mirror of
https://github.com/p2p-ld/nwb-linkml.git
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working aligned dynamic table and TimeSeriesReferenceVectorData
This commit is contained in:
parent
dd99ac24eb
commit
06a18c23a8
GPG key ID:
6DCB96EF1E4D232D
9 changed files with 307 additions and 59 deletions
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@ -5,7 +5,7 @@
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|
||||
{file = "watchdog-4.0.2.tar.gz", hash = "sha256:b4dfbb6c49221be4535623ea4474a4d6ee0a9cef4a80b20c28db4d858b64e270"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "webcolors"
|
||||
version = "24.6.0"
|
||||
version = "24.8.0"
|
||||
requires_python = ">=3.8"
|
||||
summary = "A library for working with the color formats defined by HTML and CSS."
|
||||
groups = ["default"]
|
||||
files = [
|
||||
{file = "webcolors-24.6.0-py3-none-any.whl", hash = "sha256:8cf5bc7e28defd1d48b9e83d5fc30741328305a8195c29a8e668fa45586568a1"},
|
||||
{file = "webcolors-24.6.0.tar.gz", hash = "sha256:1d160d1de46b3e81e58d0a280d0c78b467dc80f47294b91b1ad8029d2cedb55b"},
|
||||
{file = "webcolors-24.8.0-py3-none-any.whl", hash = "sha256:fc4c3b59358ada164552084a8ebee637c221e4059267d0f8325b3b560f6c7f0a"},
|
||||
{file = "webcolors-24.8.0.tar.gz", hash = "sha256:08b07af286a01bcd30d583a7acadf629583d1f79bfef27dd2c2c5c263817277d"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -2187,11 +2187,11 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "zipp"
|
||||
version = "3.19.2"
|
||||
version = "3.20.0"
|
||||
requires_python = ">=3.8"
|
||||
summary = "Backport of pathlib-compatible object wrapper for zip files"
|
||||
groups = ["dev", "plot", "tests"]
|
||||
files = [
|
||||
{file = "zipp-3.19.2-py3-none-any.whl", hash = "sha256:f091755f667055f2d02b32c53771a7a6c8b47e1fdbc4b72a8b9072b3eef8015c"},
|
||||
{file = "zipp-3.19.2.tar.gz", hash = "sha256:bf1dcf6450f873a13e952a29504887c89e6de7506209e5b1bcc3460135d4de19"},
|
||||
{file = "zipp-3.20.0-py3-none-any.whl", hash = "sha256:58da6168be89f0be59beb194da1250516fdaa062ccebd30127ac65d30045e10d"},
|
||||
{file = "zipp-3.20.0.tar.gz", hash = "sha256:0145e43d89664cfe1a2e533adc75adafed82fe2da404b4bbb6b026c0157bdb31"},
|
||||
]
|
||||
|
|
|
|||
|
|
@ -20,7 +20,7 @@ dependencies = [
|
|||
"pydantic-settings>=2.0.3",
|
||||
"tqdm>=4.66.1",
|
||||
'typing-extensions>=4.12.2;python_version<"3.11"',
|
||||
"numpydantic>=1.3.0",
|
||||
"numpydantic>=1.3.1",
|
||||
"black>=24.4.2",
|
||||
"pandas>=2.2.2",
|
||||
]
|
||||
|
|
|
|||
|
|
@ -153,8 +153,8 @@ class Adapter(BaseModel):
|
|||
# SchemaAdapters that should be located under the same
|
||||
# NamespacesAdapter when it's important to query across SchemaAdapters,
|
||||
# so skip to avoid combinatoric walking
|
||||
# if key == "imports" and type(input).__name__ == "SchemaAdapter":
|
||||
# continue
|
||||
if key == "imports" and type(input).__name__ == "SchemaAdapter":
|
||||
continue
|
||||
val = getattr(input, key)
|
||||
yield (key, val)
|
||||
if isinstance(val, (BaseModel, dict, list)):
|
||||
|
|
|
|||
|
|
@ -26,7 +26,13 @@ from linkml_runtime.utils.compile_python import file_text
|
|||
from linkml_runtime.utils.formatutils import remove_empty_items
|
||||
from linkml_runtime.utils.schemaview import SchemaView
|
||||
|
||||
from nwb_linkml.includes.hdmf import DYNAMIC_TABLE_IMPORTS, DYNAMIC_TABLE_INJECTS
|
||||
from nwb_linkml.includes.base import BASEMODEL_GETITEM
|
||||
from nwb_linkml.includes.hdmf import (
|
||||
DYNAMIC_TABLE_IMPORTS,
|
||||
DYNAMIC_TABLE_INJECTS,
|
||||
TSRVD_IMPORTS,
|
||||
TSRVD_INJECTS,
|
||||
)
|
||||
from nwb_linkml.includes.types import ModelTypeString, NamedImports, NamedString, _get_name
|
||||
|
||||
OPTIONAL_PATTERN = re.compile(r"Optional\[([\w\.]*)\]")
|
||||
|
|
@ -44,6 +50,7 @@ class NWBPydanticGenerator(PydanticGenerator):
|
|||
' is stored in an NWB file")'
|
||||
),
|
||||
'object_id: Optional[str] = Field(None, description="Unique UUID for each object")',
|
||||
BASEMODEL_GETITEM,
|
||||
)
|
||||
split: bool = True
|
||||
imports: list[Import] = field(default_factory=lambda: [Import(module="numpy", alias="np")])
|
||||
|
|
@ -232,7 +239,7 @@ class AfterGenerateClass:
|
|||
Returns:
|
||||
|
||||
"""
|
||||
if cls.cls.name == "DynamicTable":
|
||||
if cls.cls.name in "DynamicTable":
|
||||
cls.cls.bases = ["DynamicTableMixin"]
|
||||
|
||||
if cls.injected_classes is None:
|
||||
|
|
@ -254,6 +261,21 @@ class AfterGenerateClass:
|
|||
cls.cls.bases = ["DynamicTableRegionMixin", "VectorData"]
|
||||
elif cls.cls.name == "AlignedDynamicTable":
|
||||
cls.cls.bases = ["AlignedDynamicTableMixin", "DynamicTable"]
|
||||
elif cls.cls.name == "TimeSeriesReferenceVectorData":
|
||||
# in core.nwb.base, so need to inject and import again
|
||||
cls.cls.bases = ["TimeSeriesReferenceVectorDataMixin", "VectorData"]
|
||||
if cls.injected_classes is None:
|
||||
cls.injected_classes = TSRVD_INJECTS.copy()
|
||||
else:
|
||||
cls.injected_classes.extend(TSRVD_INJECTS.copy())
|
||||
|
||||
if isinstance(cls.imports, Imports):
|
||||
cls.imports += TSRVD_IMPORTS
|
||||
elif isinstance(cls.imports, list):
|
||||
cls.imports = Imports(imports=cls.imports) + TSRVD_IMPORTS
|
||||
else:
|
||||
cls.imports = TSRVD_IMPORTS.model_copy()
|
||||
|
||||
return cls
|
||||
|
||||
|
||||
|
|
|
|||
14
nwb_linkml/src/nwb_linkml/includes/base.py
Normal file
14
nwb_linkml/src/nwb_linkml/includes/base.py
Normal file
|
|
@ -0,0 +1,14 @@
|
|||
"""
|
||||
Modifications to the ConfiguredBaseModel used by all generated classes
|
||||
"""
|
||||
|
||||
BASEMODEL_GETITEM = """
|
||||
def __getitem__(self, val: Union[int, slice]) -> Any:
|
||||
\"\"\"Try and get a value from value or "data" if we have it\"\"\"
|
||||
if hasattr(self, "value") and self.value is not None:
|
||||
return self.value[val]
|
||||
elif hasattr(self, "data") and self.data is not None:
|
||||
return self.data[val]
|
||||
else:
|
||||
raise KeyError("No value or data field to index from")
|
||||
"""
|
||||
|
|
@ -535,6 +535,109 @@ class AlignedDynamicTableMixin(DynamicTableMixin):
|
|||
df.set_index((self.name, "id"), drop=True, inplace=True)
|
||||
return df
|
||||
|
||||
@model_validator(mode="before")
|
||||
@classmethod
|
||||
def create_categories(cls, model: Dict[str, Any]) -> Dict:
|
||||
"""
|
||||
Construct categories from arguments.
|
||||
|
||||
the model dict is ordered after python3.6, so we can use that minus
|
||||
anything in :attr:`.NON_COLUMN_FIELDS` to determine order implied from passage order
|
||||
"""
|
||||
if "categories" not in model:
|
||||
categories = [
|
||||
k for k in model if k not in cls.NON_CATEGORY_FIELDS and not k.endswith("_index")
|
||||
]
|
||||
model["categories"] = categories
|
||||
else:
|
||||
# add any columns not explicitly given an order at the end
|
||||
categories = [
|
||||
k
|
||||
for k in model
|
||||
if k not in cls.NON_COLUMN_FIELDS
|
||||
and not k.endswith("_index")
|
||||
and k not in model["categories"]
|
||||
]
|
||||
model["categories"].extend(categories)
|
||||
return model
|
||||
|
||||
|
||||
class TimeSeriesReferenceVectorDataMixin(VectorDataMixin):
|
||||
"""
|
||||
Mixin class for TimeSeriesReferenceVectorData -
|
||||
very simple, just indexing the given timeseries object.
|
||||
|
||||
These shouldn't have additional fields in them, just the three columns
|
||||
for index, span, and timeseries
|
||||
"""
|
||||
|
||||
idx_start: NDArray[Any, int]
|
||||
count: NDArray[Any, int]
|
||||
timeseries: NDArray[Any, BaseModel]
|
||||
|
||||
@model_validator(mode="after")
|
||||
def ensure_equal_length(self) -> "TimeSeriesReferenceVectorDataMixin":
|
||||
assert len(self.idx_start) == len(self.timeseries) == len(self.count), (
|
||||
f"Columns have differing lengths: idx: {len(self.idx_start)}, count: {len(self.count)},"
|
||||
f" timeseries: {len(self.timeseries)}"
|
||||
)
|
||||
return self
|
||||
|
||||
def __len__(self) -> int:
|
||||
"""Since we have ensured equal length, just return idx_start"""
|
||||
return len(self.idx_start)
|
||||
|
||||
@overload
|
||||
def _slice_helper(self, item: int) -> slice: ...
|
||||
|
||||
@overload
|
||||
def _slice_helper(self, item: slice) -> List[slice]: ...
|
||||
|
||||
def _slice_helper(self, item: Union[int, slice]) -> Union[slice, List[slice]]:
|
||||
if isinstance(item, (int, np.integer)):
|
||||
return slice(self.idx_start[item], self.idx_start[item] + self.count[item])
|
||||
else:
|
||||
starts = self.idx_start[item]
|
||||
ends = starts + self.count[item]
|
||||
return [slice(start, end) for start, end in zip(starts, ends)]
|
||||
|
||||
def __getitem__(self, item: Union[int, slice, Iterable]) -> Any:
|
||||
if self._index is not None:
|
||||
raise NotImplementedError(
|
||||
"VectorIndexing with TimeSeriesReferenceVectorData is not supported because it is"
|
||||
" never done in the core schema."
|
||||
)
|
||||
|
||||
if isinstance(item, (int, np.integer)):
|
||||
return self.timeseries[self._slice_helper(item)]
|
||||
elif isinstance(item, slice):
|
||||
return [self.timeseries[subitem] for subitem in self._slice_helper(item)]
|
||||
elif isinstance(item, Iterable):
|
||||
return [self.timeseries[self._slice_helper(subitem)] for subitem in item]
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Dont know how to index with {item}, must be an int, slice, or iterable"
|
||||
)
|
||||
|
||||
def __setitem__(self, key: Union[int, slice, Iterable], value: Any) -> None:
|
||||
if self._index is not None:
|
||||
raise NotImplementedError(
|
||||
"VectorIndexing with TimeSeriesReferenceVectorData is not supported because it is"
|
||||
" never done in the core schema."
|
||||
)
|
||||
if isinstance(key, (int, np.integer)):
|
||||
self.timeseries[self._slice_helper(key)] = value
|
||||
elif isinstance(key, slice):
|
||||
for subitem in self._slice_helper(key):
|
||||
self.timeseries[subitem] = value
|
||||
elif isinstance(key, Iterable):
|
||||
for subitem in key:
|
||||
self.timeseries[self._slice_helper(subitem)] = value
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Dont know how to index with {key}, must be an int, slice, or iterable"
|
||||
)
|
||||
|
||||
|
||||
DYNAMIC_TABLE_IMPORTS = Imports(
|
||||
imports=[
|
||||
|
|
@ -577,3 +680,19 @@ DYNAMIC_TABLE_INJECTS = [
|
|||
DynamicTableMixin,
|
||||
AlignedDynamicTableMixin,
|
||||
]
|
||||
|
||||
TSRVD_IMPORTS = Imports(
|
||||
imports=[
|
||||
Import(
|
||||
module="typing",
|
||||
objects=[
|
||||
ObjectImport(name="overload"),
|
||||
ObjectImport(name="Iterable"),
|
||||
ObjectImport(name="Tuple"),
|
||||
],
|
||||
),
|
||||
Import(module="pydantic", objects=[ObjectImport(name="model_validator")]),
|
||||
]
|
||||
)
|
||||
"""Imports for TimeSeriesReferenceVectorData"""
|
||||
TSRVD_INJECTS = [VectorDataMixin, TimeSeriesReferenceVectorDataMixin]
|
||||
|
|
|
|||
|
|
@ -42,6 +42,7 @@ def test_walk_fields(nwb_core_fixture):
|
|||
dtype = list(nwb_core_fixture.walk_fields(nwb_core_fixture, "dtype"))
|
||||
|
||||
dtype_havers = list(nwb_core_fixture.walk_types(nwb_core_fixture, (Dataset, Attribute)))
|
||||
dtype_havers = [haver for haver in dtype_havers if haver.dtype is not None]
|
||||
compound_dtypes = [len(d.dtype) for d in dtype_havers if isinstance(d.dtype, list)]
|
||||
expected_dtypes = np.sum(compound_dtypes) + len(dtype_havers)
|
||||
assert expected_dtypes == len(dtype)
|
||||
|
|
|
|||
|
|
@ -15,6 +15,11 @@ from nwb_linkml.models import (
|
|||
IntracellularResponsesTable,
|
||||
IntracellularStimuliTable,
|
||||
IntracellularRecordingsTable,
|
||||
VoltageClampSeries,
|
||||
VoltageClampSeriesData,
|
||||
VoltageClampStimulusSeries,
|
||||
VoltageClampStimulusSeriesData,
|
||||
TimeSeriesReferenceVectorData,
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -102,19 +107,68 @@ def units(request) -> Tuple[Units, list[np.ndarray], np.ndarray]:
|
|||
return units, spike_times, spike_idx
|
||||
|
||||
|
||||
def _icephys_stimulus_and_response(
|
||||
i: int, electrode: IntracellularElectrode
|
||||
) -> tuple[VoltageClampStimulusSeries, VoltageClampSeries]:
|
||||
generator = np.random.default_rng()
|
||||
n_samples = generator.integers(20, 50)
|
||||
stimulus = VoltageClampStimulusSeries(
|
||||
name=f"vcss_{i}",
|
||||
data=VoltageClampStimulusSeriesData(value=[i] * n_samples),
|
||||
stimulus_description=f"{i}",
|
||||
sweep_number=i,
|
||||
electrode=electrode,
|
||||
)
|
||||
response = VoltageClampSeries(
|
||||
name=f"vcs_{i}",
|
||||
data=VoltageClampSeriesData(value=[i] * n_samples),
|
||||
stimulus_description=f"{i}",
|
||||
electrode=electrode,
|
||||
)
|
||||
return stimulus, response
|
||||
|
||||
|
||||
@pytest.fixture()
|
||||
def intracellular_recordings_table() -> IntracellularRecordingsTable:
|
||||
n_recordings = 10
|
||||
generator = np.random.default_rng()
|
||||
device = Device(name="my device")
|
||||
electrode = IntracellularElectrode(
|
||||
name="my_electrode", description="an electrode", device=device
|
||||
)
|
||||
stims = []
|
||||
responses = []
|
||||
for i in range(n_recordings):
|
||||
stim, response = _icephys_stimulus_and_response(i, electrode)
|
||||
stims.append(stim)
|
||||
responses.append(response)
|
||||
|
||||
electrodes = IntracellularElectrodesTable(
|
||||
name="intracellular_electrodes", electrode=[electrode] * n_recordings
|
||||
)
|
||||
stimuli = IntracellularStimuliTable(
|
||||
name="intracellular_stimuli",
|
||||
stimulus=TimeSeriesReferenceVectorData(
|
||||
name="stimulus",
|
||||
description="this should be optional",
|
||||
idx_start=np.arange(n_recordings),
|
||||
count=generator.integers(1, 10, (n_recordings,)),
|
||||
timeseries=stims,
|
||||
),
|
||||
)
|
||||
responses = IntracellularResponsesTable()
|
||||
|
||||
recordings_table = IntracellularRecordingsTable()
|
||||
responses = IntracellularResponsesTable(
|
||||
name="intracellular_responses",
|
||||
response=TimeSeriesReferenceVectorData(
|
||||
name="response",
|
||||
description="this should be optional",
|
||||
idx_start=np.arange(n_recordings),
|
||||
count=generator.integers(1, 10, (n_recordings,)),
|
||||
timeseries=responses,
|
||||
),
|
||||
)
|
||||
|
||||
recordings_table = IntracellularRecordingsTable(
|
||||
electrodes=electrodes, stimuli=stimuli, responses=responses
|
||||
)
|
||||
return recordings_table
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
# FIXME: Make this just be the output of the provider by patching into import machinery
|
||||
from nwb_linkml.models.pydantic.core.v2_7_0.namespace import (
|
||||
|
|
@ -6,6 +7,7 @@ from nwb_linkml.models.pydantic.core.v2_7_0.namespace import (
|
|||
DynamicTableRegion,
|
||||
ElectrodeGroup,
|
||||
VectorIndex,
|
||||
VoltageClampStimulusSeries,
|
||||
)
|
||||
from .conftest import _ragged_array
|
||||
|
||||
|
|
@ -159,3 +161,39 @@ def test_dynamictable_extra_coercion():
|
|||
Extra fields should be coerced to VectorData and have their
|
||||
indexing relationships handled when passed as plain arrays.
|
||||
"""
|
||||
|
||||
|
||||
def test_aligned_dynamictable(intracellular_recordings_table):
|
||||
"""
|
||||
Multiple aligned dynamictables should be indexable with a multiindex
|
||||
"""
|
||||
# can get a single row.. (check correctness below)
|
||||
row = intracellular_recordings_table[0]
|
||||
# can get a single table with its name
|
||||
stimuli = intracellular_recordings_table["stimuli"]
|
||||
assert stimuli.shape == (10, 1)
|
||||
|
||||
# nab a few rows to make the dataframe
|
||||
rows = intracellular_recordings_table[0:3]
|
||||
assert all(
|
||||
rows.columns
|
||||
== pd.MultiIndex.from_tuples(
|
||||
[
|
||||
("electrodes", "index"),
|
||||
("electrodes", "electrode"),
|
||||
("stimuli", "index"),
|
||||
("stimuli", "stimulus"),
|
||||
("responses", "index"),
|
||||
("responses", "response"),
|
||||
]
|
||||
)
|
||||
)
|
||||
|
||||
# ensure that we get the actual values from the TimeSeriesReferenceVectorData
|
||||
# also tested separately
|
||||
# each individual cell should be an array of VoltageClampStimulusSeries...
|
||||
# and then we should be able to index within that as well
|
||||
stims = rows["stimuli", "stimulus"][0]
|
||||
for i in range(len(stims)):
|
||||
assert isinstance(stims[i], VoltageClampStimulusSeries)
|
||||
assert all([i == val for val in stims[i][:]])
|
||||
|
|
|
|||
Loading…
Reference in a new issue