示例#1
0
    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/'
示例#2
0
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