def scores(results):
tmp = copy.copy(results)
scores = []
for k, v in tmp.iteritems():
ind = analyze.nearest(v[5], v[4])
scores.append((v[0], v[1], v[2], v[3],
np.median(np.sqrt(
((v[4][ind] - v[5])**2).sum(axis=-1))),
float(len(v[5])) / len(v[4])))
return scores
def crb_compare(state0,
samples0,
state1,
samples1,
crb0=None,
crb1=None,
zlayer=None,
xlayer=None):
"""
To run, do:
s,h = pickle...
s1,h1 = pickle...
i.e. /media/scratch/bamf/vacancy/vacancy_zoom-1.tif_t002.tif-featured-v2.pkl
i.e. /media/scratch/bamf/frozen-particles/0.tif-featured-full.pkl
crb0 = diag_crb_particles(s); crb1 = diag_crb_particles(s1)
crb_compare(s,h[-25:],s1,h1[-25:], crb0, crb1)
"""
s0 = state0
s1 = state1
h0 = np.array(samples0)
h1 = np.array(samples1)
slicez = zlayer or s0.image.shape[0] // 2
slicex = xlayer or s0.image.shape[2] // 2
slicer1 = np.s_[slicez, s0.pad:-s0.pad, s0.pad:-s0.pad]
slicer2 = np.s_[s0.pad:-s0.pad, s0.pad:-s0.pad, slicex]
center = (slicez, s0.image.shape[1] // 2, slicex)
mu0 = h0.mean(axis=0)
mu1 = h1.mean(axis=0)
std0 = h0.std(axis=0)
std1 = h1.std(axis=0)
mask0 = (s0.state[s0.b_typ] == 1.) & (analyze.trim_box(
s0, mu0[s0.b_pos].reshape(-1, 3)))
mask1 = (s1.state[s1.b_typ] == 1.) & (analyze.trim_box(
s1, mu1[s1.b_pos].reshape(-1, 3)))
active0 = np.arange(s0.N)[mask0] #s0.state[s0.b_typ]==1.]
active1 = np.arange(s1.N)[mask1] #s1.state[s1.b_typ]==1.]
pos0 = mu0[s0.b_pos].reshape(-1, 3)[active0]
pos1 = mu1[s1.b_pos].reshape(-1, 3)[active1]
rad0 = mu0[s0.b_rad][active0]
rad1 = mu1[s1.b_rad][active1]
link = analyze.nearest(pos0, pos1)
dpos = pos0 - pos1[link]
drad = rad0 - rad1[link]
drift = dpos.mean(axis=0)
log.info('drift {}'.format(drift))
dpos -= drift
fig = pl.figure(figsize=(24, 10))
#=========================================================================
#=========================================================================
gs0 = ImageGrid(fig,
rect=[0.02, 0.4, 0.4, 0.60],
nrows_ncols=(2, 3),
axes_pad=0.1)
lbl(gs0[0], 'A')
for i, slicer in enumerate([slicer1, slicer2]):
ax_real = gs0[3 * i + 0]
ax_fake = gs0[3 * i + 1]
ax_diff = gs0[3 * i + 2]
diff0 = s0.get_model_image() - s0.image
diff1 = s1.get_model_image() - s1.image
a = (s0.image - s1.image)
b = (s0.get_model_image() - s1.get_model_image())
c = (diff0 - diff1)
ptp = 0.7 * max([np.abs(a).max(), np.abs(b).max(), np.abs(c).max()])
cmap = pl.cm.RdBu_r
ax_real.imshow(a[slicer], cmap=cmap, vmin=-ptp, vmax=ptp)
ax_real.set_xticks([])
ax_real.set_yticks([])
ax_fake.imshow(b[slicer], cmap=cmap, vmin=-ptp, vmax=ptp)
ax_fake.set_xticks([])
ax_fake.set_yticks([])
ax_diff.imshow(c[slicer], cmap=cmap, vmin=-ptp,
vmax=ptp) #cmap=pl.cm.RdBu, vmin=-1.0, vmax=1.0)
ax_diff.set_xticks([])
ax_diff.set_yticks([])
if i == 0:
ax_real.set_title(r"$\Delta$ Confocal image", fontsize=24)
ax_fake.set_title(r"$\Delta$ Model image", fontsize=24)
ax_diff.set_title(r"$\Delta$ Difference", fontsize=24)
ax_real.set_ylabel('x-y')
else:
ax_real.set_ylabel('x-z')
#=========================================================================
#=========================================================================
gs1 = GridSpec(1,
3,
left=0.05,
bottom=0.125,
right=0.42,
top=0.37,
wspace=0.15,
hspace=0.05)
spos0 = std0[s0.b_pos].reshape(-1, 3)[active0]
spos1 = std1[s1.b_pos].reshape(-1, 3)[active1]
srad0 = std0[s0.b_rad][active0]
srad1 = std1[s1.b_rad][active1]
def hist(ax, vals, bins, *args, **kwargs):
y, x = np.histogram(vals, bins=bins)
x = (x[1:] + x[:-1]) / 2
y /= len(vals)
ax.plot(x, y, *args, **kwargs)
def pp(ind, tarr, tsim, tcrb, var='x'):
bins = 10**np.linspace(-3, 0.0, 30)
bin2 = 10**np.linspace(-3, 0.0, 100)
bins = np.linspace(0.0, 0.2, 30)
bin2 = np.linspace(0.0, 0.2, 100)
xlim = (0.0, 0.12)
#xlim = (1e-3, 1e0)
ylim = (1e-2, 30)
ticks = ticker.FuncFormatter(
lambda x, pos: '{:0.0f}'.format(np.log10(x)))
scaler = lambda x: x #np.log10(x)
ax_crb = pl.subplot(gs1[0, ind])
ax_crb.hist(scaler(np.abs(tarr)),
bins=bins,
normed=True,
alpha=0.7,
histtype='stepfilled',
lw=1)
ax_crb.hist(scaler(np.abs(tcrb)).ravel(),
bins=bin2,
normed=True,
alpha=1.0,
histtype='step',
ls='solid',
lw=1.5,
color='k')
ax_crb.hist(scaler(np.abs(tsim).ravel()),
bins=bin2,
normed=True,
alpha=1.0,
histtype='step',
lw=3)
ax_crb.set_xlabel(r"$\Delta = |%s(t_1) - %s(t_0)|$" % (var, var),
fontsize=24)
#ax_crb.semilogx()
ax_crb.set_xlim(xlim)
#ax_crb.semilogy()
#ax_crb.set_ylim(ylim)
#ax_crb.xaxis.set_major_formatter(ticks)
ax_crb.grid(b=False, which='both', axis='both')
if ind == 0:
lbl(ax_crb, 'B')
ax_crb.set_ylabel(r"$P(\Delta)$")
else:
ax_crb.set_yticks([])
ax_crb.locator_params(axis='x', nbins=3)
f, g = 1.5, 1.95
sim = f * sim_crb_diff(spos0[:, 1], spos1[:, 1][link])
crb = g * sim_crb_diff(crb0[0][:, 1][active0], crb1[0][:,
1][active1][link])
pp(0, dpos[:, 1], sim, crb, 'x')
sim = f * sim_crb_diff(spos0[:, 0], spos1[:, 0][link])
crb = g * sim_crb_diff(crb0[0][:, 0][active0], crb1[0][:,
0][active1][link])
pp(1, dpos[:, 0], sim, crb, 'z')
sim = f * sim_crb_diff(srad0, srad1[link])
crb = g * sim_crb_diff(crb0[1][active0], crb1[1][active1][link])
pp(2, drad, sim, crb, 'a')
#ax_crb_r.locator_params(axis='both', nbins=3)
#gs1.tight_layout(fig)
#=========================================================================
#=========================================================================
gs2 = GridSpec(2,
2,
left=0.48,
bottom=0.12,
right=0.99,
top=0.95,
wspace=0.35,
hspace=0.35)
ax_hist = pl.subplot(gs2[0, 0])
ax_hist.hist(std0[s0.b_pos],
bins=np.logspace(-3.0, 0, 50),
alpha=0.7,
label='POS',
histtype='stepfilled')
ax_hist.hist(std0[s0.b_rad],
bins=np.logspace(-3.0, 0, 50),
alpha=0.7,
label='RAD',
histtype='stepfilled')
ax_hist.set_xlim((10**-3.0, 1))
ax_hist.semilogx()
ax_hist.set_xlabel(r"$\bar{\sigma}$")
ax_hist.set_ylabel(r"$P(\bar{\sigma})$")
ax_hist.legend(loc='upper right')
lbl(ax_hist, 'C')
imdiff = ((s0.get_model_image() - s0.image) /
s0._sigma_field)[s0.image_mask == 1.].ravel()
mu = imdiff.mean()
#sig = imdiff.std()
#print mu, sig
x = np.linspace(-5, 5, 10000)
ax_diff = pl.subplot(gs2[0, 1])
ax_diff.plot(x,
1.0 / np.sqrt(2 * np.pi) * np.exp(-(x - mu)**2 / 2),
'-',
alpha=0.7,
color='k',
lw=2)
ax_diff.hist(imdiff, bins=1000, histtype='step', alpha=0.7, normed=True)
ax_diff.semilogy()
ax_diff.set_ylabel(r"$P(\delta)$")
ax_diff.set_xlabel(r"$\delta = (M_i - d_i)/\sigma_i$")
ax_diff.locator_params(axis='x', nbins=5)
ax_diff.grid(b=False, which='minor', axis='y')
ax_diff.set_xlim(-5, 5)
ax_diff.set_ylim(1e-4, 1e0)
lbl(ax_diff, 'D')
pos = mu0[s0.b_pos].reshape(-1, 3)
rad = mu0[s0.b_rad]
mask = analyze.trim_box(s0, pos)
pos = pos[mask]
rad = rad[mask]
gx, gy = analyze.gofr(pos,
rad,
mu0[s0.b_zscale][0],
resolution=5e-2,
mask_start=0.5)
mask = gx < 5
gx = gx[mask]
gy = gy[mask]
ax_gofr = pl.subplot(gs2[1, 0])
ax_gofr.plot(gx, gy, '-', lw=1)
ax_gofr.set_xlabel(r"$r/a$")
ax_gofr.set_ylabel(r"$g(r/a)$")
ax_gofr.locator_params(axis='both', nbins=5)
#ax_gofr.semilogy()
lbl(ax_gofr, 'E')
gx, gy = analyze.gofr(pos, rad, mu0[s0.b_zscale][0], method='surface')
mask = gx < 5
gx = gx[mask]
gy = gy[mask]
gy[gy <= 0.] = gy[gy > 0].min()
ax_gofrs = pl.subplot(gs2[1, 1])
ax_gofrs.plot(gx, gy, '-', lw=1)
ax_gofrs.set_xlabel(r"$r/a$")
ax_gofrs.set_ylabel(r"$g_{\rm{surface}}(r/a)$")
ax_gofrs.locator_params(axis='both', nbins=5)
ax_gofrs.grid(b=False, which='minor', axis='y')
#ax_gofrs.semilogy()
lbl(ax_gofrs, 'F')
ylim = ax_gofrs.get_ylim()
ax_gofrs.set_ylim(gy.min(), ylim[1])
def crb_rad(state0, samples0, state1, samples1, crb0, crb1):
s0 = state0
s1 = state1
h0 = np.array(samples0)
h1 = np.array(samples1)
mu0 = h0.mean(axis=0)
mu1 = h1.mean(axis=0)
std0 = h0.std(axis=0)
std1 = h1.std(axis=0)
mask0 = (s0.state[s0.b_typ] == 1.) & (analyze.trim_box(
s0, mu0[s0.b_pos].reshape(-1, 3)))
mask1 = (s1.state[s1.b_typ] == 1.) & (analyze.trim_box(
s1, mu1[s1.b_pos].reshape(-1, 3)))
active0 = np.arange(s0.N)[mask0] #s0.state[s0.b_typ]==1.]
active1 = np.arange(s1.N)[mask1] #s1.state[s1.b_typ]==1.]
pos0 = mu0[s0.b_pos].reshape(-1, 3)[active0]
pos1 = mu1[s1.b_pos].reshape(-1, 3)[active1]
rad0 = mu0[s0.b_rad][active0]
rad1 = mu1[s1.b_rad][active1]
link = analyze.nearest(pos0, pos1)
dpos = pos0 - pos1[link]
drad = rad0 - rad1[link]
spos0 = std0[s0.b_pos].reshape(-1, 3)[active0]
spos1 = std1[s1.b_pos].reshape(-1, 3)[active1]
srad0 = std0[s0.b_rad][active0]
srad1 = std1[s1.b_rad][active1]
def pp(ax, tarr, tsim, tcrb, var='x'):
bins = 10**np.linspace(-3, 0.0, 30)
bin2 = 10**np.linspace(-3, 0.0, 100)
bins = np.linspace(0.0, 0.1, 30)
bin2 = np.linspace(0.0, 0.1, 100)
xlim = (0, 0.1)
#xlim = (1e-3, 1e0)
ylim = (1e-2, 30)
ticks = ticker.FuncFormatter(
lambda x, pos: '{:0.0f}'.format(np.log10(x)))
scaler = lambda x: x #np.log10(x)
ax_crb = ax
ax_crb.hist(scaler(np.abs(tarr)),
bins=bins,
normed=True,
alpha=0.7,
histtype='stepfilled',
lw=1,
label='Radii differences')
y, x = np.histogram(np.abs(tcrb).ravel(), bins=bin2, normed=True)
x = (x[1:] + x[:-1]) / 2
ax_crb.step(x, y, lw=3, color='k', ls='solid', label='CRB')
y, x = np.histogram(np.abs(tsim).ravel(), bins=bin2, normed=True)
x = (x[1:] + x[:-1]) / 2
ax_crb.step(x, y, lw=3, ls='solid', label='Estimated Error')
ax_crb.set_xlabel(r"$\Delta = |%s(t_1) - %s(t_0)|$" % (var, var),
fontsize=28)
ax_crb.set_ylabel(r"$P(\Delta)$", fontsize=28)
ax_crb.set_xlim(xlim)
ax_crb.grid(b=False, which='both', axis='both')
ax_crb.legend(loc='best', ncol=1)
fig = pl.figure()
ax = pl.gca()
f, g = 1.5, 1.85
sim = f * sim_crb_diff(srad0, srad1[link])
crb = g * sim_crb_diff(crb0[1][active0], crb1[1][active1][link])
pp(ax, drad, sim, crb, 'a')