def calc_scatter_vals(coeffs,
n_strips=21,
n_proc=4,
ax_clip=AX_CLIP,
az_clip=AZ_CLIP,
ifuncs=ifuncs):
out = {'input': {}, 'corr': {}}
theta_max = 2.55e-4
thetas = np.linspace(-theta_max, theta_max, 10001) # 10001
out['theta'] = thetas
row_slice = slice(ax_clip, N_AX - ax_clip)
col_slice = slice(az_clip, N_AZ - az_clip)
# Compute the column position of axial strips
displ = displ_x_all[row_slice, col_slice]
cols = np.linspace(0, displ.shape[1], n_strips + 1).astype(int)
cols = (cols[1:] + cols[:-1]) // 2
out['cols'] = cols
print('Calculating scatter displ (input)')
theta_arcs, scatter = calc_scatter.calc_scatter(displ[:, cols],
graze_angle=1.428,
thetas=thetas,
n_proc=n_proc)
print np.std(scatter), np.sum(scatter), np.std(displ[:, cols])
figure(1)
clf()
plot(theta_arcs, scatter)
scat = out['input']
scat['img'] = displ.copy()
scat['vals'] = scatter
stats = calc_scatter_stats(theta_arcs, scatter)
scat.update(stats)
print('Calculating scatter displ (corrected)')
adj_displ_all = calc_adj_displ(ifuncs, coeffs)
adj_displ = adj_displ_all[row_slice, col_slice]
resid = adj_displ - displ
theta_arcs, scatter = calc_scatter.calc_scatter(resid[:, cols],
graze_angle=1.428,
thetas=thetas,
n_proc=n_proc)
print np.std(scatter), np.sum(scatter), np.std(displ[:, cols])
plot(theta_arcs, scatter)
scat = out['corr']
scat['img'] = adj_displ.copy()
scat['vals'] = scatter
stats = calc_scatter_stats(theta_arcs, scatter)
scat.update(stats)
return out
def calc_scatter(displ, n_strips=20, ax_clip=None, az_clip=None, n_proc=None):
"""
Calculate the scatter intensity curve for given input displacement.
:param displ: displacement image (e.g. residual or figure error)
:param n_strips: number of evenly spaced strips for scatter intensity
:param ax_clip: pixels to clip in axial direction
:param az_clip: pixels to clip in azimuthal direction
:param n_proc: number of processors to use (do not set in IPython notebook)
"""
import calc_scatter
ax_slice, az_slice = get_ax_az_slice((ax_clip, az_clip))
displ = displ[ax_slice, az_slice]
theta_max = 2.55e-4
thetas = np.linspace(-theta_max, theta_max, 10001) # 10001
cols = np.linspace(0, displ.shape[1], n_strips + 1).astype(int)
cols = (cols[1:] + cols[:-1]) // 2
if not n_proc:
logger.info('Calculating scatter intensity')
displ_cols = displ[:, cols]
thetas, scatter = calc_scatter.calc_scatter(displ_cols,
graze_angle=1.428,
thetas=thetas,
n_proc=n_proc)
if not n_proc:
logger.info('Finished calculating scatter intensity')
stats = calc_scatter_stats(thetas, scatter)
stats['theta'] = thetas
stats['scatter'] = scatter
return stats
def calc_scatter_vals(coeffs, n_strips=21, n_proc=4, ax_clip=AX_CLIP,
az_clip=AZ_CLIP, ifuncs=ifuncs):
out = {'input': {}, 'corr': {}}
theta_max = 2.55e-4
thetas = np.linspace(-theta_max, theta_max, 10001) # 10001
out['theta'] = thetas
row_slice = slice(ax_clip, N_AX - ax_clip)
col_slice = slice(az_clip, N_AZ - az_clip)
# Compute the column position of axial strips
displ = displ_x_all[row_slice, col_slice]
cols = np.linspace(0, displ.shape[1], n_strips + 1).astype(int)
cols = (cols[1:] + cols[:-1]) // 2
out['cols'] = cols
print('Calculating scatter displ (input)')
theta_arcs, scatter = calc_scatter.calc_scatter(
displ[:, cols], graze_angle=1.428, thetas=thetas, n_proc=n_proc)
print np.std(scatter), np.sum(scatter), np.std(displ[:, cols])
figure(1)
clf()
plot(theta_arcs, scatter)
scat = out['input']
scat['img'] = displ.copy()
scat['vals'] = scatter
stats = calc_scatter_stats(theta_arcs, scatter)
scat.update(stats)
print('Calculating scatter displ (corrected)')
adj_displ_all = calc_adj_displ(ifuncs, coeffs)
adj_displ = adj_displ_all[row_slice, col_slice]
resid = adj_displ - displ
theta_arcs, scatter = calc_scatter.calc_scatter(
resid[:, cols], graze_angle=1.428, thetas=thetas, n_proc=n_proc)
print np.std(scatter), np.sum(scatter), np.std(displ[:, cols])
plot(theta_arcs, scatter)
scat = out['corr']
scat['img'] = adj_displ.copy()
scat['vals'] = scatter
stats = calc_scatter_stats(theta_arcs, scatter)
scat.update(stats)
return out
def calc_scatter(self, filename=None, calc_input=True):
theta_max = 2.55e-4
self.thetas = np.linspace(-theta_max, theta_max, 10001) # 10001
# Compute the column position of axial strips
displ = self.displ['X']['img']['clip']
cols = np.linspace(0, displ.shape[1], self.n_strips + 1).astype(int)
cols = (cols[1:] + cols[:-1]) // 2
self.scatter['cols'] = cols
if calc_input:
logging.info('Calculating scatter displ (input)')
displ = self.displ['X']['img']['clip'][:, cols]
thetas, scatter = calc_scatter.calc_scatter(
displ, graze_angle=1.428, thetas=self.thetas,
n_proc=self.n_proc)
scat = self.scatter['input']
scat['img'] = displ.copy()
scat['theta'] = thetas
scat['vals'] = scatter
hpd, rmsd = calc_scatter_stats(thetas, scatter)
scat['hpd'] = hpd
scat['rmsd'] = rmsd
for corr in self.corr_axes:
logging.info('Calculating scatter displ (corrected)')
displ = self.resid['X'][corr]['img']['clip'][:, cols]
if self.piston_tilt: # Remove piston and tilt
remove_piston_tilt(displ)
thetas, scatter = calc_scatter.calc_scatter(displ,
graze_angle=1.428,
thetas=self.thetas,
n_proc=self.n_proc)
scat = self.scatter['corr'][corr]
scat['img'] = displ.copy()
scat['theta'] = thetas
scat['vals'] = scatter
hpd, rmsd = calc_scatter_stats(thetas, scatter)
scat['hpd'] = hpd
scat['rmsd'] = rmsd
def calc_scatter(self, filename=None, calc_input=True):
theta_max = 2.55e-4
self.thetas = np.linspace(-theta_max, theta_max, 10001) # 10001
# Compute the column position of axial strips
displ = self.displ['X']['img']['clip']
cols = np.linspace(0, displ.shape[1], self.n_strips + 1).astype(int)
cols = (cols[1:] + cols[:-1]) // 2
self.scatter['cols'] = cols
if calc_input:
logging.info('Calculating scatter displ (input)')
displ = self.displ['X']['img']['clip'][:, cols]
thetas, scatter = calc_scatter.calc_scatter(displ,
graze_angle=1.428,
thetas=self.thetas,
n_proc=self.n_proc)
scat = self.scatter['input']
scat['img'] = displ.copy()
scat['theta'] = thetas
scat['vals'] = scatter
stats = calc_scatter_stats(thetas, scatter)
scat.update(stats)
for corr in self.corr_axes:
logging.info('Calculating scatter displ (corrected)')
displ = self.resid['X'][corr]['img']['clip'][:, cols]
if self.piston_tilt: # Remove piston and tilt
remove_piston_tilt(displ)
thetas, scatter = calc_scatter.calc_scatter(displ,
graze_angle=1.428,
thetas=self.thetas,
n_proc=self.n_proc)
scat = self.scatter['corr'][corr]
scat['img'] = displ.copy()
scat['theta'] = thetas
scat['vals'] = scatter
stats = calc_scatter_stats(thetas, scatter)
scat.update(stats)
def calc_scatter(self, filename=None, calc_input=True):
theta_max = 2.55e-4
self.thetas = np.linspace(-theta_max, theta_max, 10001) # 10001
# Compute the column position of axial strips
displ = self.displ["X"]["img"]["clip"]
cols = np.linspace(0, displ.shape[1], self.n_strips + 1).astype(int)
cols = (cols[1:] + cols[:-1]) // 2
self.scatter["cols"] = cols
if calc_input:
logging.info("Calculating scatter displ (input)")
displ = self.displ["X"]["img"]["clip"][:, cols]
thetas, scatter = calc_scatter.calc_scatter(
displ, graze_angle=1.428, thetas=self.thetas, n_proc=self.n_proc
)
scat = self.scatter["input"]
scat["img"] = displ.copy()
scat["theta"] = thetas
scat["vals"] = scatter
stats = calc_scatter_stats(thetas, scatter)
scat.update(stats)
for corr in self.corr_axes:
logging.info("Calculating scatter displ (corrected)")
displ = self.resid["X"][corr]["img"]["clip"][:, cols]
if self.piston_tilt: # Remove piston and tilt
remove_piston_tilt(displ)
thetas, scatter = calc_scatter.calc_scatter(
displ, graze_angle=1.428, thetas=self.thetas, n_proc=self.n_proc
)
scat = self.scatter["corr"][corr]
scat["img"] = displ.copy()
scat["theta"] = thetas
scat["vals"] = scatter
stats = calc_scatter_stats(thetas, scatter)
scat.update(stats)
