def __init__(self, settings, tstep, timeunit):
self.constrain_graph = settings('ErrorCorrection', 'constrain_graph')
self.hmm_constrain = dict()
self.classifier_dirs = dict()
self.regionnames = COrderedDict()
# settings that depend whether if the channel is checked for
# error correction or not
for channel in CHANNEL_PREFIX:
rname = self._regionname(settings, channel)
if settings('ErrorCorrection', channel) and bool(rname):
self.regionnames[channel] = rname
self.hmm_constrain[channel] = self._hmm_constrain( \
settings('ErrorCorrection', '%s_graph' %channel))
_setting = '%s_classification_envpath' % channel
self.classifier_dirs[channel] = \
settings('Classification', _setting)
self.mapping_dir = settings('General', 'plate_layout')
if settings('ErrorCorrection', 'groupby_sirna'):
self.sortby = PlateMapping.SIRNA
elif settings('ErrorCorrection', 'groupby_genesymbol'):
self.sortby = PlateMapping.GENE
else:
self.sortby = PlateMapping.FILE
# timelapse in minutes
self.overwrite_timelapse = \
settings('ErrorCorrection', 'overwrite_time_lapse')
self.timeunit = timeunit
if self.overwrite_timelapse:
self.timelapse = settings('ErrorCorrection', 'timelapse')
else:
self.timelapse = tstep
self.sorting = settings('ErrorCorrection', 'enable_sorting')
if self.sorting:
try:
self.sorting_sequence = \
eval('('+settings('ErrorCorrection', 'sorting_sequence')+',)')
except SyntaxError:
raise SyntaxError(
("Error Correction: invalid Label sequence - "
"use a comma separated list of integers"))
else:
self.sorting_sequence = None
self.tmax = settings('ErrorCorrection', 'max_time')
self.ignore_tracking_branches = \
settings('ErrorCorrection', 'ignore_tracking_branches')
self.write_gallery = settings('ErrorCorrection', 'compose_galleries')
self.n_galleries = \
settings('ErrorCorrection', 'compose_galleries_sample')
self.resampling_factor = settings('ErrorCorrection',
'resampling_factor')
self.size_gallery_image = settings('ErrorCorrection',
'size_gallery_image')
def _channel_regions(self, pchannel, chid):
"""Return a dict that contains the processing channel an the region
used for feature extraction."""
regions = COrderedDict()
if chid is None:
for prefix in (CH_PRIMARY + CH_OTHER):
if self._settings.get("Classification", "merge_%s" % prefix):
regions[prefix.title()] = \
self._settings.get("Classification", "%s_%s_region"
%(CH_VIRTUAL[0], prefix))
else:
regions[pchannel.title()] = self._settings.get( \
'Classification', "%s_classification_regionname" %pchannel)
return regions
def _channel_regions(self, p_channel):
"""Return a dict of of channel region pairs according to the classifier"""
regions = COrderedDict()
if self.CHANNELS[p_channel.lower()].is_virtual():
for prefix, channel in self.CHANNELS.iteritems():
if channel.is_virtual():
continue
if self.settings.get("Classification", "merge_%s" %prefix):
regions[prefix.title()] = \
self.settings.get("Classification", "%s_%s_region"
%(self.MERGED_CHANNEL.lower(), prefix))
else:
regions[p_channel] = self.settings.get("Classification",
self._resolve_name(p_channel, 'classification_regionname'))
return regions
def __init__(self, *args, **kw):
super(MergedChannel, self).__init__(*args, **kw)
# defines channels an regions to concatenate
self._merge_regions = COrderedDict()
self._channels = None
class MergedChannel(ChannelCore):
"""Virtual or PseudoChannel which is meant to concatenate features of
other channels. It cannot perform an segmentation or other operation
on images.
"""
NAME = 'Merged'
PREFIX = NAME.lower()
_rank = 4
_is_virtual = True
def __init__(self, *args, **kw):
super(MergedChannel, self).__init__(*args, **kw)
# defines channels an regions to concatenate
self._merge_regions = COrderedDict()
self._channels = None
@property
def merge_regions(self):
return self._merge_regions
@merge_regions.setter
def merge_regions(self, regions):
"""Set channels and regions to concatenate."""
reginfo = MetaPluginManager().region_info
reginfo.names[self.NAME.lower()] = [regions.values()]
self._merge_regions.update(regions)
@merge_regions.deleter
def merged_regions(self):
del self._merge_regions
def apply_segmentation(self, channels, master="Primary"):
self._channels = channels
self._new_container(master)
def apply_features(self, *args, **kw):
"""Concatenate features of images objects of different channels"""
holder = ObjectHolder("-".join(self._merge_regions.values()))
for cname, region_name in self._merge_regions.iteritems():
channel = self._channels[cname]
holder0 = channel.get_region(region_name)
pfx = "%s_%s" %(cname, region_name)
feature_names = ["_".join((pfx, f)) for f in holder0.feature_names]
holder.cat_samples(holder0, feature_names)
removed = holder.remove_incomplete()
if len(removed) > 0:
self.logger.info("Found incomplete samples in merged channel")
self.logger.info("removed samples: %s" %",".join([str(r) for r in removed]))
self._regions[self.regkey] = holder
self.lstFeatureNames = holder.feature_names
def _new_container(self, master):
# not sure if it makes a difference to have a master
# perhaps a method to get an rgb image
# find the region of the primary channel
# it does not feel a great piece of code ...
available_regions = self._channels[master].containers.keys()
if 'primary' in available_regions:
default_region = 'primary'
elif 'primary' in [x[:len('primary')] for x in available_regions]:
default_region = filter(lambda x: x[:len('primary')]=='primary',
available_regions)[0]
else:
default_region = available_regions[0]
mcnt = self._channels[master].containers[default_region]
self.containers[self.regkey] = mcnt
@property
def regkey(self):
return self._merge_regions.values()
def meta_images(self, alpha=1.0):
"""Return a list of image, hexcolor, alpha-value tripples, which
is used for ccore.makeRGBImage method.
"""
images = list()
ccolors = dict([(c.strChannelId, False) for c in self._channels.values()])
for channel in self._channels.values():
ccolor = channel.strChannelId
if ccolor is not None and not ccolors[channel.strChannelId]:
ccolors[channel.strChannelId] = True
images.append((channel.meta_image.image,
Colors.channel_hexcolor(ccolor),
alpha))
return images
def sub_channels(self):
for schannel, region in self._merge_regions.iteritems():
yield self._channels[schannel], region
# most of the following functions are just dummy implementations to stay
# combatible to processing channels that do actually perform segmentation
def purge(self, *args, **kw):
self._channels = None
def normalize_image(self, *args, **kw):
pass
def apply_zselection(self, *args, **kw):
pass
def apply_registration(self, *args, **kw):
pass
def apply_binning(self, *args, **kw):
pass
def __init__(self, *args, **kw):
super(MergedChannel, self).__init__(*args, **kw)
# defines channels an regions to concatenate
self._merge_regions = COrderedDict()
self._channels = None
class MergedChannel(ChannelCore):
"""Virtual or PseudoChannel which is meant to concatenate features of
other channels. It cannot perform an segmentation or other operation
on images.
"""
NAME = 'Merged'
PREFIX = NAME.lower()
_rank = 4
_is_virtual = True
def __init__(self, *args, **kw):
super(MergedChannel, self).__init__(*args, **kw)
# defines channels an regions to concatenate
self._merge_regions = COrderedDict()
self._channels = None
@property
def merge_regions(self):
return self._merge_regions
@merge_regions.setter
def merge_regions(self, regions):
"""Set channels and regions to concatenate."""
reginfo = MetaPluginManager().region_info
reginfo.names[self.NAME.lower()] = [regions.values()]
self._merge_regions.update(regions)
@merge_regions.deleter
def merged_regions(self):
del self._merge_regions
def apply_segmentation(self, channels, master="Primary"):
self._channels = channels
self._new_container(master)
def apply_features(self, *args, **kw):
"""Concatenate features of images objects of different channels"""
holder = ObjectHolder("-".join(self._merge_regions.values()))
for cname, region_name in self._merge_regions.iteritems():
channel = self._channels[cname]
holder0 = channel.get_region(region_name)
pfx = "%s_%s" % (cname, region_name)
feature_names = ["_".join((pfx, f)) for f in holder0.feature_names]
holder.cat_samples(holder0, feature_names)
removed = holder.remove_incomplete()
if len(removed) > 0:
self.logger.info("Found incomplete samples in merged channel")
self.logger.info("removed samples: %s" %
",".join([str(r) for r in removed]))
self._regions[self.regkey] = holder
self.lstFeatureNames = holder.feature_names
def _new_container(self, master):
# not sure if it makes a difference to have a master
# perhaps a method to get an rgb image
# find the region of the primary channel
# it does not feel a great piece of code ...
available_regions = self._channels[master].containers.keys()
if 'primary' in available_regions:
default_region = 'primary'
elif 'primary' in [x[:len('primary')] for x in available_regions]:
default_region = filter(lambda x: x[:len('primary')] == 'primary',
available_regions)[0]
else:
default_region = available_regions[0]
mcnt = self._channels[master].containers[default_region]
self.containers[self.regkey] = mcnt
@property
def regkey(self):
return self._merge_regions.values()
def meta_images(self, alpha=1.0):
"""Return a list of image, hexcolor, alpha-value tripples, which
is used for ccore.makeRGBImage method.
"""
images = list()
ccolors = dict([(c.strChannelId, False)
for c in self._channels.values()])
for channel in self._channels.values():
ccolor = channel.strChannelId
if ccolor is not None and not ccolors[channel.strChannelId]:
ccolors[channel.strChannelId] = True
images.append((channel.meta_image.image,
Colors.channel_hexcolor(ccolor), alpha))
return images
def sub_channels(self):
for schannel, region in self._merge_regions.iteritems():
yield self._channels[schannel], region
# most of the following functions are just dummy implementations to stay
# combatible to processing channels that do actually perform segmentation
def purge(self, *args, **kw):
self._channels = None
def normalize_image(self, *args, **kw):
pass
def apply_zselection(self, *args, **kw):
pass
def apply_registration(self, *args, **kw):
pass
def apply_binning(self, *args, **kw):
pass
def __init__(self, settings, tstep, timeunit):
if settings('ErrorCorrection', 'hmm_baumwelch'):
self.hmm_algorithm = self.HMM_BAUMWELCH
else:
self.hmm_algorithm = self.HMM_SMOOTHING
self.constrain_graph = settings('ErrorCorrection', 'constrain_graph')
self.hmm_constrain = dict()
self.classifier_dirs = dict()
self.regionnames = COrderedDict()
# settings that depend whether if the channel is checked for
# error correction or not
for channel in CHANNEL_PREFIX:
rname = self._regionname(settings, channel)
if settings('ErrorCorrection', channel) and bool(rname):
self.regionnames[channel] = rname
self.hmm_constrain[channel] = self._hmm_constrain( \
settings('ErrorCorrection', '%s_graph' %channel))
_setting = '%s_classification_envpath' % channel
self.classifier_dirs[channel] = \
settings('Classification', _setting)
self.position_labels = settings('ErrorCorrection', 'position_labels')
self.mapping_dir = settings('ErrorCorrection', 'mappingfile_path')
if settings('ErrorCorrection', 'groupby_oligoid'):
self.sortby = PlateMapping.OLIGO
elif settings('ErrorCorrection', 'groupby_genesymbol'):
self.sortby = PlateMapping.GENE
else:
self.sortby = PlateMapping.POSITION
# special case, sort by position if no mappings file is provided
if not self.position_labels:
self.sortby = PlateMapping.POSITION
# timelapse in minutes
self.overwrite_timelapse = \
settings('ErrorCorrection', 'overwrite_time_lapse')
self.timeunit = timeunit
if self.overwrite_timelapse:
self.timelapse = settings('ErrorCorrection', 'timelapse')
else:
self.timelapse = tstep
self.sorting = settings('ErrorCorrection', 'enable_sorting')
if self.sorting:
try:
self.sorting_sequence = \
eval('('+settings('ErrorCorrection', 'sorting_sequence')+',)')
except SyntaxError:
raise SyntaxError(
("Error Correction: invalid Label sequence - "
"use a comma separated list of integers"))
else:
self.sorting_sequence = None
self.tmax = settings('ErrorCorrection', 'max_time')
self.ignore_tracking_branches = \
settings('ErrorCorrection', 'ignore_tracking_branches')
self.write_gallery = settings('ErrorCorrection', 'compose_galleries')
self.n_galleries = \
settings('ErrorCorrection', 'compose_galleries_sample')
if settings('EventSelection', 'supervised_event_selection'):
self.eventselection = self.EVENTSELECTION_SUPERVISED
else:
self.eventselection = self.EVENTSELECTION_UNSUPERVISED
self.nclusters = settings('EventSelection', 'num_clusters')
self.resampling_factor = settings('ErrorCorrection',
'resampling_factor')
self.size_gallery_image = settings('ErrorCorrection',
'size_gallery_image')
