def get_measurement_columns(self, pipeline):
        '''Return a sequence describing the measurement columns needed by this module 
        '''
        cols = []
        for fd in self.images:
            name = fd[FD_IMAGE_NAME].value
            cols += [('Image', 'FileName_' + name,
                      cpm.COLTYPE_VARCHAR_FILE_NAME)]
            cols += [('Image', 'PathName_' + name,
                      cpm.COLTYPE_VARCHAR_PATH_NAME)]
            cols += [('Image', 'MD5Digest_' + name,
                      cpm.COLTYPE_VARCHAR_FORMAT % 32)]

        fd = self.images[0]
        if fd[FD_METADATA_CHOICE] == M_FILE_NAME or fd[
                FD_METADATA_CHOICE] == M_BOTH:
            tokens = cpm.find_metadata_tokens(fd[FD_FILE_METADATA].value)
            cols += [('Image', 'Metadata_' + token,
                      cpm.COLTYPE_VARCHAR_FILE_NAME) for token in tokens]

        if fd[FD_METADATA_CHOICE] == M_PATH or fd[FD_METADATA_CHOICE] == M_BOTH:
            tokens = cpm.find_metadata_tokens(fd[FD_PATH_METADATA].value)
            cols += [('Image', 'Metadata_' + token,
                      cpm.COLTYPE_VARCHAR_PATH_NAME) for token in tokens]

        return cols
 def run_objects(self, object_names, file_name, workspace):
     """Create a file (or files if there's metadata) based on the object names
     
     object_names - a sequence of object names (or Image or Experiment)
                    which tell us which objects get piled into each file
     file_name - a file name or file name with metadata tags to serve as the
                 output file.
     workspace - get the images from here.
     
     """
     if len(object_names) == 1 and object_names[0] == EXPERIMENT:
         self.make_experiment_file(file_name, workspace)
         return
     tags = cpmeas.find_metadata_tokens(file_name)
     if self.directory.is_custom_choice:
         tags += cpmeas.find_metadata_tokens(self.directory.custom_path)
     metadata_groups = workspace.measurements.group_by_metadata(tags)
     for metadata_group in metadata_groups:
         if len(object_names) == 1 and object_names[0] == IMAGE:
             self.make_image_file(file_name, 
                                  metadata_group.image_numbers, 
                                  workspace)
             if self.wants_genepattern_file.value:
                 self.make_gct_file(file_name, 
                                    metadata_group.image_numbers, 
                                    workspace)
         elif len(object_names) == 1 and object_names[0] == OBJECT_RELATIONSHIPS:
             self.make_relationships_file(file_name, 
                                          metadata_group.image_numbers, 
                                          workspace)
         else:
             self.make_object_file(object_names, file_name, 
                                   metadata_group.image_numbers, workspace)
 def run_objects(self, object_names, file_name, workspace):
     """Create a file (or files if there's metadata) based on the object names
     
     object_names - a sequence of object names (or Image or Experiment)
                    which tell us which objects get piled into each file
     file_name - a file name or file name with metadata tags to serve as the
                 output file.
     workspace - get the images from here.
     
     """
     if len(object_names) == 1 and object_names[0] == EXPERIMENT:
         self.make_experiment_file(file_name, workspace)
         return
     tags = cpmeas.find_metadata_tokens(file_name)
     if self.directory.is_custom_choice:
         tags += cpmeas.find_metadata_tokens(self.directory.custom_path)
     metadata_groups = workspace.measurements.group_by_metadata(tags)
     for metadata_group in metadata_groups:
         if len(object_names) == 1 and object_names[0] == IMAGE:
             self.make_image_file(file_name, metadata_group.image_numbers,
                                  workspace)
             if self.wants_genepattern_file.value:
                 self.make_gct_file(file_name, metadata_group.image_numbers,
                                    workspace)
         elif len(object_names
                  ) == 1 and object_names[0] == OBJECT_RELATIONSHIPS:
             self.make_relationships_file(file_name,
                                          metadata_group.image_numbers,
                                          workspace)
         else:
             self.make_object_file(object_names, file_name,
                                   metadata_group.image_numbers, workspace)
示例#4
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 def get_metadata_groups(self, workspace, settings_group=None):
     '''Find the metadata groups that are relevant for creating the file name
     
     workspace - the workspace with the image set metadata elements and
                 grouping measurements populated.
     settings_group - if saving individual objects, this is the settings
                      group that controls naming the files.
     '''
     if settings_group is None or settings_group.wants_automatic_file_name:
         tags = []
     else:
         tags = cpmeas.find_metadata_tokens(settings_group.file_name.value)
     if self.directory.is_custom_choice:
         tags += cpmeas.find_metadata_tokens(self.directory.custom_path)
     metadata_groups = workspace.measurements.group_by_metadata(tags)
     return metadata_groups
示例#5
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    def get_metadata_tags(self, fd=None):
        """Find the metadata tags for the indexed image

        fd - an image file directory from self.images
        """
        if not fd:
            s = set()
            for fd in self.images:
                s.update(self.get_metadata_tags(fd))
            tags = list(s)
            tags.sort()
            return tags
        
        tags = []
        if fd[FD_METADATA_CHOICE] in (M_FILE_NAME, M_BOTH):
            tags += cpm.find_metadata_tokens(fd[FD_FILE_METADATA].value)
        if fd[FD_METADATA_CHOICE] in (M_PATH, M_BOTH):
            tags += cpm.find_metadata_tokens(fd[FD_PATH_METADATA].value)
        return tags
    def get_metadata_tags(self, fd=None):
        """Find the metadata tags for the indexed image

        fd - an image file directory from self.images
        """
        if not fd:
            s = set()
            for fd in self.images:
                s.update(self.get_metadata_tags(fd))
            tags = list(s)
            tags.sort()
            return tags

        tags = []
        if fd[FD_METADATA_CHOICE] in (M_FILE_NAME, M_BOTH):
            tags += cpm.find_metadata_tokens(fd[FD_FILE_METADATA].value)
        if fd[FD_METADATA_CHOICE] in (M_PATH, M_BOTH):
            tags += cpm.find_metadata_tokens(fd[FD_PATH_METADATA].value)
        return tags
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 def get_measurement_columns(self, pipeline):
     '''Return a sequence describing the measurement columns needed by this module 
     '''
     cols = []
     for fd in self.images:
         name = fd[FD_IMAGE_NAME].value
         cols += [('Image','FileName_'+name, cpm.COLTYPE_VARCHAR_FILE_NAME)]
         cols += [('Image','PathName_'+name, cpm.COLTYPE_VARCHAR_PATH_NAME)]
         cols += [('Image','MD5Digest_'+name, cpm.COLTYPE_VARCHAR_FORMAT%32)]
     
     fd = self.images[0]    
     if fd[FD_METADATA_CHOICE]==M_FILE_NAME or fd[FD_METADATA_CHOICE]==M_BOTH:
         tokens = cpm.find_metadata_tokens(fd[FD_FILE_METADATA].value)
         cols += [('Image', 'Metadata_'+token, cpm.COLTYPE_VARCHAR_FILE_NAME) for token in tokens]
     
     if fd[FD_METADATA_CHOICE]==M_PATH or fd[FD_METADATA_CHOICE]==M_BOTH:
         tokens = cpm.find_metadata_tokens(fd[FD_PATH_METADATA].value)
         cols += [('Image', 'Metadata_'+token, cpm.COLTYPE_VARCHAR_PATH_NAME) for token in tokens]
     
     return cols
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    def update_imported_metadata(self):
        new_imported_metadata = []
        ipd_metadata_keys = set(getattr(self, "ipd_metadata_keys", []))
        for group in self.extraction_methods:
            if group.extraction_method == X_MANUAL_EXTRACTION:
                if group.source == XM_FILE_NAME:
                    regexp = group.file_regexp
                else:
                    regexp = group.folder_regexp
                ipd_metadata_keys.update(
                    cpmeas.find_metadata_tokens(regexp.value))
            elif group.extraction_method == X_IMPORTED_EXTRACTION:
                joiner = group.csv_joiner
                csv_path = group.csv_location.value
                if not os.path.isfile(csv_path):
                    continue
                found = False
                best_match = None
                for i, imported_metadata in enumerate(self.imported_metadata):
                    assert isinstance(imported_metadata, self.ImportedMetadata)
                    if imported_metadata.is_match(csv_path, joiner,
                                                  self.CSV_JOIN_NAME,
                                                  self.IPD_JOIN_NAME):
                        new_imported_metadata.append(imported_metadata)
                        found = True
                        break
                    elif (best_match is None
                          and imported_metadata.path == csv_path):
                        best_match = i
                if found:
                    del self.imported_metadata[i]
                else:
                    if best_match is not None:
                        imported_metadata = self.imported_metadata[i]
                        del self.imported_metadata[i]
                    else:
                        try:
                            imported_metadata = self.ImportedMetadata(csv_path)
                        except:
                            logger.debug("Failed to load csv file: %s" %
                                         csv_path)
                            continue
                    new_imported_metadata.append(imported_metadata)
                joiner.entities[self.CSV_JOIN_NAME] = \
                    imported_metadata.get_csv_metadata_keys()
                joiner.entities[self.IPD_JOIN_NAME] = \
                    list(ipd_metadata_keys)
                imported_metadata.set_joiner(joiner, self.CSV_JOIN_NAME,
                                             self.IPD_JOIN_NAME)
                ipd_metadata_keys.update(
                    imported_metadata.get_csv_metadata_keys())

        self.imported_metadata = new_imported_metadata
示例#9
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 def update_imported_metadata(self):
     new_imported_metadata = []
     ipd_metadata_keys = set(getattr(self, "ipd_metadata_keys", []))
     for group in self.extraction_methods:
         if group.extraction_method == X_MANUAL_EXTRACTION:
             if group.source == XM_FILE_NAME:
                 regexp = group.file_regexp
             else:
                 regexp = group.folder_regexp
             ipd_metadata_keys.update(cpmeas.find_metadata_tokens(regexp.value))
         elif group.extraction_method == X_IMPORTED_EXTRACTION:
             joiner = group.csv_joiner
             csv_path = group.csv_location.value
             if not os.path.isfile(csv_path):
                 continue
             found = False
             best_match = None
             for i, imported_metadata in enumerate(self.imported_metadata):
                 assert isinstance(imported_metadata, self.ImportedMetadata)
                 if imported_metadata.is_match(csv_path, joiner,
                                               self.CSV_JOIN_NAME,
                                               self.IPD_JOIN_NAME):
                     new_imported_metadata.append(imported_metadata)
                     found = True
                     break
                 elif (best_match is None and 
                       imported_metadata.path == csv_path):
                     best_match = i
             if found:
                 del self.imported_metadata[i]
             else:
                 if best_match is not None:
                     imported_metadata = self.imported_metadata[i]
                     del self.imported_metadata[i]
                 else:
                     try:
                         imported_metadata = self.ImportedMetadata(csv_path)
                     except:
                         logger.debug("Failed to load csv file: %s" % csv_path)
                         continue
                 new_imported_metadata.append(imported_metadata)
             joiner.entities[self.CSV_JOIN_NAME] = \
                 imported_metadata.get_csv_metadata_keys()
             joiner.entities[self.IPD_JOIN_NAME] = \
                 list(ipd_metadata_keys)
             imported_metadata.set_joiner(joiner,
                                          self.CSV_JOIN_NAME,
                                          self.IPD_JOIN_NAME)
             ipd_metadata_keys.update(imported_metadata.get_csv_metadata_keys())
                 
     self.imported_metadata = new_imported_metadata            
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 def validate_module(self, pipeline):
     '''Validate the module settings
     
     pipeline - current pipeline
     
     Metadata throws an exception if any of the metadata tags collide with
     tags that can be automatically extracted.
     '''
     for group in self.extraction_methods:
         if group.extraction_method == X_MANUAL_EXTRACTION:
             re_setting = (group.file_regexp if group.source == XM_FILE_NAME
                           else group.folder_regexp)
             for token in cpmeas.find_metadata_tokens(re_setting.value):
                 if token in cpmeas.RESERVED_METADATA_TAGS:
                     raise cps.ValidationError(
                         'The metadata tag, "%s", is reserved for use by CellProfiler. Please use some other tag name.' %
                         token, re_setting)
示例#11
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 def validate_module(self, pipeline):
     '''Validate the module settings
     
     pipeline - current pipeline
     
     Metadata throws an exception if any of the metadata tags collide with
     tags that can be automatically extracted.
     '''
     for group in self.extraction_methods:
         if group.extraction_method == X_MANUAL_EXTRACTION:
             re_setting = (group.file_regexp if group.source == XM_FILE_NAME
                           else group.folder_regexp)
             for token in cpmeas.find_metadata_tokens(re_setting.value):
                 if token in cpmeas.RESERVED_METADATA_TAGS:
                     raise cps.ValidationError(
                         'The metadata tag, "%s", is reserved for use by CellProfiler. Please use some other tag name.'
                         % token, re_setting)
示例#12
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 def get_metadata_keys(self):
     """Return a collection of metadata keys to be associated with files"""
     keys = set()
     self.update_imported_metadata()
     for group in self.extraction_methods:
         if group.extraction_method == X_MANUAL_EXTRACTION:
             if group.source == XM_FILE_NAME:
                 regexp = group.file_regexp
             else:
                 regexp = group.folder_regexp
             keys.update(cpmeas.find_metadata_tokens(regexp.value))
         elif group.extraction_method == X_IMPORTED_EXTRACTION:
             imported_metadata = self.get_imported_metadata_for_group(group)
             if imported_metadata is None:
                 logger.warn("Unable to import metadata from %s" % group.csv_location.value)
             keys.update(imported_metadata.metadata_keys)
         elif group.extraction_method == X_AUTOMATIC_EXTRACTION:
             # Assume that automatic extraction will populate T and Z
             keys.add(cpp.ImagePlaneDetails.MD_T)
             keys.add(cpp.ImagePlaneDetails.MD_Z)
     return list(keys)
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 def get_metadata_keys(self):
     '''Return a collection of metadata keys to be associated with files'''
     keys = set()
     self.update_imported_metadata()
     for group in self.extraction_methods:
         if group.extraction_method == X_MANUAL_EXTRACTION:
             if group.source == XM_FILE_NAME:
                 regexp = group.file_regexp
             else:
                 regexp = group.folder_regexp
             keys.update(cpmeas.find_metadata_tokens(regexp.value))
         elif group.extraction_method == X_IMPORTED_EXTRACTION:
             imported_metadata = self.get_imported_metadata_for_group(group)
             if imported_metadata is None:
                 logger.warn("Unable to import metadata from %s" %
                             group.csv_location.value)
             keys.update(imported_metadata.metadata_keys)
         elif group.extraction_method == X_AUTOMATIC_EXTRACTION:
             # Assume that automatic extraction will populate T and Z
             keys.add(cpp.ImagePlaneDetails.MD_T)
             keys.add(cpp.ImagePlaneDetails.MD_Z)
     return list(keys)