mirror of
https://github.com/p2p-ld/nwb-linkml.git
synced 2024-11-10 00:34:29 +00:00
120 lines
4.1 KiB
Python
120 lines
4.1 KiB
Python
"""
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Namespaces adapter
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Wraps the :class:`nwb_schema_language.Namespaces` and other objects with convenience methods
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for extracting information and generating translated schema
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"""
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import pdb
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from typing import List, Optional
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from pydantic import BaseModel, Field, validator, PrivateAttr
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from pprint import pformat
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from linkml_runtime.linkml_model import SchemaDefinition
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from nwb_schema_language import Namespaces
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from nwb_linkml.adapters.adapter import Adapter, BuildResult
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from nwb_linkml.adapters.schema import SchemaAdapter
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from nwb_linkml.lang_elements import NwbLangSchema
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class NamespacesAdapter(Adapter):
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namespaces: Namespaces
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schemas: List[SchemaAdapter]
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imported: List['NamespacesAdapter'] = Field(default_factory=list)
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_imports_populated = PrivateAttr(False)
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def __init__(self, **kwargs):
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super(NamespacesAdapter, self).__init__(**kwargs)
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self._populate_schema_namespaces()
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def build(self) -> BuildResult:
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if not self._imports_populated:
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self.populate_imports()
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sch_result = BuildResult()
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for sch in self.schemas:
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sch_result += sch.build()
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# recursive step
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for imported in self.imported:
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imported_build = imported.build()
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sch_result += imported_build
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# add in monkeypatch nwb types
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sch_result.schemas.append(NwbLangSchema)
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# now generate the top-level namespaces that import everything
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for ns in self.namespaces.namespaces:
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ns_schemas = [sch for sch in self.schemas if sch.namespace == ns.name]
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ns_schema = SchemaDefinition(
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name = ns.name,
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id = ns.name,
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description = ns.doc,
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version = ns.version,
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imports=[sch.name for sch in ns_schemas]
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)
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sch_result.schemas.append(ns_schema)
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return sch_result
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def _populate_schema_namespaces(self):
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# annotate for each schema which namespace imports it
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for sch in self.schemas:
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# imports seem to always be from same folder, so we can just use name part
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sch_name = sch.path.name
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# find which namespace imports this schema file
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for ns in self.namespaces.namespaces:
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sources = [sch.source for sch in ns.schema_]
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if sch_name in sources:
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sch.namespace = ns.name
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break
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def find_type_source(self, name:str) -> SchemaAdapter:
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"""
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Given some neurodata_type_inc, find the schema that it's defined in.
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"""
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# First check within the main schema
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internal_matches = []
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for schema in self.schemas:
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class_names = [cls.neurodata_type_def for cls in schema.created_classes]
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if name in class_names:
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internal_matches.append(schema)
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import_matches = []
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for imported_ns in self.imported:
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for schema in imported_ns.schemas:
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class_names = [cls.neurodata_type_def for cls in schema.created_classes]
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if name in class_names:
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import_matches.append(schema)
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all_matches = [*internal_matches, *import_matches]
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if len(all_matches)>1:
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pdb.set_trace()
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raise KeyError(f"Found multiple schemas in namespace that define {name}:\ninternal: {pformat(internal_matches)}\nimported:{pformat(import_matches)}")
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elif len(all_matches) == 1:
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return all_matches[0]
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else:
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raise KeyError(f"No schema found that define {name}")
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def populate_imports(self):
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"""
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Populate the imports that are needed for each schema file
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"""
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for sch in self.schemas:
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for needs in sch.needed_imports:
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# shouldn't be recursive references, since imports should form a tree
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depends_on = self.find_type_source(needs)
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if depends_on not in sch.imports:
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sch.imports.append(depends_on)
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# do so recursively
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for imported in self.imported:
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imported.populate_imports()
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self._imports_populated = True
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