def __init__(self, path, ioutput, **kwargs):
Snapshot.Snapshot.__init__(self, path, ioutput, "pymses", **kwargs)
'''
Load the snapshot using pymses.RamsesOutput
TODO Verify snapshot exists
'''
self._snapshot = pymses.RamsesOutput(path, ioutput)
def connect(dir="backreaction_b5_3d3yr",
out=9,
var=["d", "ux", "uy", "uz", "P", "f", "tS", "tI", "tB", "g", "w"]):
global output, grid
output = pymses.RamsesOutput(basedir + dir, out)
print output.info
grid = output.amr_source(var)
def connect(dir="L68_off",
out=11,
var=["d", "ux", "uy", "uz", "P", "f", "tS", "tI", "tB", "g"]):
global output, grid
output = pymses.RamsesOutput(basedir + dir, out)
print output.info
grid = output.amr_source(var)
def get_dx(ssnum):
# hard-code to get dx for now...
# saved in fetch_gal_fields.py
ro = pymses.RamsesOutput("output", ssnum)
ds = np.load('snapshot' + str(ssnum) + '_center_fields0123456-15.npz')
dx_vector = ds['dx_vector']
originalLevels = np.log2(1. / np.unique(dx_vector))
highestRes = 2.**(originalLevels.max() * -1)
# after finely sample unigrid
dx_pc = highestRes * get_units(ro=ro)['dx'][0]
return dx_pc
def __init__(self, path, ioutput, ro=None, verbose=False, **kwargs):
super(PymsesSnapshot, self).__init__(path, ioutput, **kwargs)
from pymses import rcConfig as pymsesrc
self.verbose = verbose
if ro is None:
self._ro = pymses.RamsesOutput(path,
ioutput,
metals=self.metals,
verbose=self.verbose,
**kwargs)
else:
self._ro = ro
self._nproc_multiprocessing = pymsesrc.multiprocessing_max_nproc
print("=================================================")
print("/!\ output_ref < output_ref_t0 /!\ ")
print("=================================================")
print("=================================================")
System.exit (0)
else:
ref=(1,0)
a_comparer=(1,0)
#-----------------------------------------
#Lecture du temps de l'output de reference
#-----------------------------------------
ro=pymses.RamsesOutput(path_simu_ref, num_output_ref)
factor_time_Myr_ref = ro.info['unit_time'].express(cst.Myr)
simulation_time_ref = ro.info['time']*factor_time_Myr_ref - ref[1]
#----------------------------------------------------------------
#Recherche des output correspondant dans la simulation a comparer
#----------------------------------------------------------------
output_manquant=0
for l in range(num_output_max):
current_output = (l+1) + int(a_comparer[0])
try:
ro=pymses.RamsesOutput(path_simu_a_comparer, current_output)
except:
#Chargement moments cinetiques de la simulation HR sans rotation #--------------------------------------------------------------- path_1_norot = '/home/users/mnt/averliat/analyses/B335_noturb_norot_hydro_hr/Moment_cinetique_tot_output_1_44_barycentre.fits' path_2_norot = '/home/users/mnt/averliat/analyses/B335_noturb_norot_hydro_hr/Integrale_valeurs_absolues_moments_cinetiques_output_1_44_barycentre.fits' path_simu_norot='/gpfs/data1/phennebe/B335_noturb_norot_hydro_hr/' num_min_norot=1 num_max_norot=44 nmbr_output_norot = num_max_norot-num_min_norot+1 if lecture_time == True: simulation_time_norot = np.zeros(nmbr_output_norot) for l in range(nmbr_output_norot): ro=pymses.RamsesOutput(path_simu_norot,l+1) factor_time_norot_Myr = ro.info['unit_time'].express(cst.Myr) simulation_time_norot[l] = ro.info['time']*factor_time_norot_Myr #---------------------------------------------------------------- #Chargement moments cinetiques de la simulation HR2 sans rotation #---------------------------------------------------------------- path_1_norot2 = '/home/users/mnt/averliat/analyses/B335_noturb_norot_hydro_hr2/Moment_cinetique_tot_output_1_102_barycentre.fits' path_2_norot2 = '/home/users/mnt/averliat/analyses/B335_noturb_norot_hydro_hr2/Integrale_valeurs_absolues_moments_cinetiques_output_1_102_barycentre.fits' path_simu_norot2='/gpfs/data1/phennebe/B335_noturb_norot_hydro_hr2/' num_min_norot2=1 num_max_norot2=102
numero_output = np.zeros(nmbr_output)
time_tab = np.zeros(nmbr_output)
time_cor_tab = np.zeros(nmbr_output)
max_rho_tab = np.zeros((nmbr_output,3))
#------------------------------------------------------------------------------------
#Boucle sur les differents output pour extraire le rayon des disques de chaque output
#------------------------------------------------------------------------------------
output_manquant=0
nbre_output_effectif=0
verif=0
for i in range(nmbr_output):
num = i+output_min
try:
ro=pymses.RamsesOutput(path,num)
except:
output_manquant += 1
if output_manquant==100:
print
print
print("=========================================================================================")
print("=========================================================================================")
print("Pas assez d'output pour la simulation : "+path )
print("=========================================================================================")
print("=========================================================================================")
break
continue
verif=1
output_manquant=0
def trace_angle_gradient_vitesse(simu,tag,output,R_min,R_max,dr,legend,marker,ang_absolu,ang_relat,omega,moment_spec,save_fig):
#------------------
#Differents chemins
#------------------
path='/drf/projets/alfven-data/averliat/'+simu+'/'
path_analyses='/home/averliat/these/analyses/'+simu+'/'
file_save_ang = 'Angle_omega_J_gradient_vitesse_log_output'+str(output)+'_Rmin'+str(R_min)+'_Rmax'+str(R_max)+'_dr'+str(dr)+'.hdf5'
#file_save_ang = 'Angle_gradient_vitesse_output'+str(output)+'comparaison_mathilde.hdf5'
#------------------------------------
#Chargement des differentes quantites
#------------------------------------
h5f = h5py.File(path_analyses+file_save_ang, 'r')
shells_au=h5f['shells_au'][:]
ang_x_tab=h5f['ang_x_tab'][:]
ang_y_tab=h5f['ang_y_tab'][:]
ang_z_tab=h5f['ang_z_tab'][:]
if omega==True:
omega_x_tab=h5f['omega_x_tab'][:]
omega_y_tab=h5f['omega_y_tab'][:]
omega_z_tab=h5f['omega_z_tab'][:]
if moment_spec==True:
moment_spec_x_tab=h5f['moment_spec_x_tab'][:]
moment_spec_y_tab=h5f['moment_spec_y_tab'][:]
moment_spec_z_tab=h5f['moment_spec_z_tab'][:]
h5f.close()
#---------------------------------------------------
#Recalage des angles par rapport à l'angle initial
#---------------------------------------------------
ang_x_tab[np.where(ang_x_tab<0)] = ang_x_tab[np.where(ang_x_tab<0)]+360
ang_y_tab[np.where(ang_y_tab<0)] = ang_y_tab[np.where(ang_y_tab<0)]+360
ang_z_tab[np.where(ang_z_tab<0)] = ang_z_tab[np.where(ang_z_tab<0)]+360
seuil_ang=200
ang_0=ang_x_tab[0]
for i in range(len(ang_x_tab)):
if np.abs(ang_0-ang_x_tab[i])>seuil_ang:
ang_x_tab[i]=ang_x_tab[i]+360
if np.abs(ang_0-ang_x_tab[i])>seuil_ang:
ang_x_tab[i]=ang_x_tab[i]-720
ang_0=ang_x_tab[i]
ang_0=ang_y_tab[0]
for i in range(len(ang_y_tab)):
if np.abs(ang_0-ang_y_tab[i])>seuil_ang:
ang_y_tab[i]=ang_y_tab[i]+360
if np.abs(ang_0-ang_y_tab[i])>seuil_ang:
ang_y_tab[i]=ang_y_tab[i]-720
ang_0=ang_y_tab[i]
ang_0=ang_z_tab[0]
for i in range(len(ang_z_tab)):
if np.abs(ang_0-ang_z_tab[i])>seuil_ang:
ang_z_tab[i]=ang_z_tab[i]+360
if np.abs(ang_0-ang_z_tab[i])>seuil_ang:
ang_z_tab[i]=ang_z_tab[i]-720
ang_0=ang_z_tab[i]
ang_x_tab_recal = ang_x_tab - ang_x_tab[0]
ang_y_tab_recal = ang_y_tab - ang_y_tab[0]
ang_z_tab_recal = ang_z_tab - ang_z_tab[0]
#---------------------------------------------------------------------------------
#Calcul des t_0 de chaque simulation ou lecture des fichiers si calculs deja faits
#---------------------------------------------------------------------------------
ro=pymses.RamsesOutput(path,output)
factor_time_yr = ro.info['unit_time'].express(cst.year)
simulation_time = ro.info['time']*factor_time_yr
seuil_rho = 1e-10
if os.path.isfile(path_analyses+'t0_seuil_rho_'+str(seuil_rho)+'.txt') == False:
ref = np.array(t_0.temps_0_simu(path_t0, seuil_rho, sortie_output=1))
np.savetxt(path_analyses+'t0_seuil_rho_'+str(seuil_rho)+'.txt', ref)
else:
ref=np.loadtxt(path_analyses+'t0_seuil_rho_'+str(seuil_rho)+'.txt')
#------------------
#Debut de la figure
#------------------
cmappts = plt.get_cmap('magma')
colorspts = [cmappts(i) for i in np.linspace(0.1,0.9,7)]
if ang_absolu==True:
plt.figure()
plt.semilogx(shells_au, ang_x_tab, marker='.',color=colorspts[2], label=r'$x$ projection')
plt.xlabel(ur'Radius (AU)')
plt.ylabel(ur"Angle (°)")
plt.legend(loc='best')
#plt.figure()
plt.semilogx(shells_au, ang_y_tab, marker='.',color=colorspts[4], label=r'$y$ projection')
plt.xlabel(ur'Radius (AU)')
plt.ylabel(ur"Angle (°)")
plt.legend(loc='best')
plt.title('Time = '+str(int(simulation_time - ref[1]*1e6))+' years ~~~~~~~~~ $ \\varepsilon= 50 \%$')
#plt.figure()
plt.semilogx(shells_au, ang_z_tab, marker='.',color=colorspts[6], label=r'$z$ projection')
plt.xlabel(ur'Radius (AU)')
plt.ylabel(ur"Angle (°)")
plt.legend(loc='best')
if save_fig==True:
plt.tight_layout(pad=0.1) #pad en inch si besoin
plt.savefig(path_analyses+'Article_1/Angle_gradient_vitesse_'+simu+'_output'+str(output)+'_Rmin'+str(R_min)+'_Rmax'+str(R_max)+'_dr'+str(dr)+'.pdf')#, bbox_inches='tight')
if ang_relat==True:
plt.figure()
plt.semilogx(shells_au, ang_x_tab_recal, marker='.',color=colorspts[2], label=r'$x$ projection')
plt.xlabel(ur'Radius (AU)')
plt.ylabel(ur"Angle (°)")
plt.legend(loc='best')
plt.title('Time = '+str(int(simulation_time - ref[1]*1e6))+' years ~~~~~~~~~ $\\varepsilon= 50 \%$')
#plt.figure()
plt.semilogx(shells_au, ang_y_tab_recal, marker='.',color=colorspts[4], label=r'$y$ projection')
plt.xlabel(ur'Radius (AU)')
plt.ylabel(ur"Angle (°)")
plt.legend(loc='best')
#plt.figure()
plt.semilogx(shells_au, ang_z_tab_recal, marker='.',color=colorspts[6], label=r'$z$ projection')
plt.xlabel(ur'Radius (AU)')
plt.ylabel(ur"Angle (°)")
plt.legend(loc='best')
if save_fig==True:
plt.tight_layout(pad=0.1) #pad en inch si besoin
plt.savefig(path_analyses+'Article_1/Angle_relatif_gradient_vitesse_'+simu+'_output'+str(output)+'_Rmin'+str(R_min)+'_Rmax'+str(R_max)+'_dr'+str(dr)+'.pdf')#, bbox_inches='tight')
if omega==True:
plt.figure()
plt.loglog(shells_au, omega_x_tab, marker='.',color=colorspts[2], label=r'$x$ projection')
plt.xlabel(ur'Radius (AU)')
plt.ylabel(ur"$\Omega$ $\left( km.s^{-1}.pc^{-1} \right)")
plt.legend(loc='best')
#plt.figure()
plt.loglog(shells_au, omega_y_tab, marker='.',color=colorspts[4], label=r'$y$ projection')
plt.xlabel(ur'Radius (AU)')
plt.ylabel(ur"$\Omega$ $\left( km.s^{-1}.pc^{-1} \right)$")
plt.legend(loc='best')
plt.title('Time = '+str(int(simulation_time - ref[1]*1e6))+' years ~~~~~~~~~ $\\varepsilon = 50 \%$')
#plt.figure()
plt.loglog(shells_au, omega_z_tab, marker='.',color=colorspts[6], label=r'$z$ projection')
plt.xlabel(ur'Radius (AU)')
plt.ylabel(ur"$\Omega$ $\left( km.s^{-1}.pc^{-1} \right)$")
plt.legend(loc='best')
if save_fig==True:
plt.tight_layout(pad=0.1) #pad en inch si besoin
plt.savefig(path_analyses+'Article_1/Amplitude_gradient_vitesse_'+simu+'_output'+str(output)+'_Rmin'+str(R_min)+'_Rmax'+str(R_max)+'_dr'+str(dr)+'.pdf')#, bbox_inches='tight')
if moment_spec==True:
plt.figure()
plt.loglog(shells_au, moment_spec_x_tab, marker='.',color=colorspts[2], label=r'$x$ projection')
plt.xlabel(ur'Radius (AU)')
plt.ylabel(ur"$j$ $\left( km.s^{-1}.pc \right)$")
plt.legend(loc='best')
#plt.figure()
plt.loglog(shells_au, moment_spec_y_tab, marker='.',color=colorspts[4], label=r'$y$ projection')
plt.xlabel(ur'Radius (AU)')
plt.ylabel(ur"$j$ $\left( km.s^{-1}.pc \right)$")
plt.legend(loc='best')
plt.title('Time = '+str(int(simulation_time - ref[1]*1e6))+' years ~~~~~~~~~ $\\varepsilon= 50 \%$')
#plt.figure()
plt.loglog(shells_au, moment_spec_z_tab, marker='.',color=colorspts[6], label=r'$z$ projection')
plt.xlabel(ur'Radius (AU)')
plt.ylabel(ur"$j$ $\left( km.s^{-1}.pc \right)$")
plt.legend(loc='best')
if save_fig==True:
plt.tight_layout(pad=0.1) #pad en inch si besoin
plt.savefig(path_analyses+'Article_1/Moment_specifique_gradient_vitesse_'+simu+'_output'+str(output)+'_Rmin'+str(R_min)+'_Rmax'+str(R_max)+'_dr'+str(dr)+'.pdf')#, bbox_inches='tight')
### ipython --pylab
import pymses
import pylab
import numpy
ro = pymses.RamsesOutput("Data/Sedov3d/output", 5)
amr = ro.amr_source(["rho"])
#sph_center = [0.5, 0.5, 0.5]
#sph_radius = 0.5
#from pymses.utils.regions import Sphere
#sph = Sphere(sph_center, sph_radius)
#from pymses.analysis import sample_points
#points = sph.random_points(1.0e6) # 1M sampling points
end = [1, 1, 1]
N = 2 * 2**ro.info["levelmax"]
points = 1. * numpy.array([end] * N)
for i in range(N):
points[i] *= (i + 1.) / N
point_dset = sample_points(amr, points)
pylab.plot(point_dset.points, point_dset['rho'])
#rho_weight_func = lambda dset: dset["rho"]
#r_bins = numpy.linspace(0.0, sph_radius, 200)
#from pymses.analysis import bin_spherical
#rho_profile = bin_spherical(point_dset, sph_center, rho_weight_func, r_bins, divide_by_counts=True)
#pylab.plot(r_bins[0:len(r_bins)-1],rho_profile)
def temps_0_simu(path_simu,
seuil_rho=1e11,
critere_mass_seuil=10,
num_min=1,
num_max=150,
sortie_output=1):
#sortie_output=1 nouveau parametre pour prendre en compte si les output sont sortie tous les pas ou tous les n pas de temps (dans ce cas mettre sortie_output=n avec n=2, 3, ...)
#--------------------------------------------
#Caracteristiques de la simulation consideree
#--------------------------------------------
#simu='/gpfs/data1/averliat/B335_noturb_rot3_hydro/'
#num_min=1
#num_max=150 #Pas obligatoirement l'output max, peut depasser
#seuil_rho = 1e-10 #kg.m^-3
nmbr_output = num_max - num_min + 1
#-------------------------------------------------
#Lecture du temps et des densites de chaque output
#-------------------------------------------------
simulation_time = np.zeros(nmbr_output)
accreted_mass = np.zeros(nmbr_output)
output_seuil = 0
time_seuil = 0
for l in range(nmbr_output):
if os.path.isdir(path_simu + 'output_' +
str(sortie_output * (l + 1)).zfill(5)) == False:
print(
"===========================================================")
print(
"===========================================================")
print(" /!\ Output manquante ou seuil trop haut /!\ ")
print(
"===========================================================")
print(
"===========================================================")
break
ro = pymses.RamsesOutput(path_simu, sortie_output * (l + 1))
factor_time_Myr = ro.info['unit_time'].express(cst.Myr)
simulation_time[l] = ro.info['time'] * factor_time_Myr
amr = ro.amr_source(["rho"])
cell_source = CellsToPoints(amr)
cells = cell_source.flatten(verbose=False)
dx = cells.get_sizes()
pos = cells.points
rho = cells["rho"]
#------------------------------------------------------------
#Facteur de conversion des unites de code en unites physiques
#------------------------------------------------------------
lbox = ro.info['boxlen']
lbox_m = ro.info['unit_length'].express(cst.m)
factor_dist_m = ro.info['unit_length'].express(cst.m)
factor_dist_cm = ro.info['unit_length'].express(cst.cm)
lbox_au = ro.info['unit_length'].express(cst.au)
lbox_cm = ro.info['unit_length'].express(cst.cm)
factor_time_yr = ro.info['unit_time'].express(cst.year)
factor_rho_Hcc = ro.info['unit_density'].express(cst.H_cc)
factor_rho = ro.info['unit_density'].express(cst.kg / (cst.m)**3)
#------------------------------
#Conversion en unites physiques
#------------------------------
simulation_time_yr = ro.info['time'] * factor_time_yr
dx *= factor_dist_m
rho_Hcc = rho * factor_rho_Hcc
rho *= factor_rho
#---------------------------------------------------------
#Definition du centre des images et de leur niveau de zoom
#---------------------------------------------------------
mask = rho_Hcc > seuil_rho
rho_masked = rho[mask]
dx_masked = dx[mask]
mass_core = np.sum(rho_masked * dx_masked**3) / 1.9889e+30 #En Msun
mass_tot = np.sum(rho * dx**3) / 1.9889e+30 #En Msun
pourcent_mass_core = mass_core / mass_tot * 100
print
print
print(
"=========================================================================="
)
print(" M_central_core = " + str(mass_core) + " Msun")
print(" M_tot = " + str(mass_tot) + " Msun")
print(" Pourcent_M_central_core = " + str(pourcent_mass_core) +
" %")
print(
"=========================================================================="
)
print
print
accreted_mass[l] = pourcent_mass_core
if accreted_mass[l] >= critere_mass_seuil:
output_seuil = sortie_output * (l + 1)
time_seuil = simulation_time[l]
print(
"===========================================================")
print(
"===========================================================")
print(" output_seuil = " + str(output_seuil))
print(" time_seuil = " + str(time_seuil) + " Myr")
print(
"===========================================================")
print(
"===========================================================")
break
if output_seuil == 0:
print("===========================================================")
print("===========================================================")
print(" /!\ Seuil de masse accretee non atteint /!\ ")
print("===========================================================")
print("===========================================================")
'''
plt.semilogy(simulation_time, max_rho, linestyle=None, marker='.')
plt.show()
'''
return (output_seuil, time_seuil)
#------------------------------------------------------------
#Chargement moments cinetiques de la simulation sans rotation
#------------------------------------------------------------
path_1_norot = '/home/users/mnt/averliat/analyses/B335_noturb_norot_hydro_hr/Moment_cinetique_tot_output_1_44_barycentre.fits'
path_2_norot = '/home/users/mnt/averliat/analyses/B335_noturb_norot_hydro_hr/Integrale_valeurs_absolues_moments_cinetiques_output_1_44_barycentre.fits'
path_simu_norot = '/gpfs/data1/phennebe/B335_noturb_norot_hydro_hr/'
num_min_norot = 1
num_max_norot = 44
nmbr_output_norot = num_max_norot - num_min_norot + 1
if lecture_time == True:
simulation_time_norot = np.zeros(nmbr_output_norot)
for l in range(nmbr_output_norot):
ro = pymses.RamsesOutput(path_simu_norot, l + 1)
factor_time_norot_Myr = ro.info['unit_time'].express(cst.Myr)
simulation_time_norot[l] = ro.info['time'] * factor_time_norot_Myr
#-------------------------------------------------------------------
#Chargement moments cinetiques de la simulation incluant 1% rotation
#-------------------------------------------------------------------
path_1_rot = '/home/users/mnt/averliat/analyses/B335_noturb_rot_hydro/Moment_cinetique_tot_output_1_98_barycentre.fits'
path_2_rot = '/home/users/mnt/averliat/analyses/B335_noturb_rot_hydro/Integrale_valeurs_absolues_moments_cinetiques_output_1_98_barycentre.fits'
path_simu_rot = '/gpfs/data1/averliat/B335_noturb_rot_hydro/'
num_min_rot = 1
num_max_rot = 98
nmbr_output_rot = num_max_rot - num_min_rot + 1
outname = "snapshot" + str(ssnum) + "_center_stars_resampled.h5"
if os.path.exists(outname):
os.system('rm ' + outname)
camera = {'center': center, 'region_size': originalSize}
loc_vec = ds['loc_vector']
level_vec = ds['level']
epoch_vec = ds['epoch']
id_vec = ds['id']
mass_vec = ds['mass']
vel_vec = ds['vel']
import pymses
ro = pymses.RamsesOutput("output", ssnum)
# resample star particle mass and compute mass-weighted avg epoch correspondingly
resample_star_mass_age(loc_vec,
level_vec,
epoch_vec,
mass_vec,
vel_vec,
outname,
camera,
old_dt_myr=100.,
young_dt_myr=10.,
ro_in=ro,
Nrefined=N,
debug=False)
#See Teyssier, R et al. 2010. n is number density in units H/cc
T_eq = 10**4 * (n / 0.3)**(-0.5)
return T_eq
if __name__ == "__main__":
if (len(sys.argv) == 1):
print 'Case 1 Usage: python %s <simdir> <ioutput> <nstar>' % sys.argv[0]
print 'Case 2 Usage: python %s <omega_m0> <h> <L_box (Mpc/h)> <ell_min> <ell_max> <nstar>' % sys.argv[
0]
sys.exit(1)
elif os.path.isdir(sys.argv[1]):
ro = pymses.RamsesOutput(sys.argv[1], int(sys.argv[2]))
omega_m0 = ro.info['omega_m']
omega_b = ro.info['omega_b']
h = ro.info['H0'] / 100
aexp = ro.info['aexp']
boxsize = ro.info[
'unit_length'].val / 3.08e22 / aexp * h # Mpc/h at z=0
lgridmin = ro.info['levelmin']
lgridmax = ro.info['levelmax']
n_star = float(sys.argv[3]) # H/cc
else:
omega_m0 = float(sys.argv[1])
omega_b = 0.045
h = float(sys.argv[2])
boxsize = float(sys.argv[3]) # Mpc/h
lgridmin = int(sys.argv[4])
# careful sometimes i used "density" (e.g., resample.py), see
# resample_fields.py to make sure
density = f["rho"].value
H2 = f["H2"].value
Pressure = f["P"].value
P_nt = f["P_nt"].value
metallicity = f["Z"].value
velx = f["vel_x"].value
vely = f["vel_y"].value
velz = f["vel_z"].value
import pymses
from pymses.utils import constants as C
ro = pymses.RamsesOutput("output", snapshot_num)
from io_modules.manipulate_fetch_gal_fields import get_units
factor_density = get_units(ro=ro)['rho'][0] # 1/cm^3 (not H/cm^3)
density *= factor_density
print density.max()
factor_vel = get_units(ro=ro)['vel'][0]
velx *= factor_vel
vely *= factor_vel
velz *= factor_vel
print velx.max(), vely.max(), velz.max()
factor_P = get_units(ro=ro)['P'][0]
Pressure *= factor_P
P_nt *= factor_P
path_2 = '/home/users/mnt/averliat/analyses/B335_noturb_norot_hydro_hr2/Integrale_valeurs_absolues_moments_cinetiques_output_1_102_par_densite_normalise.fits'
path_simu = '/gpfs/data1/phennebe/B335_noturb_norot_hydro_hr2/'
num_min = 1
num_max = 102
class_rho = np.array([1e-21, 1e-18, 1e-15, 1e-12, 1e-9, 1e-6])
#---------------------------
#Calcul de l'abscisse en Myr
#---------------------------
nmbr_output = num_max - num_min + 1
simulation_time = np.zeros(nmbr_output)
for l in range(nmbr_output):
ro = pymses.RamsesOutput(path_simu, l + 1)
factor_time_Myr = ro.info['unit_time'].express(cst.Myr)
simulation_time[l] = ro.info['time'] * factor_time_Myr
#--------------------------
#Ouverture de la sauvegarde
#--------------------------
J_tot_tab = fits.open(path_1)[0].data
J_abs_tot_tab = fits.open(path_2)[0].data
#------------------
#Debut de la figure
#------------------
plt.clf()
#output_tab = np.linspace(num_min,num_max, num_max-num_min+1
data = pickle.load(f)
center = data[str(ssnum)]['center_init']
region_size = [data[str(ssnum)]['size']
] # subregion inside which we extracted, in code unit
camera = {'center': center, 'region_size': region_size}
loc_vec = ds['loc_vector']
level_vec = ds['level']
epoch_vec = ds['epoch']
id_vec = ds['id']
mass_vec = ds['mass']
vel_vec = ds['vel']
# hard-coded for testing
amrfile = '../snapshot' + str(
ssnum) + '_center_fields0123456-15_resampled.h5'
import h5py
f = h5py.File(amrfile, "r")
N = f['rho'].shape[0]
import pymses
ro = pymses.RamsesOutput("../output", ssnum)
# resample star particle mass and compute mass-weighted avg epoch correspondingly
resample_star_mass_age(loc_vec, level_vec, epoch_vec, mass_vec, vel_vec, outname, camera, old_dt_myr=100., \
young_dt_myr=10., ro_in=ro, Nrefined=N, debug=True)
# ------
def oneOutput(output):
global prevpos
outputn = int(output.split('_')[-1])
# Load ramses output
r = pymses.RamsesOutput(ramsesdir, outputn, verbose=False)
nbin = 2**7 # int(np.sqrt(map.map.shape[0]))
percell = 50./4
vmin = 0
vmax = 4
def saveAndNotify(fname):
plt.savefig(fname) # , dpi=120)
# print(fname)
plt.clf()
##########################################
# Gas
##########################################
# Get AMR field
vx_op = ScalarOperator(lambda dset: dset["vel"][:, 0],
r.info["unit_density"])
vy_op = ScalarOperator(lambda dset: dset["vel"][:, 1],
r.info["unit_density"])
rho_op = ScalarOperator(lambda dset: dset["rho"], r.info["unit_density"])
amr = r.amr_source(['rho', 'vel'])
rhomap = SliceMap(amr, cam, rho_op, use_multiprocessing=False)
vxmap = SliceMap(amr, cam, vx_op, use_multiprocessing=False)
vymap = SliceMap(amr, cam, vy_op, use_multiprocessing=False)
lvlmap = SliceMap(amr, cam, MaxLevelOperator(), use_multiprocessing=False)
# Plot
plt.subplot(122)
plt.title('Gas map')
plt.imshow(rhomap.map.T[::-1],
extent=(0, 1, 0, 1),
aspect='auto',
vmin=vmin, vmax=vmax,
cmap='viridis')
cb = plt.colorbar()
cb.set_label(u'Density [g.cm³]')
# Velocity map
xx = np.linspace(0, 1, vxmap.map.shape[0])
yy = np.linspace(0, 1, vxmap.map.shape[1])
xs = ys = 8
plt.quiver(xx[::xs], yy[::ys],
vxmap.map.T[::xs, ::ys], vymap.map.T[::xs, ::ys],
angles='xy',
scale=70, scale_units='xy')
if args.zoom:
plt.xlim(0.4, 0.6)
else:
plt.xlim(0, 1)
plt.ylim(0, 1)
##########################################
# Particles
##########################################
# Get them
_, pos, vel, mass, lvl, cpus = read_output(output)
if prevpos is None:
prevpos = pos.copy()
# Select 1024 random particles
strain = max(pos.shape[1] // (1024), 1)
# Estimate displacement
x, y = pos[0:2, ::strain]
vx, vy = (pos[0:2, ::strain] - prevpos[0:2, ::strain])
prevpos = pos.copy()
# Projecting on grid
strain2 = 1
hist_pt, epx, epy = np.histogram2d(pos[0, ::strain2], pos[1, ::strain2],
range=[[0, 1], [0, 1]],
bins=nbin)
# Plot everything
plt.subplot(121)
plt.title('Particles')
plt.pcolormesh(epx, epy, hist_pt.T/percell,
cmap='viridis', vmin=vmin, vmax=vmax)
cb = plt.colorbar()
cb.set_label('density')
plt.quiver(x, y, vx, vy,
scale_units='xy', scale=5, angles='xy', color='white')
# Plot lvl contours (if need be)
ncontours = np.ptp(lvlmap.map)
if ncontours > 0:
plt.contour(lvlmap.map.T,
extent=(epx[0], epx[-1], epy[0], epy[-1]),
levels=np.linspace(lvlmap.map.min()+0.5,
lvlmap.map.max()-0.5,
ncontours),
alpha=1)
if args.zoom:
plt.xlim(0.4, 0.6)
else:
plt.xlim(0, 1)
plt.ylim(0, 1)
plt.suptitle('$t=%.3f$' % r.info['time'])
##########################################
# Store images
##########################################
outputn = int(output.split('_')[-1])
fname = pjoin(prefix, 'density_{:0>5}_{}.{}'.format(
outputn, now.strftime('%Hh%M_%d-%m-%y'), ext))
saveAndNotify(fname)
return pos
if owner == 'averliat':
path = '/mnt/magmist/magmist/simu_B335_averliat/' + simu + '/'
if owner == 'phennebe':
path = '/drf/projets/capucine/' + owner + '/' + simu + '/'
if owner == 'sapanais':
path = '/dsm/anais/storageA/magmist/' + simu + '/'
if owner == 'averliat_alfven':
path = '/drf/projets/alfven-data/averliat/' + simu + '/'
#path_save='/home/averliat/these/analyses/'+simu+'/'+dir_save+'/'
path_analyse = '/home/averliat/these/analyses/' + simu + '/'
#-------------------
#Lecture de l'output
#-------------------
ro = pymses.RamsesOutput(path, output)
lbox_pc = ro.info['unit_length'].express(cst.pc)
amr = ro.amr_source(["rho", "vel", "P", "phi", "g"])
cell_source = CellsToPoints(amr)
cells = cell_source.flatten(verbose=False)
dx = cells.get_sizes()
pos = cells.points
vel = cells["vel"]
rho = cells["rho"]
#------------------------------
#Conversion en unites physiques
#------------------------------
def pdf_to_singledisc_rad(path,
num,
path_out=None,
force=False,
ps=False,
xmin=0,
xmax=1.,
ymin=0,
ymax=1.,
zmin=0.,
zmax=1.,
tag='',
order='<',
do_plot=False):
print(num)
#get the mass weighted density prob density function
hist_logrho, hist_rho_edges = get_rhopdf(path,
num,
path_out=path_out,
force=force,
ps=ps,
xmin=xmin,
xmax=xmax,
ymin=ymin,
ymax=ymax,
zmin=zmin,
zmax=zmax,
tag=tag,
order=order,
do_plot=do_plot)
nbin = len(hist_logrho)
mask_min = hist_rho_edges[0:nbin] > 8.
mask_max = hist_rho_edges[0:nbin] < 11.
mask = mask_min * mask_max
amin = np.argmin(hist_logrho[mask])
#density at the disk edge
log_rho_disk = hist_rho_edges[0:nbin][mask][amin]
print 'log_rho_disk', log_rho_disk
# pdb.set_trace() # debug mode
ro = pymses.RamsesOutput(path, num)
lbox = ro.info['boxlen'] #boxlen in codeunits (=>pc)
AU, pc, Ms, Myr, scale_n, scale_d, scale_t, scale_l, scale_v, scale_T2, scale_ener, scale_mag, microG, km_s, Cwnm, scale_mass, unit_col, lbox_pc = me.normalisation_averliat(
ro)
time = ro.info['time'] * scale_t / Myr
amr = ro.amr_source(["rho", "vel", "Br"])
cell_source = CellsToPoints(amr)
cells = cell_source.flatten()
dx = cells.get_sizes()
pos = cells.points
#cells belonging to the disk
mask_rho_disk = np.log10(cells['rho']) > log_rho_disk
print 'disk cell number', np.sum(mask_rho_disk)
#mean magnetic field in the whole selected region
mrd = mask_rho_disk
#volume of the selected region
vol = np.sum(dx[mrd]**3)
mag_mean_broad = np.sum(
np.sqrt((cells['Br'][:, 0][mrd]**2 + cells['Br'][:, 1][mrd]**2 +
cells['Br'][:, 2][mrd]**2)) * dx[mrd]**3) / vol
#normalise it in cgs
mag_mean_broad = mag_mean_broad * scale_mag
#maximum density cell
mask_rho_max = cells['rho'] == np.max(cells['rho'])
#mass disk
mass_disk = np.sum(dx[mask_rho_disk]**3 * cells['rho'][mask_rho_disk])
mass_disk = mass_disk * scale_mass / Ms
#position of maximum density cell
pos_max_x = pos[:, 0][mask_rho_max][0]
pos_max_y = pos[:, 1][mask_rho_max][0]
pos_max_z = pos[:, 2][mask_rho_max][0]
#position of disk cell
pos_disk_x = pos[:, 0][mask_rho_disk] - pos_max_x
pos_disk_y = pos[:, 1][mask_rho_disk] - pos_max_y
pos_disk_z = pos[:, 2][mask_rho_disk] - pos_max_z
#position of maximum density cell
vel_max_x = cells['vel'][:, 0][mask_rho_max][0]
vel_max_y = cells['vel'][:, 1][mask_rho_max][0]
vel_max_z = cells['vel'][:, 2][mask_rho_max][0]
#position of disk cell
vel_disk_x = cells['vel'][:, 0][mask_rho_disk] - vel_max_x
vel_disk_y = cells['vel'][:, 1][mask_rho_disk] - vel_max_y
vel_disk_z = cells['vel'][:, 2][mask_rho_disk] - vel_max_z
#radial component of V
norm_pos = np.sqrt(pos_disk_x**2 + pos_disk_y**2 + pos_disk_z**2)
mask = norm_pos == 0.
norm_pos[mask] = 1.e-10
Vrad = (vel_disk_x * pos_disk_x + vel_disk_y * pos_disk_y +
vel_disk_z * pos_disk_z) / norm_pos
#non radial component of V
Vpar_x = (vel_disk_z * pos_disk_y - vel_disk_y * pos_disk_z) / norm_pos
Vpar_y = (vel_disk_x * pos_disk_z - vel_disk_z * pos_disk_x) / norm_pos
Vpar_z = (vel_disk_y * pos_disk_x - vel_disk_x * pos_disk_y) / norm_pos
Vpar = np.sqrt(Vpar_x**2 + Vpar_y**2 + Vpar_z**2)
Vpar[mask] = 1.e-10
#now select the point that have a parallel velocity larger than the radial one
mask = (-Vrad) / Vpar < 0.5
if (np.sum(mask) <= 1):
return time, 0., 0., 0., 0., log_rho_disk, mag_mean_broad, 0., 0. #, max_rad_disk, mean_rad_disk, mean_out_rad_disk
pos_disk_x = pos_disk_x[mask]
pos_disk_y = pos_disk_y[mask]
pos_disk_z = pos_disk_z[mask]
vel_disk_x = vel_disk_x[mask]
vel_disk_y = vel_disk_y[mask]
vel_disk_z = vel_disk_z[mask]
# print 'sum mask',np.sum(mask),len(pos_disk_x)
#determine the direction of the mean angular momentum
Jx = np.mean(pos_disk_y * vel_disk_z - pos_disk_z * vel_disk_y)
Jy = np.mean(pos_disk_z * vel_disk_x - pos_disk_x * vel_disk_z)
Jz = np.mean(pos_disk_x * vel_disk_y - pos_disk_y * vel_disk_x)
Jnorm = np.sqrt(Jx**2 + Jy**2 + Jz**2)
Jx = Jx / Jnorm
Jy = Jy / Jnorm
Jz = Jz / Jnorm
#project the radial vector in the disk plane
Jpos = Jx * pos_disk_x + Jy * pos_disk_y + Jz * pos_disk_z
pos_dpl_x = pos_disk_x - Jpos * Jx
pos_dpl_y = pos_disk_y - Jpos * Jy
pos_dpl_z = pos_disk_z - Jpos * Jz
if (pos_dpl_x[0]**2 + pos_dpl_x[1]**2 + pos_dpl_x[2]**2 != 0.):
pos_x = pos_dpl_x[0]
pos_y = pos_dpl_y[0]
pos_z = pos_dpl_z[0]
else:
pos_x = pos_dpl_x[1]
pos_y = pos_dpl_y[1]
pos_z = pos_dpl_z[1]
#calculate the cosinus
cos_pos = (pos_dpl_x * pos_x + pos_dpl_y * pos_y + pos_dpl_z * pos_z)
mask = pos_dpl_x * pos_dpl_x + pos_dpl_y * pos_dpl_y + pos_dpl_z * pos_dpl_z > 0.
cos_pos = cos_pos / np.sqrt(pos_x * pos_x + pos_y * pos_y + pos_z * pos_z)
cos_pos[mask] = cos_pos[mask] / np.sqrt(pos_dpl_x * pos_dpl_x +
pos_dpl_y * pos_dpl_y +
pos_dpl_z * pos_dpl_z)[mask]
#radius of disk cells
# rad_disk = np.sqrt( (pos_disk[:,0]-pos_max_x)**2 + (pos_disk[:,1]-pos_max_y)**2 + (pos_disk[:,2]-pos_max_z)**2 )
rad_disk = np.sqrt((pos_disk_x)**2 + (pos_disk_y)**2 + (pos_disk_z)**2)
#mean disk radius
mean_rad_disk = np.mean(rad_disk)
median_rad_disk = np.median(rad_disk)
arg_d = np.argsort(rad_disk)
ndisk = len(arg_d)
print 'ndisk', ndisk
mask_out_disk = arg_d[np.int(ndisk * 0.9):ndisk - 1]
#mean radius of the 90% point having higher radius
mean_out_rad_disk = np.mean(rad_disk[mask_out_disk])
#now look at the maximum in a series of direction
ndir = 500
rad_loc_v = np.zeros(ndir)
cos_min = -1.
cos_max = cos_min + 2. / (ndir + 1)
for ld in range(ndir):
mask1 = cos_pos >= cos_min
mask2 = cos_pos < cos_max
mask = mask1 * mask2
print 'len mask, sum mask', len(mask), np.sum(mask)
if (np.sum(mask) != 0):
# pdb.set_trace()
rad_loc_v[ld] = np.max(rad_disk[mask])
else:
rad_loc_v[ld] = 0.
cos_min = cos_min + 2. / (ndir + 1)
cos_max = cos_max + 2. / (ndir + 1)
max_rad_loc = np.max(
rad_loc_v[rad_loc_v < 500 / lbox / pc * AU]) * lbox * pc / AU
min_rad_loc = np.min(
rad_loc_v[rad_loc_v < 500 / lbox / pc * AU]) * lbox * pc / AU
mean_rad_loc = np.mean(
rad_loc_v[rad_loc_v < 500 / lbox / pc * AU]) * lbox * pc / AU
median_rad_loc = np.median(
rad_loc_v[rad_loc_v < 500 / lbox / pc * AU]) * lbox * pc / AU
#--------------------------
#Ajout par AV pour essayer de mieux estimer la taille des disques
rad_loc_v = rad_loc_v * lbox * pc / AU #listes des rayons en AU
taille_bin = 4 #AU
largeur_modale = 2
min_serie = np.floor(np.min(rad_loc_v))
min_serie -= min_serie % taille_bin
max_serie = np.floor(np.max(rad_loc_v)) + 1
max_serie += (taille_bin - max_serie % taille_bin)
nbr_bin = (max_serie - min_serie) / taille_bin
bins = np.arange(nbr_bin) * taille_bin + min_serie + taille_bin / 2.
count = np.zeros(len(bins))
for i in range(len(rad_loc_v)):
ind = np.int(np.floor((rad_loc_v[i] - min_serie) / taille_bin))
count[ind] += 1
bin_central = bins[np.argmax(count)]
lim_inf = bin_central - taille_bin / 2. - largeur_modale * taille_bin
lim_sup = bin_central + taille_bin / 2. + largeur_modale * taille_bin
rad_new = rad_loc_v[rad_loc_v > lim_inf]
rad_new = rad_new[rad_new < lim_sup]
rad_estim = np.median(rad_new)
#--------------------------
mean_rad_disk = mean_rad_disk * lbox * pc / AU
mean_out_rad_disk = mean_out_rad_disk * lbox * pc / AU
# pdb.set_trace() # debug mode
return time, mass_disk, max_rad_loc, min_rad_loc, mean_rad_loc, log_rho_disk, mag_mean_broad, median_rad_loc, rad_estim #, max_rad_disk, mean_rad_disk, mean_out_rad_disk
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
import h5py
f = h5py.File("snapshot28_center_densityfield_resampled.h5", "r")
density = f["density"].value
# convert from code unit density to g/cc (depending on how fetch_gal.py is implemented.)
convert_unit = True
if convert_unit:
import pymses
from pymses.utils import constants as C
ro = pymses.RamsesOutput("output", 28)
factor = ro.info["unit_density"].express(C.H_cc)
density *= factor
print density.max()
from skimage.feature import blob_dog
# blobs are assumed to be light on dark background
# min_sigma = 1 # keep it low for smaller blobs
# max_sigma = # keep it high to detect large blobs
# sigma_ratio = # ratio btw the stddev of Gaussian Kernels used.
# overlap = 0.8 # between 0 and 1; if the area of two blobs overlaps by a fraction > this, smaller blob is eliminated.
# For each blob found, the method returns its coordinates and the standard deviation of the Gaussian kernel that detected the blob.
_max = density.max()
def get_rhopdf(path_in,
num,
path_out=None,
force=False,
ps=False,
xmin=0,
xmax=1.,
ymin=0,
ymax=1.,
zmin=0.,
zmax=1.,
tag='',
order='<',
do_plot=False):
if (path_out is not None):
directory_out = path_out
else:
directory_out = path_in
if (tag != ''):
tag = tag + '_'
name_save_pdf = directory_out + 'pdf_' + tag + str(num).zfill(5) + '.save'
#a list of dictionnary to describe the plots
list_plot = []
ro = pymses.RamsesOutput(path_in, num, order=order)
lbox = ro.info['boxlen'] #boxlen in codeunits (=>pc)
amr = ro.amr_source(["rho"])
cell_source = CellsToPoints(amr)
cells = cell_source.flatten()
dx = cells.get_sizes()
mask1 = cells.points[:, 0] > xmin
mask2 = cells.points[:, 0] < xmax
mask3 = cells.points[:, 1] > ymin
mask4 = cells.points[:, 1] < ymax
mask5 = cells.points[:, 2] > zmin
mask6 = cells.points[:, 2] < zmax
mask = mask1 * mask2 * mask3 * mask4 * mask5 * mask6
AU, pc, Ms, Myr, scale_n, scale_d, scale_t, scale_l, scale_v, scale_T2, scale_ener, scale_mag, microG, km_s, Cwnm, scale_mass, unit_col, lbox_pc = me.normalisation_averliat(
ro)
vol = dx**3
mass = cells["rho"] * vol * scale_mass / Ms
log_rho = np.log10(cells['rho'])
log_min = min(log_rho)
log_max = max(log_rho)
nbin = 50
width = (log_max - log_min) / nbin
hist_logrho, hist_rho_edges = P.histogram(log_rho[mask],
bins=nbin,
range=(log_min, log_max),
weights=mass[mask])
# pdf_logrho , histrhomv_edges = P.histogram(log_rho[mask],bins=nbin,range=(log_min,log_max),weights=vol[mask])
if (do_plot):
#mass weighted histogram of density
P.clf()
P.bar(hist_rho_edges[:-1],
np.log10(hist_logrho) + 3.,
width,
-3.,
color='none',
edgecolor='k')
xlabel = '$log(n)$ (cm$^{-3}$)'
ylabel = '$log(M)$ (M$_\odot$)'
P.xlabel(r'$log(n)$ (cm$^{-3}$)')
P.ylabel(r'$log(M)$ (M$_\odot$)')
name_im = directory_out + 'pdfmw_logrho_bd_' + tag + str(num).zfill(5)
P.savefig(name_im + '.ps')
P.savefig(name_im + '.png')
return hist_logrho, hist_rho_edges
def myplot(path='.',output=-1,sink=False,center=[0.5,0.5,0.5],los='x',size=[0.5,0.5], \
minimum=None,maximum=None,typlot='rho',record=False,igrp=-1,plot_velocity=False,slice=False):
filerep = path
if (record):
savedir = os.path.join(filerep, "results")
if not os.path.exists(savedir):
os.makedirs(savedir)
#mu,alpha,lmax = parseDirSink(directory)
#savedir = checkDir(savedir, "mu" + mu)
#savedir = checkDir(savedir, "lmax" + lmax)
# if output=-1, take last output
if output == -1:
outputname = np.sort(glob.glob(os.path.join(filerep,
"output_?????")))[-1]
output = int(outputname.split('_')[-1])
# Define the file
ro = pm.RamsesOutput(filerep, output)
if igrp != -1 and (igrp > ro.info["ngrp"] or igrp < 0):
print(bold + 'PROBLEM:' + reset + ' igrp=' + str(igrp) +
' but simulation with ngrp=' + str(ro.info["ngrp"]))
sys.exit(1)
# Define the output data structure (to use with pymses5)
ro.define_amr_scalar_field("hydro", "rho", 0)
ro.define_amr_vector_field("hydro", "vel", [1, 2, 3])
ro.define_amr_vector_field("hydro", "Bl", [4, 5, 6])
ro.define_amr_vector_field("hydro", "Br", [7, 8, 9])
ro.define_amr_scalar_field("hydro", "P", 10)
ro.define_amr_multivalued_field("hydro", "Er", 11, ro.info["ngrp"])
ro.define_amr_vector_field(
"hydro", "J",
[11 + ro.info["ngrp"], 12 + ro.info["ngrp"], 13 + ro.info["ngrp"]])
ro.define_amr_scalar_field("hydro", "eint", 14 + ro.info["ngrp"])
if ro.info["eos"]:
ro.define_amr_scalar_field("hydro", "T", 15 + ro.info["ngrp"])
ro.define_amr_vector_field("grav", "g", [0, 1, 2])
time = ro.info["time"] * ro.info["unit_time"].express(ct.kyr)
if (sink):
# Read sink particles
sink_filename = os.path.join(
filerep, 'output_%05d' % output + '/sink_%05d' % output + '.csv')
print("Reading sinks : " + reset + sink_filename)
sinkp = np.loadtxt(sink_filename,
dtype={
'names':
('Id', 'mass', 'x', 'y', 'z', 'vx', 'vy', 'vz',
'rot_period', 'lx', 'ly', 'lz', 'acc_rate',
'acc_lum', 'age', 'int_lum', 'Teff'),
'formats':
('i', 'f', 'f', 'f', 'f', 'f', 'f', 'f', 'f',
'f', 'f', 'f', 'f', 'f', 'f', 'f', 'f')
},
delimiter=',')
sinkp = np.atleast_1d(sinkp) # to work even if only 1 sink
nsink = sinkp.size
# Define the accretion radius
if ("ncell_racc" in ro.info):
ncell_racc = ro.info['ncell_racc']
else:
ncell_racc = 4
dxmin = 0.5**ro.info['levelmax'] * ro.info["unit_length"].express(
ct.au)
r_acc = dxmin * ncell_racc
#area = np.pi*r_acc**2
xc, yc, zc = sinkp['x'] / ro.info['boxlen'], sinkp['y'] / ro.info[
'boxlen'], sinkp['z'] / ro.info['boxlen']
else:
nsink = 0
if nsink == 0:
nsink = 1
# Center
xc, yc, zc = np.atleast_1d(center[0]), np.atleast_1d(
center[1]), np.atleast_1d(center[2])
cmap = pl.cm.get_cmap('seismic', 100)
if typlot == 'rho':
cmap = pl.cm.get_cmap('jet', 100)
elif typlot == 'Tr':
cmap = pl.cm.get_cmap('hot', 100)
elif typlot == 'B':
cmap = pl.cm.get_cmap('PuOr', 100)
elif typlot == 'beta_plasma':
cmap = pl.cm.get_cmap('seismic', 100)
else:
cmap = pl.cm.get_cmap('jet', 100)
# Width of the region to plot
xr, yr = size[0], size[1]
xbl = (-xr / 2. * ro.info["unit_length"]).express(ct.au)
xbr = (+xr / 2. * ro.info["unit_length"]).express(ct.au)
ybl = (-yr / 2. * ro.info["unit_length"]).express(ct.au)
ybr = (+yr / 2. * ro.info["unit_length"]).express(ct.au)
extent = [xbl, xbr, ybl, ybr]
# Define camera
if los == 'x':
upvec = "z"
vx = 1
vy = 2
labelx = 'Y (AU)'
labely = 'Z (AU)'
plane = 'yz'
elif los == 'y':
upvec = "x"
vx = 2
vy = 0
labelx = 'Z (AU)'
labely = 'X (AU)'
plane = 'zx'
elif los == 'z':
upvec = "y"
vx = 0
vy = 1
labelx = 'X (AU)'
labely = 'Y (AU)'
plane = 'xy'
def plot_func(dset):
if typlot == 'rho':
return dset['rho'] * ro.info["unit_density"].express(
ct.g / ct.cm**3)
if typlot == 'eint':
return dset['eint'] * (ro.info["unit_density"] *
ro.info["unit_velocity"]**2).express(
ct.erg / ct.cm**3)
if typlot == 'T':
if ro.info["eos"]:
return dset['T']
else:
return dset['P'] / dset['rho'] * ro.info[
"unit_temperature"].express(ct.K) * ro.info["mu_gas"]
if typlot == 'B':
B = 1./4*( (dset['Bl'][:,0]+dset['Br'][:,0])**2 \
+(dset['Bl'][:,1]+dset['Br'][:,1])**2 \
+(dset['Bl'][:,2]+dset['Br'][:,2])**2)
return B
# To use with pymses5
if typlot == 'Er' or typlot == 'Tr':
if igrp == -1:
Er = np.sum(dset['Er'], axis=-1) * (
ro.info["unit_density"] *
ro.info["unit_velocity"]**2).express(ct.erg / ct.cm**3)
else:
Er = dset['Er'][:, igrp - 1] * (
ro.info["unit_density"] *
ro.info["unit_velocity"]**2).express(ct.erg / ct.cm**3)
if typlot == 'Er':
return Er
else:
return (Er / ct.a_R.express(ct.erg / ct.cm**3 / ct.K**4))**0.25
if typlot == 'entropy':
return dset['P'] / dset['rho']**ro.hydro_info["gamma"]
if typlot == 'Pth_Pdyn':
return dset['P'] / (0.5 * dset['rho'] *
np.sum(dset['vel']**2, axis=-1))
if typlot == 'beta_plasma':
B = 1./4.*( (dset['Bl'][:,0]+dset['Br'][:,0])**2 \
+(dset['Bl'][:,1]+dset['Br'][:,1])**2 \
+(dset['Bl'][:,2]+dset['Br'][:,2])**2)
if igrp == -1:
Er = np.sum(
dset['Er'], axis=-1
) #*(ro.info["unit_density"]*ro.info["unit_velocity"]**2).express(ct.erg/ct.cm**3)
else:
Er = dset[
'Er'][:, igrp -
1] #*(ro.info["unit_density"]*ro.info["unit_velocity"]**2).express(ct.erg/ct.cm**3)
return dset['P'] / (B) #/dset['rho'])
else:
print(bold + 'Problem in typlot')
sys.exit()
# Fields to be read
fields_to_read = []
if (not ro.info["eos"] and typlot == 'T') or typlot == 'entropy':
fields_to_read = ["rho", "P"]
elif typlot == 'Tr':
fields_to_read = ["Er"]
elif typlot == 'Pth_Pdyn':
fields_to_read = ["P", "rho", "vel"]
elif typlot == 'B':
fields_to_read = ["Bl", "Br"]
elif typlot == 'beta_plasma':
fields_to_read = ["Bl", "Br", "rho", "P", "Er"]
else:
fields_to_read = [typlot]
if (plot_velocity):
fields_to_read.append("vel")
if not slice:
fields_to_read.append("rho")
fields_to_read = list(set(fields_to_read)) #to remove duplicates
if slice:
source = ro.amr_source(fields_to_read)
varplot_op = ScalarOperator(plot_func)
else:
rt = raytracing.RayTracer(ro, fields_to_read)
if typlot == 'rho':
func = lambda dset: plot_func(dset) * ro.info["unit_length"
].express(ct.cm)
varplot_op = ScalarOperator(func)
else:
up_func = lambda dset: (dset["rho"] * ro.info["unit_density"].
express(ct.g / ct.cm**3) * plot_func(dset))
down_func = lambda dset: (dset["rho"] * ro.info["unit_density"].
express(ct.g / ct.cm**3))
varplot_op = FractionOperator(up_func, down_func)
for i in range(nsink):
fig = pl.figure()
ax = pl.subplot(111)
pl.xlabel(labelx)
pl.ylabel(labely)
cam = Camera(center=[xc[i], yc[i], zc[i]],line_of_sight_axis=los,region_size=[xr, yr] \
, up_vector=upvec,map_max_size=512, log_sensitive=True)
if slice:
mapx = SliceMap(source, cam, varplot_op, z=0.)
else:
# WARNING! surf_qty=True to get an integral over dz and not dSdz
# so that if typlot=='rho', get surface density map
# and if not, get a density-weighted map (and not a mass-weighted map)
mapx = rt.process(varplot_op, cam, surf_qty=True)
if typlot == 'Pth_Pdyn':
imx = pl.imshow(mapx.transpose(), extent = extent, origin='lower' \
, vmin = minimum, vmax = maximum,cmap=cmap)
#imx = pl.contourf(mapx.transpose(), extent = extent, origin='lower' \
# , vmin = minimum, vmax = maximum)
cbar = pl.colorbar()
cbar.set_label(typlot)
else:
imx = pl.imshow(np.log10(mapx.transpose()), extent = extent, origin='lower' \
, vmin = minimum, vmax = maximum,cmap=cmap)
#imx = pl.contourf(np.log10(mapx.transpose()), extent = extent, origin='lower' \
# , vmin = minimum, vmax = maximum)
cbar = pl.colorbar()
# cmap = pl.cm.get_cmap('jet',100)
if typlot == 'rho':
cbar.set_label(r'$\mathrm{log(}\rho)$', fontsize=15)
elif typlot == 'Tr':
cbar.set_label(r'$\mathrm{log(T_r)}$', fontsize=15)
elif typlot == 'Pth_Pdyn':
cbar.set_label('Pth/Pdyn')
elif typlot == 'B':
cbar.set_label('log(|B|)')
elif typlot == 'beta_plasma':
cbar.set_label(r'$\mathrm{log(}\beta)$', fontsize=15)
else:
cbar.set_label('Log(' + typlot + ')')
if (sink):
for isink in range(nsink):
# Plot sinks position
mass = sinkp[isink]["mass"]
col = cmap(int(mass * 100))
xp = (sinkp[isink]['x'] / ro.info['boxlen'] -
xc[i]) * ro.info["unit_length"].express(ct.au)
yp = (sinkp[isink]['y'] / ro.info['boxlen'] -
yc[i]) * ro.info["unit_length"].express(ct.au)
zp = (sinkp[isink]['z'] / ro.info['boxlen'] -
zc[i]) * ro.info["unit_length"].express(ct.au)
if los == 'x':
ax.add_patch(
pl.Circle((yp, zp),
radius=r_acc,
fc='white',
alpha=0.65))
ax.add_patch(
pl.Circle((yp, zp),
radius=(r_acc / ncell_racc),
fc=col,
ec=col))
#pl.plot(yp,zp,'k+',mew=2,ms=10)
elif los == 'y':
ax.add_patch(
pl.Circle((zp, xp),
radius=r_acc,
fc='white',
alpha=0.65))
ax.add_patch(
pl.Circle((zp, xp),
radius=(r_acc / ncell_racc),
fc=col,
ec=col))
#pl.plot(zp,xp,'k+',mew=2,ms=10)
elif los == 'z':
ax.add_patch(
pl.Circle((xp, yp),
radius=r_acc,
fc='white',
alpha=0.65))
ax.add_patch(
pl.Circle((xp, yp),
radius=(r_acc / ncell_racc),
fc=col,
ec=col))
#pl.plot(xp,yp,'k+',mew=2,ms=10)
# Plot velocity field
if (slice and plot_velocity):
p = cam.get_slice_points(0.0)
nx, ny = cam.get_map_size()
dset = pm.analysis.sample_points(source, p)
vel = dset["vel"] * ro.info["unit_velocity"].express(ct.km / ct.s)
rs = 32
x = np.linspace(xbl, xbr, nx)
y = np.linspace(ybl, ybr, ny)
u, v = np.zeros((nx, ny)), np.zeros((nx, ny))
mask = np.zeros((nx, ny))
for ii in range(nx):
for jj in range(ny):
if (ii % rs == 0 and jj % rs == 0):
u[ii, jj] = vel[:, vx].reshape(nx, ny)[ii, jj]
v[ii, jj] = vel[:, vy].reshape(nx, ny)[ii, jj]
else:
u[ii, jj] = 'Inf'
v[ii, jj] = 'Inf'
mask[ii, jj] = 1
u2 = u[::rs, ::rs]
v2 = v[::rs, ::rs]
x2 = x[::rs]
y2 = y[::rs]
vel_mean = np.mean(np.sqrt(u2**2 + v2**2))
vel_max = np.max(np.sqrt(u2**2 + v2**2))
#pl.quiver(x,y,u.transpose(),v.transpose(),scale=20*nx)
#Q=pl.quiver(x,y,u.transpose(),v.transpose(),scale=100,pivot='mid')
#u_masked=np.ma.masked_array(u,mask=mask)
#v_masked=np.ma.masked_array(v,mask=mask)
#vel_mean=np.mean(np.sqrt(u_masked**2+v_masked**2))
Q = pl.quiver(x2,
y2,
u2.transpose(),
v2.transpose(),
scale=100,
pivot='mid')
#pl.quiverkey(Q,0.7,0.92,vel_mean,r'%.2f km/s'%vel_mean,coordinates='figure')
pl.quiverkey(Q,
0.7,
0.92,
vel_max,
r'%.2f km/s' % vel_max,
coordinates='figure')
pl.axis(extent)
del (u, v, x, y, u2, v2, x2, y2)
if (sink):
# Print the mass of the sinks
mass = 0
for j in range(nsink):
mass = mass + sinkp["mass"][j]
pl.figtext(0.01,
00.01,
"$\mathrm{M}_*=$" + "%4.1f " % mass + "$M_\odot$",
fontsize=15)
pl.suptitle("%.3f kyr" % time)
if (record):
for format in ("png", "eps", "pdf"):
if slice:
namefig = os.path.join(
savedir, "slice_" + typlot + "_" + plane + "_eps" +
"_%.3f" % size[0] + "_out" + "_%05d" % output +
"_isink_%05d." % sinkp['Id'][i] + format)
else:
namefig = os.path.join(
savedir, "proj_" + typlot + "_" + plane + "_eps" +
"_%.3f" % size[0] + "_out" + "_%05d" % output +
"_isink_%05d." % sinkp['Id'][i] + format)
print(bold + "Save figure: " + reset + namefig)
pl.savefig(namefig)
pl.close()
else:
pl.ion()
pl.show()
if (sink):
# Print the mass of the sinks
for i in range(nsink):
mass = sinkp["mass"][i]
print(bold + "Mass(" + str(i) + "): " + reset + "%.2e Msun" % mass)
def pdf_to_singledisc_for_plot(path,
num,
path_out=None,
force=False,
ps=False,
xmin=0,
xmax=1.,
ymin=0,
ymax=1.,
zmin=0.,
zmax=1.,
tag='',
order='<',
do_plot=False):
print(num)
#get the mass weighted density prob density function
hist_logrho, hist_rho_edges = get_rhopdf(path,
num,
path_out=path_out,
force=force,
ps=ps,
xmin=xmin,
xmax=xmax,
ymin=ymin,
ymax=ymax,
zmin=zmin,
zmax=zmax,
tag=tag,
order=order,
do_plot=do_plot)
nbin = len(hist_logrho)
mask_min = hist_rho_edges[0:nbin] > 8.
mask_max = hist_rho_edges[0:nbin] < 11.
mask = mask_min * mask_max
amin = np.argmin(hist_logrho[mask])
#density at the disk edge
log_rho_disk = hist_rho_edges[0:nbin][mask][amin]
print 'log_rho_disk', log_rho_disk
rho_min_disk = 10**log_rho_disk
# pdb.set_trace() # debug mode
ro = pymses.RamsesOutput(path, num)
lbox = ro.info['boxlen'] #boxlen in codeunits (=>pc)
AU, pc, Ms, Myr, scale_n, scale_d, scale_t, scale_l, scale_v, scale_T2, scale_ener, scale_mag, microG, km_s, Cwnm, scale_mass, unit_col, lbox_pc = me.normalisation_averliat(
ro)
#time = ro.info['time'] * scale_t / Myr
amr = ro.amr_source(["rho", "vel"]) #,"Br"])
cell_source = CellsToPoints(amr)
cells = cell_source.flatten()
dx = cells.get_sizes()
pos = cells.points
#cells belonging to the disk
mask_rho_disk = np.log10(cells['rho']) > log_rho_disk
print 'disk cell number', np.sum(mask_rho_disk)
#mean magnetic field in the whole selected region
#mrd=mask_rho_disk
#volume of the selected region
#vol = np.sum(dx[mrd]**3)
#mag_mean_broad=np.sum(np.sqrt((cells['Br'][:,0][mrd]**2+cells['Br'][:,1][mrd]**2+cells['Br'][:,2][mrd]**2))*dx[mrd]**3)/vol
#normalise it in cgs
#mag_mean_broad = mag_mean_broad*scale_mag
#maximum density cell
mask_rho_max = cells['rho'] == np.max(cells['rho'])
#mass disk
#mass_disk = np.sum(dx[mask_rho_disk]**3 * cells['rho'][mask_rho_disk])
#mass_disk = mass_disk * scale_mass / Ms
#position of maximum density cell
pos_max_x = pos[:, 0][mask_rho_max][0]
pos_max_y = pos[:, 1][mask_rho_max][0]
pos_max_z = pos[:, 2][mask_rho_max][0]
#velocity of maximum density cell
vel_max_x = cells['vel'][:, 0][mask_rho_max][0]
vel_max_y = cells['vel'][:, 1][mask_rho_max][0]
vel_max_z = cells['vel'][:, 2][mask_rho_max][0]
#if(np.sum(mask) <= 1):
# return time, 0., 0., 0., 0. , log_rho_disk, mag_mean_broad #, max_rad_disk, mean_rad_disk, mean_out_rad_disk
#pos_disk_x = pos_disk_x[mask]
#pos_disk_y = pos_disk_y[mask]
#pos_disk_z = pos_disk_z[mask]
#vel_disk_x = vel_disk_x[mask]
#vel_disk_y = vel_disk_y[mask]
#vel_disk_z = vel_disk_z[mask]
#radius of disk cells
# rad_disk = np.sqrt( (pos_disk[:,0]-pos_max_x)**2 + (pos_disk[:,1]-pos_max_y)**2 + (pos_disk[:,2]-pos_max_z)**2 )
#rad_disk = np.sqrt( (pos_disk_x)**2 + (pos_disk_y)**2 + (pos_disk_z)**2 )
#mean disk radius
#mean_rad_disk = np.mean(rad_disk)
return rho_min_disk, pos_max_x, pos_max_y, pos_max_z, vel_max_x, vel_max_y, vel_max_z
path_2_hr2 = '/home/users/mnt/averliat/analyses/B335_noturb_norot_hydro_hr2/Integrale_valeurs_absolues_moments_cinetiques_output_1_36.fits' path_3_hr2 = '/home/users/mnt/averliat/analyses/B335_noturb_norot_hydro_hr2/Moment_cinetique_tot_output_1_55_barycentre.fits' path_4_hr2 = '/home/users/mnt/averliat/analyses/B335_noturb_norot_hydro_hr2/Integrale_valeurs_absolues_moments_cinetiques_output_1_55_barycentre.fits' path_simu_hr2='/gpfs/data1/phennebe/B335_noturb_norot_hydro_hr2/' num_min_hr2=1 num_max_hr2=36 nmbr_output_hr2 = num_max_hr2-num_min_hr2+1 simulation_time_hr2 = np.zeros(nmbr_output_hr2) for l in range(nmbr_output_hr2): ro=pymses.RamsesOutput(path_simu_hr2,l+1) factor_time_hr2_Myr = ro.info['unit_time'].express(cst.Myr) simulation_time_hr2[l] = ro.info['time']*factor_time_hr2_Myr #-------------------------- #Ouverture de la sauvegarde #-------------------------- J_tot_tab_hr2 = fits.open(path_1_hr2)[0].data[0:nmbr_output_hr2] J_abs_tot_tab_hr2 = fits.open(path_2_hr2)[0].data[0:nmbr_output_hr2] J_tot_tab_hr2_barycentre = fits.open(path_3_hr2)[0].data[0:nmbr_output_hr2] J_abs_tot_tab_hr2_barycentre = fits.open(path_4_hr2)[0].data[0:nmbr_output_hr2]
path='/gpfs/data1/'+owner+'/'+simu+'/' if owner=='phennebe': path='/drf/projets/capucine/'+owner+'/'+simu+'/' path_save='/gpfs/data1/averliat/analyses/'+simu+'/' nbre_output=output_max-output_min+1 pourcent_mass_core = np.zeros(nbre_output) simulation_time = np.zeros(nbre_output) output_manquant=0 for l in range(nbre_output): current_output = (l+1) try: ro=pymses.RamsesOutput(path, current_output) except: output_manquant += 1 continue #------------------- #Lecture de l'output #------------------- lbox_pc = ro.info['unit_length'].express(cst.pc) amr = ro.amr_source(["rho","vel","P","phi","g"]) cell_source = CellsToPoints(amr) cells = cell_source.flatten(verbose=False) dx = cells.get_sizes()
from optparse import OptionParser
parser = OptionParser()
parser.usage = "%prog ramses_directory ramses_output_number map_max_resolution=512"
(opts, args) = parser.parse_args()
try:
fileDir = args[0]
outNumber = int(args[1])
except:
fileDir = None
outNumber = None
try:
mms = int(args[2])
except:
mms = 512
ro = pymses.RamsesOutput(fileDir, outNumber)
outNumber = ro.iout
source = ro.amr_source(["rho"])
cam = Camera(center=[0.5, 0.5, 0.5],
line_of_sight_axis='z',
region_size=[1., 1.],
up_vector='y',
distance=0.0,
far_cut_depth=0.0,
map_max_size=mms)
op = ScalarOperator(lambda dset: dset["rho"])
from time import time
t0 = time()
# Optional CameraOctreeDatasource creation (may be faster...)
#from pymses.sources.ramses.octree import CameraOctreeDatasource
def masse_dans_coeur_output_unique(simu, owner, num_output, rho_seuil):
#--------------------------------------------------------------
#Chemin de la simulation et du dossier de sauvegarde des images
#--------------------------------------------------------------
if owner=='averliat':
path='/gpfs/data1/'+owner+'/'+simu+'/'
if owner=='phennebe':
path='/drf/projets/capucine/'+owner+'/'+simu+'/'
path_save='/gpfs/data1/averliat/analyses/'+simu+'/'
#-------------------
#Lecture de l'output
#-------------------
ro=pymses.RamsesOutput(path,num_output)
lbox_pc = ro.info['unit_length'].express(cst.pc)
amr = ro.amr_source(["rho","vel","P","phi","g"])
cell_source = CellsToPoints(amr)
cells = cell_source.flatten(verbose=False)
dx = cells.get_sizes()
pos = cells.points
rho = cells["rho"]
#------------------------------------------------------------
#Facteur de conversion des unites de code en unites physiques
#------------------------------------------------------------
lbox=ro.info['boxlen']
lbox_m = ro.info['unit_length'].express(cst.m)
factor_dist_m = ro.info['unit_length'].express(cst.m)
factor_dist_cm = ro.info['unit_length'].express(cst.cm)
lbox_au = ro.info['unit_length'].express(cst.au)
lbox_cm = ro.info['unit_length'].express(cst.cm)
factor_time_yr = ro.info['unit_time'].express(cst.year)
factor_rho_Hcc = ro.info['unit_density'].express(cst.H_cc)
factor_rho = ro.info['unit_density'].express(cst.kg/(cst.m)**3)
#------------------------------
#Conversion en unites physiques
#------------------------------
simulation_time = ro.info['time']*factor_time_yr
dx *= factor_dist_m
rho_Hcc = rho * factor_rho_Hcc
rho *= factor_rho
#---------------------------------------------------------
#Definition du centre des images et de leur niveau de zoom
#---------------------------------------------------------
mask = rho_Hcc > rho_seuil
rho_masked = rho[mask]
dx_masked = dx[mask]
mass_core = np.sum( rho_masked * dx_masked**3 ) / 1.9889e+30 #En Msun
mass_tot = np.sum( rho * dx**3 ) / 1.9889e+30 #En Msun
pourcent_mass_core = mass_core / mass_tot *100
print
print("==========================================================================")
print(" M_central_core = "+str(mass_core)+ " Msun")
print(" M_tot = "+str(mass_tot)+ " Msun")
print(" Pourcent_M_central_core = "+str(pourcent_mass_core)+ " %")
print("==========================================================================")
print
return (mass_core, mass_tot, pourcent_mass_core)
#if simu == 'B335_noturb_norot_hydro_pert_asym_aleatoire50_vhr':
# path_t0='/mnt/magmist/magmist/simu_B335_averliat/'+simu+'/'
#else:
# path_t0=path
path_t0=path
if save==True:
if os.path.isdir(path_save) == False:
os.mkdir(path_save)
#-------------------
#Lecture de l'output
#-------------------
ro=pymses.RamsesOutput(path,num_output)
lbox_pc = ro.info['unit_length'].express(cst.pc)
amr = ro.amr_source(["rho","vel","P","phi","g"])
cell_source = CellsToPoints(amr)
cells = cell_source.flatten()
pos = cells.points
rho = cells["rho"]
#------------------------------------------------------------
#Facteur de conversion des unites de code en unites physiques
#------------------------------------------------------------
#------------------------------------------------------------------ #Chargement moments cinetiques de la simulation perturbee a la main #------------------------------------------------------------------ path_1_pert_llf = '/home/users/mnt/averliat/analyses/B335_noturb_norot_hydro_pert_llf/Moment_cinetique_tot_output_1_48_par_densite.fits' path_2_pert_llf = '/home/users/mnt/averliat/analyses/B335_noturb_norot_hydro_pert_llf/Integrale_valeurs_absolues_moments_cinetiques_output_1_48_par_densite.fits' path_simu_pert_llf='/gpfs/data1/phennebe/B335_noturb_norot_hydro_pert_llf/' num_min_pert_llf=1 num_max_pert_llf=48 nmbr_output_pert_llf = num_max_pert_llf-num_min_pert_llf+1 simulation_time_pert_llf = np.zeros(nmbr_output_pert_llf) for l in range(nmbr_output_pert_llf): ro=pymses.RamsesOutput(path_simu_pert_llf,l+1) factor_time_pert_llf_Myr = ro.info['unit_time'].express(cst.Myr) simulation_time_pert_llf[l] = ro.info['time']*factor_time_pert_llf_Myr #-------------------------------------------------- #Chargement moments cinetiques de la simulation HR2 #-------------------------------------------------- path_1_hr2 = '/home/users/mnt/averliat/analyses/B335_noturb_norot_hydro_hr2/Moment_cinetique_tot_output_1_78_par_densite.fits' path_2_hr2 = '/home/users/mnt/averliat/analyses/B335_noturb_norot_hydro_hr2/Integrale_valeurs_absolues_moments_cinetiques_output_1_78_par_densite.fits' path_simu_hr2='/gpfs/data1/phennebe/B335_noturb_norot_hydro_hr2/' num_min_hr2=1 num_max_hr2=78