From 23a5412854ab98c5f168c867bf4ea6e03321a54e Mon Sep 17 00:00:00 2001 From: sneakers-the-rat Date: Thu, 19 Oct 2023 04:01:48 -0700 Subject: [PATCH] remove unused code --- nwb_linkml/src/nwb_linkml/io/hdf5.py | 74 ---------------------------- 1 file changed, 74 deletions(-) diff --git a/nwb_linkml/src/nwb_linkml/io/hdf5.py b/nwb_linkml/src/nwb_linkml/io/hdf5.py index 100f9f3..485f2ee 100644 --- a/nwb_linkml/src/nwb_linkml/io/hdf5.py +++ b/nwb_linkml/src/nwb_linkml/io/hdf5.py @@ -91,7 +91,6 @@ class HDF5IO(): else: return queue.completed[path].result - def make_provider(self) -> SchemaProvider: """ Create a :class:`~.providers.schema.SchemaProvider` by @@ -120,56 +119,6 @@ class HDF5IO(): return provider - def process_group(self, group:h5py.Group|h5py.File) -> dict | list: - attrs = dict(group.attrs) - - # how to process the group? - # -------------------------------------------------- - # list-like - # -------------------------------------------------- - # a list of data classes - if 'neurodata_type' not in attrs and \ - all([isinstance(v, h5py.Group) for v in group.values()]) and \ - all(['neurodata_type' in v.attrs for v in group.values()]): - - return [self.process_group(v) for v in group.values()] - - # -------------------------------------------------- - # dict-like - # -------------------------------------------------- - - res = {} - - - for key, val in group.items(): - if isinstance(val, h5py.Group): - res[key] = self.process_group(val) - elif isinstance(val, h5py.Dataset): - res[key] = self.process_dataset(val) - return res - - def process_dataset(self, data: h5py.Dataset) -> dict | list: - if len(data.shape) == 1: - return list(data[:]) - - -def finish_root_hackily(queue: ReadQueue) -> dict: - root = {'name': 'root'} - for k, v in queue.queue.items(): - if isinstance(v.result, dict): - res_dict = {} - for inner_k, inner_v in v.result.items(): - if isinstance(inner_v, HDF5_Path): - inner_res = queue.completed.get(inner_v) - if inner_res is not None: - res_dict[inner_k] = inner_res.result - else: - res_dict[inner_k] = inner_v - root[res_dict['name']] = res_dict - else: - root[v.path.split('/')[-1]] = v.result - return root - def read_specs_as_dicts(group: h5py.Group) -> dict: """ Utility function to iterate through the `/specifications` group and @@ -197,18 +146,6 @@ def read_specs_as_dicts(group: h5py.Group) -> dict: return spec_dict -def get_model(cls: h5py.Group | h5py.Dataset) -> Type[BaseModel]: - attrs = cls.attrs - ns = attrs.get('namespace') - model_name = attrs.get('neurodata_type') - - try: - return SchemaProvider().get_class(ns, model_name) - except: - # try to get parent class - mod = get_model(cls.parent) - return mod.model_fields[cls.name.split('/')[-1]].annotation - def find_references(h5f: h5py.File, path: str) -> List[str]: """ Find all objects that make a reference to a given object in @@ -264,8 +201,6 @@ def find_references(h5f: h5py.File, path: str) -> List[str]: return references - - def truncate_file(source: Path, target: Optional[Path] = None, n:int=10) -> Path: """ Create a truncated HDF5 file where only the first few samples are kept. @@ -350,12 +285,3 @@ def truncate_file(source: Path, target: Optional[Path] = None, n:int=10) -> Path return target -def submodel_by_path(model: BaseModel, path:str) -> Type[BaseModel | dict | list]: - """ - Given a pydantic model and an absolute HDF5 path, get the type annotation - """ - parts = path.split('/') - for part in parts: - ann = model.model_fields[part].annotation - -