p2p-ld/nwb-linkml
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omfg working serialization (with forthcoming updates to numpydantic)

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sneakers-the-rat 2024-10-03 01:17:08 -07:00
parent 4672f54630
commit af53ee1ea9
Signed by untrusted user who does not match committer: jonny
GPG key ID: 6DCB96EF1E4D232D
2 changed files with 10 additions and 4 deletions
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8
nwb_linkml/tests/test_io/test_io_nwb.py
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@ -73,8 +73,14 @@ def test_nwbfile_dump(read_nwbfile):
electrodes = read_nwbfile.general.extracellular_ephys.electrodes.model_dump_json( electrodes = read_nwbfile.general.extracellular_ephys.electrodes.model_dump_json(
round_trip=True round_trip=True
) )
data = read_nwbfile.general.model_dump_json(round_trip=True)
file = read_nwbfile.model_dump_json(
round_trip=True, exclude_none=True, exclude_unset=True, exclude_defaults=True
)
# data = read_nwbfile.model_dump_json(round_trip=True, serialize_as_any=True) # data = read_nwbfile.model_dump_json(round_trip=True, serialize_as_any=True)
# pdb.set_trace() pdb.set_trace()
def test_timeseries(read_nwbfile, read_pynwb): def test_timeseries(read_nwbfile, read_pynwb):

6
nwb_models/src/nwb_models/models/pydantic/core/v2_7_0/core_nwb_base.py
View file

@ -17,10 +17,10 @@ from typing import (
Optional, Optional,
Tuple, Tuple,
Type, Type,
TypeVar,
Union, Union,
overload, overload,
) )
from typing_extensions import TypeVar
import numpy as np import numpy as np
from numpydantic import NDArray, Shape from numpydantic import NDArray, Shape
@ -154,7 +154,7 @@ class LinkMLMeta(RootModel):
NUMPYDANTIC_VERSION = "1.2.1" NUMPYDANTIC_VERSION = "1.2.1"
T = TypeVar("T", bound=NDArray) T = TypeVar("T", default=NDArray)
class VectorDataMixin(ConfiguredBaseModel, Generic[T]): class VectorDataMixin(ConfiguredBaseModel, Generic[T]):
@ -364,7 +364,7 @@ class TimeSeriesReferenceVectorData(TimeSeriesReferenceVectorDataMixin, VectorDa
description="""Number of data samples available in this time series, during this epoch""", description="""Number of data samples available in this time series, during this epoch""",
json_schema_extra={"linkml_meta": {"array": {"exact_number_dimensions": 1}}}, json_schema_extra={"linkml_meta": {"array": {"exact_number_dimensions": 1}}},
) )
timeseries: NDArray[Shape["*"], TimeSeries] = Field( timeseries: List[TimeSeries] = Field(
..., ...,
description="""The TimeSeries that this index applies to""", description="""The TimeSeries that this index applies to""",
json_schema_extra={"linkml_meta": {"array": {"exact_number_dimensions": 1}}}, json_schema_extra={"linkml_meta": {"array": {"exact_number_dimensions": 1}}},