def prepare_scan(self, label=None):
"""
Prepare scan specific parameters and create a label if necessary
"""
if label is None:
label = 'Scan%05d' % self.label_idx
self.label_idx += 1
try:
# return the scan if already prepared
return self.scans[label]
except KeyError:
# get standarad parameters
# Create a dictionary specific for the scan.
scan = u.Param()
self.scans[label] = scan
scan.label = label
# make a copy of model ditionary
scan.pars = self.p.copy(depth=3)
# Look for a scan-specific entry in the input parameters
scan_specific_parameters = self.scans_pars.get(label, None)
scan.pars.update(scan_specific_parameters, Replace=False)
# prepare the tags
t = scan.pars.tags
if str(t) == t:
scan.pars.tags = [tag.strip().lower() for tag in t.split(',')]
# also create positions
scan.pos_theory = xy.from_pars(scan.pars.xy)
return scan
def prepare_scan(self,label=None):
"""
Prepare scan specific parameters and create a label if necessary
"""
if label is None:
label = 'Scan%05d' % self.label_idx
self.label_idx +=1
try:
# return the scan if already prepared
return self.scans[label]
except KeyError:
# get standarad parameters
# Create a dictionary specific for the scan.
scan = u.Param()
self.scans[label] = scan
scan.label = label
# make a copy of model ditionary
scan.pars = self.p.copy(depth=3)
# Look for a scan-specific entry in the input parameters
scan_specific_parameters = self.scans_pars.get(label, None)
scan.pars.update(scan_specific_parameters,Replace=False)
# prepare the tags
t = scan.pars.tags
if str(t)==t:
scan.pars.tags = [tag.strip().lower() for tag in t.split(',')]
# also create positions
scan.pos_theory = xy.from_pars(scan.pars.xy)
return scan
def new_data(self):
"""
Get all new diffraction patterns and create all views and pods
accordingly.
"""
parallel.barrier()
# Nothing to do if there are no new data.
if not self.ptycho.datasource.data_available: return 'ok'
logger.info('Processing new data.')
used_scans = []
not_initialized = []
for dp in self.ptycho.datasource.feed_data():
"""
A dp (data package) contains the following:
common : dict or Param
Meta information common to all datapoints in the
data package. Variable names need to be consistent with those
in the rest of ptypy package.
(TODO further description)
Important info:
------------------------
shape : (tuple or array)
expected frame shape
label : (string)
a string signifying to which scan this package belongs
iterable : An iterable structure that yields for each iteration
a dict with the following fields:
data : (np.2darray, float)
diffraction data
In MPI case, data can be None if distributed to other nodes
mask : (np.2darray, bool)
masked out areas in diffraction data array
index : (int)
diffraction datapoint index in scan
position : (tuple or array)
scan position
"""
meta = dp['common']
label = meta['label']
# we expect a string for the label.
assert label == str(label)
used_scans.append(label)
logger.info('Importing data from %s as scan %s.' %
(meta['label_original'], label))
# prepare scan dictionary or dig up the already prepared one
scan = self.prepare_scan(label)
scan.meta = meta
# empty buffer
scan.iterable = []
# prepare the scan geometry if not already done.
if scan.get('geometries') is None:
# ok now that we have meta we can check if the geometry fits
scan.geometries = []
geo = scan.pars.geometry
for key in ['lam', 'energy', 'psize_det', 'z', 'prop_type']:
if geo[key] is None or scan.pars.if_conflict_use_meta:
mk = scan.meta.get(key)
if mk is not None: # None existing key or None values in meta dict are treated alike
geo[key] = mk
for ii, fac in enumerate(self.p.coherence.energies):
geoID = geometry.Geo._PREFIX + '%02d' % ii + label
g = geometry.Geo(self.ptycho, geoID, pars=geo)
# now we fix the sample pixel size, This will make the frame size adapt
g.p.psize_sam_is_fix = True
g.energy *= fac
scan.geometries.append(g)
# create a buffer
scan.iterable = []
scan.diff_views = []
scan.mask_views = []
# Remember the order in which these scans were fed the manager
self.scan_labels.append(label)
# Remember also that these new scans are probably not initialized yet
not_initialized.append(label)
# Buffer incoming data and evaluate if we got Nones in data
for dct in dp['iterable']:
dct['active'] = dct['data'] is not None
scan.iterable.append(dct)
# Ok data transmission is over for now.
# Lets see what scans have received data and create the views for those
for label in used_scans:
# get scan Param
scan = self.scans[label]
# pick one of the geometries for calculating the frame shape
geo = scan.geometries[0]
sh = np.array(scan.meta.get('shape', geo.N))
# Storage generation if not already existing
if scan.get('diff') is None:
# this scan is brand new so we create storages for it
scan.diff = self.ptycho.diff.new_storage(shape=(1, sh[-2],
sh[-1]),
psize=geo.psize_det,
padonly=True,
layermap=None)
old_diff_views = []
old_diff_layers = []
else:
# ok storage exists already. Views most likely also. Let's do some analysis and deactivate the old views
old_diff_views = self.ptycho.diff.views_in_storage(
scan.diff, active=False)
old_diff_layers = []
for v in old_diff_views:
old_diff_layers.append(v.layer)
#v.active = False
# same for mask
if scan.get('mask') is None:
scan.mask = self.ptycho.mask.new_storage(shape=(1, sh[-2],
sh[-1]),
psize=geo.psize_det,
padonly=True,
layermap=None)
old_mask_views = []
old_mask_layers = []
else:
old_mask_views = self.ptycho.mask.views_in_storage(
scan.mask, active=False)
old_mask_layers = []
for v in old_mask_views:
old_mask_layers.append(v.layer)
#v.active = False
# Prepare for View genereation
AR_diff_base = DEFAULT_ACCESSRULE.copy()
AR_diff_base.shape = geo.N #None
AR_diff_base.coord = 0.0
AR_diff_base.psize = geo.psize_det
AR_mask_base = AR_diff_base.copy()
AR_diff_base.storageID = scan.diff.ID
AR_mask_base.storageID = scan.mask.ID
diff_views = []
mask_views = []
positions = []
positions_theory = xy.from_pars(scan.pars.xy)
for dct in scan.iterable:
index = dct['index']
active = dct['active']
#tpos = positions_theory[index]
if not scan.pars.if_conflict_use_meta and positions_theory is not None:
pos = positions_theory[index]
else:
pos = dct.get('position') #,positions_theory[index])
AR_diff = AR_diff_base #.copy()
AR_mask = AR_mask_base #.copy()
AR_diff.layer = index
AR_mask.layer = index
# check here: is there already a view to this layer? Is it active?
try:
old_view = old_diff_views[old_diff_layers.index(index)]
old_active = old_view.active
old_view.active = active
# also set this for the attached pods' exit views
for pod in old_view.pods.itervalues():
pod.ex_view.active = active
logger.debug(
'Diff view with layer/index %s of scan %s exists. \nSetting view active state from %s to %s'
% (index, label, old_active, active))
except ValueError:
v = View(self.ptycho.diff,
accessrule=AR_diff,
active=active)
diff_views.append(v)
logger.debug(
'Diff view with layer/index %s of scan %s does not exist. \nCreating view with ID %s and set active state to %s'
% (index, label, v.ID, active))
# append position also
positions.append(pos)
try:
old_view = old_mask_views[old_mask_layers.index(index)]
old_view.active = active
except ValueError:
v = View(self.ptycho.mask,
accessrule=AR_mask,
active=active)
mask_views.append(v)
# so now we should have the right views to this storages. Let them reformat()
# that will create the right sizes and the datalist access
scan.diff.reformat()
scan.mask.reformat()
#parallel.barrier()
#print 'this'
for dct in scan.iterable:
parallel.barrier()
if not dct['active']:
continue
data = dct['data']
idx = dct['index']
scan.diff.datalist[idx][:] = data #.astype(scan.diff.dtype)
scan.mask.datalist[idx][:] = dct.get(
'mask', np.ones_like(data)) #.astype(scan.mask.dtype)
#del dct['data']
#print 'hallo'
scan.diff.nlayers = parallel.MPImax(scan.diff.layermap) + 1
scan.mask.nlayers = parallel.MPImax(scan.mask.layermap) + 1
# empty iterable buffer
#scan.iterable = []
scan.new_positions = positions
scan.new_diff_views = diff_views
scan.new_mask_views = mask_views
scan.diff_views += diff_views
scan.mask_views += mask_views
self._update_stats(scan)
# Create PODs
new_pods, new_probe_ids, new_object_ids = self._create_pods(used_scans)
logger.info(
'Process %d created %d new PODs, %d new probes and %d new objects.'
% (parallel.rank, len(new_pods), len(new_probe_ids),
len(new_object_ids)),
extra={'allprocesses': True})
# Adjust storages
self.ptycho.probe.reformat(True)
self.ptycho.obj.reformat(True)
self.ptycho.exit.reformat()
self._initialize_probe(new_probe_ids)
self._initialize_object(new_object_ids)
self._initialize_exit(new_pods)
def new_data(self):
"""
Get all new diffraction patterns and create all views and pods
accordingly.
"""
parallel.barrier()
# Nothing to do if there are no new data.
if not self.ptycho.datasource.data_available: return 'ok'
logger.info('Processing new data.')
used_scans = []
not_initialized = []
for dp in self.ptycho.datasource.feed_data():
"""
A dp (data package) contains the following:
common : dict or Param
Meta information common to all datapoints in the
data package. Variable names need to be consistent with those
in the rest of ptypy package.
(TODO further description)
Important info:
------------------------
shape : (tuple or array)
expected frame shape
label : (string)
a string signifying to which scan this package belongs
iterable : An iterable structure that yields for each iteration
a dict with the following fields:
data : (np.2darray, float)
diffraction data
In MPI case, data can be None if distributed to other nodes
mask : (np.2darray, bool)
masked out areas in diffraction data array
index : (int)
diffraction datapoint index in scan
position : (tuple or array)
scan position
"""
meta = dp['common']
label = meta['label']
# we expect a string for the label.
assert label == str(label)
used_scans.append(label)
logger.info('Importing data from %s as scan %s.' % (meta['label_original'],label))
# prepare scan dictionary or dig up the already prepared one
scan = self.prepare_scan(label)
scan.meta = meta
# empty buffer
scan.iterable = []
# prepare the scan geometry if not already done.
if scan.get('geometries') is None:
# ok now that we have meta we can check if the geometry fits
scan.geometries=[]
geo = scan.pars.geometry
for key in ['lam','energy','psize_det','z','prop_type']:
if geo[key] is None or scan.pars.if_conflict_use_meta:
mk = scan.meta.get(key)
if mk is not None: # None existing key or None values in meta dict are treated alike
geo[key] = mk
for ii,fac in enumerate(self.p.coherence.energies):
geoID = geometry.Geo._PREFIX + '%02d' %ii + label
g = geometry.Geo(self.ptycho,geoID,pars=geo)
# now we fix the sample pixel size, This will make the frame size adapt
g.p.psize_sam_is_fix = True
g.energy *= fac
scan.geometries.append(g)
# create a buffer
scan.iterable = []
scan.diff_views = []
scan.mask_views = []
# Remember the order in which these scans were fed the manager
self.scan_labels.append(label)
# Remember also that these new scans are probably not initialized yet
not_initialized.append(label)
# Buffer incoming data and evaluate if we got Nones in data
for dct in dp['iterable']:
dct['active'] = dct['data'] is not None
scan.iterable.append(dct)
# Ok data transmission is over for now.
# Lets see what scans have received data and create the views for those
for label in used_scans:
# get scan Param
scan = self.scans[label]
# pick one of the geometries for calculating the frame shape
geo = scan.geometries[0]
sh = np.array(scan.meta.get('shape',geo.N))
# Storage generation if not already existing
if scan.get('diff') is None:
# this scan is brand new so we create storages for it
scan.diff = self.ptycho.diff.new_storage(shape=(1,sh[-2],sh[-1]), psize=geo.psize_det, padonly=True, layermap=None)
old_diff_views = []
old_diff_layers= []
else:
# ok storage exists already. Views most likely also. Let's do some analysis and deactivate the old views
old_diff_views = self.ptycho.diff.views_in_storage(scan.diff,active=False)
old_diff_layers =[]
for v in old_diff_views:
old_diff_layers.append(v.layer)
#v.active = False
# same for mask
if scan.get('mask') is None:
scan.mask = self.ptycho.mask.new_storage(shape=(1,sh[-2],sh[-1]), psize=geo.psize_det, padonly=True, layermap=None)
old_mask_views = []
old_mask_layers= []
else:
old_mask_views = self.ptycho.mask.views_in_storage(scan.mask,active=False)
old_mask_layers =[]
for v in old_mask_views:
old_mask_layers.append(v.layer)
#v.active = False
# Prepare for View genereation
AR_diff_base = DEFAULT_ACCESSRULE.copy()
AR_diff_base.shape = geo.N #None
AR_diff_base.coord = 0.0
AR_diff_base.psize = geo.psize_det
AR_mask_base = AR_diff_base.copy()
AR_diff_base.storageID = scan.diff.ID
AR_mask_base.storageID = scan.mask.ID
diff_views = []
mask_views = []
positions = []
positions_theory = xy.from_pars(scan.pars.xy)
for dct in scan.iterable:
index = dct['index']
active = dct['active']
#tpos = positions_theory[index]
if not scan.pars.if_conflict_use_meta and positions_theory is not None:
pos = positions_theory[index]
else:
pos = dct.get('position') #,positions_theory[index])
AR_diff = AR_diff_base #.copy()
AR_mask = AR_mask_base #.copy()
AR_diff.layer = index
AR_mask.layer = index
# check here: is there already a view to this layer? Is it active?
try:
old_view = old_diff_views[old_diff_layers.index(index)]
old_active = old_view.active
old_view.active = active
# also set this for the attached pods' exit views
for pod in old_view.pods.itervalues():
pod.ex_view.active = active
logger.debug('Diff view with layer/index %s of scan %s exists. \nSetting view active state from %s to %s' % (index,label,old_active,active) )
except ValueError:
v = View(self.ptycho.diff, accessrule=AR_diff, active=active)
diff_views.append(v)
logger.debug('Diff view with layer/index %s of scan %s does not exist. \nCreating view with ID %s and set active state to %s' % (index,label,v.ID,active))
# append position also
positions.append(pos)
try:
old_view = old_mask_views[old_mask_layers.index(index)]
old_view.active = active
except ValueError:
v = View(self.ptycho.mask, accessrule=AR_mask, active=active)
mask_views.append(v)
# so now we should have the right views to this storages. Let them reformat()
# that will create the right sizes and the datalist access
scan.diff.reformat()
scan.mask.reformat()
#parallel.barrier()
#print 'this'
for dct in scan.iterable:
parallel.barrier()
if not dct['active']:
continue
data = dct['data']
idx = dct['index']
scan.diff.datalist[idx][:]=data #.astype(scan.diff.dtype)
scan.mask.datalist[idx][:]=dct.get('mask',np.ones_like(data)) #.astype(scan.mask.dtype)
#del dct['data']
#print 'hallo'
scan.diff.nlayers = parallel.MPImax(scan.diff.layermap)+1
scan.mask.nlayers = parallel.MPImax(scan.mask.layermap)+1
# empty iterable buffer
#scan.iterable = []
scan.new_positions = positions
scan.new_diff_views = diff_views
scan.new_mask_views = mask_views
scan.diff_views += diff_views
scan.mask_views += mask_views
self._update_stats(scan)
# Create PODs
new_pods, new_probe_ids, new_object_ids = self._create_pods(used_scans)
logger.info('Process %d created %d new PODs, %d new probes and %d new objects.' % (
parallel.rank,len(new_pods), len(new_probe_ids), len(new_object_ids)),extra={'allprocesses':True})
# Adjust storages
self.ptycho.probe.reformat(True)
self.ptycho.obj.reformat(True)
self.ptycho.exit.reformat()
self._initialize_probe(new_probe_ids)
self._initialize_object(new_object_ids)
self._initialize_exit(new_pods)
