumi[i,j] = star[9]
Radi[i,j] = star[10]
Temp[i,j] = star[11]
N_iter = i
N_part = j
x_cm = np.array([np.average(X[i], weights=M[i]) for i in range(N_iter)])
y_cm = np.array([np.average(Y[i], weights=M[i]) for i in range(N_iter)])
z_cm = np.array([np.average(Z[i], weights=M[i]) for i in range(N_iter)])
lim_min = np.min((X,Y))
lim_max = np.max((X,Y))
Alpha = np.clip((np.log(Lumi) - np.min(np.log(Lumi)))/(np.max(np.log(Lumi)) - np.min(np.log(Lumi))), 0.1, 1)
# * Output choice
print("\033[36m"+"Choose figures:"+"\033[0m")
figure_1 = input("\033[32m"+"\tFigure 1 (energy, angular momentum, time): "+"\033[0m").upper() in YES
figure_2 = input("\033[32m"+"\tFigure 2a and 2b (positions, speed): "+"\033[0m").upper() in YES
figure_3 = input("\033[32m"+"\tFigure 3 (mass distribution): "+"\033[0m").upper() in YES
figure_4 = input("\033[32m"+"\tFigure 4 (density distribution): "+"\033[0m").upper() in YES
figure_5 = input("\033[32m"+"\tFigure 5 (HR diagram): "+"\033[0m").upper() in YES
print("")
print("\033[36m"+"General settings:"+"\033[0m")
time = np.int32(input("\033[32m"+"\tStatic images drawing instant ({} < int < {}): ".format(0,N_iter)+"\033[0m"))
if input("\033[32m"+"\tWindow size restriction (y/n): "+"\033[0m").upper() in YES:
lim = np.float32(input("\033[32m"+"\t\tLimit [pc]: "+"\033[0m"))
lim_min = -lim/2
lim_max = +lim/2
print("")
draw_time = scatter_time = distrib_time = diagram_time = time
if figure_1:
print("\033[36m"+"Figure 1 (energy, angular momentum, time):"+"\033[0m")
plot_E = input("\033[32m"+"\tPlot energy vs. time (y/n): "+"\033[0m").upper() in YES
plot_L = input("\033[32m"+"\tPlot angular momentum vs. time (y/n): "+"\033[0m").upper() in YES
plot_save = input("\033[32m"+"\tSave figure (y/n): "+"\033[0m").upper() in YES
print("")
else: plot_E = plot_L = plot_save = False
if figure_2:
print("\033[36m"+"Figure 2a and 2b (positions, speed):"+"\033[0m")
scatter_2D = input("\033[32m"+"\tScatter positions in 2D (y/n): "+"\033[0m").upper() in YES
scatter_3D = input("\033[32m"+"\tScatter positions in 3D (y/n): "+"\033[0m").upper() in YES
scatter_speed = input("\033[32m"+"\tShow speed vectors (y/n): "+"\033[0m").upper() in YES
scatter_save = input("\033[32m"+"\tSave figure (y/n): "+"\033[0m").upper() in YES
scatter_animate = input("\033[32m"+"\tAnimate over time (y/n): "+"\033[0m").upper() in YES
print("")
else: scatter_2D = scatter_3D = scatter_speed = scatter_save = scatter_animate = False
if figure_3:
print("\033[36m"+"Figure 3 (mass distribution):"+"\033[0m")
distrib_mass = input("\033[32m"+"\tShow mass distribution (y/n): "+"\033[0m").upper() in YES
distrib_bins = np.int32(input("\033[32m"+"\tNumber of bins (int): "+"\033[0m"))
distrib_save = input("\033[32m"+"\tSave figure (y/n): "+"\033[0m").upper() in YES
print("")
else: distrib_mass = distrib_bins = distrib_save = False
if figure_4:
print("\033[36m"+"Figure 4 (density distribution):"+"\033[0m")
draw_density = input("\033[32m"+"\tShow density distribution (y/n): "+"\033[0m").upper() in YES
draw_bins = np.float32(input("\033[32m"+"\tWidth of bins [pc]: "+"\033[0m"))
draw_max = np.float32(input("\033[32m"+"\tMaximum distance [pc]: "+"\033[0m"))
draw_save = input("\033[32m"+"\tSave figure (y/n): "+"\033[0m").upper() in YES
draw_animate = input("\033[32m"+"\tAnimate over time (y/n): "+"\033[0m").upper() in YES
print("")
else: draw_density = draw_bins = draw_max = draw_save = draw_animate = False
if figure_5:
print("\033[36m"+"Figure 5 (HR diagram):"+"\033[0m")
diagram_HT = input("\033[32m"+"\tDraw HR diagram (y/n): "+"\033[0m").upper() in YES
diagram_save = input("\033[32m"+"\tSave figure (y/n): "+"\033[0m").upper() in YES
diagram_animate = input("\033[32m"+"\tAnimate over time (y/n): "+"\033[0m").upper() in YES
else : diagram_HT = diagram_save = diagram_animate = False
# * Plotting
if plot_E or plot_L:
fig1, axs1 = plt.subplots(2, sharex=True)
if plot_E and not plot_L: # Just the energy
axs1[1].set_xlabel("$t\\ [\\mathrm{{Myr}}]$")
axs1[0].set_ylabel("$E\\ [\\mathrm{{pc^2\\ M_\\odot\\ Myr^{{-2}}}}]$")
axs1[1].set_ylabel("$\\Delta E/E_0$")
axs1[0].plot(T, E, color="C3")
axs1[1].plot(T, DE, color="C3")
elif plot_L and not plot_E: # Just the angular momentum
axs1[1].set_xlabel("$t\\ [\\mathrm{{Myr}}]$")
axs1[0].set_ylabel("$L\\ [\\mathrm{{pc^2\\ M_\\odot\\ Myr^{{-1}}}}]$")
axs1[1].set_ylabel("$\\Delta L/L_0$")
axs1[0].plot(T, L, color="C4")
axs1[1].plot(T, DL, color="C4")
elif plot_L and plot_E: # ...both
xas1 = [axs1[i].twinx() for i in range(len(axs1))]
axs1[1].set_xlabel("$t\\ [\\mathrm{{Myr}}]$")
axs1[0].set_ylabel("$E\\ [\\mathrm{{pc^2\\ M_\\odot\\ Myr^{{-2}}}}]$")
axs1[1].set_ylabel("$\\Delta E/E_0$")
xas1[0].set_ylabel("$L\\ [\\mathrm{{pc^2\\ M_\\odot\\ Myr^{{-1}}}}]$")
xas1[1].set_ylabel("$\\Delta L/L_0$")
axs1[0].plot(T, E, color="C3")
axs1[1].plot(T, DE, color="C3")
xas1[0].plot(T, L, color="C4")
xas1[1].plot(T, DL, color="C4")
axs1[0].tick_params(axis='y', which="both", colors='C3')
axs1[0].yaxis.label.set_color('C3')
xas1[0].tick_params(axis='y', which="both", colors='C4')
xas1[0].yaxis.label.set_color('C4')
axs1[1].tick_params(axis='y', which="both", colors='C3')
axs1[1].yaxis.label.set_color('C3')
xas1[1].tick_params(axis='y', which="both", colors='C4')
xas1[1].yaxis.label.set_color('C4')
if plot_save:
fig1.tight_layout()
fig1.savefig(out_directory+date_prefix+"_figure_1")
print("\033[94m"+"Info: Figure saved as " + "\033[0m" + date_prefix+"_figure_1")
if scatter_2D or scatter_3D:
if scatter_2D:
fig2a, ax2a = plt.subplots(1)
# ax2a.set_facecolor('k')
i = scatter_time
ax2a.set_title("$t =$ {:.2f} Myr".format(T[i]))
if scatter_speed:
qvr2a = ax2a.quiver(X[i]-x_cm[i],Y[i]-y_cm[i],VX[i],VY[i], alpha=0.1, headwidth=2, headlength=2, headaxislength=2, color="k")
ax2a.quiverkey(qvr2a, 0.95, 0.05, 1, "Velocity scale: $1\mathrm{{~km\\cdot s^{{-1}}}}$", labelpos='W', coordinates='figure', color="k", alpha=1)
sct2a = ax2a.scatter(X[i]-x_cm[i],Y[i]-y_cm[i],s=np.clip(Radi[i]**2,0.5,30), c=Temp[i], alpha=Alpha[i], cmap="RdYlBu", vmin=Tmin, vmax=Tmax)
ax2a.set_xlabel("$x$ [pc]")
ax2a.set_ylabel("$y$ [pc]")
ax2a.set_xlim(lim_min, lim_max)
ax2a.set_ylim(lim_min, lim_max)
ax2a.set_aspect("equal")
fig2a.colorbar(sct2a, label="Stellar effective temperature $T$ [K]", extend='both')
fig2a.tight_layout()
if scatter_save:
fig2a.savefig(out_directory+date_prefix+"_figure_2a")
print("\033[94m"+"Info: Figure saved as " + "\033[0m" + date_prefix+"_figure_2a")
if scatter_3D:
fig2b, ax2b = plt.subplots(1, subplot_kw={"projection": "3d"})
# ax2b.set_facecolor('k')
i = scatter_time
ax2b.set_title("$t =$ {:.2f} Myr".format(T[i]))
if scatter_speed:
qvr2b = ax2b.quiver(X[i]-x_cm[i],Y[i]-y_cm[i],Z[i]-z_cm[i],VX[i],VY[i],VZ[i], alpha=0.1, color="k")
# ax2b.quiverkey(qvr2b, 0.95, 0.05, 2, "Velocity scale: $2\mathrm{{~km\\cdot s^{{-1}}}}$", labelpos='W', coordinates='figure')
sct2b = ax2b.scatter(X[i]-x_cm[i],Y[i]-y_cm[i],Z[i]-z_cm[i],s=np.clip(Radi[i]**2,0.5,30), c=Temp[i], alpha=Alpha[i], cmap="RdYlBu", vmin=Tmin, vmax=Tmax)
ax2b.set_xlabel("$x$ [pc]")
ax2b.set_ylabel("$y$ [pc]")
ax2b.set_zlabel("$z$ [pc]")
ax2b.set_xlim(lim_min, lim_max)
ax2b.set_ylim(lim_min, lim_max)
ax2b.set_zlim(lim_min, lim_max)
ax2b.set_aspect("equal")
fig2b.colorbar(sct2b, label="Stellar effective temperature $T$ [K]", location="left", extend='both')
fig2b.tight_layout()
if scatter_save:
fig2b.savefig(out_directory+date_prefix+"_figure_2b")
print("\033[94m"+"Info: Figure saved as " + "\033[0m" + date_prefix+"_figure_2b")
if distrib_mass:
fig3, ax3 = plt.subplots(1)
i = distrib_time
ax3.set_title("$t =$ {:.2f} Myr".format(T[i]))
nbins = distrib_bins
hist, lbin = np.histogram(M[i], nbins)
cbins = 0.5*(lbin[1:]+ lbin[: -1])
ax3.scatter(cbins, hist, s=5)
ax3.set_xscale ("log")
ax3.set_yscale ("log")
ax3.set_xlabel("Masses $M\\ [\\mathrm{{M_\\odot}}]$")
ax3.set_ylabel("Star distribution")
if distrib_save:
fig3.savefig(out_directory+date_prefix+"_figure_3")
print("\033[94m"+"Info: Figure saved as " + "\033[0m" + date_prefix+"_figure_3")
if draw_density:
fig4, ax4 = plt.subplots(1)
i = draw_time
bin_width = draw_bins
bin_max = draw_max
bins = np.arange(0,bin_max,bin_width)
Rho = density_hist(X,Y,M,T,bins,i)
cbins = 0.5*(bins[1:]+ bins[: -1])
ax4.set_title("$t =$ {:.2f} Myr".format(T[i]))
ax4.set_xscale ("log")
ax4.set_yscale ("log")
ax4.set_xlabel("Distance from center $r$ [pc]")
ax4.set_ylabel("Stellar masses density $\\rho\\ [\\mathrm{{M_\\odot\\ pc^{{-3}}}}]$")
ax4.scatter(cbins, Rho, s=5)
fig4.tight_layout()
if draw_save:
fig4.savefig(out_directory+date_prefix+"_figure_4")
print("\033[94m"+"Info: Figure saved as " + "\033[0m" + date_prefix+"_figure_4")
if diagram_HT:
fig5, ax5 = plt.subplots(1)
ax5.invert_xaxis()
i = diagram_time
ax5.set_title("$t =$ {:.2f} Myr".format(T[i]))
ax5.set_xlabel("Temperature $T$ [K]")
ax5.set_ylabel("Luminosity $L\\ [\\mathrm{{L_\\odot}}]$")
ax5.set_xscale ("log")
ax5.set_yscale ("log")
ax5.scatter(Temp[i], Lumi[i], s=5, alpha=Alpha[i], c=Temp[i], cmap="RdYlBu", vmin=Tmin, vmax=Tmax)
fig5.tight_layout()
if diagram_save:
fig5.savefig(out_directory+date_prefix+"_figure_5")
print("\033[94m"+"Info: Figure saved as " + "\033[0m" + date_prefix+"_figure_5")
if scatter_animate:
figA2a,axA2a = plt.subplots(1)
# ax2a.set_facecolor('k')
axA2a.set_title("$t =$ {:.2f} Myr".format(T[0]))
if scatter_speed:
qvr = axA2a.quiver(X[0]-x_cm[0],Y[0]-y_cm[0],VX[0],VY[0], alpha=0.5, headwidth=2, headlength=2, headaxislength=2, color="k")
axA2a.quiverkey(qvr, 0.95, 0.05, 1, "Velocity scale: $1\mathrm{{~km\\cdot s^{{-1}}}}$", labelpos='W', coordinates='figure', color="k", alpha=1)
sctA2a = axA2a.scatter(X[0]-x_cm[0],Y[0]-y_cm[0],s=np.clip(Radi[0]**2,0.5,30), c=Temp[0], alpha=Alpha[0], cmap="RdYlBu", vmin=Tmin, vmax=Tmax)
figA2a.colorbar(sctA2a, label="Stellar effective temperature $T$ [K]", extend='both')
axA2a.set_xlabel("$x$ [pc]")
axA2a.set_ylabel("$y$ [pc]")
axA2a.set_aspect("equal")
axA2a.set_xlim(lim_min, lim_max)
axA2a.set_ylim(lim_min, lim_max)
figA2a.tight_layout()
def anim2a(i):
if scatter_speed:
qvr.set_offsets(np.array([X[i]-x_cm[i],Y[i]-y_cm[i]]).T)
qvr.set_UVC(VX[i],VY[i])
axA2a.set_title("$t =$ {:.2f} Myr".format(T[i]))
sctA2a.set_offsets(np.array([X[i]-x_cm[i],Y[i]-y_cm[i]]).T)
sctA2a.set_array(Temp[i])
sctA2a.set_sizes(np.clip(Radi[i]**2,0.5,30))
sctA2a.set_alpha(Alpha[i])
return sctA2a,
ani2a = animation.FuncAnimation(fig=figA2a, func=anim2a, frames=N_iter, interval=100)
if scatter_save:
ani2a.save(out_directory+date_prefix+"_animation_2a.png", dpi=300, fps=10)
ani2a.save(out_directory+date_prefix+"_animation_2a.pdf", dpi=300, fps=10)
print("\033[94m"+"Info: Animation saved as " + "\033[0m" + date_prefix+"_animation_2a")
if draw_animate:
figA4, axA4 = plt.subplots(1)
bin_width = draw_bins
bin_max = draw_max
bins = np.arange(0,bin_max,bin_width)
cbins = 0.5*(bins[1:]+ bins[: -1])
Rho = np.array([density_hist(X,Y,M,T,bins,k) for k in range(N_iter)])
axA4.set_title("$t =$ {:.2f} Myr".format(T[0]))
sctA4 = axA4.scatter(cbins, Rho[0], s=5)
axA4.set_xscale ("log")
axA4.set_yscale ("log")
axA4.set_ylim(top=np.max(Rho))
axA4.set_xlabel("Distance from center $r$ [pc]")
axA4.set_ylabel("Stellar masses density $\\rho\\ [\\mathrm{{M_\\odot\\ pc^{{-3}}}}]$")
figA4.tight_layout()
def anim4(i):
axA4.set_title("$t =$ {:.2f} Myr".format(T[i]))
sctA4.set_offsets(np.array([cbins, Rho[i]]).T)
return sctA4
ani4 = animation.FuncAnimation(fig=figA4, func=anim4, frames=N_iter, interval=100)
if scatter_save:
ani4.save(out_directory+date_prefix+"_animation_4.png", dpi=300, fps=10)
ani4.save(out_directory+date_prefix+"_animation_4.pdf", dpi=300, fps=10)
print("\033[94m"+"Info: Animation saved as " + "\033[0m" + date_prefix+"_animation_4")
if diagram_animate:
figA5, axA5 = plt.subplots(1)
axA5.invert_xaxis()
axA5.set_title("$t =$ {:.2f} Myr".format(T[0]))
axA5.set_xlabel("Temperature $T$ [K]")
axA5.set_ylabel("Luminosity $L\\ [\\mathrm{{L_\\odot}}]$")
axA5.set_xscale ("log")
axA5.set_yscale ("log")
axA5.set_xlim(np.max(Temp), np.min(Temp))
axA5.set_ylim(np.min(Lumi), np.max(Lumi))
sctA5 = axA5.scatter(Temp[0], Lumi[0], s=5, alpha=Alpha[0], c=Temp[0], cmap="RdYlBu", vmin=Tmin, vmax=Tmax)
fig5.tight_layout()
def anim5(i):
axA5.set_title("$t =$ {:.2f} Myr".format(T[i]))
sctA5.set_offsets(np.array([Temp[i], Lumi[i]]).T)
sctA5.set_array(Temp[i])
ani5 = animation.FuncAnimation(fig=figA5, func=anim5, frames=N_iter, interval=100)
if diagram_save:
ani5.save(out_directory+date_prefix+"_animation_5.png", dpi=300, fps=10)
ani5.save(out_directory+date_prefix+"_animation_5.pdf", dpi=300, fps=10)
print("\033[94m"+"Info: Animation saved as " + "\033[0m" + date_prefix+"_animation_5")
# * SHOW !
plt.show(block=False)
input("(press enter to quit)")
Reference in New Issue View Git Blame Copy Permalink