if __name__ == '__main__':
# ->> initialization <<- #
sec = 'Rect'
init_dict = myDict(param_dict) + myDict(prog_control)
p = pc.prog_init(section=sec, **init_dict)
p.smooth_R = p.smooth_r
root = p.folder
''' ->> import data <<- '''
print 'likelihood fname:', p.likelihood_test_fname
z_init = 100.
# ->> import testing data <<- #
dd=rd.rblock(p.likelihood_test_fname, p.ngrid**3*7, \
dtype='float').reshape(7,p.ngrid,p.ngrid,p.ngrid)
#disp, disp_model = dd[:3,...], dd[3:,...]
bd = 10
disp, disp_model = dd[:3, bd:-bd, bd:-bd, bd:-bd], dd[3:, bd:-bd, bd:-bd,
bd:-bd],
di = dd[-1, ...]
#
print 'mean:', disp.min(), disp_model.min()
#->> final density <<- #
df=rd.rblock(p.original_density_fname, p.ngrid**3, \
dtype='float').reshape(p.ngrid, p.ngrid, p.ngrid)
# ->> define some other useful variables <<- #
init_dict = myDict(param_dict) + myDict(prog_control)
p = pc.prog_init(section=sec, **init_dict)
p.smooth_R = p.smooth_r
root = p.folder
# ->> import data <<- #
if (p.py_import_density_field == True):
if p.import_format == 'cita_simulation':
p.rec_fname = p.reconstructed_fname + '_' + p.smooth_type + '_R' + str(
p.smooth_R) + '.dat'
print 'reading data ... ', p.rec_fname
f_rec = rd.rblock(p.rec_fname, p.ngrid**3 * 3,
dtype='float').reshape(3, p.ngrid, p.ngrid,
p.ngrid)
drec, d_disp, d_shift = f_rec
d_ori = rd.rblock(p.original_density_fname,
p.ngrid**3,
dtype='float').reshape(p.ngrid, p.ngrid, p.ngrid)
print 'density shape:', drec.shape, d_disp.shape, d_shift.shape, d_ori.shape
print 'density min/max:', drec.min(), drec.max(), d_disp.min(
), d_disp.max(), d_shift.min(), d_shift.max()
else:
raise Exception
# ->> power spectrum measurement <<- #
}
if __name__ == '__main__':
# ->> initialization <<- #
sec = 'Rect'
init_dict = myDict(param_dict) + myDict(prog_control)
p = pc.prog_init(section=sec, **init_dict)
p.smooth_R = p.smooth_r
root = p.folder
if (p.cal_rect_transfer_func == True):
nblock = 6
dd = rd.rblock(p.raw_disp_field_fname,
p.ngrid**3 * nblock,
dtype='float').reshape(nblock, p.ngrid, p.ngrid,
p.ngrid)
#->> discard boundary data <<- #
bd = 5
bsize = p.boxsize - 2. * bd * p.boxsize / float(p.ngrid)
#bsize=p.boxsize
print 'bsize:', bsize
disp, disp_model = dd[:3, bd:-bd, bd:-bd, bd:-bd], dd[3:, bd:-bd,
bd:-bd, bd:-bd],
#disp, disp_model = dd[:3], dd[3:]
if True:
nplt, ncol = 2, 2
fig,ax=mpl.mysubplots(nplt,ncol_max=ncol,subp_size=5.,\
#->> <<-#
print len(pid), pid.min(), pid.max()
ll = np.arange(len(pid)) + 1
err = ll - pid
print 'len of non-zeros: ', len(np.where(err != 0)[0])
if do_displacement_test:
#->>
#fn_disp='/mnt/scratch-lustre/xwang/data/baorec/cubep3m_dm_sml_pid/rec_data/stat_disp_0_100.dat'
fn_disp = '/mnt/scratch-lustre/xwang/data/baorec/cubep3m_dm_sml_pid/rec_data/disp_0_100.dat'
nblock = 6
dd = rd.rblock(fn_disp, p.ngrid**3 * nblock,
dtype='float').reshape(nblock, p.ngrid, p.ngrid,
p.ngrid)
print dd.shape
disp, disp_lpt = dd[:3], dd[3:]
for i in range(3):
print 'disp :', i, disp[i].min(), disp[i].max()
if True:
# ->> 1D histogram <<- #
nplt, ncol = 3, 2
fig,ax=mpl.mysubplots(nplt,ncol_max=ncol,subp_size=5.,\
gap_size=0.5,return_figure=True)
n_bin = 500
color = ['g', 'r', 'b', 'y']