def setUp(self):
self.config = build_config([])
self.rules_file = path.get_full_path(__file__, "..", "cluster_rules", "strict.txt")
self.signature_file = path.get_full_path(__file__, "..", "data", "hmmdetails.txt")
self.signature_names = {sig.name for sig in core.get_signature_profiles()}
self.valid_categories = {cat.name for cat in core.get_rule_categories()}
self.filter_file = path.get_full_path(__file__, "..", "filterhmmdetails.txt")
self.results_by_id = {
"GENE_1": [
FakeHSPHit("modelA", "GENE_1", 0, 10, 50, 0),
FakeHSPHit("modelB", "GENE_1", 0, 10, 50, 0)
],
"GENE_2": [
FakeHSPHit("modelC", "GENE_2", 0, 10, 50, 0),
FakeHSPHit("modelB", "GENE_2", 0, 10, 50, 0)
],
"GENE_3": [
FakeHSPHit("modelC", "GENE_3", 0, 10, 50, 0),
FakeHSPHit("modelF", "GENE_3", 0, 10, 50, 0)
],
"GENE_4": [
FakeHSPHit("modelA", "GENE_4", 0, 10, 50, 0),
FakeHSPHit("modelE", "GENE_4", 0, 10, 50, 0)
],
"GENE_5": [
FakeHSPHit("modelA", "GENE_5", 0, 10, 50, 0),
FakeHSPHit("modelG", "GENE_5", 0, 10, 50, 0)
]
}
self.feature_by_id = {
"GENE_1": DummyCDS(0, 30000, locus_tag="GENE_1"),
"GENE_2": DummyCDS(30000, 50000, locus_tag="GENE_2"),
"GENE_3": DummyCDS(70000, 90000, locus_tag="GENE_3"),
"GENE_X": DummyCDS(95000, 100000, locus_tag="GENE_X"), # no hits
"GENE_4": DummyCDS(125000, 140000, locus_tag="GENE_4"),
"GENE_5": DummyCDS(130000, 150000, locus_tag="GENE_5")
}
self.test_names = {"modelA", "modelB", "modelC", "modelF", "modelG",
"a", "b", "c", "d"}
self.categories = {"Cat"}
self.rules = rule_parser.Parser("\n".join([
"RULE MetaboliteA CATEGORY Cat CUTOFF 10 NEIGHBOURHOOD 5 CONDITIONS modelA",
"RULE MetaboliteB CATEGORY Cat CUTOFF 10 NEIGHBOURHOOD 5 CONDITIONS cds(modelA and modelB)",
"RULE MetaboliteC CATEGORY Cat CUTOFF 10 NEIGHBOURHOOD 5 CONDITIONS (modelA and modelB)",
"RULE MetaboliteD CATEGORY Cat CUTOFF 20 NEIGHBOURHOOD 5 CONDITIONS minimum(2,[modelC,modelB]) and modelA",
"RULE Metabolite0 CATEGORY Cat CUTOFF 1 NEIGHBOURHOOD 3 CONDITIONS modelF",
"RULE Metabolite1 CATEGORY Cat CUTOFF 1 NEIGHBOURHOOD 3 CONDITIONS modelG"]),
self.test_names, self.categories).rules
self.features = []
for gene_id in self.feature_by_id:
self.features.append(self.feature_by_id[gene_id])
self.features.sort(key=lambda x: x.location.start) # vital for py3 < 3.5
self.record = Record()
self.record._record.seq = Seq("A"*150000)
for feature in self.features:
self.record.add_cds_feature(feature)
def get_supported_cluster_types() -> List[str]:
""" Returns a list of all cluster types for which there are rules
"""
signature_names = {sig.name for sig in get_signature_profiles()}
with open(path.get_full_path(__file__, 'cluster_rules.txt'), "r") as rulefile:
rules = rule_parser.Parser("".join(rulefile.readlines()), signature_names).rules
clustertypes = [rule.name for rule in rules]
return clustertypes
def create_rules(rule_file: str, signature_names: Set[str]
) -> List[rule_parser.DetectionRule]:
""" Creates DetectionRule instances from the default rules file
Args:
rule_file: A path to a file containing cluster rules to use.
Returns:
A list of DetectionRules.
"""
rules = []
with open(rule_file, "r") as ruledata:
parser = rule_parser.Parser("".join(ruledata.readlines()), signature_names)
for rule in parser.rules:
rules.append(rule)
return rules
def get_supported_cluster_types(strictness: str,
category: Optional[str] = None) -> List[str]:
""" Returns a list of all cluster types for which there are rules
"""
signature_names = {sig.name for sig in get_signature_profiles()}
category_names = {cat.name for cat in get_rule_categories()}
rules: List[rule_parser.DetectionRule] = []
aliases: Dict[str, List[rule_parser.Token]] = {}
for rule_file in _get_rule_files_for_strictness(strictness):
with open(rule_file) as rulefile:
rules = rule_parser.Parser("".join(rulefile.readlines()),
signature_names, category_names, rules,
aliases).rules
clustertypes = [
rule.name for rule in rules
if category is None or rule.category == category
]
return clustertypes
def create_rules(
rule_file: str,
signature_names: Set[str],
existing_rules: List[rule_parser.DetectionRule] = None
) -> List[rule_parser.DetectionRule]:
""" Creates DetectionRule instances from the default rules file
Args:
rule_file: A path to a file containing cluster rules to use.
signature_names: the set of all known profile/signature names
existing_rules: a list of existing rules, if any
Returns:
A list of DetectionRules.
"""
rules = existing_rules or []
with open(rule_file, "r") as ruledata:
parser = rule_parser.Parser("".join(ruledata.readlines()),
signature_names, rules)
return parser.rules
