def generate_training_data():
meshes = {}
cube_features, cube_target = [[] for i in range(len(cube_sizes))
], [[] for i in range(len(cube_sizes))]
for seed in seeds:
mesh = {}
partp = tools.readbigfile(path + ftype % (bs, nc, seed, step) +
'dynamic/1/Position/')
mesh['cic'] = tools.paintcic(partp, bs, ncp)
#mesh['decic'] = tools.decic(mesh['cic'], kk, kny)
mesh['R1'] = tools.fingauss(mesh['cic'], kk, R1, kny)
#mesh['R2'] = tools.fingauss(mesh['cic'], kk, R2, kny)
#mesh['GD'] = mesh['R1'] - mesh['R2']
hmesh = {}
hpath = path + ftype % (bs, ncf, seed, stepf) + 'galaxies_n05/galcat/'
hposd = tools.readbigfile(hpath + 'Position/')
massd = tools.readbigfile(hpath + 'Mass/').reshape(-1) * 1e10
galtype = tools.readbigfile(hpath +
'gal_type/').reshape(-1).astype(bool)
hmesh['pnn'] = tools.paintnn(hposd, bs, ncp)
hmesh['mnn'] = tools.paintnn(hposd, bs, ncp, massd)
hmesh['pnnsat'] = tools.paintnn(hposd[galtype], bs, ncp)
hmesh['pnncen'] = tools.paintnn(hposd[~galtype], bs, ncp)
meshes[seed] = [mesh, hmesh]
print('All the mesh have been generated for seed = %d' % seed)
#Create training voxels
ftlist = [mesh[i].copy() for i in ftname]
ftlistpad = [np.pad(i, pad, 'wrap') for i in ftlist]
# targetmesh = hmesh['pnn']
targetmesh = [hmesh[i].copy() for i in tgname]
for i, size in enumerate(cube_sizes):
print('For size = ', size)
if size == nc:
features = [np.stack(ftlistpad, axis=-1)]
target = [np.stack(targetmesh, axis=-1)]
else:
numcubes = int(num_cubes / size * 4)
features, target = dtools.randomvoxels(ftlistpad,
targetmesh,
numcubes,
max_offset[i],
size,
cube_sizesft[i],
seed=seed,
rprob=0)
cube_features[i] = cube_features[i] + features
cube_target[i] = cube_target[i] + target
# #
for i in range(cube_sizes.size):
cube_target[i] = np.stack(cube_target[i], axis=0)
cube_features[i] = np.stack(cube_features[i], axis=0)
print(cube_features[i].shape, cube_target[i].shape)
return meshes, cube_features, cube_target
def generate_training_data():
meshes = {}
cube_features, cube_target = [[] for i in range(len(cube_sizes))
], [[] for i in range(len(cube_sizes))]
for seed in seeds:
mesh, hmesh = get_meshes(seed)
meshes[seed] = [mesh, hmesh]
print('All the mesh have been generated for seed = %d' % seed)
#Create training voxels
ftlist = [mesh[i].copy() for i in ftname]
ftlistpad = [np.pad(i, pad, 'wrap') for i in ftlist]
targetmesh = [hmesh[i].copy() for i in tgname]
for i, size in enumerate(cube_sizes):
print('For size = ', size)
if size == nc:
features = [np.stack(ftlistpad, axis=-1)]
target = [np.stack(targetmesh, axis=-1)]
else:
numcubes = int(num_cubes / size * 4)
if size == 16: numcubes = 100
features, target = dtools.randomvoxels(ftlistpad,
targetmesh,
numcubes,
max_offset[i],
size,
cube_sizesft[i],
seed=seed,
rprob=0)
cube_features[i] = cube_features[i] + features
cube_target[i] = cube_target[i] + target
for i in range(cube_sizes.size):
cube_target[i] = np.stack(cube_target[i], axis=0)
cube_features[i] = np.stack(cube_features[i], axis=0)
print(cube_features[i].shape, cube_target[i].shape)
return meshes, cube_features, cube_target
meshes[seed] = [mesh, hmesh]
print('All the mesh have been generated for seed = %d' % seed, file=fname)
#Create training voxels
ftlist = [mesh[i].copy() for i in ftname]
ftlistpad = [np.pad(i, pad, 'wrap') for i in ftlist]
targetmesh = hmesh['pnn']
#Round off things to 1 again
targetmesh[targetmesh > 1] = 1
features, target = dtools.randomvoxels(ftlistpad,
targetmesh,
num_cubes,
max_offset,
cube_size,
cube_sizeft,
seed=seed,
rprob=rprob)
cube_features = cube_features + features
cube_target = cube_target + target
#
cube_target = np.stack(cube_target, axis=0).reshape(
(-1, cube_size, cube_size, cube_size, 1))
print(cube_target.sum(), cube_target.size, targetmesh.sum())
print(len(cube_features))
print(cube_features[0].shape)
cube_features = np.stack(cube_features, axis=0).reshape(
(-1, cube_sizeft, cube_sizeft, cube_sizeft, nchannels))
trainingsize = cube_features.shape[0]