def collate(collation, output="table", layout="horizontal", segmentation=True, near_match=False, astar=False, detect_transpositions=False, debug_scores=False, properties_filter=None):
if not astar:
algorithm = EditGraphAligner(collation, near_match=near_match, detect_transpositions=detect_transpositions, debug_scores=debug_scores, properties_filter=properties_filter)
else:
algorithm = ExperimentalAstarAligner(collation, near_match=near_match, debug_scores=debug_scores)
# build graph
graph = VariantGraph()
algorithm.collate(graph, collation)
# join parallel segments
if segmentation:
join(graph)
# check which output format is requested: graph or table
if output == "svg":
return display_variant_graph_as_SVG(graph)
if output=="graph":
return graph
# create alignment table
table = AlignmentTable(collation, graph, layout)
if output == "json":
return export_alignment_table_as_json(table)
if output == "html":
return display_alignment_table_as_HTML(table)
if output == "html2":
return visualizeTableVerticallyWithColors(table, collation)
if output == "table":
return table
else:
raise Exception("Unknown output type: "+output)
def collate_pretokenized_json(json,
output='table',
layout='horizontal',
**kwargs):
# Takes more or less the same arguments as collate() above, but with some restrictions.
# Only output types 'json' and 'table' are supported.
if output not in ['json', 'table', 'html2']:
raise UnsupportedError("Output type " + output +
" not supported for pretokenized collation")
if 'segmentation' in kwargs and kwargs['segmentation']:
raise UnsupportedError(
"Segmented output not supported for pretokenized collation")
kwargs['segmentation'] = False
# For each witness given, make a 'shadow' witness based on the normalization tokens
# that will actually be collated.
tokenized_witnesses = []
collation = Collation()
for witness in json["witnesses"]:
collation.add_witness(witness)
tokenized_witnesses.append(witness["tokens"])
at = collate(collation, output="table", **kwargs)
if output == "html2":
return visualizeTableVerticallyWithColors(at, collation)
# record whether there is variation in each of the columns (horizontal) or rows (vertical layout)
has_variation_array = []
for column in at.columns:
has_variation_array.append(column.variant)
tokenized_at = AlignmentTable(collation, layout=layout)
for row, tokenized_witness in zip(at.rows, tokenized_witnesses):
new_row = Row(row.header)
tokenized_at.rows.append(new_row)
token_counter = 0
for cell in row.cells:
new_row.cells.append(
tokenized_witness[token_counter] if cell else None)
if cell:
token_counter += 1
# In order to have the same information as in the non pretokenized alignment table we
# add variation information to the pretokenized alignment table.
tokenized_at.has_rank_variation = has_variation_array
if output == "json":
return export_alignment_table_as_json(tokenized_at)
if output == "table":
# transform JSON objects to "t" form.
for row in tokenized_at.rows:
row.cells = [cell["t"] if cell else None for cell in row.cells]
return tokenized_at
def collate(collation,
output="table",
layout="horizontal",
segmentation=True,
near_match=False,
astar=False,
detect_transpositions=False,
debug_scores=False,
properties_filter=None):
if not astar:
algorithm = EditGraphAligner(
collation,
near_match=near_match,
detect_transpositions=detect_transpositions,
debug_scores=debug_scores,
properties_filter=properties_filter)
else:
algorithm = ExperimentalAstarAligner(collation,
near_match=near_match,
debug_scores=debug_scores)
# build graph
graph = VariantGraph()
algorithm.collate(graph, collation)
# join parallel segments
if segmentation:
join(graph)
# check which output format is requested: graph or table
if output == "svg":
return display_variant_graph_as_SVG(graph)
if output == "graph":
return graph
# create alignment table
table = AlignmentTable(collation, graph, layout)
if output == "json":
return export_alignment_table_as_json(table)
if output == "html":
return display_alignment_table_as_HTML(table)
if output == "html2":
return visualizeTableVerticallyWithColors(table, collation)
if output == "table":
return table
else:
raise Exception("Unknown output type: " + output)
def collate_pretokenized_json(json, output='table', layout='horizontal', **kwargs):
# Takes more or less the same arguments as collate() above, but with some restrictions.
# Only output types 'json' and 'table' are supported.
if output not in ['json', 'table', 'html2']:
raise UnsupportedError("Output type " + output + " not supported for pretokenized collation")
if 'segmentation' in kwargs and kwargs['segmentation']:
raise UnsupportedError("Segmented output not supported for pretokenized collation")
kwargs['segmentation'] = False
# For each witness given, make a 'shadow' witness based on the normalization tokens
# that will actually be collated.
tokenized_witnesses = []
collation = Collation()
for witness in json["witnesses"]:
collation.add_witness(witness)
tokenized_witnesses.append(witness["tokens"])
at = collate(collation, output="table", **kwargs)
if output == "html2":
return visualizeTableVerticallyWithColors(at, collation)
# record whether there is variation in each of the columns (horizontal) or rows (vertical layout)
has_variation_array = []
for column in at.columns:
has_variation_array.append(column.variant)
tokenized_at = AlignmentTable(collation, layout=layout)
for row, tokenized_witness in zip(at.rows, tokenized_witnesses):
new_row = Row(row.header)
tokenized_at.rows.append(new_row)
token_counter = 0
for cell in row.cells:
new_row.cells.append(tokenized_witness[token_counter] if cell else None)
if cell:
token_counter += 1
# In order to have the same information as in the non pretokenized alignment table we
# add variation information to the pretokenized alignment table.
tokenized_at.has_rank_variation = has_variation_array
if output == "json":
return export_alignment_table_as_json(tokenized_at)
if output == "table":
# transform JSON objects to "t" form.
for row in tokenized_at.rows:
row.cells = [cell["t"] if cell else None for cell in row.cells]
return tokenized_at
def collate(collation,
output="table",
layout="horizontal",
segmentation=True,
near_match=False,
astar=False,
detect_transpositions=False,
debug_scores=False,
properties_filter=None,
svg_output=None,
indent=False,
scheduler=Scheduler()):
# collation may be collation or json; if it's the latter, use it to build a real collation
if isinstance(collation, dict):
json_collation = Collation()
for witness in collation["witnesses"]:
json_collation.add_witness(witness)
collation = json_collation
# assume collation is collation (by now); no error trapping
if not astar:
algorithm = EditGraphAligner(
collation,
near_match=False,
detect_transpositions=detect_transpositions,
debug_scores=debug_scores,
properties_filter=properties_filter)
else:
algorithm = ExperimentalAstarAligner(collation,
near_match=False,
debug_scores=debug_scores)
# build graph
graph = VariantGraph()
algorithm.collate(graph, collation)
ranking = VariantGraphRanking.of(graph)
if near_match:
# Segmentation not supported for near matching; raise exception if necessary
if segmentation:
raise SegmentationError(
'segmentation must be set to False for near matching')
highestRank = ranking.byVertex[graph.end]
witnessCount = len(collation.witnesses)
# do-while loop to avoid looping through ranking while modifying it
rank = highestRank - 1
condition = True
while condition:
rank = process_rank(scheduler, rank, collation, ranking,
witnessCount)
rank -= 1
condition = rank > 0
# # Verify that nodes have been moved
# print("\nLabels at each rank at end of processing: ")
# for rank in ranking.byRank:
# print("\nRank: " + str(rank))
# print([node.label for node in ranking.byRank[rank]])
# join parallel segments
if segmentation:
join(graph)
ranking = VariantGraphRanking.of(graph)
# check which output format is requested: graph or table
if output == "svg" or output == "svg_simple":
return display_variant_graph_as_SVG(graph, svg_output, output)
if output == "graph":
return graph
# create alignment table
table = AlignmentTable(collation, graph, layout, ranking)
if output == "json":
return export_alignment_table_as_json(table)
if output == "html":
return display_alignment_table_as_HTML(table)
if output == "html2":
return visualizeTableVerticallyWithColors(table, collation)
if output == "table":
return table
if output == "xml":
return export_alignment_table_as_xml(table)
if output == "tei":
return export_alignment_table_as_tei(table, indent)
else:
raise Exception("Unknown output type: " + output)
def collate(collation, output="table", layout="horizontal", segmentation=True, near_match=False, astar=False,
detect_transpositions=False, debug_scores=False, properties_filter=None, svg_output=None, indent=False, scheduler=Scheduler()):
# collation may be collation or json; if it's the latter, use it to build a real collation
if isinstance(collation, dict):
json_collation = Collation()
for witness in collation["witnesses"]:
json_collation.add_witness(witness)
collation = json_collation
# assume collation is collation (by now); no error trapping
if not astar:
algorithm = EditGraphAligner(collation, near_match=False, detect_transpositions=detect_transpositions, debug_scores=debug_scores, properties_filter=properties_filter)
else:
algorithm = ExperimentalAstarAligner(collation, near_match=False, debug_scores=debug_scores)
# build graph
graph = VariantGraph()
algorithm.collate(graph, collation)
ranking = VariantGraphRanking.of(graph)
if near_match:
# Segmentation not supported for near matching; raise exception if necessary
if segmentation:
raise SegmentationError('segmentation must be set to False for near matching')
highestRank = ranking.byVertex[graph.end]
witnessCount = len(collation.witnesses)
# do-while loop to avoid looping through ranking while modifying it
rank = highestRank - 1
condition = True
while condition:
rank = process_rank(scheduler, rank, collation, ranking, witnessCount)
rank -= 1
condition = rank > 0
# # Verify that nodes have been moved
# print("\nLabels at each rank at end of processing: ")
# for rank in ranking.byRank:
# print("\nRank: " + str(rank))
# print([node.label for node in ranking.byRank[rank]])
# join parallel segments
if segmentation:
join(graph)
ranking = VariantGraphRanking.of(graph)
# check which output format is requested: graph or table
if output == "svg" or output == "svg_simple":
return display_variant_graph_as_SVG(graph, svg_output, output)
if output == "graph":
return graph
# create alignment table
table = AlignmentTable(collation, graph, layout, ranking)
if output == "json":
return export_alignment_table_as_json(table)
if output == "html":
return display_alignment_table_as_HTML(table)
if output == "html2":
return visualizeTableVerticallyWithColors(table, collation)
if output == "table":
return table
if output == "xml":
return export_alignment_table_as_xml(table)
if output == "tei":
return export_alignment_table_as_tei(table, indent)
else:
raise Exception("Unknown output type: " + output)
