def test_settings(self):
toml = NestedDotDict.read_toml(get_test_resource("settings.toml"))
x = Settings.load(toml)
assert x.taxon == 1111
assert x.min_pchembl == 15
assert x.min_confidence_score == 2
assert x.min_phase == 0
assert str(x.cache_path) == "~"
assert x.n_retries == 100
assert not x.fast_save
assert x.timeout_sec == 0
def search_for(
what: What,
compounds: Union[Sequence[str], PurePath],
config: Union[None, Mapping[str, Any], Path],
) -> Tup[pd.DataFrame, Sequence[Triple]]:
"""
Args:
what:
compounds:
config:
Returns:
"""
if isinstance(compounds, (PurePath, str)):
compounds = Path(compounds).read_text(encoding="utf8").splitlines()
compounds = [c.strip() for c in compounds if len(c.strip()) > 0]
if config is None:
settings = Settings.load(NestedDotDict({}))
elif isinstance(config, PurePath):
settings = Settings.load(NestedDotDict.read_toml(config))
elif isinstance(config, NestedDotDict):
settings = config
else:
settings = Settings.load(NestedDotDict(config))
settings.set()
compounds = list(compounds)
api = ChemblApi.wrap(Chembl)
taxonomy = TaxonomyCaches.load(settings.taxon)
hits = what.clazz(api, settings, taxonomy).find_all(compounds)
# collapse over and sort the triples
triples = sorted(list({hit.to_triple() for hit in hits}))
df = pd.DataFrame([
pd.Series({f: getattr(h, f)
for f in what.clazz.hit_fields()}) for h in hits
])
return df, triples
def __init__(self):
path = os.environ.get("VALARDAGGER_CONFIG",
Path("/etc", "valardagger.toml"))
self.config = NestedDotDict.read_toml(path)
self.valar = valarpy.Valar()
self.model = None
def test_empty(self):
toml = NestedDotDict.read_toml(
get_test_resource("settings-empty.toml"))
x = Settings.load(toml)
assert x.min_phase == 3
def from_toml_file(
cls,
path: PathLike,
*,
warn: Union[bool, Callable[[Response], Any]] = True) -> Notifier:
return cls.from_dict(NestedDotDict.read_toml(path), warn=warn)