gtd0 = 100. #Gas to dust ratio (H2 vs Dust)
gtdratio = Model.gastodust(gtd0, NPoints)
#-----------------------------
#WRITING DATA for LIME
#-----------------------------
prop = {
'dens_H2': density.total,
'temp_gas': temperature.total,
'vel_x': vel.x,
'vel_y': vel.y,
'vel_z': vel.z,
'abundance': abundance,
'gtdratio': gtdratio
}
lime = rt.Lime(GRID)
lime.finalmodel(prop)
#-----------------------------
#PRINTING resultant PROPERTIES
#-----------------------------
Model.PrintProperties(density, temperature, GRID)
#-------
#TIMING
#-------
print('Ellapsed time: %.3fs' % (time.time() - t0))
print(
'-------------------------------------------------\n-------------------------------------------------\n'
)
f1 = sf.FilamentModel([0,0,0], [1,0,0], -0.2*pc, 0.2*pc, 4e-3*pc)
def new_temp(R,theta,z, *temp_pars):
TR, R0, pR, zh = temp_pars
cR = TR*R0**-pR
return cR*R**pR * np.exp(np.abs(z)/zh)
f1.func_temp = new_temp
f1.cylinder(0.1*pc, 1e-4*pc,
dens_pars = [7e10, 0.03*pc, 2.0],
temp_pars = [200, 0.02*pc, -0.15, -0.17*pc],
abund_pars = 1e-4)
pm.scatter3D(f1.GRID, f1.density, np.min(f1.density), axisunit = pc,
colordim = f1.density,
colorlabel = 'T [K]',
NRand = 10000, show=True)
prop = {'dens_H': f1.density,
'temp_gas': f1.temperature,
'abundance': f1.abundance,
'gtdratio': f1.gtdratio,
'vel_x': f1.vel.x,
'vel_y': f1.vel.y,
'vel_z': f1.vel.z,
}
lime = rt.Lime(f1.GRID)
lime.submodel(prop, output='filament.dat')
npoints=100000)
#*****************************************
#Computing relevant physical distributions
#*****************************************
density = transition.powerlaw_cavity(grid=grid, **kwargs_dens)
gtdratio = 1. / transition.powerlaw_cavity(
grid=grid, **kwargs_dtg) #Converting to gas-to-dust ratio for LIME
temperature = np.zeros(grid.NPoints) + 100. #Constant temperature 100 K
prop = {'dens_H2': density, 'gtdratio': gtdratio, 'temp_gas': temperature}
#**********************
#Writing files for LIME
#**********************
lime = rt.Lime(grid)
lime.submodel(prop,
output='datatab.dat',
folder='./',
lime_npoints=True,
lime_header=True)
print('Output columns', lime.columns)
print('Ellapsed time:', time.time() - t0)
#******************
#PLOTTING BLOCK
#******************
SMALL_SIZE = 11
MEDIUM_SIZE = 15
BIGGER_SIZE = 18
def do_overlap():
#******************************
#PATH AND TAG
#******************************
cwd = os.getcwd()
cloud_kind = cwd.split('Lime-arepo/')[-1].split('-Lime')
cloud_kind = cloud_kind[0] + cloud_kind[1]
home = os.environ['HOME']
base = home + '/Downloads/Manchester_2018/Rowan-data/Andres/data/datasets/'
base += cloud_kind
base_content = os.popen('ls ' + base).readlines()
snapshot = ''
for file in base_content:
if 'Restest' in file: snapshot = '/' + file.split('\n')[0]
#******************************
#AREPO 3D-grids (not projected)
#******************************
rsnap.io_flags['mc_tracer'] = True
rsnap.io_flags['time_steps'] = True
rsnap.io_flags['sgchem'] = True
rsnap.io_flags['variable_metallicity'] = False
#rsnap.io_flags['MHD']=True #There is no magnetic field information
#******************************
#READING SNAPSHOT
#******************************
f = base + snapshot
data, header = rsnap.read_snapshot(f)
nparts = header['num_particles']
ngas = nparts[0]
nsinks = nparts[5]
for key in data:
print(key, np.shape(data[key]))
#****************************
#GLOBAL GRID
#****************************
pos_max = np.max(data['pos'], axis=0)
pos_min = np.min(data['pos'], axis=0)
pos_mean = 0.5 * (pos_max + pos_min) * ulength / 100.
sizex = sizey = sizez = 0.5 * np.max(pos_max - pos_min) * ulength / 100.
#print (sizex, sizey, sizez)
Nx = Ny = Nz = 200
GRID = Model.grid([sizex, sizey, sizez], [Nx, Ny, Nz],
include_zero=False) #, rt_code='radmc3d')
columns = [
'id', 'x', 'y', 'z', 'dens_H2', 'dens_H', 'dens_Hplus', 'temp_gas',
'temp_dust', 'vel_x', 'vel_y', 'vel_z', 'abundance', 'gtdratio',
'doppler'
]
"""
overlap = grid.Overlap(GRID)
overlap.min_values['dens_H2'] = 0.0
newprop = overlap.fromfiles(columns, submodels=['datatab.dat'])
"""
props2use = ['dens_H', 'dens_H2', 'abundance', 'dens_Hplus']
newprop = {}
datatab = np.loadtxt('./Subgrids/datatab.dat') #[:-30000]
header_tab = np.loadtxt('./Subgrids/header.dat')
coords = np.array(GRID.XYZ).T
data_prop = {}
Lime = rt.Lime(GRID)
for prop in props2use:
column, = np.where(Lime.sf3d_header[prop] == header_tab)
data_prop[prop] = datatab[:, column]
subgrid = Model.Struct(XYZ=[datatab[:, i] for i in range(1, 4)],
NPoints=len(datatab))
fill = grid.fillgrid.Random(subgrid)
fill_rand = fill.spherical(
data_prop,
#prop_fill = {'dens_H2': 1.0},
r_min=10 * pc,
r_max=np.max(subgrid.XYZ) * np.sqrt(3),
n_dummy=subgrid.NPoints / 3)
coords_sub = np.array(subgrid.XYZ).T
for prop in props2use:
print('Merging %s ...' % prop)
ind_nodummie = data_prop[prop] > 2 * data_prop[prop][-1]
min_val = data_prop[prop][ind_nodummie].min()
newprop[prop] = griddata(coords_sub,
data_prop[prop],
coords,
fill_value=0.0) #, method='nearest')
newprop[prop][
newprop[prop] <
min_val] = min_val #very low value, just to get rid of zeroes
#newprop[prop][np.isnan(newprop[prop])] = datatab[:,column].min() #newprop[prop][np.isnan(newprop[prop])].min()
sink_pos = data['pos'][ngas:] * ulength / 100 - pos_mean
dummy_pos.append(coords_sub[-int(subgrid.NPoints / 2):])
return GRID, sizex, newprop, sink_pos, nsinks