def surface_density_bycells(S, agecut, poscut, a, h, cell_length=0.3, surface_frac=0.8, density_max_cell_center=True):
#r = np.random.randn(100,3)
r = [S['p'][:,0][agecut][poscut], S['p'][:,1][agecut][poscut], S['p'][:,2][agecut][poscut]]
minx = np.min(S['p'][:,0][agecut][poscut])*a/h
maxx = np.max(S['p'][:,0][agecut][poscut])*a/h
miny = np.min(S['p'][:,1][agecut][poscut])*a/h
maxy = np.max(S['p'][:,1][agecut][poscut])*a/h
minz = np.min(S['p'][:,2][agecut][poscut])*a/h
maxz = np.max(S['p'][:,2][agecut][poscut])*a/h
numxcells = int((maxx-minx)/cell_length)+1
numycells = int((maxy-miny)/cell_length)+1
numzcells = int((maxz-minz)/cell_length)+1
xrange = [minx*h/a, (minx*h/a + float(numxcells)*cell_length*h/a)]
yrange = [miny*h/a, (miny*h/a + float(numycells)*cell_length*h/a)]
zrange = [minz*h/a, (minz*h/a + float(numzcells)*cell_length*h/a)]
# print 'diagnostic x',(xrange[1] - xrange[0])/(cell_length*h/a), numxcells
# print 'diagnostic y',(yrange[1] - yrange[0])/(cell_length*h/a), numycells
# print 'diagnostic x',(zrange[1] - zrange[0])/(cell_length*h/a), numzcells
#rad should be in physical kpc
#num_cells=int(rad*2.0/cell_length)
H, edges = np.histogramdd(r, bins = (numxcells, numycells, numzcells), range=(xrange,yrange,zrange), weights=S['m'][agecut][poscut])
cut = H>0
ix, iy, iz = np.where(cut)
count = 0
Nums = []
cenx = []
ceny = []
cenz = []
#print 'iy ', iy
#print 'len iy ',len(iy)
#print 'edges ',edges
#print 'len edges ',len(edges)
while (count < len(ix)):
theNum = H[ix[count],iy[count],iz[count]]
thecenx = (edges[0][ix[count]] + edges[0][ix[count]+1])/2.0
theceny = (edges[1][iy[count]] + edges[1][iy[count]+1])/2.0
thecenz = (edges[2][iz[count]] + edges[2][iz[count]+1])/2.0
if (density_max_cell_center):
cellxmin_cut = S['p'][:,0][agecut][poscut] > edges[0][ix[count]]
cellxmax_cut = S['p'][:,0][agecut][poscut] < edges[0][ix[count]+1]
cellymin_cut = S['p'][:,1][agecut][poscut] > edges[1][iy[count]]
cellymax_cut = S['p'][:,1][agecut][poscut] < edges[1][iy[count]+1]
cellzmin_cut = S['p'][:,2][agecut][poscut] > edges[2][iz[count]]
cellzmax_cut = S['p'][:,2][agecut][poscut] < edges[2][iz[count]+1]
allcut = cellxmin_cut* cellxmax_cut* cellymin_cut* cellymax_cut * cellzmin_cut * cellzmax_cut
#print 'test test ',len(S['p'][:,0][agecut][poscut][allcut]), S['p'][:,0][agecut][poscut][allcut]
if (len(S['p'][:,0][agecut][poscut][allcut]) > 5):
#print 'first center ',[thecenx, theceny, thecenz], theNum
S_COM = SF.gaussian_KDE_center(S['p'][:,0][agecut][poscut][allcut], S['p'][:,1][agecut][poscut][allcut] , S['p'][:,2][agecut][poscut][allcut] , downsample=False)
[thecenx, theceny, thecenz] = S_COM
#print 'adjusting center ',[thecenx, theceny, thecenz]
Nums.append(theNum)
cenx.append(thecenx)
ceny.append(theceny)
cenz.append(thecenz)
count+=1
cenx = np.array(cenx)
ceny = np.array(ceny)
cenz = np.array(cenz)
Nums = np.array(Nums)
order_of_densities = np.argsort(Nums)[::-1]
#cumsumdens = np.cumsum(Nums[order_of_densities])
sum_of_densities = np.sum(Nums)
current_sum = 0
count = 0
final_x = []
final_y = []
final_z = []
final_Nums = []
#print 'densities in order ',Nums[order_of_densities]
while (current_sum <= surface_frac * sum_of_densities):
current_sum += Nums[order_of_densities][count]
final_x.append(cenx[order_of_densities][count])
final_y.append(ceny[order_of_densities][count])
final_z.append(cenz[order_of_densities][count])
final_Nums.append( Nums[order_of_densities][count])
count+=1
return [final_x, final_y, final_z, final_Nums]
omega_L = S['header'][11]
h = S['header'][12]
AHF_haloX = x[count]
AHF_haloY = y[count]
AHF_haloZ = z[count]
AHF_Rvir = Rvir[count]
AHF_a = 1.0 / (1.0 + Zs[count])
if (adaptive_Rstar):
Rstars = Inner_SF_thresh * Rvir[count]
RstarsPhys = Rstars * AHF_a / h
Sdists = SF.calcdist2(AHF_haloX, AHF_haloY, AHF_haloZ, S['p'][:, 0],
S['p'][:, 1], S['p'][:, 2], boxsize)
Sinner = Sdists < Rstars
S_COM = SF.gaussian_KDE_center(S['p'][:, 0][Sinner],
S['p'][:, 1][Sinner],
S['p'][:, 2][Sinner],
downsample=True)
Shalo = Sdists < AHF_Rvir
S_COM_grand = SF.gaussian_KDE_center(S['p'][:, 0][Shalo],
S['p'][:, 1][Shalo],
S['p'][:, 2][Shalo],
downsample=True)
iSinX.append(S_COM[0])
iSinY.append(S_COM[1])
iSinZ.append(S_COM[2])
ShalX.append(S_COM_grand[0])
ShalY.append(S_COM_grand[1])
ShalZ.append(S_COM_grand[2])
D = readsnap(the_snapdir,
def surface_density_bycells(S,
agecut,
poscut,
a,
h,
cell_length=0.3,
surface_frac=0.8,
density_max_cell_center=True):
#r = np.random.randn(100,3)
r = [
S['p'][:, 0][agecut][poscut], S['p'][:, 1][agecut][poscut],
S['p'][:, 2][agecut][poscut]
]
minx = np.min(S['p'][:, 0][agecut][poscut]) * a / h
maxx = np.max(S['p'][:, 0][agecut][poscut]) * a / h
miny = np.min(S['p'][:, 1][agecut][poscut]) * a / h
maxy = np.max(S['p'][:, 1][agecut][poscut]) * a / h
minz = np.min(S['p'][:, 2][agecut][poscut]) * a / h
maxz = np.max(S['p'][:, 2][agecut][poscut]) * a / h
numxcells = int((maxx - minx) / cell_length) + 1
numycells = int((maxy - miny) / cell_length) + 1
numzcells = int((maxz - minz) / cell_length) + 1
xrange = [
minx * h / a, (minx * h / a + float(numxcells) * cell_length * h / a)
]
yrange = [
miny * h / a, (miny * h / a + float(numycells) * cell_length * h / a)
]
zrange = [
minz * h / a, (minz * h / a + float(numzcells) * cell_length * h / a)
]
# print 'diagnostic x',(xrange[1] - xrange[0])/(cell_length*h/a), numxcells
# print 'diagnostic y',(yrange[1] - yrange[0])/(cell_length*h/a), numycells
# print 'diagnostic x',(zrange[1] - zrange[0])/(cell_length*h/a), numzcells
#rad should be in physical kpc
#num_cells=int(rad*2.0/cell_length)
H, edges = np.histogramdd(r,
bins=(numxcells, numycells, numzcells),
range=(xrange, yrange, zrange),
weights=S['m'][agecut][poscut])
cut = H > 0
ix, iy, iz = np.where(cut)
count = 0
Nums = []
cenx = []
ceny = []
cenz = []
#print 'iy ', iy
#print 'len iy ',len(iy)
#print 'edges ',edges
#print 'len edges ',len(edges)
while (count < len(ix)):
theNum = H[ix[count], iy[count], iz[count]]
thecenx = (edges[0][ix[count]] + edges[0][ix[count] + 1]) / 2.0
theceny = (edges[1][iy[count]] + edges[1][iy[count] + 1]) / 2.0
thecenz = (edges[2][iz[count]] + edges[2][iz[count] + 1]) / 2.0
if (density_max_cell_center):
cellxmin_cut = S['p'][:, 0][agecut][poscut] > edges[0][ix[count]]
cellxmax_cut = S['p'][:,
0][agecut][poscut] < edges[0][ix[count] + 1]
cellymin_cut = S['p'][:, 1][agecut][poscut] > edges[1][iy[count]]
cellymax_cut = S['p'][:,
1][agecut][poscut] < edges[1][iy[count] + 1]
cellzmin_cut = S['p'][:, 2][agecut][poscut] > edges[2][iz[count]]
cellzmax_cut = S['p'][:,
2][agecut][poscut] < edges[2][iz[count] + 1]
allcut = cellxmin_cut * cellxmax_cut * cellymin_cut * cellymax_cut * cellzmin_cut * cellzmax_cut
#print 'test test ',len(S['p'][:,0][agecut][poscut][allcut]), S['p'][:,0][agecut][poscut][allcut]
if (len(S['p'][:, 0][agecut][poscut][allcut]) > 5):
#print 'first center ',[thecenx, theceny, thecenz], theNum
S_COM = SF.gaussian_KDE_center(
S['p'][:, 0][agecut][poscut][allcut],
S['p'][:, 1][agecut][poscut][allcut],
S['p'][:, 2][agecut][poscut][allcut],
downsample=False)
[thecenx, theceny, thecenz] = S_COM
#print 'adjusting center ',[thecenx, theceny, thecenz]
Nums.append(theNum)
cenx.append(thecenx)
ceny.append(theceny)
cenz.append(thecenz)
count += 1
cenx = np.array(cenx)
ceny = np.array(ceny)
cenz = np.array(cenz)
Nums = np.array(Nums)
order_of_densities = np.argsort(Nums)[::-1]
#cumsumdens = np.cumsum(Nums[order_of_densities])
sum_of_densities = np.sum(Nums)
current_sum = 0
count = 0
final_x = []
final_y = []
final_z = []
final_Nums = []
#print 'densities in order ',Nums[order_of_densities]
while (current_sum <= surface_frac * sum_of_densities):
current_sum += Nums[order_of_densities][count]
final_x.append(cenx[order_of_densities][count])
final_y.append(ceny[order_of_densities][count])
final_z.append(cenz[order_of_densities][count])
final_Nums.append(Nums[order_of_densities][count])
count += 1
return [final_x, final_y, final_z, final_Nums]
omega_matter = S['header'][10] omega_L = S['header'][11] h = S['header'][12] AHF_haloX = x[count] AHF_haloY = y[count] AHF_haloZ = z[count] AHF_Rvir = Rvir[count] AHF_a = 1.0 / (1.0 + Zs[count]) if (adaptive_Rstar): Rstars = Inner_SF_thresh*Rvir[count] RstarsPhys = Rstars * AHF_a / h Sdists = SF.calcdist2(AHF_haloX, AHF_haloY, AHF_haloZ, S['p'][:,0], S['p'][:,1], S['p'][:,2], boxsize) Sinner = Sdists<Rstars S_COM = SF.gaussian_KDE_center(S['p'][:,0][Sinner], S['p'][:,1][Sinner], S['p'][:,2][Sinner], downsample=True) Shalo = Sdists < AHF_Rvir S_COM_grand = SF.gaussian_KDE_center(S['p'][:,0][Shalo], S['p'][:,1][Shalo], S['p'][:,2][Shalo], downsample=True) iSinX.append(S_COM[0]) iSinY.append(S_COM[1]) iSinZ.append(S_COM[2]) ShalX.append(S_COM_grand[0]) ShalY.append(S_COM_grand[1]) ShalZ.append(S_COM_grand[2]) D = readsnap(the_snapdir, Nsnapstring, 1, snapshot_name=the_prefix, extension=the_suffix) Ddists = SF.calcdist2(AHF_haloX, AHF_haloY, AHF_haloZ, D['p'][:,0], D['p'][:,1], D['p'][:,2], boxsize) Dinner = Ddists < Rstars Dhalo = Ddists < AHF_Rvir D_COM_inner = SF.gaussian_KDE_center(D['p'][:,0][Dinner], D['p'][:,1][Dinner], D['p'][:,2][Dinner], downsample=True)
Rvir, Vsig, M = SF.check_Rvir_growth(halo_to_do[workcount], a, Rvir, Vsig, M, therod=use_fixed_halos) if (adaptive_Rstar): Rstars = Inner_SF_thresh*Rvir RstarsPhys = Rstars * a / h Sdists = SF.calcdist2(haloX, haloY, haloZ, S['p'][:,0], S['p'][:,1], S['p'][:,2], boxsize) Sinner_young = Sdists[Syoung] < Rstars if (use_KDE_cent): Sinner = Sdists<Rstars S_COM = SF.gaussian_KDE_center(S['p'][:,0][Sinner], S['p'][:,1][Sinner], S['p'][:,2][Sinner], downsample=True) [haloX, haloY, haloZ] = [S_COM[0], S_COM[1], S_COM[2]] Sdists = SF.calcdist2(haloX, haloY, haloZ, S['p'][:,0], S['p'][:,1], S['p'][:,2], boxsize) Sinner_young = Sdists[Syoung] < Rstars elif (use_darkKDE_cent): #dark matter fun Ddists = SF.calcdist2(haloX, haloY, haloZ, D['p'][:,0], D['p'][:,1], D['p'][:,2], boxsize) Dinner = Ddists<Rstars D_COM = SF.gaussian_KDE_center(D['p'][:,0][Dinner], D['p'][:,1][Dinner], D['p'][:,2][Dinner], downsample=True) [haloX, haloY, haloZ] = [D_COM[0], D_COM[1], D_COM[2]] Sdists = SF.calcdist2(haloX, haloY, haloZ, S['p'][:,0], S['p'][:,1], S['p'][:,2], boxsize) Sinner_young = Sdists[Syoung] < Rstars [x_cells, y_cells, z_cells, cellmass ]= potter.surface_density_bycells(S, Syoung, Sinner_young, a, h, surface_frac=the_surface_frac) Nnewstars = len(Sdists[Syoung][Sinner_young])
