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[DATALAD RUNCMD] run codespell throughout fixing typo automagically but ignoring the failure due to ambigous typos
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11 changed files with 14 additions and 14 deletions
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@ -11,7 +11,7 @@
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* the [register_container_type](https://github.com/hdmf-dev/hdmf/blob/dd39b3878523c4b03f5286fc740752befd192d8b/src/hdmf/build/manager.py#L727-L736) method in hdmf's TypeMap class seems to overwrite the loaded schema???
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* `__NS_CATALOG` seems to actually hold references to the schema but it doesn't seem to be used anywhere except within `__TYPE_MAP` ?
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* [NWBHDF5IO](https://github.com/NeurodataWithoutBorders/pynwb/blob/dev/src/pynwb/__init__.py#L237-L238) uses `TypeMap` to greate a `BuildManager`
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* Parent class [HDF5IO](https://github.com/hdmf-dev/hdmf/blob/dd39b3878523c4b03f5286fc740752befd192d8b/src/hdmf/backends/hdf5/h5tools.py#L37) then reimplements a lot of basic functionality from elsehwere
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* Parent class [HDF5IO](https://github.com/hdmf-dev/hdmf/blob/dd39b3878523c4b03f5286fc740752befd192d8b/src/hdmf/backends/hdf5/h5tools.py#L37) then reimplements a lot of basic functionality from elsewhere
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* Parent-parent metaclass [HDMFIO](https://github.com/hdmf-dev/hdmf/blob/dev/src/hdmf/backends/io.py) appears to be the final writing class?
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* `BuildManager.build` then [calls `TypeMap.build`](https://github.com/hdmf-dev/hdmf/blob/dd39b3878523c4b03f5286fc740752befd192d8b/src/hdmf/build/manager.py#L171) ???
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* `TypeMap.build` ...
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@ -72,7 +72,7 @@ is relatively complex, and so to use a schema extension one must also
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program the python classes or mappings to python class attributes
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needed to use them, configuration for getter and setter methods,
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i/o routines, etc. Since schema extensions are relatively hard to make,
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to accomodate heterogeneous data NWB uses `DynamicTable`s, which can be
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to accommodate heterogeneous data NWB uses `DynamicTable`s, which can be
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given arbitrary new columns.
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The loose coupling between schema and code has a few impacts:
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@ -2,4 +2,4 @@
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## v0.1.0 - Package exists
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thats about as much as can be said
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that's about as much as can be said
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@ -243,7 +243,7 @@ class NamespacesAdapter(Adapter):
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ns = ns[0]
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break
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else:
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raise NameError(f"Couldnt find namespace {name}")
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raise NameError(f"Couldn't find namespace {name}")
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else:
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ns = ns[0]
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@ -311,7 +311,7 @@ def truncate_file(source: Path, target: Optional[Path] = None, n:int=10) -> Path
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try:
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obj.resize(n, axis=0)
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except TypeError:
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# contiguous arrays cant be trivially resized, so we have to copy and create a new dataset
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# contiguous arrays can't be trivially resized, so we have to copy and create a new dataset
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tmp_name = obj.name + '__tmp'
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original_name = obj.name
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obj.parent.move(obj.name, tmp_name)
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@ -326,7 +326,7 @@ def truncate_file(source: Path, target: Optional[Path] = None, n:int=10) -> Path
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# use h5repack to actually remove the items from the dataset
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if shutil.which('h5repack') is None:
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warnings.warn('Truncated file made, but since h5repack not found in path, file wont be any smaller')
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warnings.warn('Truncated file made, but since h5repack not found in path, file won't be any smaller')
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return target
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print('Repacking hdf5...')
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@ -22,7 +22,7 @@ FlatDType = EnumDefinition(
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DTypeTypes = []
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for nwbtype, linkmltype in flat_to_linkml.items():
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# skip the dtypes that are the same as the builtin linkml types (which should alredy exist)
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# skip the dtypes that are the same as the builtin linkml types (which should already exist)
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# to avoid a recursion error
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if linkmltype == nwbtype:
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continue
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@ -815,7 +815,7 @@ def resolve_references(src: dict, completed: Dict[str, H5ReadResult]) -> Tuple[d
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if isinstance(item, HDF5_Path):
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other_item = completed.get(item, None)
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if other_item is None:
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errors.append(f"Couldnt find: {item}")
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errors.append(f"Couldn't find: {item}")
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res[path] = other_item.result
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completes.append(item)
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@ -23,7 +23,7 @@ def model_from_dynamictable(group:h5py.Group, base:Optional[BaseModel] = None) -
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nptype = group[col].dtype.type
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if nptype == np.void:
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warnings.warn(f"Cant handle numpy void type for column {col} in {group.name}")
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warnings.warn(f"Can't handle numpy void type for column {col} in {group.name}")
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continue
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type_ = Optional[NDArray[Any, nptype]]
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@ -64,7 +64,7 @@ def dynamictable_to_model(
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# # dask can't handle this, we just arrayproxy it
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items[col] = NDArrayProxy(h5f_file=group.file.filename, path=group[col].name)
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#else:
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# warnings.warn(f"Dask cant handle object type arrays like {col} in {group.name}. Skipping")
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# warnings.warn(f"Dask can't handle object type arrays like {col} in {group.name}. Skipping")
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# pdb.set_trace()
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# # can't auto-chunk with "object" type
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# items[col] = da.from_array(group[col], chunks=-1)
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@ -189,7 +189,7 @@ class NDArrayProxy():
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obj = h5f.get(self.path)
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return obj[slice]
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def __setitem__(self, slice, value):
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raise NotImplementedError(f"Cant write into an arrayproxy yet!")
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raise NotImplementedError(f"Can't write into an arrayproxy yet!")
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@classmethod
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@ -16,7 +16,7 @@ def test_build_base(nwb_schema):
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assert len(base.classes) == 1
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img = base.classes[0]
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assert img.name == "Image"
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# no parent class, tree_root shoudl be true
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# no parent class, tree_root should be true
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assert img.tree_root
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assert len(img.attributes) == 3
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@ -340,12 +340,12 @@ groups:
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each point in time is assumed to be 2-D (has only x & y dimensions).'
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groups:
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- neurodata_type_inc: CorrectedImageStack
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doc: Reuslts from motion correction of an image stack.
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doc: Results from motion correction of an image stack.
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quantity: '+'
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- neurodata_type_def: CorrectedImageStack
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neurodata_type_inc: NWBDataInterface
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doc: Reuslts from motion correction of an image stack.
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doc: Results from motion correction of an image stack.
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groups:
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- name: corrected
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neurodata_type_inc: ImageSeries
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