modpath,
data,
pad,
maxiter=maxiter,
gtol=gtol,
anneal=anneal,
resnorm=resnorm,
loss=loss,
log=logit,
inverse=inverse)
#
initval = None
initval = np.random.normal(0, 1, size=nc**3).reshape(nc, nc, nc).astype(
config['dtype']) #truth
if stdinit: initval = standardinit(config, datap, hposd, final, R=8)
#initval = tools.readbigfile(dpath + ftype%(bs, nc, 900, step) + 'mesh/s/')
#initval = np.ones((nc, nc, nc))
if truthinit: initval = truth.copy()
losses = []
literals = {'losses': losses, 'truemeshes': truemeshes, 'bs': bs, 'nc': nc}
tstart = time()
lcallback = lambda x: loss_callback(x,
literals=literals,
nprint=nprint,
nsave=nsave,
maxiter=maxiter,
t0=tstart)
with tf.Session(graph=recong) as session:
print('\nDo reconstruction\n')
recong = rmods.graphhposft1(config,
modpath,
data,
pad,
maxiter=maxiter,
gtol=gtol,
anneal=anneal,
resnorm=resnorm)
#
initval = None
initval = np.random.normal(1, 0.5, size=nc**3).reshape(nc, nc, nc).astype(
config['dtype']) #truth
initval = standardinit(config, data, hposd, final, R=8)
#initval = tools.readbigfile(dpath + ftype%(bs, nc, 900, step) + 'mesh/s/')
#initval = np.ones((nc, nc, nc))
#initval = truth.copy()
losses = []
literals = {'losses': losses, 'truemeshes': truemeshes, 'bs': bs, 'nc': nc}
tstart = time()
lcallback = lambda x: loss_callback(x,
literals=literals,
nprint=nprint,
nsave=nsave,
maxiter=maxiter,
t0=tstart)
with tf.Session(graph=recong) as session:
def main(_):
infield = True
dtype = tf.float32
mesh_shape = mtf.convert_to_shape(FLAGS.mesh_shape)
nc, bs = FLAGS.nc, FLAGS.box_size
a0, a, nsteps = FLAGS.a0, FLAGS.af, FLAGS.nsteps
stages = np.linspace(a0, a, nsteps, endpoint=True)
numd = 1e-3
##Begin here
klin = np.loadtxt('../flowpm/data/Planck15_a1p00.txt').T[0]
plin = np.loadtxt('../flowpm/data/Planck15_a1p00.txt').T[1]
ipklin = iuspline(klin, plin)
#pypath = '/global/cscratch1/sd/chmodi/cosmo4d/output/version2/L0400_N0128_05step-fof/lhd_S0100/n10/opt_s999_iM12-sm3v25off/meshes/'
final = tools.readbigfile(
'/project/projectdirs/m3058/chmodi/cosmo4d/data/L0400_N0128_S0100_05step/mesh/d/'
)
ic = tools.readbigfile(
'/project/projectdirs/m3058/chmodi/cosmo4d/data/L0400_N0128_S0100_05step/mesh/s/'
)
fpos = tools.readbigfile(
'/project/projectdirs/m3058/chmodi/cosmo4d/data/L0400_N0128_S0100_05step/dynamic/1/Position/'
)
aa = 1
zz = 1 / aa - 1
rsdfactor = float(100 / (aa**2 * cosmo.H(zz).value**1))
print('\nRsdfactor used is : ', rsdfactor)
hpos = tools.readbigfile(
'/project/projectdirs/m3058/chmodi/cosmo4d/data/L0400_N0512_S0100_40step/FOF/PeakPosition//'
)[1:int(bs**3 * numd)]
hvel = tools.readbigfile(
'/project/projectdirs/m3058/chmodi/cosmo4d/data/L0400_N0512_S0100_40step/FOF/CMVelocity//'
)[1:int(bs**3 * numd)]
rsdpos = hpos + hvel * rsdfactor * np.array([0, 0, 1])
print('Effective displacement : ', (hvel[:, -1] * rsdfactor).std())
hmass = tools.readbigfile(
'/project/projectdirs/m3058/chmodi/cosmo4d/data/L0400_N0512_S0100_40step/FOF/Mass//'
)[1:int(bs**3 * numd)].flatten()
meshpos = tools.paintcic(rsdpos, bs, nc)
meshmass = tools.paintcic(rsdpos, bs, nc, hmass.flatten() * 1e10)
data = meshmass
kv = tools.fftk([nc, nc, nc], bs, symmetric=True, dtype=np.float32)
datasm = tools.fingauss(data, kv, 3, np.pi * nc / bs)
ic, data = np.expand_dims(ic, 0), np.expand_dims(data,
0).astype(np.float32)
datasm = np.expand_dims(datasm, 0).astype(np.float32)
print("Min in data : %0.4e" % datasm.min())
#
####################################################
stdinit = srecon.standardinit(bs, nc, meshpos, hpos, final, R=8)
recon_estimator = tf.estimator.Estimator(model_fn=model_fn,
model_dir=fpath)
def predict_input_fn(data=data,
M0=0.,
w=3.,
R0=0.,
off=None,
istd=None,
x0=None):
features = {}
features['datasm'] = data
features['rsdfactor'] = rsdfactor
features['M0'] = M0
features['w'] = w
features['R0'] = R0
features['off'] = off
features['istd'] = istd
features['x0'] = x0
return features, None
eval_results = recon_estimator.predict(
input_fn=lambda: predict_input_fn(x0=ic), yield_single_examples=False)
for i, pred in enumerate(eval_results):
if i > 0: break
suff = '-model'
dg.saveimfig(suff, [pred['ic'], pred['model']], [ic, data],
fpath + '/figs/')
dg.save2ptfig(suff, [pred['ic'], pred['model']], [ic, data],
fpath + '/figs/', bs)
np.save(fpath + '/reconmeshes/ic_true' + suff, pred['ic'])
np.save(fpath + '/reconmeshes/fin_true' + suff, pred['final'])
np.save(fpath + '/reconmeshes/model_true' + suff, pred['model'])
#
randominit = np.random.normal(size=data.size).reshape(data.shape)
#eval_results = recon_estimator.predict(input_fn=lambda : predict_input_fn(x0 = np.expand_dims(stdinit, 0)), yield_single_examples=False)
eval_results = recon_estimator.predict(
input_fn=lambda: predict_input_fn(x0=randominit),
yield_single_examples=False)
for i, pred in enumerate(eval_results):
if i > 0: break
suff = '-init'
dg.saveimfig(suff, [pred['ic'], pred['model']], [ic, data],
fpath + '/figs/')
dg.save2ptfig(suff, [pred['ic'], pred['model']], [ic, data],
fpath + '/figs/', bs)
np.save(fpath + '/reconmeshes/ic_init' + suff, pred['ic'])
np.save(fpath + '/reconmeshes/fin_init' + suff, pred['final'])
np.save(fpath + '/reconmeshes/model_init' + suff, pred['model'])
#
# Train and evaluate model.
mms = [1e12, 1e11]
wws = [1., 2., 3.]
RRs = [4., 2., 1., 0.5, 0.]
niter = 100
iiter = 0
for mm in mms:
noisefile = '/project/projectdirs/m3058/chmodi/cosmo4d/train/L0400_N0128_05step-n10/width_3/Wts_30_10_1/r1rf1/hlim-13_nreg-43_batch-5/eluWts-10_5_1/blim-20_nreg-23_batch-100/hist_M%d_na.txt' % (
np.log10(mm) * 10)
offset, ivar = setnoise(datasm, noisefile, noisevar=0.25)
istd = ivar**0.5
if not FLAGS.offset: offset = None
if not FLAGS.istd: istd = None
for R0 in RRs:
for ww in wws:
print('\nFor iteration %d\n' % iiter)
print('With mm=%0.2e, R0=%0.2f, ww=%d \n' % (mm, R0, ww))
def train_input_fn():
features = {}
features['datasm'] = datasm
features['rsdfactor'] = rsdfactor
features['M0'] = mm
features['w'] = ww
features['R0'] = R0
features['off'] = offset
features['istd'] = istd
features['x0'] = np.expand_dims(
stdinit, 0
) #np.random.normal(size=datasm.size).reshape(datasm.shape)
features['lr'] = 0.01
return features, None
recon_estimator.train(input_fn=train_input_fn,
max_steps=iiter + niter)
eval_results = recon_estimator.predict(
input_fn=predict_input_fn, yield_single_examples=False)
for i, pred in enumerate(eval_results):
if i > 0: break
iiter += niter #
suff = '-%d-M%d-R%d-w%d' % (iiter, np.log10(mm), R0, ww)
dg.saveimfig(suff, [pred['ic'], pred['model']], [ic, data],
fpath + '/figs/')
dg.save2ptfig(suff, [pred['ic'], pred['model']], [ic, data],
fpath + '/figs/', bs)
suff = '-M%d-R%d-w%d' % (np.log10(mm), R0, ww)
np.save(fpath + '/reconmeshes/ic' + suff, pred['ic'])
np.save(fpath + '/reconmeshes/fin' + suff, pred['final'])
np.save(fpath + '/reconmeshes/model' + suff, pred['model'])
RRs = [1., 0.5, 0.]
wws = [3.]
niter = 200
sys.exit(0)
##
exit(0)