def simulate_basic_with_pods(ptypy_pars_tree=None, sim_pars=None, save=False):
"""
Basic Simulation
"""
p = DEFAULT.copy()
ppt = ptypy_pars_tree
if ppt is not None:
p.update(ppt.get('simulation'))
if sim_pars is not None:
p.update(sim_pars)
P = ptypy.core.Ptycho(ppt, level=1)
# make a data source that has is basicaly empty
P.datasource = make_sim_datasource(P.modelm, p.pos_drift, p.pos_scale,
p.pos_noise)
P.modelm.new_data()
u.parallel.barrier()
P.print_stats()
# Propagate and apply psf for simulationg partial coherence (if not done so with modes)
for name, pod in P.pods.iteritems():
if not pod.active: continue
pod.diff += conv(u.abs2(pod.fw(pod.exit)), p.psf)
# Filter storage data similar to a detector.
if p.detector is not None:
Det = Detector(p.detector)
save_dtype = Det.dtype
for ID, Sdiff in P.diff.S.items():
# get the mask storage too although their content will be overriden
Smask = P.mask.S[ID]
dat, mask = Det.filter(Sdiff.data)
if p.frame_size is not None:
hplanes = u.expect2(p.frame_size) - u.expect2(dat.shape[-2:])
dat = u.crop_pad(dat, hplanes, axes=[-2, -1]).astype(dat.dtype)
mask = u.crop_pad(mask, hplanes, axes=[-2,
-1]).astype(mask.dtype)
Sdiff.fill(dat)
Smask.fill(mask)
else:
save_dtype = None
if save:
P.modelm.collect_diff_mask_meta(save=save, dtype=save_dtype)
u.parallel.barrier()
return P
def simulate_basic_with_pods(ptypy_pars_tree=None,sim_pars=None,save=False):
"""
Basic Simulation
"""
p = DEFAULT.copy()
ppt = ptypy_pars_tree
if ppt is not None:
p.update(ppt.get('simulation'))
if sim_pars is not None:
p.update(sim_pars)
P = ptypy.core.Ptycho(ppt,level=1)
# make a data source that has is basicaly empty
P.datasource = make_sim_datasource(P.modelm,p.pos_drift,p.pos_scale,p.pos_noise)
P.modelm.new_data()
u.parallel.barrier()
P.print_stats()
# Propagate and apply psf for simulationg partial coherence (if not done so with modes)
for name,pod in P.pods.iteritems():
if not pod.active: continue
pod.diff += conv(u.abs2(pod.fw(pod.exit)),p.psf)
# Filter storage data similar to a detector.
if p.detector is not None:
Det = Detector(p.detector)
save_dtype = Det.dtype
for ID,Sdiff in P.diff.S.items():
# get the mask storage too although their content will be overriden
Smask = P.mask.S[ID]
dat, mask = Det.filter(Sdiff.data)
if p.frame_size is not None:
hplanes = u.expect2(p.frame_size)-u.expect2(dat.shape[-2:])
dat = u.crop_pad(dat,hplanes,axes=[-2,-1]).astype(dat.dtype)
mask = u.crop_pad(mask,hplanes,axes=[-2,-1]).astype(mask.dtype)
Sdiff.fill(dat)
Smask.fill(mask)
else:
save_dtype = None
if save:
P.modelm.collect_diff_mask_meta(save=save,dtype=save_dtype)
u.parallel.barrier()
return P
def bw(self, W):
"""
computes backward propagated wavefront of input wavefront W
"""
# check for cropping
if (self.crop_pad != 0).any():
w = u.crop_pad(W, self.crop_pad)
else:
w = W
# compute transform
w = self.isc * self.post_ifft * self.ifft(self.pre_ifft * w)
# cropping again
if (self.crop_pad != 0).any():
return u.crop_pad(w, -self.crop_pad)
else:
return w
def bw(self,W):
"""
computes backward propagated wavefront of input wavefront W
"""
# check for cropping
if (self.crop_pad != 0).any() :
w = u.crop_pad(W,self.crop_pad)
else:
w = W
# compute transform
w = self.isc * self.post_ifft * self.ifft(self.pre_ifft * w)
# cropping again
if (self.crop_pad != 0).any() :
return u.crop_pad(w,-self.crop_pad)
else:
return w
def plot_storage(self,storage,title="",typ='obj'):
# get plotting paramters
pp=storage.plot
axes=self.axes_list[pp.axes_index]
weight=pp.get('weight')
# plotting mask for ramp removal
sh=storage.data.shape[-2:]
x,y=np.indices(sh)-np.reshape(np.array(sh)//2,(len(sh),)+len(sh)*(1,))
mask= (x**2+y**2 < 0.1*min(sh)**2)
pp.mask=mask
# cropping
crop=np.array(sh)*np.array(pp.crop)//2
crop=-crop.astype(int)
data=u.crop_pad(storage.data,crop,axes=[-2,-1])
plot_mask=u.crop_pad(mask,crop,axes=[-2,-1])
for ii,ind in enumerate([(l,a) for l in pp.layers for a in pp.auto_display]):
#print ii, ind
if ii >= len(axes):
break
# get the layer
dat=data[ind[0]]
if ind[1]=='p' or ind[1]=='c':
if pp.rm_pr:
if weight is None:
ndat = U.rmphaseramp(dat, np.abs(dat) * plot_mask.astype(float))
mean_ndat = (ndat*plot_mask).sum() / plot_mask.sum()
else:
ndat = U.rmphaseramp(dat, np.abs(dat) * weight)
mean_ndat = (ndat*weight).sum() / weight.sum()
else:
ndat=dat.copy()
mean_ndat = (ndat*plot_mask).sum() / plot_mask.sum()
else:
ndat=dat.copy()
if typ=='obj':
mm = np.mean(np.abs(ndat*plot_mask)**2)
info = 'T=%.2f' % mm
else:
mm = np.sum(np.abs(ndat)**2)
info = 'P=%1.1e' % mm
if ind[1]=='c':
dat_i = U.imsave(np.flipud(ndat))
if not axes[ii].images:
axes[ii].imshow(dat_i)
self.pp.setp(axes[ii].get_xticklabels(), fontsize=8)
self.pp.setp(axes[ii].get_yticklabels(), fontsize=8)
else:
axes[ii].images[0].set_data(dat_i)
axes[ii].set_title('%s#%d (C)\n%s' % (title,ind[0],info),size=12)
continue
if ind[1]=='p':
d = np.angle(ndat / mean_ndat)
ttl = '%s#%d (P)' % (title,ind[0]) #% (ind[0],ind[1])
cmap = self.pp.get_cmap(pp.cmaps[1])
clims = pp.clims[1]
elif ind[1]=='a':
d = np.abs(ndat)
ttl = '%s#%d (M)\n%s' % (title,ind[0],info)
cmap = self.pp.get_cmap(pp.cmaps[0])
clims = pp.clims[0]
vmin = d[plot_mask].min() if clims is None else clims[0]
vmax = d[plot_mask].max() if clims is None else clims[1]
if not axes[ii].images:
axes[ii].imshow(d,vmin=vmin, vmax=vmax,cmap=cmap)
self.pp.setp(axes[ii].get_xticklabels(), fontsize=8)
self.pp.setp(axes[ii].get_yticklabels(), fontsize=8)
else:
axes[ii].images[0].set_data(d)
axes[ii].images[0].set_clim(vmin=vmin, vmax=vmax)
axes[ii].set_title(ttl,size=12)
def read(self, scan=None, **kwargs):
"""\
Read in the data
TODO: (maybe?) MPI to avoid loading all data in a single process for large scans.
"""
scan = scan if scan is not None else self.p.scan
logger.info('Processing scan number %s' % str(scan))
self.scan = self.get_nexus_file(scan)
logger.debug('Data will be read from path: %s' % self.scan)
self.exp = load(self.scan, self.nxs.frame)
try:
self.motors = load(self.scan, self.nxs.motors)
except:
self.motors = None
self.command = load(self.scan, self.nxs.command)
self.data = load(self.scan, self.nxs.frame).astype(float)
self.label = load(self.scan, self.nxs.label)[0]
if self.p.experimentID is None:
try:
experimentID = load(self.scan, self.nxs.experiment)[0]
except:
logger.debug(
'Could not find experiment ID from nexus file %s.' %
self.scan)
experimentID = os.path.split(base_path[:-1])[1]
logger.debug('experimentID: "%s" (automatically set).' %
experimentID)
else:
logger.debug('experimentID: "%s".' % self.p.experimentID)
self.experimentID = self.p.experimentID
dpsize = self.p.dpsize
ctr = self.p.ctr
sh = self.data.shape[-2:]
fullframe = False
if dpsize is None:
dpsize = sh
logger.debug(
'Full frames (%d x %d) will be saved (so no recentering).' %
(sh))
fullframe = True
self.p.dpsize = expect2(dpsize)
#data_filename = self.get_save_filename(scan_number, dpsize)
#logger.info( 'Data will be saved to %s' % data_filename)
f = self.data
if not fullframe:
# Compute center of mass
if self.mask is None:
ctr_auto = mass_center(f.sum(0))
else:
ctr_auto = mass_center(f.sum(0) * self.mask)
print ctr_auto
# Check for center position
if ctr is None:
ctr = ctr_auto
logger.debug('Using center: (%d, %d)' % (ctr[0], ctr[1]))
#elif ctr == 'inter':
#import matplotlib as mpl
#fig = mpl.pyplot.figure()
#ax = fig.add_subplot(1,1,1)
#ax.imshow(np.log(f))
#ax.set_title('Select center point (hit return to finish)')
#s = u.Multiclicks(ax, True, mode='replace')
#mpl.pyplot.show()
#s.wait_until_closed()
#ctr = np.round(np.array(s.pts[0][::-1]));
#logger.debug( 'Using center: (%d, %d) - I would have guessed it is (%d, %d)' % (ctr[0], ctr[1], ctr_auto[0], ctr_auto[1]))
else:
logger.debug(
'Using center: (%d, %d) - I would have guessed it is (%d, %d)'
% (ctr[0], ctr[1], ctr_auto[0], ctr_auto[1]))
self.dpsize = dpsize
self.ctr = np.array(ctr)
lim_inf = -np.ceil(ctr - dpsize / 2.).astype(int)
lim_sup = np.ceil(ctr + dpsize / 2.).astype(int) - np.array(sh)
hplane_list = [(lim_inf[0], lim_sup[0]), (lim_inf[1], lim_sup[1])]
logger.debug('Going from %s to %s (hplane_list = %s)' %
(str(sh), str(dpsize), str(hplane_list)))
if self.mask is not None:
self.mask = u.crop_pad(self.mask, hplane_list).astype(bool)
if self.flat is not None:
self.flat = u.crop_pad(self.flat, hplane_list, fillpar=1.)
if self.dark is not None:
self.dark = u.crop_pad(self.dark, hplane_list)
if self.data is not None:
self.data = u.crop_pad(self.data, hplane_list)
def read(self, scan=None, **kwargs):
"""\
Read in the data
TODO: (maybe?) MPI to avoid loading all data in a single process for large scans.
"""
scan=scan if scan is not None else self.p.scan
logger.info( 'Processing scan number %s' % str(scan))
self.scan = self.get_nexus_file(scan)
logger.debug( 'Data will be read from path: %s' % self.scan)
self.exp = load(self.scan,self.nxs.frame)
try:
self.motors = load(self.scan,self.nxs.motors)
except:
self.motors=None
self.command = load(self.scan,self.nxs.command)
self.data = load(self.scan, self.nxs.frame).astype(float)
self.label = load(self.scan, self.nxs.label)[0]
if self.p.experimentID is None:
try:
experimentID = load(self.scan, self.nxs.experiment)[0]
except:
logger.debug('Could not find experiment ID from nexus file %s.' % self.scan)
experimentID = os.path.split(base_path[:-1])[1]
logger.debug( 'experimentID: "%s" (automatically set).' % experimentID)
else:
logger.debug( 'experimentID: "%s".' % self.p.experimentID)
self.experimentID = self.p.experimentID
dpsize = self.p.dpsize
ctr = self.p.ctr
sh = self.data.shape[-2:]
fullframe = False
if dpsize is None:
dpsize = sh
logger.debug( 'Full frames (%d x %d) will be saved (so no recentering).' % (sh))
fullframe = True
self.p.dpsize = expect2(dpsize)
#data_filename = self.get_save_filename(scan_number, dpsize)
#logger.info( 'Data will be saved to %s' % data_filename)
f = self.data
if not fullframe:
# Compute center of mass
if self.mask is None:
ctr_auto = mass_center(f.sum(0))
else:
ctr_auto = mass_center(f.sum(0)*self.mask)
print ctr_auto
# Check for center position
if ctr is None:
ctr = ctr_auto
logger.debug( 'Using center: (%d, %d)' % (ctr[0],ctr[1]))
#elif ctr == 'inter':
#import matplotlib as mpl
#fig = mpl.pyplot.figure()
#ax = fig.add_subplot(1,1,1)
#ax.imshow(np.log(f))
#ax.set_title('Select center point (hit return to finish)')
#s = u.Multiclicks(ax, True, mode='replace')
#mpl.pyplot.show()
#s.wait_until_closed()
#ctr = np.round(np.array(s.pts[0][::-1]));
#logger.debug( 'Using center: (%d, %d) - I would have guessed it is (%d, %d)' % (ctr[0], ctr[1], ctr_auto[0], ctr_auto[1]))
else:
logger.debug( 'Using center: (%d, %d) - I would have guessed it is (%d, %d)' % (ctr[0], ctr[1], ctr_auto[0], ctr_auto[1]))
self.dpsize = dpsize
self.ctr = np.array(ctr)
lim_inf = -np.ceil(ctr - dpsize/2.).astype(int)
lim_sup = np.ceil(ctr + dpsize/2.).astype(int) - np.array(sh)
hplane_list = [(lim_inf[0], lim_sup[0]), (lim_inf[1], lim_sup[1])]
logger.debug( 'Going from %s to %s (hplane_list = %s)' % (str(sh), str(dpsize), str(hplane_list)))
if self.mask is not None: self.mask = u.crop_pad(self.mask, hplane_list).astype(bool)
if self.flat is not None: self.flat = u.crop_pad(self.flat, hplane_list,fillpar=1.)
if self.dark is not None: self.dark = u.crop_pad(self.dark, hplane_list)
if self.data is not None: self.data = u.crop_pad(self.data, hplane_list)
