def describe_filter(self):
sel = self.filter
r = []
if sel.components:
r.append("(ᴄᴏᴍᴩ, ꜰᴏʀ) ⊇ {{{0}}}".format(
string_helper.format_array(sel.components)))
if sel.genes:
r.append("ᴀʟʟ ⊇ {{{0}}}".format(
string_helper.format_array(sel.genes)))
if sel.fusions:
r.append("ᴀʟʟ ⊇ {{{0}}}".format(
string_helper.format_array(sel.fusions)))
if sel.splits:
r.append("ꜱᴩʟɪᴛ ∈ {{{0}}}".format(
string_helper.format_array(sel.splits)))
if sel.text:
r.append('"{0}" ∈ ꜱᴩʟɪᴛ'.format(sel.text))
if not r:
return "No filter"
return " ∧ ".join(r)
def to_details(self):
r = []
r.append("MAJOR-SE: {}".format(
string_helper.format_array(self.major_genes, sort=True)))
r.append("MINOR-SE: {}".format(
string_helper.format_array(self.minor_genes, sort=True)))
r.append("MINOR-SS: {}".format(
string_helper.format_array(self.minor_domains)))
r.append("INCOMING: {}".format(
string_helper.format_array(self.incoming_components(), sort=True)))
r.append("OUTGOING: {}".format(
string_helper.format_array(self.outgoing_components(), sort=True)))
return "\n".join(r)
def create_supertrees(algorithm: supertree_algorithms.Algorithm) -> None:
"""
Creates the supertrees/subgraphs for the model.
Requisites: `create_pregraphs`
:param algorithm: Algorithm to use, see `algorithm_help`.
:return: Nothing is returned, the state is saved into the model.
"""
# Check we're ready to go
model = global_view.current_model()
model.get_status(STAGES.SUPERTREES_14).assert_create()
# Create the subgraphs
subgraphs = []
for subset in model.subsets:
subgraph = __create_supertree(algorithm, subset)
subgraphs.append((subset, subgraph))
# Collect the sources and destinations
destinations = set()
sources = set()
for subset, subgraph in subgraphs:
sequences = lego_graph.get_sequence_data(subgraph)
ffn = lego_graph.get_fusion_formation_nodes(subgraph)
if not ffn:
raise ValueError(
"The subgraph («{}») of the subset «{}» («{}») doesn't appear to have any fusion point nodes. Refusing to continue because this means the subgraph's position in the NRFG is unavailable."
.format(string_helper.format_array(subgraph.nodes), subset,
string_helper.format_array(subset.contents)))
for node in ffn: # type:MNode
formation: Formation = node.data
if any(x in sequences for x in formation.pertinent_inner):
destinations.add(node.uid)
else:
sources.add(node.uid)
model.subgraphs_destinations = tuple(destinations)
model.subgraphs_sources = tuple(sources)
model.subgraphs = tuple(
Subgraph(subgraph, subset, repr(algorithm))
for subset, subgraph in subgraphs)
return EChanges.MODEL_DATA
def __subset_to_possible_graphs(subset: Subset):
"""
Converts a subset of genes into the possible graphs representing these genes (1 graph per component).
:remarks:
This isn't as simple as just pulling out the genes from the component trees, we need to address the following issues:
1. Our resulting graphs might contain distinct fusions points which may, in fact, be the same.
This causes problems in the supertree stage.
We address this issue by coalescing identical fusions (`LegoPoint`s into `LegoFormation`s).
2. The "intermediaries" (the clades which hold our gene subset together to form a coherent graph)
should generally just be boring clades, but occasionally we'll pull a fusion node into them.
This also causes problems at the supertree stage.
We address this issue by swapping them out these out for clades.
"""
graphs: List[Pregraph] = []
LOG("{} :::: {}", subset, subset.contents)
for component in subset.model.components:
intermediaries = analysing.get_intermediaries(
component.tree, lambda x: x.data in subset.contents)
LOG("{} :::: {}", subset, component)
graph = component.tree.copy(nodes=intermediaries)
# Hold up!
for node in graph:
LOG("{} :::: {}", subset, node)
if lego_graph.is_clade(node):
continue
if lego_graph.is_fusion_point(node):
if node.data in subset.contents:
node.data = node.data.formation
else:
# Substitute for clade
print("SUB {}".format(node))
node.data = None
continue
if lego_graph.is_sequence_node(node):
if node.data in subset.contents:
continue
raise ValueError(
"Subset graph contains the node «{}», which does not appear in the actual subset «{}»."
.format(node, subset))
if sum(1 for _ in graph.nodes.roots) > 1:
raise LogicError("Graph of subset has multiple roots: {}".format(
string_helper.format_array(graph.nodes.roots)))
if graph.nodes:
graphs.append(Pregraph(graph, subset, component))
subset.pregraphs = tuple(graphs)
def print_subsets() -> EChanges:
"""
Prints NRFG subsets.
"""
model = global_view.current_model()
for x in sorted( model.subsets, key = cast( Any, str ) ):
assert isinstance( x, Subset )
print( "{} - {} elements: {}".format( x, len( x ), string_helper.format_array( x.contents, sort = True, autorange = True ) ) )
return EChanges.INFORMATION
def __list_comp(comparison, name, html, calculated, original, ini: TIniData):
html.append('<table border=1 style="border-collapse: collapse;">')
html.append("<tr><td colspan=6><b>{} QUARTETS</b></td></tr>".format(name))
ini_sect: TIniSection = {}
ini[name.lower()] = ini_sect
for quartet in comparison:
calc = calculated[quartet.get_unsorted_key()]
orig = original[quartet.get_unsorted_key()]
html.append(
"<tr><td>{}</td><td>{}</td><td>{}</td><td>{}</td><td>{}</td><td>{}</td></tr>"
.format(*quartet.get_unsorted_key(), calc, orig))
ini_sect["{}_calculated".format(
string_helper.format_array(
quartet.get_unsorted_key()))] = str(calc)
ini_sect["{}_original".format(
string_helper.format_array(
quartet.get_unsorted_key()))] = str(orig)
html.append("</table><br/>")
def print_fusions() -> EChanges:
"""
Prints model fusions.
"""
results: List[str] = []
model = global_view.current_model()
for event in model.fusions:
results.append("- name {}".format(event))
results.append(" components in {}".format(event.components_in))
results.append(" component out {}".format(event.component_out))
results.append(" index {}".format(event.index))
results.append(" points {}".format(
string_helper.format_array(event.points)))
for point in event.points:
results.append(" - name {}".format(point))
results.append(" point_component {}".format(
point.point_component))
results.append(" count {}".format(
point.count))
results.append(" outer sequences {}".format(
string_helper.format_array(point.outer_sequences)))
results.append(" pertinent inner {}".format(
string_helper.format_array(point.pertinent_inner)))
results.append(" pertinent outer {}".format(
string_helper.format_array(point.pertinent_outer)))
results.append(" sequences {}".format(
string_helper.format_array(point.genes)))
results.append("")
results.append("")
print("\n".join(results))
return EChanges.INFORMATION
def on_refresh_data(self):
tvw = self.ui.LST_MAIN
tvw.clear()
model = self.get_model()
accepted = 0
rejected = 0
if model.splits:
for split in model.splits:
if not self.check_filter(split):
rejected += 1
continue
accepted += 1
assert isinstance(split, Split)
item = QTreeWidgetItem()
col = qt.tree_helper.get_or_create_column(tvw, "Inside")
txt = string_helper.format_array(split.split.inside)
item.setText(col, txt)
col = qt.tree_helper.get_or_create_column(tvw, "Outside")
txt = string_helper.format_array(split.split.outside)
item.setText(col, txt)
col = qt.tree_helper.get_or_create_column(tvw, "Components")
txt = string_helper.format_array(split.components)
item.setText(col, txt)
col = qt.tree_helper.get_or_create_column(tvw, "For")
txt = string_helper.format_array(split.evidence_for)
item.setText(col, txt)
col = qt.tree_helper.get_or_create_column(tvw, "Against")
txt = string_helper.format_array(split.evidence_against)
item.setText(col, txt)
col = qt.tree_helper.get_or_create_column(tvw, "Unused")
txt = string_helper.format_array(split.evidence_unused)
item.setText(col, txt)
qt.tree_helper.set_data(item, split)
tvw.addTopLevelItem(item)
if rejected:
self.ui.LBL_TITLE.setText(
"{} splits, {} rejected due to filter".format(
accepted, rejected))
else:
self.ui.LBL_TITLE.setText("{} splits".format(accepted))
self.ui.LBL_FILTER.setText(self.describe_filter())
def create_consensus(cutoff: float = 0.5) -> EChanges:
"""
Filters the candidate splits.
NRFG PHASE II.
Collect consensus evidence.
:remarks:
----------------------------------------------------------------------------------------------------
| The second stage of the consensus. |
| We collect evidence from the graphs to support or reject our splits. |
| Unlike a normal majority rule consensus, there's no guarantee that our splits are in the graphs, |
| so, in addition to support/reject evidence, we have a third category, whereby the graph neither |
| supports nor rejects a split. |
----------------------------------------------------------------------------------------------------
:param cutoff: Cutoff to be used in the consensus
"""
model = global_view.current_model()
__LOG_EVIDENCE.pause("▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ EVIDENCE ▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒")
model.get_status(STAGES.CONSENSUS_11).assert_create()
__LOG_EVIDENCE("BEGIN EVIDENCE ({} splits)".format(len(model.splits)))
viable_splits: Set[Split] = set()
for split in model.splits:
assert isinstance(split, Split), split
if split.split.is_empty:
__LOG_EVIDENCE("SPLIT IS EMPTY: {}".format(split))
continue
evidence_for = set()
evidence_against = set()
evidence_unused = set()
for component in model.components:
component_splits = component.splits
has_evidence = None
for component_split in component_splits:
evidence = split.is_evidenced_by(component_split)
if evidence is True:
has_evidence = True
break
elif evidence is False:
has_evidence = False
if has_evidence is True:
evidence_for.add(component)
elif has_evidence is False:
evidence_against.add(component)
else:
evidence_unused.add(component)
if not evidence_for:
raise LogicError(
"There is no evidence for (F{} A{} U{}) this split «{}», but the split must have come from somewhere."
.format(len(evidence_for), len(evidence_against),
len(evidence_unused), split))
total_evidence: int = len(evidence_for) + len(evidence_against)
frequency: float = len(evidence_for) / total_evidence
accept: bool = frequency > cutoff
split.evidence_for = frozenset(evidence_for)
split.evidence_against = frozenset(evidence_against)
split.evidence_unused = frozenset(evidence_unused)
__LOG_EVIDENCE(
"{} {} = {}% -- FOR: ({}) {}, AGAINST: ({}) {}, UNUSED: ({}) {}",
"✔" if accept else "✘", ansi_helper.ljust(str(split), 80),
int(frequency * 100), len(evidence_for),
string_helper.format_array(evidence_for, sort=True),
len(evidence_against),
string_helper.format_array(evidence_against, sort=True),
len(evidence_unused),
string_helper.format_array(evidence_unused, sort=True))
if accept:
viable_splits.add(split)
model.consensus = frozenset(viable_splits)
return EChanges.MODEL_DATA
def __format_elements(y):
return string_helper.format_array(y, join=",", sort=True, autorange=True)
def get_details(self):
return string_helper.format_array(self.contents)
def compare_graphs(calc_graph_: INamedGraph,
orig_graph_: INamedGraph) -> Report:
"""
Compares graphs using quartets.
:param calc_graph_: The model graph. Data is `ILeaf` or `None`.
:param orig_graph_: The source graph. Data is `str`.
:return: A `Report` object with an `TIniData` as its `raw_data`.
"""
differences = []
differences.append("<html><body>")
differences.append(
"<h1>Results for comparison of graphs {} and {}</h1>".format(
calc_graph_, orig_graph_))
calc_graph = calc_graph_.graph
orig_graph = orig_graph_.graph
ccs = analysing.find_connected_components(calc_graph)
if len(ccs) != 1:
raise ValueError(
"The graph has more than 1 connected component ({}).".format(
len(ccs)))
calc_genes: Set[object] = set(
x.data for x in analysing.realise_node_predicate_as_set(
calc_graph, lego_graph.is_sequence_node))
orig_genes: Set[object] = set(
x.data for x in analysing.realise_node_predicate_as_set(
orig_graph, lego_graph.is_sequence_node))
if not calc_genes:
raise ValueError("The calculated graph contains no genes.")
if not orig_genes:
raise ValueError("The original graph contains no genes.")
if calc_genes != orig_genes:
raise ValueError(
"The calculated graph has a different gene set to the original. Missing: {}; additional: {}."
.format(
string_helper.format_array(
orig_genes - calc_genes,
sort=True,
format=lambda x: "{}:{}".format(type(x).__name__, x)),
string_helper.format_array(
calc_genes - orig_genes,
sort=True,
format=lambda x: "{}:{}".format(type(x).__name__, x))))
calc_quartets = __get_quartets_with_progress(calc_graph, "calculated")
orig_quartets = __get_quartets_with_progress(orig_graph, "original")
comparison: QuartetComparison = calc_quartets.compare(orig_quartets)
html = []
ini_data: TIniData = {}
# QUARTETS
html.append('<table border=1 style="border-collapse: collapse;">')
html.append("<tr><td colspan=2><b>QUARTETS</b></td></tr>")
ini_data["quartets"] = q = {}
__add_row(html, q, "total_quartets", len(comparison))
__add_row(
html, q, "match_quartets",
string_helper.percent(len(comparison.match), len(comparison.all)))
__add_row(
html, q, "mismatch_quartets",
string_helper.percent(len(comparison.mismatch), len(comparison.all)))
__add_row(
html, q, "new_quartets",
string_helper.percent(len(comparison.missing_in_left),
len(comparison.all)))
__add_row(
html, q, "missing_quartets",
string_helper.percent(len(comparison.missing_in_right),
len(comparison.all)))
# GENE COMBINATIONS
__enumerate_2genes(calc_genes, comparison, html, 1, ini_data)
__enumerate_2genes(calc_genes, comparison, html, 2, ini_data)
__enumerate_2genes(calc_genes, comparison, html, 3, ini_data)
c = calc_quartets.get_unsorted_lookup()
o = orig_quartets.get_unsorted_lookup()
__list_comp(comparison.match, "MATCHING", html, c, o, ini_data)
__list_comp(comparison.mismatch, "MISMATCH", html, c, o, ini_data)
if comparison.missing_in_left:
__list_comp(comparison.missing_in_left, "MISSING IN LEFT", html, c, o,
ini_data)
if comparison.missing_in_right:
__list_comp(comparison.missing_in_right, "MISSING IN RIGHT", html, c,
o, ini_data)
differences.append("</body></html>")
report = Report("{} -vs- {}".format(orig_graph_, calc_graph_),
"\n".join(html))
report.raw_data = ini_data
return report
def __enumerate_2genes(calc_seq: Set[object], comparison: QuartetComparison,
html: List[str], n: int, ini_data: TIniData) -> None:
if array_helper.get_num_combinations(calc_seq, n) > 100:
return
html.append('<table border=1 style="border-collapse: collapse;">')
html.append(
"<tr><td colspan=5><b>BREAKDOWN FOR COMBINATIONS OF {}</b></td></tr>".
format(n))
html.append(
"<tr><td>total</td><td>hit</td><td>miss</td><td>missing in left</td><td>missing in right</td></tr>"
)
ini_sect: TIniSection = {}
ini_data["n_quartets_{}".format(n)] = ini_sect
for comb in sorted(itertools.combinations(calc_seq, n),
key=cast(Callable, str)): # type: Iterable[object]
n_tot = []
n_hit = []
n_mis = []
n_mil = []
n_mir = []
for quartet in comparison.all:
assert isinstance(quartet, AbstractQuartet)
if all(x in quartet.get_unsorted_key() for x in comb):
n_tot.append(quartet)
if quartet in comparison.match:
n_hit.append(quartet)
elif quartet in comparison.mismatch:
n_mis.append(quartet)
elif quartet in comparison.missing_in_left:
n_mil.append(quartet)
elif quartet in comparison.missing_in_right:
n_mir.append(quartet)
else:
raise SwitchError("quartet(in)", quartet)
if not n_mis and not n_mil and not n_mir:
continue
html.append("<tr>")
i = []
# COMBINATION NAME
name = string_helper.format_array(comb)
html.append("<td>{}</td>".format(name))
# HIT
txt = string_helper.percent(len(n_hit), len(n_tot)) if n_hit else ""
html.append("<td>{}</td>".format(txt))
i.append(txt)
# MISS
txt = string_helper.percent(len(n_mis), len(n_tot)) if n_mis else ""
html.append("<td>{}</td>".format(txt))
i.append(txt)
# MISSING IN LEFT
txt = string_helper.percent(len(n_mil), len(n_tot)) if n_mil else ""
html.append("<td>{}</td>".format(txt))
i.append(txt)
# MISSING IN RIGHT
txt = string_helper.percent(len(n_mir), len(n_tot)) if n_mil else ""
html.append("<td>{}</td>".format(txt))
i.append(txt)
html.append("</tr>")
ini_sect[name] = "; ".join(str(x) for x in i)
# Write out full quartets (if < 10)
i = []
if len(n_hit) < len(n_mis) < 10:
for quartet in n_mis:
html.append("<tr>")
html.append("<td></td>")
html.append("<td colspan=4>{}</td>".format(quartet))
html.append("</tr>")
i.append(quartet)
ini_sect[name + "_list"] = "; ".join(str(x) for x in i)
html.append("</table><br/>")
def create_major( tol: int = 0, debug: bool = False ) -> EChanges:
"""
Detects model components.
First step of finding the components.
We classify each component as a set of "major" genes.
Components are defined as sets of genes that share a similarity path between them, where each edge between element 𝓧 and 𝓨 in that path:
* Is sourced from no less than 𝓧's length, less the tolerance
* Is targeted to no less than 𝓨's length, less the tolerance
* The difference between 𝓧 and 𝓨's length is less than the tolerance
We'll grab the minor domains that this component extends into in the next step.
Requisites: Sequence similarity (BLAST data) must have been loaded
:param debug: Assert the creation.
:param tol: Tolerance value
:returns: Nothing, the components are written to :ivar:`model.components`.
"""
model = global_view.current_model()
model.get_status( STAGES.MAJOR_4 ).assert_create()
model.components.clear()
# Find connected components
components = ComponentFinder()
# Basic assertions
LOG_MAJOR( "There are {} sequences.", len( model.genes ) )
missing_edges = []
for sequence in model.genes:
edges = model.edges.find_gene( sequence )
if not edges:
missing_edges.append( sequence )
if missing_edges:
raise ValueError( "Refusing to detect components because some sequences have no edges: «{}»".format( string_helper.format_array( missing_edges ) ) )
# Iterate sequences
for sequence_alpha in model.genes:
assert isinstance( sequence_alpha, Gene )
alpha_edges = model.edges.find_gene( sequence_alpha )
any_accept = False
LOG_MAJOR( "Sequence {} contains {} edges.", sequence_alpha, len( alpha_edges ) )
for edge in alpha_edges:
assert isinstance( edge, Edge )
source_difference = abs( edge.left.length - edge.left.gene.length )
destination_difference = abs( edge.right.length - edge.right.gene.length )
total_difference = abs( edge.left.gene.length - edge.right.gene.length )
LOG_MAJOR_V( "{}", edge )
LOG_MAJOR_V( "-- Source difference ({})", source_difference )
LOG_MAJOR_V( "-- Destination difference ({})", destination_difference )
LOG_MAJOR_V( "-- Total difference ({})", total_difference )
if source_difference > tol:
LOG_MAJOR_V( "-- ==> REJECTED (SOURCE)" )
continue
elif destination_difference > tol:
LOG_MAJOR_V( "-- ==> REJECTED (DEST)" )
continue
elif total_difference > tol:
LOG_MAJOR_V( "-- ==> REJECTED (TOTAL)" )
continue
else:
LOG_MAJOR_V( "-- ==> ACCEPTED" )
if debug and edge.left.gene.accession[0] != edge.right.gene.accession[0]:
raise ValueError( "Debug assertion failed. This edge not rejected: {}".format( edge ) )
any_accept = True
beta = edge.opposite( sequence_alpha ).gene
LOG_MAJOR( "-- {:<40} LINKS {:<5} AND {:<5}", edge, sequence_alpha, beta )
components.join( sequence_alpha, beta )
if debug and not any_accept:
raise ValueError( "Debug assertion failed. This sequence has no good edges: {}".format( sequence_alpha ) )
# Create the components!
sequences_in_components = set()
for index, sequence_list in enumerate( components.tabulate() ):
model.components.add( Component( model, index, sequence_list ) )
LOG_MAJOR( "COMPONENT MAJOR: {}", sequence_list )
sequences_in_components.update( sequence_list )
# Create components for orphans
for sequence in model.genes:
if sequence not in sequences_in_components:
LOG_MAJOR( "ORPHAN: {}", sequence )
model.components.add( Component( model, len( model.components ), (sequence,) ) )
# An assertion
for component in model.components:
assert isinstance( component, Component )
if len( component.major_genes ) == 1:
warnings.warn( "There are components with just one sequence in them. Maybe you meant to use a tolerance higher than {}?".format( tol ), UserWarning )
break
pr.printx( "<verbose>{} components detected.</verbose>".format( len( model.components ) ) )
return EChanges.COMPONENTS
def __create_supertree(algorithm: supertree_algorithms.Algorithm,
subset: Subset) -> MGraph:
"""
Generates a supertree from a set of trees.
:param algorithm: Algorithm to use. See `algorithm_help`.
:param subset: Subset of genes from which we generate the consensus from
:return: The consensus graph (this may be a reference to one of the input `graphs`)
"""
# Get our algorithm
ins = FunctionInspector(algorithm.function)
# We allow two kinds of algorithm
# - Python algorithms, which takes a `LegoSubset` instance
# - External algorithms, which takes a newick-formatted string
if ins.args[0].annotation == Subset:
# Python algorithms get the subset instance
input = subset
else:
# External algorithms get newick strings for each possible tree in the subset
input_lines = __graphs_to_newick(subset.pregraphs)
if __is_redundant(subset.pregraphs, input_lines):
return subset.pregraphs[0].graph
input = "\n".join(input_lines) + "\n"
# Run the algorithm!
output = external_runner.run_in_temporary(algorithm, input)
# We allow two types of result
# - `MGraph` objects
# - `str` objects, which denote a newick-formatted string
if isinstance(output, MGraph):
result = output
elif isinstance(output, str):
# We don't reclade the newick, it's pointless at this stage and we remove redundancies during the NRFG_CLEAN stage anyway
result = lego_graph.import_newick(output, subset.model, reclade=False)
else:
raise SwitchError("create_supertree::output", output, instance=True)
# Assert the result
# - All elements of the subset are in the supertree
for element in subset.contents:
if isinstance(element, Gene):
if element in result.nodes.data:
continue
elif isinstance(element, Point):
if element.formation in result.nodes.data:
continue
raise ValueError(
_MSG1.format(
element,
string_helper.format_array(
result.nodes.data,
format=lambda x: "{}:{}".format(type(x).__name__, x),
sort=True),
type(element).__name__))
# - All (non-clade) elements of the supertree are in the subset
for node in result.nodes:
if lego_graph.is_clade(node):
continue
if lego_graph.is_formation(node):
if any(x.formation is node.data for x in subset.contents
if isinstance(x, Point)):
continue
if lego_graph.is_sequence_node(node):
if node.data in subset.contents:
continue
raise ValueError(
_MSG2.format(
node.data,
string_helper.format_array(
subset.contents,
format=lambda x: "{}:{}".format(type(x).__name__, x),
sort=True),
type(node.data).__name__))
return result
def print_minor(component: Optional[Component] = None,
verbose: bool = False) -> EChanges:
"""
Prints the edges between the component subsequences.
Each line is of the form:
`FROM <minor> TO <major> [ <start> : <end> ] <length>`
Where:
`minor` is the source component
`major` is the destination component
`start` is the average of the start of the destination entry point
`end` is the average of the end of the destination entry point
`length` is the average length of the sequences in the destination
:param component: Component to print.
If not specified prints a summary of all components.
:param verbose: Print all the things!
"""
model = global_view.current_model()
if not model.components:
raise ValueError(
"Cannot print components because components have not been calculated."
)
if verbose:
rows = []
rows.append(["component", "origins", "destinations"])
for comp in model.components:
assert isinstance(comp, Component)
if component is not None and component is not comp:
continue
major_genes = string_helper.format_array(comp.major_genes,
join="\n")
minor_domains = string_helper.format_array(comp.minor_domains,
join="\n")
rows.append([comp, major_genes, minor_domains])
with pr.pr_section("all components"):
pr.pr_table(rows)
if component:
title = str(component)
else:
title = "all components"
average_lengths = __get_average_component_lengths(model)
rows = []
rows.append([
"source", "destination", "sequence", "seq-length", "start", "end",
"edge-length"
])
for comp in model.components:
if component is not None and component is not comp:
continue
major_genes = list(comp.major_genes)
for minor in model.components:
if comp is minor:
continue
start = 0
end = 0
failed = False
for sequence in major_genes:
# subsequences that are in major sequence is a major sequence of major and are a minor subsequence of minor
subsequences = [
x for x in minor.minor_domains if x.sequence is sequence
]
if subsequences:
start += subsequences[0].start
end += subsequences[-1].end
if component is not None:
rows.append([
minor, comp, sequence.accession, sequence.length,
subsequences[0].start, subsequences[-1].end,
subsequences[-1].end - subsequences[0].start
])
else:
failed = True
if failed:
continue
start /= len(major_genes)
end /= len(major_genes)
rows.append([
minor, comp, "AVG*{}".format(len(major_genes)),
round(average_lengths[comp]),
round(start),
round(end),
round(end - start)
])
with pr.pr_section(title):
pr.pr_table(rows)
return EChanges.INFORMATION