def _cpui_level_iterator(cpu, amr_filename, bisection_order, maxlevel, ndim) :
f = file(amr_filename, 'rb')
header = read_fortran_series(f, ramses_amr_header)
skip_fortran(f, 13)
n_per_level = read_fortran(f, _int_type, header['nlevelmax']*header['ncpu']).reshape(( header['nlevelmax'], header['ncpu']))
skip_fortran(f,1)
if header['nboundary']>0 :
skip_fortran(f,2)
n_per_level_boundary = read_fortran(f, _int_type, header['nlevelmax']*header['nboundary']).reshape(( header['nlevelmax'], header['nboundary']))
skip_fortran(f,2)
if bisection_order :
skip_fortran(f, 5)
else :
skip_fortran(f, 1)
skip_fortran(f, 3)
offset = np.array(header['ng'],dtype='f8')/2
offset-=0.5
for level in xrange(maxlevel or header['nlevelmax']) :
# loop through those CPUs with grid data (includes ghost regions)
for cpuf in 1+np.where(n_per_level[level,:]!=0)[0] :
#print "CPU=",cpu,"CPU on disk=",cpuf,"npl=",n_per_level[level,cpuf-1]
if cpuf==cpu :
# this is the data we want
skip_fortran(f,3) # grid, next, prev index
# store the coordinates in temporary arrays. We only want
# to copy it if the cell is not refined
x0,y0,z0 = [read_fortran(f, _float_type, n_per_level[level,cpu-1]) for ar in range(ndim)]
skip_fortran(f,1 # father index
+ 2*ndim # nbor index
+ 2*(2**ndim) # son index,cpumap,refinement map
)
refine = np.array([read_fortran(f,_int_type,n_per_level[level,cpu-1]) for i in xrange(2**ndim)])
if level==maxlevel :
refine[:] = 0
x0-=offset[0]; y0-=offset[1]; z0-=offset[2]
yield (x0,y0,z0),refine,cpuf,level
else :
# skip ghost regions from other CPUs
skip_fortran(f,3+ndim+1+2*ndim+3*2**ndim)
if header['nboundary']>0 :
for boundaryf in np.where(n_per_level_boundary[level, :]!=0)[0] :
skip_fortran(f,3+ndim+1+2*ndim+3*2**ndim)
def _load_particle_cpuid(self) :
ind0_dm = 0
ind0_star = 0
for i, star_mask, nstar in zip(self._cpus, self._star_mask, self._nstar) :
f = file(self._particle_filename(i))
header = read_fortran_series(f, ramses_particle_header)
f.close()
ind1_dm = ind0_dm+header['npart']-nstar
ind1_star = ind0_star+nstar
self.dm['cpu'][ind0_dm:ind1_dm] = i
self.star['cpu'][ind0_star:ind1_star] = i
ind0_dm, ind0_star = ind1_dm, ind1_star
def _cpui_count_particles(filename) :
distinguisher_field = int(particle_distinguisher[0])
distinguisher_type = np.dtype(particle_distinguisher[1])
f = file(filename)
header = read_fortran_series(f, ramses_particle_header)
npart_this = header['npart']
try:
skip_fortran(f,distinguisher_field)
data = read_fortran(f,distinguisher_type,header['npart'])
except TypeError:
data = np.zeros(npart_this)
my_mask = (data!=0)
nstar_this = (data!=0).sum()
return npart_this, nstar_this, my_mask
def _cpui_load_particle_block(filename, dm_ar, star_ar, offset, ind0_dm, ind0_star, _type, star_mask, nstar) :
f = file(filename)
header = read_fortran_series(f, ramses_particle_header)
skip_fortran(f, offset)
ind1_dm = ind0_dm+header['npart']-nstar
ind1_star = ind0_star+nstar
data = read_fortran(f, _type, header['npart'])
if len(star_mask)>0 :
dm_ar[ind0_dm:ind1_dm]=data[~star_mask]
star_ar[ind0_star:ind1_star]=data[star_mask]
else :
dm_ar[ind0_dm:ind1_dm]=data
f.close()
def _cpui_load_particle_block(filename, dm_ar, star_ar, offset, ind0_dm, ind0_star, _type, star_mask, nstar) :
f = file(filename)
header = read_fortran_series(f, ramses_particle_header)
skip_fortran(f, offset)
ind1_dm = ind0_dm+header['npart']-nstar
ind1_star = ind0_star+nstar
data = read_fortran(f, _type, header['npart'])
try:
if len(star_mask)>0 :
dm_ar[ind0_dm:ind1_dm]=data[~star_mask]
star_ar[ind0_star:ind1_star]=data[star_mask]
else :
dm_ar[ind0_dm:ind1_dm]=data
except ValueError:
# this translates into the data block loaded from disk not being
# long enough
raise IOError, "Could not load particle block"
f.close()
def _cpui_load_gas_vars(dims, maxlevel, ndim, filename, cpu, lia, i1,
mode = _gv_load_hydro ) :
if config['verbose'] :
print>>sys.stderr, cpu,
sys.stderr.flush()
nvar = len(dims)
grid_info_iter = _cpui_level_iterator(*lia)
f = file(filename)
check_nvar_file = False
if mode is _gv_load_hydro :
header = read_fortran_series(f, ramses_hydro_header)
nvar_file = header['nvarh']
else :
header = read_fortran_series(f, ramses_grav_header)
nvar_file=4
if nvar_file<nvar :
warnings.warn("Fewer hydro variables are in this RAMSES dump than are defined in config.ini (expected %d, got %d in file)"%(nvar, nvar_file), RuntimeWarning)
nvar = nvar_file
dims = dims[:nvar]
elif nvar_file>nvar :
warnings.warn("More hydro variables are in this RAMSES dump than are defined in config.ini", RuntimeWarning)
for level in xrange(maxlevel or header['nlevelmax']) :
for cpuf in xrange(1,header['ncpu']+1) :
flevel = read_fortran(f, 'i4')[0]
ncache = read_fortran(f, 'i4')[0]
assert flevel-1==level
if ncache>0 :
if cpuf==cpu :
coords, refine, gi_cpu, gi_level = grid_info_iter.next()
mark = np.where(refine==0)
assert gi_level==level
assert gi_cpu==cpu
if cpuf==cpu and len(mark[0])>0 :
for icel in xrange(2**ndim) :
i0 = i1
i1 = i0+(refine[icel]==0).sum()
for ar in dims :
ar[i0:i1] = read_fortran(f, _float_type, ncache)[(refine[icel]==0)]
skip_fortran(f, (nvar_file-nvar))
else :
skip_fortran(f, (2**ndim)*nvar_file)
for boundary in xrange(header['nboundary']) :
flevel = read_fortran(f, 'i4')[0]
ncache = read_fortran(f, 'i4')[0]
if ncache>0 :
skip_fortran(f, (2**ndim)*nvar_file)
