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"0f2d9fc76cf3788fbdefc6f7b06afb7267c5fe2967970389907a5a9c4864334a" +content-hash = "6c8e41c25a97368f31f190b4d76c3dd839a3b2b364c8653dcdd00ffc258a2547" diff --git a/nwb_linkml/pyproject.toml b/nwb_linkml/pyproject.toml index 1f59cf5..196319c 100644 --- a/nwb_linkml/pyproject.toml +++ b/nwb_linkml/pyproject.toml @@ -30,7 +30,9 @@ pytest-cov = {version = "^4.1.0", optional = true} coveralls = {version = "^3.3.1", optional = true} pytest-profiling = {version = "^1.7.0", optional = true} pydantic-settings = "^2.0.3" -pandas = "^2.1.1" +dask = "^2023.9.2" +blosc2 = "^2.2.7" + [tool.poetry.extras] tests = [ diff --git a/nwb_linkml/src/nwb_linkml/annotations.py b/nwb_linkml/src/nwb_linkml/annotations.py index b068936..27d45ef 100644 --- a/nwb_linkml/src/nwb_linkml/annotations.py +++ b/nwb_linkml/src/nwb_linkml/annotations.py @@ -2,6 +2,7 @@ Utility functions for introspection on python annotations """ import typing +from typing import Any, List def unwrap_optional(annotation): @@ -12,6 +13,17 @@ def unwrap_optional(annotation): annotation = args[0] return annotation +def get_inner_types(annotation) -> List[Any]: + types = [] + args = typing.get_args(annotation) + for arg in args: + # try and get inner types to see if we're a terminal type + inner_args = typing.get_args(arg) + if len(inner_args) == 0: + types.append(arg) + else: + types.extend(get_inner_types(arg)) + return types def take_outer_type(annotation): if typing.get_origin(annotation) is list: diff --git a/nwb_linkml/src/nwb_linkml/maps/hdmf.py b/nwb_linkml/src/nwb_linkml/maps/hdmf.py index df1e724..91638fa 100644 --- a/nwb_linkml/src/nwb_linkml/maps/hdmf.py +++ b/nwb_linkml/src/nwb_linkml/maps/hdmf.py @@ -2,6 +2,7 @@ Mapping functions for handling HDMF classes like DynamicTables """ import pdb +import warnings from typing import List, Type, Optional, Any import ast from nwb_linkml.types import DataFrame @@ -10,52 +11,114 @@ from pydantic import create_model, BaseModel from nwb_linkml.maps import dtype import numpy as np from nwb_linkml.types.hdf5 import HDF5_Path -from nwb_linkml.types.ndarray import NDArray +from nwb_linkml.types.ndarray import NDArray, NDArrayProxy +from nwb_linkml.annotations import get_inner_types +import dask.array as da +import nptyping def model_from_dynamictable(group:h5py.Group, base:Optional[BaseModel] = None) -> Type[DataFrame]: + """ + Create a pydantic model from a dynamic table + """ colnames = group.attrs['colnames'] types = {} for col in colnames: - idxname = col + '_index' - if idxname in group.keys(): - idx = group.get(idxname)[0] - dset = group.get(col) - item = dset[idx] - else: - dset = group.get(col) - item = dset[0] - # read the first entry to see what we got + # idxname = col + '_index' + # if idxname in group.keys(): + # idx = group.get(idxname)[0] + # dset = group.get(col) + # item = dset[idx] + # else: + # dset = group.get(col) + # item = dset[0] + # # read the first entry to see what we got + # + # if isinstance(item, bytes): + # item = item.decode('utf-8') + # if isinstance(item, str): + # # try to see if this is actually a list or smth encoded as a string + # try: + # item = ast.literal_eval(item) + # except (ValueError, SyntaxError): + # pass - if isinstance(item, bytes): - item = item.decode('utf-8') - if isinstance(item, str): - # try to see if this is actually a list or smth encoded as a string - try: - item = ast.literal_eval(item) - except (ValueError, SyntaxError): - pass + # Get a nptypes type for the array + #pdb.set_trace() + + # type_ = type(item) + # type_ = dtype.np_to_python.get(type_, type_) + # if type_ is h5py.h5r.Reference: + # #type_ = HDF5_Path + # type_ = 'String' + # elif type_ is np.ndarray: + # item: np.ndarray + # type_ = dtype.flat_to_npytyping[item.dtype.name] + + #if type_ is not np.void: + #type_ = NDArray[Any, getattr(nptyping, dtype.flat_to_npytyping[item.dtype.name])] + + #nptype = nptyping.typing_.name_per_dtype[group[col].dtype.type] + nptype = group[col].dtype.type + type_ = Optional[NDArray[Any, nptype]] - type_ = type(item) - type_ = dtype.np_to_python.get(type_, type_) - if type_ is h5py.h5r.Reference: - type_ = HDF5_Path - elif type_ is np.ndarray: - type_ = NDArray - if type_ is not np.void: # FIXME: handling nested column types that appear only in some versions? - types[col] = (List[type_ | None], ...) + #types[col] = (List[type_ | None], ...) + types[col] = (type_, None) if base is None: - base = DataFrame + #base = DataFrame + base = BaseModel else: - base = (DataFrame, base) + base = (BaseModel, base) + #base = (DataFrame, base) model = create_model(group.name.split('/')[-1], **types, __base__=base) return model +def dynamictable_to_model( + group:h5py.Group, + model:Optional[Type[DataFrame]]=None, + base:Optional[BaseModel] = None) -> BaseModel: + """ + Instantiate a dynamictable model + + Calls :func:`.model_from_dynamictable` if model is not provided. + """ + if model is None: + model = model_from_dynamictable(group, base) + + items = {} + for col, col_type in model.model_fields.items(): + if col not in group.keys(): + if col in group.attrs: + items[col] = group.attrs[col] + continue + + if col_type.annotation is HDF5_Path: + items[col] = [HDF5_Path(group[d].name) for d in group[col][:]] + else: + try: + items[col] = da.from_array(group[col]) + except NotImplementedError: + if str in get_inner_types(col_type.annotation): + # dask can't handle this, we just arrayproxy it + items[col] = NDArrayProxy(h5f_file=group.file.filename, path=group[col].name) + else: + warnings.warn(f"Dask cant handle object type arrays like {col} in {group.name}. Skipping") + # pdb.set_trace() + # # can't auto-chunk with "object" type + # items[col] = da.from_array(group[col], chunks=-1) + + return model.model_construct(hdf5_path = group.name, + name = group.name.split('/')[-1], + **items) + + + + def dynamictable_to_df(group:h5py.Group, model:Optional[Type[DataFrame]]=None, base:Optional[BaseModel] = None) -> DataFrame: diff --git a/nwb_linkml/src/nwb_linkml/types/df.py b/nwb_linkml/src/nwb_linkml/types/df.py index b89530e..df32fb6 100644 --- a/nwb_linkml/src/nwb_linkml/types/df.py +++ b/nwb_linkml/src/nwb_linkml/types/df.py @@ -116,25 +116,3 @@ class DataFrame(BaseModel, pd.DataFrame): for k, v in out.items() } return nxt(self.__class__(**out)) - - -class DataFrameProxy(DataFrame): - def __init__(self, hdf5_file: str, hdf5_path:str, **kwargs): - # pdb.set_trace() - super().__init__(**kwargs) - - self.hdf5_file = hdf5_file - self.hdf5_path = hdf5_path - - - def _load(self): - cols = {} - with h5py.File(self.hdf5_file, 'r') as h5f: - obj = h5f.get(self.hdf5_path) - for col in self.model_fields.keys(): - if col in obj.keys(): - setattr(self, col, obj[col][:]) - - def __getitem__(self, item:str): - pass - diff --git a/nwb_linkml/src/nwb_linkml/types/ndarray.py b/nwb_linkml/src/nwb_linkml/types/ndarray.py index 93525ad..7bba66b 100644 --- a/nwb_linkml/src/nwb_linkml/types/ndarray.py +++ b/nwb_linkml/src/nwb_linkml/types/ndarray.py @@ -3,6 +3,7 @@ Extension of nptyping NDArray for pydantic that allows for JSON-Schema serializa * Order to store data in (row first) """ +import base64 import pdb from pathlib import Path from typing import ( @@ -12,6 +13,8 @@ from typing import ( Generic, TypeVar ) +import sys +from copy import copy from pydantic_core import core_schema from pydantic import ( @@ -24,6 +27,8 @@ from pydantic.json_schema import JsonSchemaValue import numpy as np import h5py +from dask.array.core import Array as DaskArray +import blosc2 from nptyping import NDArray as _NDArray from nptyping.ndarray import NDArrayMeta @@ -32,6 +37,7 @@ from nptyping.shape_expression import check_shape from nwb_linkml.maps.dtype import np_to_python + class NDArray(_NDArray): """ Following the example here: https://docs.pydantic.dev/latest/usage/types/custom/#handling-third-party-types @@ -56,7 +62,11 @@ class NDArray(_NDArray): assert value.dtype == dtype, f"Invalid dtype! expected {dtype}, got {value.dtype}" return value def validate_array(value: Any) -> np.ndarray: - assert cls.__instancecheck__(value), f'Invalid shape! expected shape {shape.prepared_args}, got shape {value.shape}' + if isinstance(value, np.ndarray): + assert cls.__instancecheck__(value), f'Invalid shape! expected shape {shape.prepared_args}, got shape {value.shape}' + elif isinstance(value, DaskArray): + assert shape is Any or check_shape(value.shape, shape), f'Invalid shape! expected shape {shape.prepared_args}, got shape {value.shape}' + return value def coerce_list(value: Any) -> np.ndarray: @@ -106,36 +116,60 @@ class NDArray(_NDArray): ) + def array_to_list(instance: np.ndarray | DaskArray) -> list|dict: + if isinstance(instance, DaskArray): + arr = instance.__array__() + else: + arr = instance + + # If we're larger than 16kB then compress array! + if sys.getsizeof(arr) > 16 * 1024: + packed = blosc2.pack_array2(arr) + packed = base64.b64encode(packed) + ret= { + 'array': packed, + 'shape': copy(arr.shape), + 'dtype': copy(arr.dtype.name), + 'unpack_fns': ['base64.b64decode', 'blosc2.unpack_array2'] + } + return ret + else: + return arr.tolist() + + + + return core_schema.json_or_python_schema( json_schema=list_schema, python_schema=core_schema.chain_schema( [ core_schema.no_info_plain_validator_function(coerce_list), - core_schema.is_instance_schema(np.ndarray), + core_schema.union_schema([ + core_schema.is_instance_schema(cls=np.ndarray), + core_schema.is_instance_schema(cls=DaskArray) + ]), core_schema.no_info_plain_validator_function(validate_dtype), core_schema.no_info_plain_validator_function(validate_array) ] ), serialization=core_schema.plain_serializer_function_ser_schema( - lambda instance: instance.tolist(), + array_to_list, when_used='json' ) ) - - class NDArrayProxy(): """ Thin proxy to numpy arrays stored within hdf5 files, only read into memory when accessed, but otherwise passthrough all attempts to access attributes. """ - def __init__(self, h5f_file: Path, path: str): + def __init__(self, h5f_file: Path|str, path: str): """ Args: h5f_file (:class:`pathlib.Path`): Path to source HDF5 file path (str): Location within HDF5 file where this array is located """ - self.h5f_file = h5f_file + self.h5f_file = Path(h5f_file) self.path = path def __getattr__(self, item): @@ -148,3 +182,20 @@ class NDArrayProxy(): return obj[slice] def __setitem__(self, slice, value): raise NotImplementedError(f"Cant write into an arrayproxy yet!") + + + @classmethod + def __get_pydantic_core_schema__( + cls, + _source_type: _NDArray, + _handler: Callable[[Any], core_schema.CoreSchema], + + ) -> core_schema.CoreSchema: + # return core_schema.no_info_after_validator_function( + # serialization=core_schema.plain_serializer_function_ser_schema( + # lambda instance: instance.tolist(), + # when_used='json' + # ) + # ) + + return NDArray_.__get_pydantic_core_schema__(cls, _source_type, _handler) diff --git a/nwb_linkml/tests/test_annotations.py b/nwb_linkml/tests/test_annotations.py new file mode 100644 index 0000000..f08b698 --- /dev/null +++ b/nwb_linkml/tests/test_annotations.py @@ -0,0 +1,17 @@ +import pytest + +from typing import List, Optional, Union +from types import NoneType +from nwb_linkml.annotations import get_inner_types + +@pytest.mark.parametrize( + ('annotation', 'inner_types'), + [ + (List[str | None], (str, NoneType)) + ] +) +def test_get_inner_types(annotation, inner_types): + got_inner_types = get_inner_types(annotation) + assert len(got_inner_types) == len(inner_types) + for got, expected in zip(got_inner_types, inner_types): + assert got is expected \ No newline at end of file diff --git a/nwb_linkml/tests/test_maps/test_hdmf.py b/nwb_linkml/tests/test_maps/test_hdmf.py index d81ec7a..c3bc784 100644 --- a/nwb_linkml/tests/test_maps/test_hdmf.py +++ b/nwb_linkml/tests/test_maps/test_hdmf.py @@ -1,16 +1,24 @@ +import pdb + import pytest import h5py +import time -from nwb_linkml.maps.hdmf import model_from_dynamictable, dynamictable_to_df +from nwb_linkml.maps.hdmf import model_from_dynamictable, dynamictable_to_model NWBFILE = '/Users/jonny/Dropbox/lab/p2p_ld/data/nwb/sub-738651046_ses-760693773.nwb' @pytest.mark.skip() def test_make_dynamictable(): h5f = h5py.File(NWBFILE, 'r') - group = h5f['intervals']['drifting_gratings_presentations'] + group = h5f['units'] + start_time = time.time() model = model_from_dynamictable(group) - data = dynamictable_to_df(group, model) + data = dynamictable_to_model(group, model) + ser = data.model_dump_json() + end_time = time.time() + total_time = end_time - start_time + pdb.set_trace()