return k, paramsk, mod
if __name__ == "__main__":
#bs, nc = 256, 128
bs, nc = 1024, 256
model = 'ModelA'
ik = 50
ii = 50
ffile = '../../data/bparams-L%04d-N%04d-%s.txt' % (bs, nc, model)
header = 'b1, b2, bg, b0, bk \nFit bias upto 0.3\nFit tf upto 1.0'
pm = ParticleMesh(BoxSize=bs, Nmesh=[nc, nc, nc])
rank = pm.comm.rank
grid = pm.mesh_coordinates() * bs / nc
lin = BigFileMesh(
'/global/cscratch1/sd/chmodi/m3127/cm_lowres/5stepT-B1/%d-%d-9100/linear'
% (bs, nc), 'LinearDensityK').paint()
tosave = []
for aa in [0.1429, 0.2000, 0.3333]:
zz = 1 / aa - 1
print(aa)
dyn = BigFileCatalog(
'/global/cscratch1/sd/chmodi/m3127/cm_lowres/5stepT-B1/%d-%d-9100/fastpm_%0.4f/1'
% (bs, nc, aa))
hmesh = BigFileMesh(
'/global/cscratch1/sd/chmodi/m3127/H1mass/highres/%d-9100/fastpm_%0.4f/HImesh-N%04d/'
if not args.evaluateOnly:
#target map
if args.target == 'dm':
targetmap = load_TNG_map(TNG_basepath=args.TNGDarkpath,
snapNum=args.snapNum,
field=args.target,
pm=pm)
else:
targetmap = load_TNG_map(TNG_basepath=args.TNGpath,
snapNum=args.snapNum,
field=args.target,
pm=pm)
#split among training, validation and test set
index = pm.mesh_coordinates()
Nmesh_test = int(0.44 * args.Nmesh)
Nmesh_validate = args.Nmesh - Nmesh_test
select_test = ((index[:, 0] < Nmesh_test) & (index[:, 1] < Nmesh_test) &
(index[:, 2] < Nmesh_test)).reshape(targetmap.shape)
select_validate = ((index[:, 0] >= Nmesh_test) &
(index[:, 1] < Nmesh_validate) &
(index[:, 2] < Nmesh_validate)).reshape(targetmap.shape)
mask_train = np.ones_like(targetmap, dtype='?')
mask_validate = np.zeros_like(targetmap, dtype='?')
mask_test = np.zeros_like(targetmap, dtype='?')
mask_train[select_test] = False
mask_train[select_validate] = False
mask_validate[select_validate] = True
mask_test[select_test] = True
