def elltraj_test(p, a):
#->>
ln_a = np.linspace(-4.6, 0., 500)
a = np.exp(ln_a)
F, ee, pp = 0.5, 0.2, 0.1
#F, ee, pp=1.686, 0., 0.
l = shape_to_eigval(F, ee, pp)
print 'testing lambda:', l
traj = get_elliptraj_one(p, a, l, freeze=False)
#print 'traj:', traj
print 'traj shape:', traj.shape
shape = eigval_to_shape(traj[:, 0], traj[:, 1], traj[:, 2])
#print 'shape', shape
# ->> plot <<- #
nplt, ncol = 3, 3
fig, ax = mpl.mysubplots(nplt,
ncol_max=ncol,
subp_size=5.,
gap_size=0.5,
return_figure=True)
for i in range(3):
#ax[i].plot(a, shape[i])
ax[i].plot(a, traj[:, i] / a, 'k-')
#ax[i].plot(a, traj[:,i+3], 'b-')
pl.show()
return
#->> 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 <<- #
lw1 = ['k-', 'r-', 'b-', 'g-']
# ->> get the divergence of the displacement field <<- #
#->>
if False:
print 'disp rk fname:', p.disp_transfunc_fname
nplt, ncol = 1, 1
fig,ax=mpl.mysubplots(nplt,ncol_max=ncol,subp_size=5.,\
gap_size=0.5,return_figure=True)
lw1 = ['k-', 'r-', 'b-', 'g-']
lw2 = ['k--', 'r--', 'b--', 'g--']
cd_k, cd_p = [], []
for i in range(3):
k1, pk1 = psor.cross(disp[i], disp_model[i], boxsize=p.boxsize)
k2, pk2 = psor.pk(disp_model[i], boxsize=p.boxsize)
k3, pk3 = psor.pk(disp[i], boxsize=p.boxsize)
cr = pk1 / np.sqrt(pk3 * pk2)
cd_k.append(k1)
cd_p.append(cr)
def Testing_portal(p, data, import_data_type='field'):
if True:
if import_data_type == 'field':
# ->>
print 'boxsize=', p.boxsize
dmean = np.mean(data)
delta = data / dmean - 1.
phi, phi_ij=ptt.Poisson3d(delta, boxsize=p.boxsize, return_hessian=True, \
smooth_R=None, smooth_type=None)
#phi, phi_ij=ptt.Poisson3d(delta, boxsize=p.boxsize, return_hessian=True, \
# smooth_R=p.smooth_R, smooth_type=p.smooth_type)
# ->>
d = dmean * (1 + phi_ij[0, 0] + phi_ij[1, 1] + phi_ij[2, 2])
print 'd shape:', d.shape, d.min(), d.max()
print 'd err:', np.max(np.fabs((d - data) / data))
# ->>
da = dmean * (1. +
ptt.Laplacian(phi, boxsize=p.boxsize, Lap_type=1))
print 'da shape:', da.shape, da.min(), da.max()
print 'da err:', np.max(np.fabs((da - data) / data))
if True:
nplt = 3
ncol = 3
fig, ax = mpl.mysubplots(nplt,
ncol_max=ncol,
subp_size=2.,
gap_size=0.15,
return_figure=True)
dd1, dd2, dd3 = data[100, :, :], d[100, :, :], da[100, :, :]
print 'min, max:', dd1.min(), dd1.max(), dd2.min(), dd2.max(
), dd3.min(), dd3.max()
ax[0].imshow(np.flipud(dd1),
norm=colors.LogNorm(vmin=dd1.min(),
vmax=dd1.max()))
ax[1].imshow(np.flipud(dd2),
norm=colors.LogNorm(vmin=dd2.min(),
vmax=dd2.max()))
#ax[2].imshow(np.flipud(dd3), norm=colors.LogNorm(vmin=dd3.min(), vmax=dd3.max()) )
ax[2].imshow(np.flipud(phi[100, :, :]))
pl.show()
if True:
if import_data_type == 'field':
dmean = np.mean(data)
delta = data / dmean - 1.
#phi=ptt.Poisson3d(delta, boxsize=p.boxsize, smooth_R=None, smooth_type=None)
#phi=ptt.Poisson3d(delta, boxsize=p.boxsize, smooth_R=p.smooth_R, smooth_type=p.smooth_type)
phi, phi_i = ptt.Poisson3d(delta,
boxsize=p.boxsize,
smooth_R=None,
smooth_type=None,
return_gradient=True)
print 'phi, phi_i shape:', phi.shape, phi_i.shape
dl = p.boxsize / float(p.nbin)
gd = np.array(np.gradient(phi)) / dl
print gd.shape
print 'gradient error 1:', [
np.max(np.fabs((phi[i] - gd[i]) / phi[i])) for i in range(3)
]
print 'gradient error 2:', [
np.max(np.fabs((phi[i] - gd[i]) / gd[i])) for i in range(3)
]
if True:
# ->> testing <<- #
if True:
nplt = 10
ncol = 4
fig, ax = mpl.mysubplots(nplt,
ncol_max=ncol,
subp_size=2.,
gap_size=0.15,
return_figure=True)
ax[0].imshow(np.flipud(phi[100, :, :]))
for i in range(3):
dd_ = np.fabs(gd[i, 100, :, :])
ax[i + 1].imshow(np.flipud(dd_),
norm=colors.LogNorm(vmin=dd_.min(),
vmax=dd_.max()))
_dd = gd[0, 100, :, :]**2. + gd[1, 100, :, :]**2. + gd[
2, 100, :, :]**2.
ax[4].imshow(np.flipud(_dd),
norm=colors.LogNorm(vmin=_dd.min(),
vmax=_dd.max()))
dd1 = data[100, :, :]
ax[5].imshow(np.flipud(dd1),
norm=colors.LogNorm(vmin=dd1.min(),
vmax=dd1.max()))
#->> comparison <<- #
for i in range(3):
dd_ = np.fabs(phi_i[i, 100, :, :])
ax[i + 6].imshow(np.flipud(dd_),
norm=colors.LogNorm(vmin=dd_.min(),
vmax=dd_.max()))
_dd = phi_i[0, 100, :, :]**2. + phi_i[
1, 100, :, :]**2. + phi_i[2, 100, :, :]**2.
ax[9].imshow(np.flipud(_dd),
norm=colors.LogNorm(vmin=_dd.min(),
vmax=_dd.max()))
pl.show()
if False:
# ->> testing <<- #
pl.plot(y[:, :, 100] * p.nbin, z[:, :, 100] * p.nbin, 'k.', alpha=0.3)
#_dd=np.swapaxes(den[100,:,:], 0, 1)
#_dd=den[100,:,:]
_dd = np.flipud(den[100, :, :])
#pl.pcolormesh(_dd, norm=colors.LogNorm(vmin=_dd.min(), vmax=_dd.max()), alpha=0.7)
pl.imshow(_dd,
norm=colors.LogNorm(vmin=_dd.min(), vmax=_dd.max()),
alpha=0.8)
pl.show()
quit()
return
pos = (s['pos']/1.e3).reshape(nbin**3,3)
print 'pos.shape', pos.shape, 'pos boundary:', pos[...,0].min(), pos[...,0].max()
''' ->> run CIC density estimation <<- '''
npart=pos.shape[0]
d=mcic.cic(npart, nbin, pos, pmass=1.e5)
print d.shape, d.min(), d.max()
if True:
#fig=pl.figure(figsize=(20, 20))
#ax=fig.add_subplot(111)
nplt, ncol=2, 2
fig,ax=mpl.mysubplots(nplt,ncol_max=ncol,subp_size=8.,gap_size=0.15,return_figure=True)
data=d[...,100] +1e-3
ax[0].imshow(np.flipud(data), norm=colors.LogNorm(vmin=data.min(),vmax=data.max()) )
# ->> read field <<- #
dd=rd.rgrid(root+fname_field, ngrid=nbin, dtype='float', comp=1)
data_1 = dd[...,100]
ax[1].imshow(np.flipud(data_1), norm=colors.LogNorm(vmin=data_1.min(),vmax=data_1.max()) )
fig.savefig('dtest_cmp.png')
def lag_rec_ZA(p,
mpart,
dmap,
smooth_R=None,
smooth_type=None,
rect_type='ZA_displaced'):
# -> reconstruction of initial state, return density map <<- #
# ->> get displacement field <<- #
phi, si = get_ZA_displacement(p,
dmap,
smooth_R=smooth_R,
smooth_type=smooth_type)
print '->> phi, si shape:', phi.shape, si.shape
npt = p.nbin**3.
if rect_type == 'ZA_displaced_shifted':
#->> get particles <<- #
#pos_disp=np.copy(np.swapaxes(displaced_ZA(p,si,mpart).reshape(3, p.nbin**3),0,1))
#pos_shift=np.copy(np.swapaxes(shifted_ZA(p, si).reshape(3, p.nbin**3),0,1))
pos_disp = displaced_ZA(p, si, mpart)
pos_shift = shifted_ZA(p, si)
print 'particle shape:', mpart.shape, pos_disp.shape, pos_shift.shape
#->> converting density map <<- #
d_disp = mcic.cic(p.cp,
npt,
p.nbin,
p.boxsize,
pos_disp,
pmass=p.particle_mass)
d_shift = mcic.cic(p.cp,
npt,
p.nbin,
p.boxsize,
pos_shift,
pmass=p.particle_mass)
_test_draw_ = True
if _test_draw_:
shift_ = pos_shift.reshape(p.nbin, p.nbin, p.nbin, 3)
nplt, ncol = 4, 2
fig, ax = mpl.mysubplots(nplt,
ncol_max=ncol,
subp_size=7.,
gap_size=1,
return_figure=True)
ax[0].imshow(d_shift[:, :, 100])
ax[1].plot(shift_[:, :, 100, 1], shift_[:, :, 100, 2], 'k.')
ax[2].imshow(1. + d_disp[:, :, 100], norm=colors.LogNorm())
ax[3].imshow(1. + dmap[:, :, 100], norm=colors.LogNorm())
pl.show()
d_rec = d_disp - d_shift
return d_rec, d_disp, d_shift, pos_disp, pos_shift
elif rect_type == 'ZA_displaced':
return
else:
raise Exception
"""
