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65ea1b673f
* save tiledb array to s3, dont cache user annotations * Add option to disable annotation filename prompt (#1787) Co-authored-by: Madison Dunitz <dunitzm@gmail.com> * set tiledb default context in cxg_adaptor Co-authored-by: maniarathi <arathi.mani@chanzuckerberg.com> Co-authored-by: Severiano Badajoz <sbadajoz@chanzuckerberg.com>
71 lines
2.4 KiB
Python
71 lines
2.4 KiB
Python
from abc import ABCMeta, abstractmethod
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import fastobo
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import fsspec
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from server.common.errors import OntologyLoadFailure
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from server.common.utils.type_conversion_utils import get_schema_type_hint_of_array
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class Annotations(metaclass=ABCMeta):
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""" baseclass for annotations, including ontologies"""
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""" our default ontology is the PURL for the Cell Ontology.
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See http://www.obofoundry.org/ontology/cl.html """
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DefaultOnotology = "http://purl.obolibrary.org/obo/cl.obo"
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def __init__(self):
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self.ontology_data = None
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def load_ontology(self, path):
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"""Load and parse ontologies - currently support OBO files only."""
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if path is None:
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path = self.DefaultOnotology
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try:
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with fsspec.open(path) as f:
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obo = fastobo.iter(f)
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terms = filter(lambda stanza: type(stanza) is fastobo.term.TermFrame, obo)
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names = [tag.name for term in terms for tag in term if type(tag) is fastobo.term.NameClause]
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self.ontology_data = names
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except FileNotFoundError as e:
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raise OntologyLoadFailure("Unable to find OBO ontology path") from e
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except SyntaxError as e:
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raise OntologyLoadFailure("Syntax error loading OBO ontology") from e
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except Exception as e:
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raise OntologyLoadFailure("Error loading OBO file") from e
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def get_schema(self, data_adaptor):
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schema = []
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labels = self.read_labels(data_adaptor)
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if labels is not None and not labels.empty:
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for col in labels.columns:
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col_schema = dict(name=col, writable=True)
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col_schema.update(get_schema_type_hint_of_array(labels[col]))
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schema.append(col_schema)
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return schema
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@abstractmethod
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def set_collection(self, name):
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"""set or create a new annotation collection"""
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pass
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@abstractmethod
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def read_labels(self, data_adaptor):
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"""Return the labels as a pandas.DataFrame"""
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pass
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@abstractmethod
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def write_labels(self, df, data_adaptor):
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"""Write the labels (df) to a persistent storage such that it can later be read"""
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pass
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@abstractmethod
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def update_parameters(self, parameters, data_adaptor):
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"""Update configuration parameters that describe information about the annotations feature"""
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pass
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