def __getitem__(self, item, flatten=True, cpu=None):
'''
Return an object containing this field.
flatten - Return a numpy array containing all points.
cpu - Return only the domain belonging to this CPU.
If flatten == False and cpu=None, return an iterable
'''
source = self._snapshot.source
ndim = '3D'
fields = [
v.name for v in output.RamsesOutput.amr_field_descrs_by_file[ndim]
['hydro']
]
if item in fields:
#Load and return (will work with multiple fields too!)
#amr_source = self.amr_source(item)
cells = CellsToPoints(source)
if flatten:
return cells.flatten()
elif cpu is None:
return cells.iter_dsets()
else:
return cells.get_domain_dset(cpu)
else:
#Particle field
#part_source = self.particle_source(item)
if flatten:
return source.flatten()
elif cpu is None:
return source.iter_dsets()
else:
return source.get_domain_dset(cpu)
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
#------------------------------------------------------------
lbox=ro.info['boxlen']
lbox_m = ro.info['unit_length'].express(cst.m)
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_vel_km_s = ro.info['unit_velocity'].express(cst.km/cst.s)
import sys
# Parse Arguments
if len(sys.argv) == 1:
iout = 1
elif len(sys.argv) == 2:
iout = int(sys.argv[1])
# Prepare Output
output = RamsesOutput(".", iout)
source = output.amr_source(["rho"])
# Convert Leaf-Cells to Points
cell_source = CellsToPoints(source)
cells = cell_source.flatten()
# Get Density & Cell Size
rho = cells["rho"]
dx = output.info["boxlen"] * cells.get_sizes()
dV = dx**output.info["ndim"]
# Print Stuff
# print cells["rho"] # contains densities
# print rho # contains densities
# print cells.points # contains coordinates
# print dx
print ""
# Compute Total Mass
mtotal = np.sum(rho * dx**3.)
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
#------------------------------
lbox_au = ro.info['unit_length'].express(cst.au)
lbox_pc = ro.info['unit_length'].express(cst.pc)
factor_dist = ro.info['unit_length'].express(cst.m)
factor_vel = ro.info['unit_velocity'].express(cst.m / cst.s)
factor_rho = ro.info['unit_density'].express(cst.kg / (cst.m)**3)
factor_time_Myr = ro.info['unit_time'].express(cst.Myr)
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)
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 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
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)
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
def image(simu, owner, num_output, dir_save):
save = True
radius_zoom = 1
v_proj = True
title_time = True
title_time_cor = False
seuil_rho = 1e-10
fleche_vel = False
nbre_fleche = 20 #En fait plus c'est faible plus y'a de fleches...
selon_x = True
selon_y = True
selon_z = True
vmin_vel = None #-4.5
vmax_vel = None #-vmin_vel
vmin_dens = None #22.7
vmax_dens = None #28.9
color_sink_colmap = 'firebrick'
transparence_sink_colmap = 0.4
color_sink_velmap = 'limegreen'
transparence_sink_velmap = 0.7
reposition_fig = False #Pour repositionner les figures ouvertes par matplotlib
#Pour fermer toutes les figures avec matplotlib : plt.close('all')
#--------------------------------------------------------------
#Chemin de la simulation et du dossier de sauvegarde des images
#--------------------------------------------------------------
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 + '/'
#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
#------------------------------------------------------------
lbox = ro.info['boxlen']
lbox_m = ro.info['unit_length'].express(cst.m)
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_vel_km_s = ro.info['unit_velocity'].express(cst.km / cst.s)
simulation_time = ro.info['time'] * factor_time_yr
#---------------------------------------------------------------------------------
#Calcul des t_0 de chaque simulation ou lecture des fichiers si calculs deja faits
#---------------------------------------------------------------------------------
if title_time_cor == True:
if os.path.isfile(path_analyse + '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_analyse + 't0_seuil_rho_' + str(seuil_rho) + '.txt', ref)
else:
ref = np.loadtxt(path_analyse + 't0_seuil_rho_' + str(seuil_rho) +
'.txt')
#Erreur si on cherche a etudier un fichier inferieur au t0 de la simulation de reference
if num_output < ref[0]:
print
print
print("=================================================")
print("=================================================")
print("/!\ output_ref < output_ref_t0 /!\ ")
print("=================================================")
print("=================================================")
title_time_cor = 0
#---------------------------------------------------------
#Definition du centre des images et de leur niveau de zoom
#---------------------------------------------------------
#Position du "centre" de la simulation = endroit le plus dense
if radius_zoom == 5:
center = [0.5, 0.5, 0.5]
else:
arg_centre = np.argmax(rho)
center = [
pos[:, 0][arg_centre], pos[:, 1][arg_centre], pos[:, 2][arg_centre]
]
zoom_v = [0.045, 0.015, 0.005, 0.005 / 3., 0.5]
if 'bigbox' or 'jet' in simu:
zoom_v = [
0.045 / 2, 0.015 / 2, 0.005 / 2, 0.005 / 3. / 2, 0.5, 0.5 / 2,
0.005 / 20.
]
if radius_zoom == 6:
center = [0.5, 0.5, 0.5]
if 'hugebox' in simu:
zoom_v = [
0.045 / 4, 0.015 / 4, 0.005 / 4, 0.005 / 3. / 4, 0.5, 0.5 / 4
]
if radius_zoom == 6:
center = [0.5, 0.5, 0.5]
radius = zoom_v[radius_zoom - 1]
#radius=float(zoom_v[np.where(zoom_v==radius_zoom)]) #0.015#0.005 #Niveau de zoom correspondant au niveau '3' des images de "pipeline_image_unique.py"
#------------------------------------------------------------------------
#Get the properties of the particules with just the particules' positions
#Copie depuis module_extract.py
#------------------------------------------------------------------------
def read_sink_csv(num, directory, no_cvs=None):
name = directory + 'output_' + str(num).zfill(5) + '/sink_' + str(
num).zfill(5) + '.csv'
print 'read sinks from ', name
if (no_cvs is None):
sinks = np.loadtxt(name,
delimiter=',',
ndmin=2,
usecols=(0, 1, 2, 3, 4, 5, 6, 7,
8)) #,9,10,11,12))
else:
sinks = np.loadtxt(name,
ndmin=2,
usecols=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
12))
return sinks
#------------------------------
#Pour visualiser les sinks
#Adapted from module_extract.py
#------------------------------
def visualise_sink_AU(path, num_output):
sinks = read_sink_csv(num_output, path)
if len(sinks) != 0:
mass_sinks = sinks[:, 1] #masse des sinks en masses solaires
x_sinks = sinks[:,
3] * lbox_au / lbox #coordonnees x des sinks en AU
y_sinks = sinks[:,
4] * lbox_au / lbox #coordonnees y des sinks en AU
z_sinks = sinks[:,
5] * lbox_au / lbox #coordonnees z des sinks en AU
return mass_sinks, x_sinks, y_sinks, z_sinks
#---------------------------------------
#Lecture du fichier sink.csv s'il existe
#---------------------------------------
sink_plot = False
if os.path.isfile(path + 'output_' + str(num_output).zfill(5) + '/sink_' +
str(num_output).zfill(5) + '.csv') == True:
#sink_plot=True
sinks = read_sink_csv(num_output, path)
if len(sinks) != 0:
sink_plot = True
if sink_plot == True:
m_sinks, x_sinks, y_sinks, z_sinks = visualise_sink_AU(
path, num_output)
#size_sinks=20*np.tanh(25*m_sinks)
size_sinks = 20 * np.sqrt(m_sinks)
if radius_zoom != 5: #centrage sur la plus grosse sink au lieu de rho max
arg_biggest_sink = np.argmax(m_sinks)
x_biggest_sink = x_sinks[arg_biggest_sink] / lbox_au
y_biggest_sink = y_sinks[arg_biggest_sink] / lbox_au
z_biggest_sink = z_sinks[arg_biggest_sink] / lbox_au
center = [x_biggest_sink, y_biggest_sink, z_biggest_sink]
#---------------------------------------------------------------------------------------------------------------
#Definition prise sur https://stackoverflow.com/questions/20144529/shifted-colorbar-matplotlib/20146989#20146989
#pour avoir le centre de la colorbar a 0
#---------------------------------------------------------------------------------------------------------------
class MidpointNormalize(Normalize):
def __init__(self, vmin=None, vmax=None, midpoint=None, clip=False):
self.midpoint = midpoint
Normalize.__init__(self, vmin, vmax, clip)
def __call__(self, value, clip=None):
# I'm ignoring masked values and all kinds of edge cases to make a
# simple example...
x, y = [self.vmin, self.midpoint, self.vmax], [0, 0.5, 1]
return np.ma.masked_array(np.interp(value, x, y))
#-----------------
#-----------------
#Calcul des cartes
#-----------------
#-----------------
if selon_x == True:
#--------------------------------------------
#Calcul de la carte ou l'on regarde suivant x
#--------------------------------------------
cam_x = Camera(center=center,
line_of_sight_axis='x',
region_size=[2. * radius, 2. * radius],
distance=radius,
far_cut_depth=radius,
up_vector='z',
map_max_size=512)
rho_op = ScalarOperator(lambda dset: dset["rho"],
ro.info["unit_density"])
rt = raytracing.RayTracer(amr, ro.info, rho_op)
datamap = rt.process(cam_x, surf_qty=True)
map_col = np.log10(datamap.map.T * lbox_cm)
if v_proj == True:
Vx_op = ScalarOperator(
lambda dset: dset["vel"][..., 0] * dset["rho"],
ro.info["unit_velocity"])
rt = raytracing.RayTracer(amr, ro.info, Vx_op)
datamap_vx = rt.process(cam_x, surf_qty=True)
map_Vx = datamap_vx.map.T / datamap.map.T * factor_vel_km_s
if fleche_vel == True:
Vy_depuis_x_op = ScalarOperator(
lambda dset: dset["vel"][..., 1] * dset["rho"],
ro.info["unit_velocity"])
rt = raytracing.RayTracer(amr, ro.info, Vy_depuis_x_op)
datamap_vy_depuis_x = rt.process(cam_x, surf_qty=True)
map_vy_depuis_x = datamap_vy_depuis_x.map.T / datamap.map.T * factor_vel_km_s
map_vy_depuis_x_red = map_vy_depuis_x[::nbre_fleche, ::nbre_fleche]
Vz_depuis_x_op = ScalarOperator(
lambda dset: dset["vel"][..., 2] * dset["rho"],
ro.info["unit_velocity"])
rt = raytracing.RayTracer(amr, ro.info, Vz_depuis_x_op)
datamap_vz_depuis_x = rt.process(cam_x, surf_qty=True)
map_vz_depuis_x = datamap_vz_depuis_x.map.T / datamap.map.T * factor_vel_km_s
map_vz_depuis_x_red = map_vz_depuis_x[::nbre_fleche, ::nbre_fleche]
plt.figure()
im = plt.imshow(map_col,
extent=[(-radius + center[1]) * lbox_au,
(radius + center[1]) * lbox_au,
(-radius + center[2]) * lbox_au,
(radius + center[2]) * lbox_au],
origin='lower')
plt.xlabel('$y$ (AU)')
plt.ylabel('$z$ (AU)')
cbar = plt.colorbar()
cbar.set_label(r'$log(N) \, \, (cm^{-2})$')
if title_time == True:
plt.title('Time = ' + str(int(simulation_time)) + ' years')
if title_time_cor == True:
plt.title('Time = ' + str(int(simulation_time)) +
' years \n Corrected time = ' +
str(int(simulation_time - ref[1] * 1e6)) + ' years')
if radius_zoom == 5:
plt.xlim([0, lbox_au])
plt.ylim([0, lbox_au])
if save == True:
plt.savefig(path_save + 'dens_x_' + str(radius_zoom) + '_' +
str(num_output) + '.pdf',
bbox_inches='tight')
nx = map_vy_depuis_x_red.shape[0]
ny = map_vy_depuis_x_red.shape[1]
vec_x = (np.arange(nx) * 2. / nx * radius - radius + center[0] +
radius / nx) * lbox_au
vec_y = (np.arange(ny) * 2. / ny * radius - radius + center[1] +
radius / nx) * lbox_au
xx, yy = np.meshgrid(vec_x, vec_y)
plt.quiver(xx, yy, map_vy_depuis_x_red, map_vz_depuis_x_red)
else:
plt.figure()
plt.imshow(map_col,
extent=[(-radius + center[1]) * lbox_au,
(radius + center[1]) * lbox_au,
(-radius + center[2]) * lbox_au,
(radius + center[2]) * lbox_au],
origin='lower',
vmin=vmin_dens,
vmax=vmax_dens)
if sink_plot == True:
for i in range(len(m_sinks)):
if (y_sinks[i] > (-radius + center[1]) * lbox_au) & (
y_sinks[i] <
(radius + center[1]) * lbox_au) & (
z_sinks[i] > (-radius + center[2]) * lbox_au) & (
z_sinks[i] < (radius + center[2]) * lbox_au):
plt.plot(y_sinks[i],
z_sinks[i],
'.',
color=color_sink_colmap,
markersize=size_sinks[i],
alpha=transparence_sink_colmap)
plt.xlabel('$y$ (AU)')
plt.ylabel('$z$ (AU)')
cbar = plt.colorbar()
if title_time == True:
plt.title('Time = ' + str(int(simulation_time)) + ' years')
if title_time_cor == True:
plt.title('Time = ' + str(int(simulation_time)) +
' years \n Corrected time = ' +
str(int(simulation_time - ref[1] * 1e6)) + ' years')
cbar.set_label(r'$log(N) \, \, (cm^{-2})$')
if radius_zoom == 5:
plt.xlim([0, lbox_au])
plt.ylim([0, lbox_au])
if save == True:
plt.savefig(path_save + 'dens_x_' + str(radius_zoom) +
'_%05d' % num_output + '.png',
bbox_inches='tight')
plt.close()
if v_proj == True:
plt.figure()
norm = MidpointNormalize(
midpoint=0) #Pour avoir le centre de la colormap a 0
plt.imshow(map_Vx,
extent=[(-radius + center[1]) * lbox_au,
(radius + center[1]) * lbox_au,
(-radius + center[2]) * lbox_au,
(radius + center[2]) * lbox_au],
origin='lower',
cmap='RdBu_r',
norm=norm,
vmin=vmin_vel,
vmax=vmax_vel)
if sink_plot == True:
for i in range(len(m_sinks)):
if (y_sinks[i] > (-radius + center[1]) * lbox_au) & (
y_sinks[i] <
(radius + center[1]) * lbox_au) & (
z_sinks[i] > (-radius + center[2]) * lbox_au) & (
z_sinks[i] < (radius + center[2]) * lbox_au):
plt.plot(y_sinks[i],
z_sinks[i],
'.',
color=color_sink_velmap,
markersize=size_sinks[i],
alpha=transparence_sink_velmap)
plt.xlabel('$y$ (AU)')
plt.ylabel('$z$ (AU)')
cbar = plt.colorbar()
cbar.set_label(r'$v_x \, (km.s^{-1})$')
if title_time == True:
plt.title('Time = ' + str(int(simulation_time)) + ' years')
if title_time_cor == True:
plt.title('Time = ' + str(int(simulation_time)) +
' years \n Corrected time = ' +
str(int(simulation_time - ref[1] * 1e6)) + ' years')
if radius_zoom == 5:
plt.xlim([0, lbox_au])
plt.ylim([0, lbox_au])
if save == True:
plt.savefig(path_save + 'vel_x_' + str(radius_zoom) +
'_%05d' % num_output + '.png',
bbox_inches='tight')
plt.close()
if selon_y == True:
#--------------------------------------------
#Calcul de la carte ou l'on regarde suivant y
#--------------------------------------------
cam_y = Camera(center=center,
line_of_sight_axis='y',
region_size=[2. * radius, 2. * radius],
distance=radius,
far_cut_depth=radius,
up_vector='x',
map_max_size=512)
rho_op = ScalarOperator(lambda dset: dset["rho"],
ro.info["unit_density"])
rt = raytracing.RayTracer(amr, ro.info, rho_op)
datamap = rt.process(cam_y, surf_qty=True)
map_col = np.log10(datamap.map.T * lbox_cm)
if v_proj == True:
Vy_op = ScalarOperator(
lambda dset: dset["vel"][..., 1] * dset["rho"],
ro.info["unit_velocity"])
rt = raytracing.RayTracer(amr, ro.info, Vy_op)
datamap_vy = rt.process(cam_y, surf_qty=True)
map_Vy = datamap_vy.map.T / datamap.map.T * factor_vel_km_s
if fleche_vel == True:
Vx_depuis_y_op = ScalarOperator(
lambda dset: dset["vel"][..., 0] * dset["rho"],
ro.info["unit_velocity"])
rt = raytracing.RayTracer(amr, ro.info, Vx_depuis_y_op)
datamap_vx_depuis_y = rt.process(cam_y, surf_qty=True)
map_vx_depuis_y = datamap_vx_depuis_y.map.T / datamap.map.T * factor_vel_km_s
map_vx_depuis_y_red = map_vx_depuis_y[::nbre_fleche, ::nbre_fleche]
Vz_depuis_y_op = ScalarOperator(
lambda dset: dset["vel"][..., 2] * dset["rho"],
ro.info["unit_velocity"])
rt = raytracing.RayTracer(amr, ro.info, Vz_depuis_y_op)
datamap_vz_depuis_y = rt.process(cam_y, surf_qty=True)
map_vz_depuis_y = datamap_vz_depuis_y.map.T / datamap.map.T * factor_vel_km_s
map_vz_depuis_y_red = map_vz_depuis_y[::nbre_fleche, ::nbre_fleche]
plt.figure()
im = plt.imshow(map_col,
extent=[(-radius + center[2]) * lbox_au,
(radius + center[2]) * lbox_au,
(-radius + center[0]) * lbox_au,
(radius + center[0]) * lbox_au],
origin='lower')
plt.xlabel('$z$ (AU)')
plt.ylabel('$x$ (AU)')
cbar = plt.colorbar()
cbar.set_label(r'$log(N) \, \, (cm^{-2})$')
if title_time == True:
plt.title('Time = ' + str(int(simulation_time)) + ' years')
if title_time_cor == True:
plt.title('Time = ' + str(int(simulation_time)) +
' years \n Corrected time = ' +
str(int(simulation_time - ref[1] * 1e6)) + ' years')
if radius_zoom == 5:
plt.xlim([0, lbox_au])
plt.ylim([0, lbox_au])
if save == True:
plt.savefig(path_save + 'dens_y_' + str(radius_zoom) + '_' +
str(num_output) + '.pdf',
bbox_inches='tight')
nx = map_vx_depuis_y_red.shape[0]
ny = map_vx_depuis_y_red.shape[1]
vec_x = (np.arange(nx) * 2. / nx * radius - radius + center[0] +
radius / nx) * lbox_au
vec_y = (np.arange(ny) * 2. / ny * radius - radius + center[1] +
radius / nx) * lbox_au
xx, yy = np.meshgrid(vec_x, vec_y)
plt.quiver(xx, yy, map_vz_depuis_y_red, map_vx_depuis_y_red)
else:
plt.figure()
im = plt.imshow(map_col,
extent=[(-radius + center[2]) * lbox_au,
(radius + center[2]) * lbox_au,
(-radius + center[0]) * lbox_au,
(radius + center[0]) * lbox_au],
origin='lower',
vmin=vmin_dens,
vmax=vmax_dens)
if sink_plot == True:
for i in range(len(m_sinks)):
if (z_sinks[i] > (-radius + center[2]) * lbox_au) & (
z_sinks[i] <
(radius + center[2]) * lbox_au) & (
x_sinks[i] > (-radius + center[0]) * lbox_au) & (
x_sinks[i] < (radius + center[0]) * lbox_au):
plt.plot(z_sinks[i],
x_sinks[i],
'.',
color=color_sink_colmap,
markersize=size_sinks[i],
alpha=transparence_sink_colmap)
plt.xlabel('$z$ (AU)')
plt.ylabel('$x$ (AU)')
cbar = plt.colorbar()
cbar.set_label(r'$log(N) \, \, (cm^{-2})$')
if title_time == True:
plt.title('Time = ' + str(int(simulation_time)) + ' years')
if title_time_cor == True:
plt.title('Time = ' + str(int(simulation_time)) +
' years \n Corrected time = ' +
str(int(simulation_time - ref[1] * 1e6)) + ' years')
if radius_zoom == 5:
plt.xlim([0, lbox_au])
plt.ylim([0, lbox_au])
if save == True:
plt.savefig(path_save + 'dens_y_' + str(radius_zoom) +
'_%05d' % num_output + '.png',
bbox_inches='tight')
plt.close()
if v_proj == True:
plt.figure()
norm = MidpointNormalize(
midpoint=0) #Pour avoir le centre de la colormap a 0
plt.imshow(map_Vy,
extent=[(-radius + center[2]) * lbox_au,
(radius + center[2]) * lbox_au,
(-radius + center[0]) * lbox_au,
(radius + center[0]) * lbox_au],
origin='lower',
cmap='RdBu_r',
norm=norm,
vmin=vmin_vel,
vmax=vmax_vel)
if sink_plot == True:
for i in range(len(m_sinks)):
if (z_sinks[i] > (-radius + center[2]) * lbox_au) & (
z_sinks[i] <
(radius + center[2]) * lbox_au) & (
x_sinks[i] > (-radius + center[0]) * lbox_au) & (
x_sinks[i] < (radius + center[0]) * lbox_au):
plt.plot(z_sinks[i],
x_sinks[i],
'.',
color=color_sink_velmap,
markersize=size_sinks[i],
alpha=transparence_sink_velmap)
plt.xlabel('$z$ (AU)')
plt.ylabel('$x$ (AU)')
cbar = plt.colorbar()
cbar.set_label(r'$v_y \, (km.s^{-1})$')
if title_time == True:
plt.title('Time = ' + str(int(simulation_time)) + ' years')
if title_time_cor == True:
plt.title('Time = ' + str(int(simulation_time)) +
' years \n Corrected time = ' +
str(int(simulation_time - ref[1] * 1e6)) + ' years')
if radius_zoom == 5:
plt.xlim([0, lbox_au])
plt.ylim([0, lbox_au])
if save == True:
plt.savefig(path_save + 'vel_y_' + str(radius_zoom) +
'_%05d' % num_output + '.png',
bbox_inches='tight')
plt.close()
if selon_z == True:
#--------------------------------------------
#Calcul de la carte ou l'on regarde suivant z
#--------------------------------------------
cam_z = Camera(center=center,
line_of_sight_axis='z',
region_size=[2. * radius, 2. * radius],
distance=radius,
far_cut_depth=radius,
up_vector='y',
map_max_size=512)
rho_op = ScalarOperator(lambda dset: dset["rho"],
ro.info["unit_density"])
rt = raytracing.RayTracer(amr, ro.info, rho_op)
datamap = rt.process(cam_z, surf_qty=True)
map_col = np.log10(datamap.map.T * lbox_cm)
if v_proj == True:
Vz_op = ScalarOperator(
lambda dset: dset["vel"][..., 2] * dset["rho"],
ro.info["unit_velocity"])
rt = raytracing.RayTracer(amr, ro.info, Vz_op)
datamap_vz = rt.process(cam_z, surf_qty=True)
map_Vz = datamap_vz.map.T / datamap.map.T * factor_vel_km_s
if fleche_vel == True:
Vx_depuis_z_op = ScalarOperator(
lambda dset: dset["vel"][..., 0] * dset["rho"],
ro.info["unit_velocity"])
rt = raytracing.RayTracer(amr, ro.info, Vx_depuis_z_op)
datamap_vx_depuis_z = rt.process(cam_z, surf_qty=True)
map_vx_depuis_z = datamap_vx_depuis_z.map.T / datamap.map.T * factor_vel_km_s
map_vx_depuis_z_red = map_vx_depuis_z[::nbre_fleche, ::nbre_fleche]
Vy_depuis_z_op = ScalarOperator(
lambda dset: dset["vel"][..., 1] * dset["rho"],
ro.info["unit_velocity"])
rt = raytracing.RayTracer(amr, ro.info, Vy_depuis_z_op)
datamap_vy_depuis_z = rt.process(cam_z, surf_qty=True)
map_vy_depuis_z = datamap_vy_depuis_z.map.T / datamap.map.T * factor_vel_km_s
map_vy_depuis_z_red = map_vy_depuis_z[::nbre_fleche, ::nbre_fleche]
plt.figure()
im = plt.imshow(map_col,
extent=[(-radius + center[0]) * lbox_au,
(radius + center[0]) * lbox_au,
(-radius + center[1]) * lbox_au,
(radius + center[1]) * lbox_au],
origin='lower')
plt.xlabel('$x$ (AU)')
plt.ylabel('$y$ (AU)')
cbar = plt.colorbar()
cbar.set_label(r'$log(N) \, \, (cm^{-2})$')
if title_time == True:
plt.title('Time = ' + str(int(simulation_time)) + ' years')
if title_time_cor == True:
plt.title('Time = ' + str(int(simulation_time)) +
' years \n Corrected time = ' +
str(int(simulation_time - ref[1] * 1e6)) + ' years')
if radius_zoom == 5:
plt.xlim([0, lbox_au])
plt.ylim([0, lbox_au])
if save == True:
plt.savefig(path_save + 'dens_z_' + str(radius_zoom) + '_' +
str(num_output) + '.pdf',
bbox_inches='tight')
nx = map_vx_depuis_z_red.shape[0]
ny = map_vx_depuis_z_red.shape[1]
vec_x = (np.arange(nx) * 2. / nx * radius - radius + center[0] +
radius / nx) * lbox_au
vec_y = (np.arange(ny) * 2. / ny * radius - radius + center[1] +
radius / nx) * lbox_au
xx, yy = np.meshgrid(vec_x, vec_y)
plt.quiver(xx, yy, map_vx_depuis_z_red, map_vy_depuis_z_red)
else:
plt.figure()
im = plt.imshow(map_col,
extent=[(-radius + center[0]) * lbox_au,
(radius + center[0]) * lbox_au,
(-radius + center[1]) * lbox_au,
(radius + center[1]) * lbox_au],
origin='lower',
vmin=vmin_dens,
vmax=vmax_dens)
if sink_plot == True:
for i in range(len(m_sinks)):
if (x_sinks[i] > (-radius + center[0]) * lbox_au) & (
x_sinks[i] <
(radius + center[0]) * lbox_au) & (
y_sinks[i] > (-radius + center[1]) * lbox_au) & (
y_sinks[i] < (radius + center[1]) * lbox_au):
plt.plot(x_sinks[i],
y_sinks[i],
'.',
color=color_sink_colmap,
markersize=size_sinks[i],
alpha=transparence_sink_colmap)
plt.xlabel('$x$ (AU)')
plt.ylabel('$y$ (AU)')
cbar = plt.colorbar()
cbar.set_label(r'$log(N) \, \, (cm^{-2})$')
if title_time == True:
plt.title('Time = ' + str(int(simulation_time)) + ' years')
if title_time_cor == True:
plt.title('Time = ' + str(int(simulation_time)) +
' years \n Corrected time = ' +
str(int(simulation_time - ref[1] * 1e6)) + ' years')
if radius_zoom == 5:
plt.xlim([0, lbox_au])
plt.ylim([0, lbox_au])
if save == True:
plt.savefig(path_save + 'dens_z_' + str(radius_zoom) +
'_%05d' % num_output + '.png',
bbox_inches='tight')
plt.close()
if v_proj == True:
plt.figure()
norm = MidpointNormalize(
midpoint=0) #Pour avoir le centre de la colormap a 0
plt.imshow(map_Vz,
extent=[(-radius + center[0]) * lbox_au,
(radius + center[0]) * lbox_au,
(-radius + center[1]) * lbox_au,
(radius + center[1]) * lbox_au],
origin='lower',
cmap='RdBu_r',
norm=norm,
vmin=vmin_vel,
vmax=vmax_vel)
if sink_plot == True:
for i in range(len(m_sinks)):
if (x_sinks[i] > (-radius + center[0]) * lbox_au) & (
x_sinks[i] <
(radius + center[0]) * lbox_au) & (
y_sinks[i] > (-radius + center[1]) * lbox_au) & (
y_sinks[i] < (radius + center[1]) * lbox_au):
plt.plot(x_sinks[i],
y_sinks[i],
'.',
color=color_sink_velmap,
markersize=size_sinks[i],
alpha=transparence_sink_velmap)
plt.xlabel('$x$ (AU)')
plt.ylabel('$y$ (AU)')
cbar = plt.colorbar()
cbar.set_label(r'$v_z \, (km.s^{-1})$')
if title_time == True:
plt.title('Time = ' + str(int(simulation_time)) + ' years')
if title_time_cor == True:
plt.title('Time = ' + str(int(simulation_time)) +
' years \n Corrected time = ' +
str(int(simulation_time - ref[1] * 1e6)) + ' years')
if radius_zoom == 5:
plt.xlim([0, lbox_au])
plt.ylim([0, lbox_au])
if save == True:
plt.savefig(path_save + 'vel_z_' + str(radius_zoom) +
'_%05d' % num_output + '.png',
bbox_inches='tight')
plt.close()
if v_proj == True and reposition_fig == True:
for i in range(3):
i *= 2
for j in range(2):
plt.figure(i + j + 1)
mngr = plt.get_current_fig_manager()
geom = mngr.window.geometry()
x, y, dx, dy = geom.getRect()
mngr.window.setGeometry(1920 / 3 * (i / 2), 1080 / 2 * j, dx,
dy)
plt.figure(1) #Oblige sinon figure 1 mal placee...
mngr = plt.get_current_fig_manager()
geom = mngr.window.geometry()
x, y, dx, dy = geom.getRect()
#mngr.window.setGeometry(65,52,dx,dy)
mngr.window.setGeometry(1, 1, dx, dy)
return
def SFR_calculation(simu,owner,output_min,output_max,colorsink,colorgas,colortot,legend):
#--------------------------------------------------------------
#Chemin de la simulation et du dossier de sauvegarde des images
#--------------------------------------------------------------
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_analyse='/home/averliat/these/analyses/'+simu+'/'
path_t0=path
#------------------------------------------------------------------------
#Get the properties of the particules with just the particules' positions
#Copie depuis module_extract.py
#------------------------------------------------------------------------
def read_sink_csv(num,directory,no_cvs=None):
name = directory+'output_'+str(num).zfill(5)+'/sink_'+str(num).zfill(5)+'.csv'
print 'read sinks from ', name
if(no_cvs is None):
sinks = np.loadtxt(name,delimiter=',',ndmin=2,usecols=(0,1,2,3,4,5,6,7,8)) #,9,10,11,12))
else:
sinks = np.loadtxt(name,ndmin=2,usecols=(0,1,2,3,4,5,6,7,8,9,10,11,12))
return sinks
#---------------------------------------
#Lecture du fichier sink.csv s'il existe
#---------------------------------------
mass_sinks_tab=np.zeros(output_max+1-output_min,dtype=object)
mass_gas_tab=np.zeros(output_max+1-output_min)
numero_output=np.zeros(output_max+1-output_min)
simulation_time=np.zeros(output_max+1-output_min)
ind_output=0
for num_output in range(output_min,output_max+1):
m_sinks=None
if os.path.isfile(path+'output_'+str(num_output).zfill(5)+'/sink_'+str(num_output).zfill(5)+'.csv') == True:
sinks=read_sink_csv(num_output,path)
if len(sinks) != 0:
m_sinks=sinks[:,1] #masse des sinks en masses solaires
else:
m_sinks=0.
mass_sinks_tab[ind_output]=np.array(m_sinks)
numero_output[ind_output]=num_output
#-------------------
#lecture de l'output
#-------------------
ro=pymses.RamsesOutput(path,num_output)
amr = ro.amr_source(["rho"])
cell_source = CellsToPoints(amr)
cells = cell_source.flatten(verbose=False)
dx = cells.get_sizes()
rho = cells["rho"]
#------------------------------
#conversion en unites physiques
#------------------------------
lbox_au = ro.info['unit_length'].express(cst.au)
factor_dist= ro.info['unit_length'].express(cst.m)
factor_vel = ro.info['unit_velocity'].express(cst.m/cst.s)
factor_rho = ro.info['unit_density'].express(cst.kg/(cst.m)**3)
factor_time_Myr = ro.info['unit_time'].express(cst.Myr)
simulation_time[ind_output] = ro.info['time']*factor_time_Myr
dx *= factor_dist
rho *= factor_rho
mass_tot = np.sum( rho * dx**3 ) / 1.9889e+30 #En Msun
mass_gas_tab[ind_output]=mass_tot
ind_output+=1
mass_sinks_sum_tab=np.zeros(output_max+1-output_min)
for i in range(len(mass_sinks_tab)):
mass_sinks_sum_tab[i]=np.sum(mass_sinks_tab[i])
#---------------------------------------------------
#Trace de la masse des sinks en fonction de l'output
#---------------------------------------------------
plt.figure(20)
plt.plot(numero_output,mass_sinks_sum_tab,linestyle='solid',marker='.',color=colorsink)
plt.xlabel(r"Numero de l'output")
plt.ylabel(r"Masse des sinks ($M_\odot$)")
plt.figure(21)
plt.plot(simulation_time,mass_sinks_sum_tab,linestyle='solid',marker='.',color=colorsink)
plt.xlabel(r"Temps (Myr)")
plt.ylabel(r"Masse des sinks ($M_\odot$)")
plt.figure(22)
plt.plot(numero_output,mass_sinks_sum_tab+mass_gas_tab,linestyle='solid',marker='.',color=colortot)
plt.xlabel(r"Numero de l'output")
plt.ylabel(r"Masse totale ($M_\odot$)")
plt.figure(23)
plt.semilogy(simulation_time,mass_gas_tab,linestyle='solid',marker='.',color=colorgas,label='Gas'+legend)
plt.semilogy(simulation_time,mass_sinks_sum_tab,linestyle='solid',marker='.',color=colorsink,label='Sinks'+legend)
plt.semilogy(simulation_time,mass_sinks_sum_tab+mass_gas_tab,linestyle='solid',marker='.',color=colortot,label='Gas + sinks'+legend)
plt.xlabel(r"Temps (Myr)")
plt.ylabel(r"Masse ($M_\odot$)")
plt.legend(loc='best')
def temps_0_simu(path_simu,
seuil_rho=1e-10,
num_min=2,
num_max=150,
sortie_output=2):
#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)
max_rho = 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()
rho = cells["rho"]
factor_rho = ro.info['unit_density'].express(cst.kg / (cst.m)**3)
rho *= factor_rho
max_rho[l] = np.max(rho)
print
print
print("===========================================================")
print('max(rho) = ' + str(np.max(rho)) + ' kg.m^-3')
print("===========================================================")
print
print
print
if max_rho[l] >= seuil_rho:
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 densité non atteint /!\ ")
print("===========================================================")
print("===========================================================")
'''
plt.semilogy(simulation_time, max_rho, linestyle=None, marker='.')
plt.show()
'''
return (output_seuil, time_seuil)
def mstar_halomass_tree(self, tree, ds):
snapshot = self._snapshot
ro = snapshot.raw_snapshot()
parts = ro.particle_source(["mass", "epoch"])
amr = ro.amr_source(['rho'])
#Figure out the smallest possible cell size in code units
boxlen = ro.info['boxlen']
lmax = ro.info['levelmax']
min_dx = boxlen/float(2**(lmax))
offset = tree.get_offset()
snap_num = self._snapshot.output_number() - offset
halos = tree.sub_catalogue('snap_num', snap_num)
#idx = np.where(tree._tree[:]['snap_num'] == self._snapshot.output_number()-30)[0]
#halos = tree[idx] # Is this right?
print 'Loaded halos have snap_num:', halos[1]['snap_num']
mstar = []
mhalo = []
i = 1
for halo in halos:
#Check if distinct halo
if not halo['pid'].value == -1:
print 'Halo %d skipped: PID = %d'%(halo['id'].value, halo['pid'].value)
continue
#Unit conversion
cen = halo['pos']
cen = ds.arr(cen.value, cen.unit)
r = halo['Rvir']
r = ds.arr(r.value, r.unit)
#Create the sphere
cen = cen.in_units('code_length').value
r = r.in_units('code_length').value
r_amr = float(r)
#If the halo radius is smaller than the min cell size,
#we need to try and approximate the masses enclosed.
#Not sure how correct this is...
factor = 1
if r < min_dx:
r_amr += min_dx
vol_halo = (4./3.)*np.pi*r**3
vol_cell = (4./3.)*np.pi*r_amr**3
factor = vol_halo/vol_cell
#Define the regions
region_part = Sphere(cen, r)
region_amr = Sphere(cen, r_amr)
#Filter the particles
filt_parts = RegionFilter(region_part, parts)
part_source = filt_parts.flatten()
dm = np.where(part_source['epoch'] == 0)[0]
stars = np.where(part_source['epoch'] != 0)[0]
part_mass = part_source['mass']*ro.info['unit_mass'].express(C.Msun)
if len(part_mass[dm]) < 50:
print 'Discarding halo with less than 200 particles. Mvir=%e'%halo['Mvir'].value
continue
#Filter the AMR data
filt_amr = RegionFilter(region_amr, amr)
cell_source = CellsToPoints(filt_amr)
cells = cell_source.flatten()
rho = cells['rho']*ro.info['unit_density'].express(C.Msun/C.kpc**3)
vol = (cells.get_sizes()*ro.info['unit_length'].express(C.kpc))**3
cell_mass = rho*vol
#Compute the total mass enclosed
gas_mass = np.sum(cell_mass)*factor # Multiply by factor incase we had to inflate the sphere
stellar_mass = np.sum(part_mass[stars])
particle_mass = np.sum(part_mass)
total_mass = gas_mass + particle_mass
mstar.append(stellar_mass)
mhalo.append(total_mass)
i+=1
print i
#print i
if(config.verbose and (i%100)==0): print 'Processed %d halos...'%i
return np.array(mstar), np.array(mhalo)
def fgas_halomass(self, halos=None):
snapshot = self._snapshot
ro = snapshot.raw_snapshot()
amr = ro.amr_source(['rho'])
parts = ro.particle_source(["mass"])
#Figure out the smallest possible cell size in code units
boxlen = ro.info['boxlen']
lmax = ro.info['levelmax']
min_dx = boxlen/float(2**(lmax))
if config.verbose: print 'min_dx=', min_dx
if (halos == None):
halos = snapshot.halos()
if config.verbose: print 'Processing %d halos'%len(halos)
fgas = []
mhalo = []
#z = self._snapshot.current_redshift()
#a = 1./(1.+z)
i = 0
for halo in halos:
if halo['num_p'] < 150:
break
#if tree:
#Find this halo and count number of progenitors.
#If too high, skip (frequent mergers -> tidal stripping)
#idx_tree_halo = (tree._tree[:]['orig_halo_id'] == halo['id'].value) &\
# (tree._tree[:]['snap_num'] == snapshot.rockstar_output_number())
#print idx_tree_halo
#tree_halo = tree._tree[idx_tree_halo]
#assert(len(tree_halo)==1)
#all_halos = []
#tree.find_progs(np.array([tree_halo]), all_halos)
#num_progs = len(all_halos)
#print 'Halo %d has %d progenitors'%(halo['id'], num_progs)
#if num_progs > 150: continue
#Create the sphere
cen = halo['pos'].in_units('code_length').value
r = halo['Rvir'].in_units('code_length').value
r_amr = float(r)
#If the halo radius is smaller than the min cell size,
#we need to try and approximate the masses enclosed.
#Not sure how correct this is...
factor = 1
if r < min_dx:
r_amr += min_dx
vol_halo = (4./3.)*np.pi*r**3
vol_cell = (4./3.)*np.pi*r_amr**3
factor = vol_halo/vol_cell
#Define the regions
region_part = Sphere(cen, r)
region_amr = Sphere(cen, r_amr)
#Filter the particles
filt_parts = RegionFilter(region_part, parts)
part_source = filt_parts.flatten()
part_mass = part_source['mass']*ro.info['unit_mass'].express(C.Msun)
#Filter the AMR data
filt_amr = RegionFilter(region_amr, amr)
cell_source = CellsToPoints(filt_amr)
cells = cell_source.flatten()
rho = cells['rho']*ro.info['unit_density'].express(C.Msun/C.kpc**3)
vol = (cells.get_sizes()*ro.info['unit_length'].express(C.kpc))**3
cell_mass = rho*vol
#Compute the total mass enclosed
gas_mass = np.sum(cell_mass)*factor # Multiply by factor incase we had to inflate the sphere
particle_mass = np.sum(part_mass)
total_mass = gas_mass + particle_mass
fgas.append(gas_mass/total_mass)
mhalo.append(total_mass)
i+=1
#print i
if(config.verbose and (i%100)==0): print 'Processes %d halos...'%i
return np.array(fgas), np.array(mhalo)