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COSMIC/old/COSMIC-VIS_v2.py
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385
COSMIC/old/COSMIC-VIS_v2.py
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"""
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* COSMIC-VIS : Cluster Orbital SysteM Integration Code - Visualisation
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* Version 2 - 2024-02-27
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@ Yaël Moussouni
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@ Unistra, P&E, MSc1-MoFP
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@ Observatory of Strasbourg (Intern)
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.animation as animation
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import os
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# * Parameters
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if "YII" in plt.style.available: plt.style.use("YII")
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if "YII_light" in plt.style.available: light = "YII_light"
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else: light = "default"
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# * Variables
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in_directory = "./COSMIC_output/"
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out_directory = "../Figs/COSMIC_figs/"
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cluster_suffix = "_cluster.csv"
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stars_suffix = "_stars.csv"
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YES = ["Y", "YES", "1", "OUI"]
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MARKER = ["o", "s", "D", "v", "^", "<", ">"]
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Tmin = 2000 # K - Temperature truncation
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Tmax = 10000 # K - Temperature truncation
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# * Functions
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def density_hist(X,Y,M,T,bins,i):
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x_cm = np.average(X[i], weights=M[i])
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y_cm = np.average(Y[i], weights=M[i])
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Radius = np.sqrt((X[i]-x_cm)**2 + (Y[i]-y_cm)**2)
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Rho = np.zeros(len(bins)-1)
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for k in range(len(bins)-1):
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r1 = bins[k]
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r2 = bins[k+1]
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w = np.where((Radius > r1) & (Radius < r2))
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Rho[k] = np.sum(M[i,w])/(4*np.pi*r2**3/3 - 4*np.pi*r1**3/3)
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return Rho
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# * Listing files for selection
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filelist = np.sort(os.listdir(in_directory))
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filelist = [f for f in filelist if f[0] != "."]
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dates = []
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for f in filelist:
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if len(f) == len("YYYY-MM-DD_HH:MM_cluster.csv") or len(f) == len("YYYY-MM-DD_HH:MM_stars.csv"):
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date = f[0:16]
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if not date in dates:
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dates.append(date)
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# * File selection
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print("\033[36m"+"Available dates:"+"\033[0m")
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for i in range(len(dates)):
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print("\033[36m"+"\t{}: ".format(i+1) + "\033[0m" + "{}".format(dates[i].replace("h", ":").replace("_", " ")))
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j = int(input("\033[32m"+"Select a date number: "+"\033[0m"))-1
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print("")
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date_prefix = dates[j]
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# * Data collection
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cluster = np.loadtxt(in_directory+date_prefix+cluster_suffix, skiprows=1, comments="#", delimiter=",")
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stars = np.loadtxt(in_directory+date_prefix+stars_suffix, skiprows=1, comments="#", delimiter=",")
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T = cluster[:,1]
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E = cluster[:,2]
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DE = cluster[:,3]
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L = cluster[:,4]
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DL = cluster[:,5]
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M = np.zeros(np.int32(stars[-1,0:2])+1)
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X = np.zeros(np.int32(stars[-1,0:2])+1)
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Y = np.zeros(np.int32(stars[-1,0:2])+1)
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Z = np.zeros(np.int32(stars[-1,0:2])+1)
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VX = np.zeros(np.int32(stars[-1,0:2])+1)
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VY = np.zeros(np.int32(stars[-1,0:2])+1)
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VZ = np.zeros(np.int32(stars[-1,0:2])+1)
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Lumi = np.zeros(np.int32(stars[-1,0:2])+1)
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Radi = np.zeros(np.int32(stars[-1,0:2])+1)
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Temp = np.zeros(np.int32(stars[-1,0:2])+1)
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for star in stars:
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i = np.int32(star[0])
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j = np.int32(star[1])
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M[i,j] = star[2]
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X[i,j] = star[3]
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Y[i,j] = star[4]
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Z[i,j] = star[5]
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VX[i,j] = star[6]
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VY[i,j] = star[7]
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VZ[i,j] = star[8]
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Lumi[i,j] = star[9]
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Radi[i,j] = star[10]
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Temp[i,j] = star[11]
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N_iter = i
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N_part = j
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x_cm = np.array([np.average(X[i], weights=M[i]) for i in range(N_iter)])
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y_cm = np.array([np.average(Y[i], weights=M[i]) for i in range(N_iter)])
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z_cm = np.array([np.average(Z[i], weights=M[i]) for i in range(N_iter)])
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lim_min = np.min((X,Y))
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lim_max = np.max((X,Y))
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Alpha = np.clip((np.log(Lumi) - np.min(np.log(Lumi)))/(np.max(np.log(Lumi)) - np.min(np.log(Lumi))), 0.1, 1)
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# * Output choice
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print("\033[36m"+"Choose figures:"+"\033[0m")
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figure_1 = input("\033[32m"+"\tFigure 1 (energy, angular momentum, time): "+"\033[0m").upper() in YES
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figure_2 = input("\033[32m"+"\tFigure 2a and 2b (positions, speed): "+"\033[0m").upper() in YES
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figure_3 = input("\033[32m"+"\tFigure 3 (mass distribution): "+"\033[0m").upper() in YES
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figure_4 = input("\033[32m"+"\tFigure 4 (density distribution): "+"\033[0m").upper() in YES
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figure_5 = input("\033[32m"+"\tFigure 5 (HR diagram): "+"\033[0m").upper() in YES
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print("")
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print("\033[36m"+"General settings:"+"\033[0m")
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time = np.int32(input("\033[32m"+"\tStatic images drawing instant ({} < int < {}): ".format(0,N_iter)+"\033[0m"))
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if input("\033[32m"+"\tWindow size restriction (y/n): "+"\033[0m").upper() in YES:
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lim = np.float32(input("\033[32m"+"\t\tLimit [pc]: "+"\033[0m"))
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lim_min = -lim/2
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lim_max = +lim/2
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print("")
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draw_time = scatter_time = distrib_time = diagram_time = time
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if figure_1:
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print("\033[36m"+"Figure 1 (energy, angular momentum, time):"+"\033[0m")
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plot_E = input("\033[32m"+"\tPlot energy vs. time (y/n): "+"\033[0m").upper() in YES
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plot_L = input("\033[32m"+"\tPlot angular momentum vs. time (y/n): "+"\033[0m").upper() in YES
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plot_save = input("\033[32m"+"\tSave figure (y/n): "+"\033[0m").upper() in YES
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print("")
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else: plot_E = plot_L = plot_save = False
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if figure_2:
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print("\033[36m"+"Figure 2a and 2b (positions, speed):"+"\033[0m")
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scatter_2D = input("\033[32m"+"\tScatter positions in 2D (y/n): "+"\033[0m").upper() in YES
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scatter_3D = input("\033[32m"+"\tScatter positions in 3D (y/n): "+"\033[0m").upper() in YES
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scatter_speed = input("\033[32m"+"\tShow speed vectors (y/n): "+"\033[0m").upper() in YES
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scatter_save = input("\033[32m"+"\tSave figure (y/n): "+"\033[0m").upper() in YES
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scatter_animate = input("\033[32m"+"\tAnimate over time (y/n): "+"\033[0m").upper() in YES
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print("")
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else: scatter_2D = scatter_3D = scatter_speed = scatter_save = scatter_animate = False
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if figure_3:
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print("\033[36m"+"Figure 3 (mass distribution):"+"\033[0m")
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distrib_mass = input("\033[32m"+"\tShow mass distribution (y/n): "+"\033[0m").upper() in YES
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distrib_bins = np.int32(input("\033[32m"+"\tNumber of bins (int): "+"\033[0m"))
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distrib_save = input("\033[32m"+"\tSave figure (y/n): "+"\033[0m").upper() in YES
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print("")
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else: distrib_mass = distrib_bins = distrib_save = False
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if figure_4:
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print("\033[36m"+"Figure 4 (density distribution):"+"\033[0m")
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draw_density = input("\033[32m"+"\tShow density distribution (y/n): "+"\033[0m").upper() in YES
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draw_bins = np.float32(input("\033[32m"+"\tWidth of bins [pc]: "+"\033[0m"))
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draw_max = np.float32(input("\033[32m"+"\tMaximum distance [pc]: "+"\033[0m"))
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draw_save = input("\033[32m"+"\tSave figure (y/n): "+"\033[0m").upper() in YES
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draw_animate = input("\033[32m"+"\tAnimate over time (y/n): "+"\033[0m").upper() in YES
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print("")
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else: draw_density = draw_bins = draw_max = draw_save = draw_animate = False
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if figure_5:
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print("\033[36m"+"Figure 5 (HR diagram):"+"\033[0m")
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diagram_HT = input("\033[32m"+"\tDraw HR diagram (y/n): "+"\033[0m").upper() in YES
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diagram_save = input("\033[32m"+"\tSave figure (y/n): "+"\033[0m").upper() in YES
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diagram_animate = input("\033[32m"+"\tAnimate over time (y/n): "+"\033[0m").upper() in YES
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else : diagram_HT = diagram_save = diagram_animate = False
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# * Plotting
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if plot_E or plot_L:
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fig1, axs1 = plt.subplots(2, sharex=True)
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if plot_E and not plot_L: # Just the energy
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axs1[1].set_xlabel("$t\\ [\\mathrm{{Myr}}]$")
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axs1[0].set_ylabel("$E\\ [\\mathrm{{pc^2\\ M_\\odot\\ Myr^{{-2}}}}]$")
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axs1[1].set_ylabel("$\\Delta E/E_0$")
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axs1[0].plot(T, E, color="C3")
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axs1[1].plot(T, DE, color="C3")
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elif plot_L and not plot_E: # Just the angular momentum
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axs1[1].set_xlabel("$t\\ [\\mathrm{{Myr}}]$")
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axs1[0].set_ylabel("$L\\ [\\mathrm{{pc^2\\ M_\\odot\\ Myr^{{-1}}}}]$")
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axs1[1].set_ylabel("$\\Delta L/L_0$")
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axs1[0].plot(T, L, color="C4")
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axs1[1].plot(T, DL, color="C4")
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elif plot_L and plot_E: # ...both
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xas1 = [axs1[i].twinx() for i in range(len(axs1))]
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axs1[1].set_xlabel("$t\\ [\\mathrm{{Myr}}]$")
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axs1[0].set_ylabel("$E\\ [\\mathrm{{pc^2\\ M_\\odot\\ Myr^{{-2}}}}]$")
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axs1[1].set_ylabel("$\\Delta E/E_0$")
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xas1[0].set_ylabel("$L\\ [\\mathrm{{pc^2\\ M_\\odot\\ Myr^{{-1}}}}]$")
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xas1[1].set_ylabel("$\\Delta L/L_0$")
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axs1[0].plot(T, E, color="C3")
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axs1[1].plot(T, DE, color="C3")
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xas1[0].plot(T, L, color="C4")
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xas1[1].plot(T, DL, color="C4")
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axs1[0].tick_params(axis='y', which="both", colors='C3')
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axs1[0].yaxis.label.set_color('C3')
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xas1[0].tick_params(axis='y', which="both", colors='C4')
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xas1[0].yaxis.label.set_color('C4')
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axs1[1].tick_params(axis='y', which="both", colors='C3')
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axs1[1].yaxis.label.set_color('C3')
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xas1[1].tick_params(axis='y', which="both", colors='C4')
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xas1[1].yaxis.label.set_color('C4')
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if plot_save:
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fig1.tight_layout()
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fig1.savefig(out_directory+date_prefix+"_figure_1")
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print("\033[94m"+"Info: Figure saved as " + "\033[0m" + date_prefix+"_figure_1")
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if scatter_2D or scatter_3D:
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if scatter_2D:
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fig2a, ax2a = plt.subplots(1)
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# ax2a.set_facecolor('k')
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i = scatter_time
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ax2a.set_title("$t =$ {:.2f} Myr".format(T[i]))
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if scatter_speed:
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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")
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ax2a.quiverkey(qvr2a, 0.95, 0.05, 1, "Velocity scale: $1\mathrm{{~km\\cdot s^{{-1}}}}$", labelpos='W', coordinates='figure', color="k", alpha=1)
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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)
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ax2a.set_xlabel("$x$ [pc]")
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ax2a.set_ylabel("$y$ [pc]")
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ax2a.set_xlim(lim_min, lim_max)
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ax2a.set_ylim(lim_min, lim_max)
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ax2a.set_aspect("equal")
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fig2a.colorbar(sct2a, label="Stellar effective temperature $T$ [K]", extend='both')
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fig2a.tight_layout()
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if scatter_save:
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fig2a.savefig(out_directory+date_prefix+"_figure_2a")
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print("\033[94m"+"Info: Figure saved as " + "\033[0m" + date_prefix+"_figure_2a")
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if scatter_3D:
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fig2b, ax2b = plt.subplots(1, subplot_kw={"projection": "3d"})
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# ax2b.set_facecolor('k')
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i = scatter_time
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ax2b.set_title("$t =$ {:.2f} Myr".format(T[i]))
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if scatter_speed:
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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")
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# ax2b.quiverkey(qvr2b, 0.95, 0.05, 2, "Velocity scale: $2\mathrm{{~km\\cdot s^{{-1}}}}$", labelpos='W', coordinates='figure')
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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)
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ax2b.set_xlabel("$x$ [pc]")
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ax2b.set_ylabel("$y$ [pc]")
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ax2b.set_zlabel("$z$ [pc]")
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ax2b.set_xlim(lim_min, lim_max)
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ax2b.set_ylim(lim_min, lim_max)
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ax2b.set_zlim(lim_min, lim_max)
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ax2b.set_aspect("equal")
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fig2b.colorbar(sct2b, label="Stellar effective temperature $T$ [K]", location="left", extend='both')
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fig2b.tight_layout()
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if scatter_save:
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fig2b.savefig(out_directory+date_prefix+"_figure_2b")
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print("\033[94m"+"Info: Figure saved as " + "\033[0m" + date_prefix+"_figure_2b")
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if distrib_mass:
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fig3, ax3 = plt.subplots(1)
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i = distrib_time
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ax3.set_title("$t =$ {:.2f} Myr".format(T[i]))
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nbins = distrib_bins
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hist, lbin = np.histogram(M[i], nbins)
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cbins = 0.5*(lbin[1:]+ lbin[: -1])
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ax3.scatter(cbins, hist, s=5)
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ax3.set_xscale ("log")
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ax3.set_yscale ("log")
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ax3.set_xlabel("Masses $M\\ [\\mathrm{{M_\\odot}}]$")
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ax3.set_ylabel("Star distribution")
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if distrib_save:
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fig3.savefig(out_directory+date_prefix+"_figure_3")
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print("\033[94m"+"Info: Figure saved as " + "\033[0m" + date_prefix+"_figure_3")
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if draw_density:
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fig4, ax4 = plt.subplots(1)
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i = draw_time
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bin_width = draw_bins
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bin_max = draw_max
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bins = np.arange(0,bin_max,bin_width)
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Rho = density_hist(X,Y,M,T,bins,i)
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cbins = 0.5*(bins[1:]+ bins[: -1])
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ax4.set_title("$t =$ {:.2f} Myr".format(T[i]))
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ax4.set_xscale ("log")
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||||
ax4.set_yscale ("log")
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ax4.set_xlabel("Distance from center $r$ [pc]")
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ax4.set_ylabel("Stellar masses density $\\rho\\ [\\mathrm{{M_\\odot\\ pc^{{-3}}}}]$")
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ax4.scatter(cbins, Rho, s=5)
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fig4.tight_layout()
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if draw_save:
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fig4.savefig(out_directory+date_prefix+"_figure_4")
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print("\033[94m"+"Info: Figure saved as " + "\033[0m" + date_prefix+"_figure_4")
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if diagram_HT:
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fig5, ax5 = plt.subplots(1)
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ax5.invert_xaxis()
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i = diagram_time
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ax5.set_title("$t =$ {:.2f} Myr".format(T[i]))
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ax5.set_xlabel("Temperature $T$ [K]")
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ax5.set_ylabel("Luminosity $L\\ [\\mathrm{{L_\\odot}}]$")
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ax5.set_xscale ("log")
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ax5.set_yscale ("log")
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ax5.scatter(Temp[i], Lumi[i], s=5, alpha=Alpha[i], c=Temp[i], cmap="RdYlBu", vmin=Tmin, vmax=Tmax)
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fig5.tight_layout()
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if diagram_save:
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fig5.savefig(out_directory+date_prefix+"_figure_5")
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print("\033[94m"+"Info: Figure saved as " + "\033[0m" + date_prefix+"_figure_5")
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if scatter_animate:
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figA2a,axA2a = plt.subplots(1)
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# ax2a.set_facecolor('k')
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axA2a.set_title("$t =$ {:.2f} Myr".format(T[0]))
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if scatter_speed:
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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")
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axA2a.quiverkey(qvr, 0.95, 0.05, 1, "Velocity scale: $1\mathrm{{~km\\cdot s^{{-1}}}}$", labelpos='W', coordinates='figure', color="k", alpha=1)
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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
Block a user