p2p-ld/nwb-linkml
1
0
Fork 0
mirror of https://github.com/p2p-ld/nwb-linkml.git synced 2024-11-14 10:44:28 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

Merge pull request #16 from p2p-ld/tests-generator
Some checks failed
Lint / Ruff Linting (push) Has been cancelled
Details
Lint / Black Formatting (push) Has been cancelled
Details
Lint / Check for spelling errors (push) Has been cancelled
Details
Model Rebuild / build_models (push) Has been cancelled
Details
Tests / test (3.10) (push) Has been cancelled
Details
Tests / test (3.11) (push) Has been cancelled
Details
Tests / test (3.12) (push) Has been cancelled
Details
Tests / finish-coverage (push) Has been cancelled
Details

Browse source 
Restore generator tests
This commit is contained in:
Jonny Saunders 2024-10-02 20:40:57 -07:00 committed by GitHub
commit 77a852913c
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
167 changed files with 9957 additions and 4347 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

6
nwb_linkml/src/nwb_linkml/adapters/dataset.py
View file

@ -642,7 +642,7 @@ class MapNVectors(DatasetMap):
cls.name is None
and cls.neurodata_type_def is None
and cls.neurodata_type_inc
and cls.neurodata_type_inc != "VectorData"
and cls.neurodata_type_inc not in ("VectorData", "VectorIndex")
and cls.quantity in ("*", "+")
)
@ -681,10 +681,10 @@ class MapNVectorData(DatasetMap):
Check for being an unnamed multivalued vector class that IS VectorData
"""
return (
cls.name is None
(cls.name is None or cls.name in ("vector_data", "vector_index"))
and cls.neurodata_type_def is None
and cls.neurodata_type_inc
and cls.neurodata_type_inc == "VectorData"
and cls.neurodata_type_inc in ("VectorData", "VectorIndex")
and cls.quantity in ("*", "+")
)

2
nwb_linkml/src/nwb_linkml/adapters/group.py
View file

@ -230,7 +230,7 @@ class GroupAdapter(ClassAdapter):
)
if self.debug: # pragma: no cover - only used in development
slot.annotations["group_adapter"] = {
slot.annotations["slot_adapter"] = {
"tag": "slot_adapter",
"value": "container_value_slot",
}

35
nwb_linkml/src/nwb_linkml/generators/pydantic.py
View file

@ -7,8 +7,6 @@ See class and module docstrings for details :)
import re
from dataclasses import dataclass, field
from pathlib import Path
from types import ModuleType
from typing import Callable, ClassVar, Dict, List, Optional, Tuple
from linkml.generators import PydanticGenerator
@ -27,7 +25,7 @@ from linkml_runtime.utils.schemaview import SchemaView
from nwb_linkml.includes.base import (
BASEMODEL_CAST_WITH_VALUE,
BASEMODEL_COERCE_CHILD,
BASEMODEL_COERCE_SUBCLASS,
BASEMODEL_COERCE_VALUE,
BASEMODEL_EXTRA_TO_VALUE,
BASEMODEL_GETITEM,
@ -58,7 +56,7 @@ class NWBPydanticGenerator(PydanticGenerator):
BASEMODEL_GETITEM,
BASEMODEL_COERCE_VALUE,
BASEMODEL_CAST_WITH_VALUE,
BASEMODEL_COERCE_CHILD,
BASEMODEL_COERCE_SUBCLASS,
BASEMODEL_EXTRA_TO_VALUE,
)
split: bool = True
@ -146,7 +144,6 @@ class NWBPydanticGenerator(PydanticGenerator):
cls = AfterGenerateClass.inject_dynamictable(cls)
cls = AfterGenerateClass.wrap_dynamictable_columns(cls, sv)
cls = AfterGenerateClass.inject_dynamictable_imports(cls, sv, self._get_element_import)
cls = AfterGenerateClass.strip_vector_data_slots(cls, sv)
return cls
def before_render_template(self, template: PydanticModule, sv: SchemaView) -> PydanticModule:
@ -157,25 +154,6 @@ class NWBPydanticGenerator(PydanticGenerator):
del template.meta["source_file"]
return template
def compile_module(
self, module_path: Path = None, module_name: str = "test", **kwargs
) -> ModuleType: # pragma: no cover - replaced with provider
"""
Compiles generated python code to a module
:return:
"""
pycode = self.serialize(**kwargs)
if module_path is not None:
module_path = Path(module_path)
init_file = module_path / "__init__.py"
with open(init_file, "w") as ifile:
ifile.write(" ")
try:
return compile_python(pycode, module_path, module_name)
except NameError as e:
raise e
class AfterGenerateSlot:
"""
@ -373,15 +351,6 @@ class AfterGenerateClass:
cls.imports += imp
return cls
@staticmethod
def strip_vector_data_slots(cls: ClassResult, sv: SchemaView) -> ClassResult:
"""
Remove spurious ``vector_data`` slots from DynamicTables
"""
if "vector_data" in cls.cls.attributes:
del cls.cls.attributes["vector_data"]
return cls
def wrap_preserving_optional(annotation: str, wrap: str) -> str:
"""

2
nwb_linkml/src/nwb_linkml/includes/base.py
View file

@ -44,7 +44,7 @@ BASEMODEL_CAST_WITH_VALUE = """
raise e1
"""
BASEMODEL_COERCE_CHILD = """
BASEMODEL_COERCE_SUBCLASS = """
@field_validator("*", mode="before")
@classmethod
def coerce_subclass(cls, v: Any, info) -> Any:

68
nwb_linkml/tests/fixtures/schema.py vendored
View file

@ -5,8 +5,10 @@ from typing import Dict, Optional
import pytest
from linkml_runtime.dumpers import yaml_dumper
from linkml_runtime.linkml_model import (
from linkml_runtime.linkml_model.meta import (
ArrayExpression,
ClassDefinition,
DimensionExpression,
Prefix,
SchemaDefinition,
SlotDefinition,
@ -88,10 +90,6 @@ def linkml_schema_bare() -> TestSchemas:
equals_string="toplevel",
identifier=True,
),
SlotDefinition(name="array", range="MainTopLevel__Array"),
SlotDefinition(
name="SkippableSlot", description="A slot that was meant to be skipped!"
),
SlotDefinition(
name="inline_dict",
description=(
@ -103,35 +101,51 @@ def linkml_schema_bare() -> TestSchemas:
inlined_as_list=False,
any_of=[{"range": "OtherClass"}, {"range": "StillAnotherClass"}],
),
],
),
ClassDefinition(
name="MainTopLevel__Array",
description="Main class's array",
is_a="Arraylike",
attributes=[
SlotDefinition(name="x", range="numeric", required=True),
SlotDefinition(name="y", range="numeric", required=True),
SlotDefinition(
name="z",
range="numeric",
required=False,
maximum_cardinality=3,
minimum_cardinality=3,
name="named_slot",
description=(
"A slot that should use the Named[] generic to set the name param"
),
annotations=[{"named": True}],
range="OtherClass",
inlined=True,
),
SlotDefinition(
name="a",
name="value",
description="Main class's array",
range="numeric",
required=False,
minimum_cardinality=4,
maximum_cardinality=4,
any_of=[
{
"array": ArrayExpression(
dimensions=[
DimensionExpression(alias="x"),
DimensionExpression(alias="y"),
]
)
},
{
"array": ArrayExpression(
dimensions=[
DimensionExpression(alias="x"),
DimensionExpression(alias="y"),
DimensionExpression(alias="z", exact_cardinality=3),
]
)
},
{
"array": ArrayExpression(
dimensions=[
DimensionExpression(alias="x"),
DimensionExpression(alias="y"),
DimensionExpression(alias="z", exact_cardinality=3),
DimensionExpression(alias="a", exact_cardinality=4),
]
)
},
],
),
],
),
ClassDefinition(
name="skippable",
description="A class that lives to be skipped!",
),
ClassDefinition(
name="OtherClass",
description="Another class yno!",

192
nwb_linkml/tests/test_generators/test_generator_pydantic.py
View file

@ -7,7 +7,6 @@ because it's tested in the base linkml package.
# ruff: noqa: F821 - until the tests here settle down
import re
import sys
import typing
from types import ModuleType
@ -15,8 +14,10 @@ from typing import Optional, TypedDict
import numpy as np
import pytest
from linkml_runtime.utils.compile_python import compile_python
from numpydantic.dtype import Float
from numpydantic.ndarray import NDArrayMeta
from pydantic import BaseModel
from pydantic import ValidationError
from nwb_linkml.generators.pydantic import NWBPydanticGenerator
@ -35,7 +36,6 @@ class TestModules(TypedDict):
TestModules.__test__ = False
@pytest.mark.xfail()
def generate_and_import(
linkml_schema: TestSchemas, split: bool, generator_kwargs: Optional[dict] = None
) -> TestModules:
@ -45,7 +45,6 @@ def generate_and_import(
"split": split,
"emit_metadata": True,
"gen_slots": True,
"pydantic_version": "2",
**generator_kwargs,
}
@ -68,20 +67,13 @@ def generate_and_import(
sys.path.append(str(linkml_schema.core_path.parents[1]))
core = compile_python(
str(linkml_schema.core_path.with_suffix(".py")), module_name="test_schema.core"
)
imported = compile_python(
str(linkml_schema.imported_path.with_suffix(".py")), module_name="test_schema.imported"
)
namespace = compile_python(
str(linkml_schema.namespace_path.with_suffix(".py")), module_name="test_schema.namespace"
)
core = compile_python(str(linkml_schema.core_path.with_suffix(".py")))
imported = compile_python(str(linkml_schema.imported_path.with_suffix(".py")))
namespace = compile_python(str(linkml_schema.namespace_path.with_suffix(".py")))
return TestModules(core=core, imported=imported, namespace=namespace, split=split)
@pytest.mark.xfail()
@pytest.fixture(scope="module", params=["split", "unsplit"])
def imported_schema(linkml_schema, request) -> TestModules:
"""
@ -90,99 +82,14 @@ def imported_schema(linkml_schema, request) -> TestModules:
"""
split = request.param == "split"
yield generate_and_import(linkml_schema, split)
del sys.modules["test_schema.core"]
del sys.modules["test_schema.imported"]
del sys.modules["test_schema.namespace"]
return generate_and_import(linkml_schema, split)
def _model_correctness(modules: TestModules):
def test_array(imported_schema):
"""
Shared assertions for model correctness.
Only tests very basic things like type and existence,
more specific tests are in their own test functions!
"""
assert issubclass(modules["core"].MainTopLevel, BaseModel)
assert issubclass(modules["core"].Skippable, BaseModel)
assert issubclass(modules["core"].OtherClass, BaseModel)
assert issubclass(modules["core"].StillAnotherClass, BaseModel)
assert issubclass(modules["imported"].MainThing, BaseModel)
Arraylike classes are converted to slots that specify nptyping arrays.
@pytest.mark.xfail()
def test_generate(linkml_schema):
"""
Base case, we can generate pydantic models from linkml schema
Tests basic functionality of serializer including
- serialization
- compilation (loading as a python model)
- existence and correctness of attributes
"""
modules = generate_and_import(linkml_schema, split=False)
assert isinstance(modules["core"], ModuleType)
assert isinstance(modules["imported"], ModuleType)
assert isinstance(modules["namespace"], ModuleType)
_model_correctness(modules)
# unsplit modules should have all the classes present, even if they aren't defined in it
assert modules["core"].MainThing.__module__ == "test_schema.core"
assert issubclass(modules["core"].MainTopLevel, modules["core"].MainThing)
del sys.modules["test_schema.core"]
del sys.modules["test_schema.imported"]
del sys.modules["test_schema.namespace"]
@pytest.mark.xfail()
def test_generate_split(linkml_schema):
"""
We can generate schema split into separate files
"""
modules = generate_and_import(linkml_schema, split=True)
assert isinstance(modules["core"], ModuleType)
assert isinstance(modules["imported"], ModuleType)
assert isinstance(modules["namespace"], ModuleType)
_model_correctness(modules)
# split modules have classes defined once and imported
assert modules["core"].MainThing.__module__ == "test_schema.imported"
# can't assert subclass here because of the weird way relative imports work
# when we don't actually import using normal python import machinery
assert modules["core"].MainTopLevel.__mro__[1].__module__ == "test_schema.imported"
del sys.modules["test_schema.core"]
del sys.modules["test_schema.imported"]
del sys.modules["test_schema.namespace"]
@pytest.mark.xfail()
def test_versions(linkml_schema):
"""
We can use explicit versions that import from relative paths generated by
SchemaProvider
"""
# here all we do is check that we have the correct relative import, since we test
# the actual generation of these path structures elsewhere in the provider tests
core_str = NWBPydanticGenerator(
str(linkml_schema.core_path), versions={"imported": "v4.2.0"}, split=True
).serialize()
# the import should be like
# from ...imported.v4_2_0.imported import (
# MainThing
# )
match = re.findall(r"from \.\.\.imported\.v4_2_0.*?MainThing.*?\)", core_str, flags=re.DOTALL)
assert len(match) == 1
@pytest.mark.xfail()
def test_arraylike(imported_schema):
"""
Arraylike classes are converted to slots that specify nptyping arrays
Test that we can use any_of with the array slot (unlike the upstream generator, currently)
array: Optional[Union[
NDArray[Shape["* x, * y"], Number],
@ -191,19 +98,18 @@ def test_arraylike(imported_schema):
]] = Field(None)
"""
# check that we have gotten an NDArray annotation and its shape is correct
array = imported_schema["core"].MainTopLevel.model_fields["array"].annotation
array = imported_schema["core"].MainTopLevel.model_fields["value"].annotation
args = typing.get_args(array)
for i, _ in enumerate(("* x, * y", "* x, * y, 3 z", "* x, * y, 3 z, 4 a")):
for i, shape in enumerate(("* x, * y", "* x, * y, 3 z", "* x, * y, 3 z, 4 a")):
assert isinstance(args[i], NDArrayMeta)
assert args[i].__args__[0].__args__
assert args[i].__args__[1] == np.number
assert args[i].__args__[0].__args__[0] == shape
assert args[i].__args__[1] == Float
# we shouldn't have an actual class for the array
assert not hasattr(imported_schema["core"], "MainTopLevel__Array")
assert not hasattr(imported_schema["core"], "MainTopLevelArray")
@pytest.mark.xfail()
def test_inject_fields(imported_schema):
"""
Our root model should have the special fields we injected
@ -213,35 +119,6 @@ def test_inject_fields(imported_schema):
assert "object_id" in base.model_fields
@pytest.mark.xfail()
def test_linkml_meta(imported_schema):
"""
We should be able to store some linkml metadata with our classes
"""
meta = imported_schema["core"].LinkML_Meta
assert "tree_root" in meta.model_fields
assert imported_schema["core"].MainTopLevel.linkml_meta.default.tree_root
assert not imported_schema["core"].OtherClass.linkml_meta.default.tree_root
@pytest.mark.xfail()
def test_skip(linkml_schema):
"""
We can skip slots and classes
"""
modules = generate_and_import(
linkml_schema,
split=False,
generator_kwargs={
"SKIP_SLOTS": ("SkippableSlot",),
"SKIP_CLASSES": ("Skippable", "skippable"),
},
)
assert not hasattr(modules["core"], "Skippable")
assert "SkippableSlot" not in modules["core"].MainTopLevel.model_fields
@pytest.mark.xfail()
def test_inline_with_identifier(imported_schema):
"""
By default, if a class has an identifier attribute, it is inlined
@ -256,7 +133,6 @@ def test_inline_with_identifier(imported_schema):
assert stillanother is imported_schema["core"].StillAnotherClass
@pytest.mark.xfail()
def test_namespace(imported_schema):
"""
Namespace schema import all classes from the other schema
@ -269,23 +145,39 @@ def test_namespace(imported_schema):
("MainThing", "test_schema.imported"),
("Arraylike", "test_schema.imported"),
("MainTopLevel", "test_schema.core"),
("Skippable", "test_schema.core"),
("OtherClass", "test_schema.core"),
("StillAnotherClass", "test_schema.core"),
):
assert hasattr(ns, classname)
if imported_schema["split"]:
assert getattr(ns, classname).__module__ == modname
module_end_name = ".".join(getattr(ns, classname).__module__.split(".")[-2:])
assert module_end_name == modname
@pytest.mark.xfail()
def test_get_set_item(imported_schema):
"""We can get and set without explicitly addressing array"""
cls = imported_schema["core"].MainTopLevel(array=np.array([[1, 2, 3], [4, 5, 6]]))
cls[0] = 50
assert (cls[0] == 50).all()
assert (cls.array[0] == 50).all()
def test_get_item(imported_schema):
"""We can get without explicitly addressing array"""
cls = imported_schema["core"].MainTopLevel(value=np.array([[1, 2, 3], [4, 5, 6]], dtype=float))
assert np.array_equal(cls[0], np.array([1, 2, 3], dtype=float))
cls[1, 1] = 100
assert cls[1, 1] == 100
assert cls.array[1, 1] == 100
def test_named_slot(imported_schema):
"""
Slots that have a ``named`` annotation should get their ``name`` attribute set automatically
"""
OtherClass = imported_schema["core"].OtherClass
MainClass = imported_schema["core"].MainTopLevel
# We did in fact get the outer annotation
# this is a wild ass way to get the function name but hey
annotation = MainClass.model_fields["named_slot"].annotation.__args__[0]
validation_fn_name = annotation.__metadata__[0].func.__name__
assert validation_fn_name == "_get_name"
# we can't instantiate OtherClass without the ``name``
with pytest.raises(ValidationError, match=".*name.*"):
_ = OtherClass()
# but when we instantiate MainClass the name gets set automatically
instance = MainClass(named_slot={})
assert isinstance(instance.named_slot, OtherClass)
assert instance.named_slot.name == "named_slot"

45
nwb_linkml/tests/test_includes/test_base.py Normal file
View file

@ -0,0 +1,45 @@
"""
Base includes
"""
import pytest
@pytest.mark.skip
def test_basemodel_getitem(imported_schema):
"""
We can get a value from ``value`` if we have it
"""
pass
@pytest.mark.skip
def test_basemodel_coerce_value(imported_schema):
"""
We can instantiate something by trying to grab it's "value" item
"""
pass
@pytest.mark.skip
def test_basemodel_cast_with_value(imported_schema):
"""
Opposite of above, we try to cast **into** the ``value`` field
"""
pass
@pytest.mark.skip
def test_basemodel_coerce_subclass(imported_schema):
"""
We try to rescue by coercing to a child class if possible
"""
pass
@pytest.mark.skip
def test_basemodel_extra_to_value(imported_schema):
"""
We gather extra fields and put them into a value dict when it's present
"""
pass

15
nwb_models/src/nwb_models/models/pydantic/core/v2_2_0/core_nwb_base.py generated
View file

@ -170,9 +170,9 @@ class Image(NWBData):
description: Optional[str] = Field(None, description="""Description of the image.""")
value: Optional[
Union[
NDArray[Shape["* x, * y"], float],
NDArray[Shape["* x, * y, 3 r_g_b"], float],
NDArray[Shape["* x, * y, 4 r_g_b_a"], float],
NDArray[Shape["* x, * y"], float | int],
NDArray[Shape["* x, * y, 3 r_g_b"], float | int],
NDArray[Shape["* x, * y, 4 r_g_b_a"], float | int],
]
] = Field(None)
@ -333,13 +333,16 @@ class ProcessingModule(NWBContainer):
{"from_schema": "core.nwb.base", "tree_root": True}
)
value: Optional[List[Union[DynamicTable, NWBDataInterface]]] = Field(
name: str = Field(...)
description: str = Field(
..., description="""Description of this collection of processed data."""
)
value: Optional[Dict[str, Union[DynamicTable, NWBDataInterface]]] = Field(
None,
json_schema_extra={
"linkml_meta": {"any_of": [{"range": "NWBDataInterface"}, {"range": "DynamicTable"}]}
},
)
name: str = Field(...)
class Images(NWBDataInterface):
@ -353,7 +356,7 @@ class Images(NWBDataInterface):
name: str = Field("Images", json_schema_extra={"linkml_meta": {"ifabsent": "string(Images)"}})
description: str = Field(..., description="""Description of this collection of images.""")
image: List[Image] = Field(..., description="""Images stored in this collection.""")
image: List[str] = Field(..., description="""Images stored in this collection.""")
# Model rebuild

60
nwb_models/src/nwb_models/models/pydantic/core/v2_2_0/core_nwb_behavior.py generated
View file

@ -213,6 +213,16 @@ class SpatialSeriesData(ConfiguredBaseModel):
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Optional[str] = Field(
"meters",
description="""Base unit of measurement for working with the data. The default value is 'meters'. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
@ -220,8 +230,8 @@ class SpatialSeriesData(ConfiguredBaseModel):
)
value: Optional[
Union[
NDArray[Shape["* num_times"], float],
NDArray[Shape["* num_times, * num_features"], float],
NDArray[Shape["* num_times"], float | int],
NDArray[Shape["* num_times, * num_features"], float | int],
]
] = Field(None)
@ -235,10 +245,13 @@ class BehavioralEpochs(NWBDataInterface):
{"from_schema": "core.nwb.behavior", "tree_root": True}
)
value: Optional[List[IntervalSeries]] = Field(
name: str = Field(
"BehavioralEpochs",
json_schema_extra={"linkml_meta": {"ifabsent": "string(BehavioralEpochs)"}},
)
value: Optional[Dict[str, IntervalSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "IntervalSeries"}]}}
)
name: str = Field(...)
class BehavioralEvents(NWBDataInterface):
@ -250,10 +263,13 @@ class BehavioralEvents(NWBDataInterface):
{"from_schema": "core.nwb.behavior", "tree_root": True}
)
value: Optional[List[TimeSeries]] = Field(
name: str = Field(
"BehavioralEvents",
json_schema_extra={"linkml_meta": {"ifabsent": "string(BehavioralEvents)"}},
)
value: Optional[Dict[str, TimeSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "TimeSeries"}]}}
)
name: str = Field(...)
class BehavioralTimeSeries(NWBDataInterface):
@ -265,10 +281,13 @@ class BehavioralTimeSeries(NWBDataInterface):
{"from_schema": "core.nwb.behavior", "tree_root": True}
)
value: Optional[List[TimeSeries]] = Field(
name: str = Field(
"BehavioralTimeSeries",
json_schema_extra={"linkml_meta": {"ifabsent": "string(BehavioralTimeSeries)"}},
)
value: Optional[Dict[str, TimeSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "TimeSeries"}]}}
)
name: str = Field(...)
class PupilTracking(NWBDataInterface):
@ -280,10 +299,12 @@ class PupilTracking(NWBDataInterface):
{"from_schema": "core.nwb.behavior", "tree_root": True}
)
value: Optional[List[TimeSeries]] = Field(
name: str = Field(
"PupilTracking", json_schema_extra={"linkml_meta": {"ifabsent": "string(PupilTracking)"}}
)
value: Optional[Dict[str, TimeSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "TimeSeries"}]}}
)
name: str = Field(...)
class EyeTracking(NWBDataInterface):
@ -295,10 +316,12 @@ class EyeTracking(NWBDataInterface):
{"from_schema": "core.nwb.behavior", "tree_root": True}
)
value: Optional[List[SpatialSeries]] = Field(
name: str = Field(
"EyeTracking", json_schema_extra={"linkml_meta": {"ifabsent": "string(EyeTracking)"}}
)
value: Optional[Dict[str, SpatialSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "SpatialSeries"}]}}
)
name: str = Field(...)
class CompassDirection(NWBDataInterface):
@ -310,10 +333,13 @@ class CompassDirection(NWBDataInterface):
{"from_schema": "core.nwb.behavior", "tree_root": True}
)
value: Optional[List[SpatialSeries]] = Field(
name: str = Field(
"CompassDirection",
json_schema_extra={"linkml_meta": {"ifabsent": "string(CompassDirection)"}},
)
value: Optional[Dict[str, SpatialSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "SpatialSeries"}]}}
)
name: str = Field(...)
class Position(NWBDataInterface):
@ -325,10 +351,12 @@ class Position(NWBDataInterface):
{"from_schema": "core.nwb.behavior", "tree_root": True}
)
value: Optional[List[SpatialSeries]] = Field(
name: str = Field(
"Position", json_schema_extra={"linkml_meta": {"ifabsent": "string(Position)"}}
)
value: Optional[Dict[str, SpatialSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "SpatialSeries"}]}}
)
name: str = Field(...)
# Model rebuild

118
nwb_models/src/nwb_models/models/pydantic/core/v2_2_0/core_nwb_ecephys.py generated
View file

@ -190,11 +190,12 @@ class ElectricalSeries(TimeSeries):
)
name: str = Field(...)
data: Union[
NDArray[Shape["* num_times"], float],
NDArray[Shape["* num_times, * num_channels"], float],
NDArray[Shape["* num_times, * num_channels, * num_samples"], float],
] = Field(..., description="""Recorded voltage data.""")
channel_conversion: Optional[NDArray[Shape["* num_channels"], float]] = Field(
None,
description="""Channel-specific conversion factor. Multiply the data in the 'data' dataset by these values along the channel axis (as indicated by axis attribute) AND by the global conversion factor in the 'conversion' attribute of 'data' to get the data values in Volts, i.e, data in Volts = data * data.conversion * channel_conversion. This approach allows for both global and per-channel data conversion factors needed to support the storage of electrical recordings as native values generated by data acquisition systems. If this dataset is not present, then there is no channel-specific conversion factor, i.e. it is 1 for all channels.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_channels"}]}}},
)
data: ElectricalSeriesData = Field(..., description="""Recorded voltage data.""")
electrodes: Named[DynamicTableRegion] = Field(
...,
description="""DynamicTableRegion pointer to the electrodes that this time series was generated from.""",
@ -207,11 +208,6 @@ class ElectricalSeries(TimeSeries):
}
},
)
channel_conversion: Optional[NDArray[Shape["* num_channels"], float]] = Field(
None,
description="""Channel-specific conversion factor. Multiply the data in the 'data' dataset by these values along the channel axis (as indicated by axis attribute) AND by the global conversion factor in the 'conversion' attribute of 'data' to get the data values in Volts, i.e, data in Volts = data * data.conversion * channel_conversion. This approach allows for both global and per-channel data conversion factors needed to support the storage of electrical recordings as native values generated by data acquisition systems. If this dataset is not present, then there is no channel-specific conversion factor, i.e. it is 1 for all channels.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_channels"}]}}},
)
description: Optional[str] = Field(
"no description",
description="""Description of the time series.""",
@ -249,6 +245,41 @@ class ElectricalSeries(TimeSeries):
)
class ElectricalSeriesData(ConfiguredBaseModel):
"""
Recorded voltage data.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.ecephys"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["volts"] = Field(
"volts",
description="""Base unit of measurement for working with the data. This value is fixed to 'volts'. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion' and 'channel_conversion' (if present).""",
json_schema_extra={"linkml_meta": {"equals_string": "volts", "ifabsent": "string(volts)"}},
)
value: Optional[
Union[
NDArray[Shape["* num_times"], float | int],
NDArray[Shape["* num_times, * num_channels"], float | int],
NDArray[Shape["* num_times, * num_channels, * num_samples"], float | int],
]
] = Field(None)
class SpikeEventSeries(ElectricalSeries):
"""
Stores snapshots/snippets of recorded spike events (i.e., threshold crossings). This may also be raw data, as reported by ephys hardware. If so, the TimeSeries::description field should describe how events were detected. All SpikeEventSeries should reside in a module (under EventWaveform interface) even if the spikes were reported and stored by hardware. All events span the same recording channels and store snapshots of equal duration. TimeSeries::data array structure: [num events] [num channels] [num samples] (or [num events] [num samples] for single electrode).
@ -259,15 +290,17 @@ class SpikeEventSeries(ElectricalSeries):
)
name: str = Field(...)
data: Union[
NDArray[Shape["* num_events, * num_samples"], float],
NDArray[Shape["* num_events, * num_channels, * num_samples"], float],
] = Field(..., description="""Spike waveforms.""")
data: SpikeEventSeriesData = Field(..., description="""Spike waveforms.""")
timestamps: NDArray[Shape["* num_times"], float] = Field(
...,
description="""Timestamps for samples stored in data, in seconds, relative to the common experiment master-clock stored in NWBFile.timestamps_reference_time. Timestamps are required for the events. Unlike for TimeSeries, timestamps are required for SpikeEventSeries and are thus re-specified here.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}},
)
channel_conversion: Optional[NDArray[Shape["* num_channels"], float]] = Field(
None,
description="""Channel-specific conversion factor. Multiply the data in the 'data' dataset by these values along the channel axis (as indicated by axis attribute) AND by the global conversion factor in the 'conversion' attribute of 'data' to get the data values in Volts, i.e, data in Volts = data * data.conversion * channel_conversion. This approach allows for both global and per-channel data conversion factors needed to support the storage of electrical recordings as native values generated by data acquisition systems. If this dataset is not present, then there is no channel-specific conversion factor, i.e. it is 1 for all channels.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_channels"}]}}},
)
electrodes: Named[DynamicTableRegion] = Field(
...,
description="""DynamicTableRegion pointer to the electrodes that this time series was generated from.""",
@ -280,11 +313,6 @@ class SpikeEventSeries(ElectricalSeries):
}
},
)
channel_conversion: Optional[NDArray[Shape["* num_channels"], float]] = Field(
None,
description="""Channel-specific conversion factor. Multiply the data in the 'data' dataset by these values along the channel axis (as indicated by axis attribute) AND by the global conversion factor in the 'conversion' attribute of 'data' to get the data values in Volts, i.e, data in Volts = data * data.conversion * channel_conversion. This approach allows for both global and per-channel data conversion factors needed to support the storage of electrical recordings as native values generated by data acquisition systems. If this dataset is not present, then there is no channel-specific conversion factor, i.e. it is 1 for all channels.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_channels"}]}}},
)
description: Optional[str] = Field(
"no description",
description="""Description of the time series.""",
@ -317,6 +345,40 @@ class SpikeEventSeries(ElectricalSeries):
)
class SpikeEventSeriesData(ConfiguredBaseModel):
"""
Spike waveforms.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.ecephys"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["volts"] = Field(
"volts",
description="""Unit of measurement for waveforms, which is fixed to 'volts'.""",
json_schema_extra={"linkml_meta": {"equals_string": "volts", "ifabsent": "string(volts)"}},
)
value: Optional[
Union[
NDArray[Shape["* num_events, * num_samples"], float | int],
NDArray[Shape["* num_events, * num_channels, * num_samples"], float | int],
]
] = Field(None)
class FeatureExtraction(NWBDataInterface):
"""
Features, such as PC1 and PC2, that are extracted from signals stored in a SpikeEventSeries or other source.
@ -415,10 +477,12 @@ class EventWaveform(NWBDataInterface):
{"from_schema": "core.nwb.ecephys", "tree_root": True}
)
value: Optional[List[SpikeEventSeries]] = Field(
name: str = Field(
"EventWaveform", json_schema_extra={"linkml_meta": {"ifabsent": "string(EventWaveform)"}}
)
value: Optional[Dict[str, SpikeEventSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "SpikeEventSeries"}]}}
)
name: str = Field(...)
class FilteredEphys(NWBDataInterface):
@ -430,10 +494,12 @@ class FilteredEphys(NWBDataInterface):
{"from_schema": "core.nwb.ecephys", "tree_root": True}
)
value: Optional[List[ElectricalSeries]] = Field(
name: str = Field(
"FilteredEphys", json_schema_extra={"linkml_meta": {"ifabsent": "string(FilteredEphys)"}}
)
value: Optional[Dict[str, ElectricalSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "ElectricalSeries"}]}}
)
name: str = Field(...)
class LFP(NWBDataInterface):
@ -445,10 +511,10 @@ class LFP(NWBDataInterface):
{"from_schema": "core.nwb.ecephys", "tree_root": True}
)
value: Optional[List[ElectricalSeries]] = Field(
name: str = Field("LFP", json_schema_extra={"linkml_meta": {"ifabsent": "string(LFP)"}})
value: Optional[Dict[str, ElectricalSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "ElectricalSeries"}]}}
)
name: str = Field(...)
class ElectrodeGroup(NWBContainer):
@ -591,7 +657,9 @@ class Clustering(NWBDataInterface):
# Model rebuild
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
ElectricalSeries.model_rebuild()
ElectricalSeriesData.model_rebuild()
SpikeEventSeries.model_rebuild()
SpikeEventSeriesData.model_rebuild()
FeatureExtraction.model_rebuild()
EventDetection.model_rebuild()
EventWaveform.model_rebuild()

3
nwb_models/src/nwb_models/models/pydantic/core/v2_2_0/core_nwb_epoch.py generated
View file

@ -247,9 +247,6 @@ class TimeIntervals(DynamicTable):
description="""Array of unique identifiers for the rows of this dynamic table.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_rows"}]}}},
)
vector_index: Optional[List[VectorIndex]] = Field(
None, description="""Indices for the vector columns of this dynamic table."""
)
class TimeIntervalsTimeseries(VectorData):

50
nwb_models/src/nwb_models/models/pydantic/core/v2_2_0/core_nwb_file.py generated
View file

@ -211,28 +211,28 @@ class NWBFile(NWBContainer):
...,
description="""Date and time corresponding to time zero of all timestamps. The date is stored in UTC with local timezone offset as ISO 8601 extended formatted string: 2018-09-28T14:43:54.123+02:00. Dates stored in UTC end in \"Z\" with no timezone offset. Date accuracy is up to milliseconds. All times stored in the file use this time as reference (i.e., time zero).""",
)
acquisition: Optional[List[Union[DynamicTable, NWBDataInterface]]] = Field(
acquisition: Optional[Dict[str, Union[DynamicTable, NWBDataInterface]]] = Field(
None,
description="""Data streams recorded from the system, including ephys, ophys, tracking, etc. This group should be read-only after the experiment is completed and timestamps are corrected to a common timebase. The data stored here may be links to raw data stored in external NWB files. This will allow keeping bulky raw data out of the file while preserving the option of keeping some/all in the file. Acquired data includes tracking and experimental data streams (i.e., everything measured from the system). If bulky data is stored in the /acquisition group, the data can exist in a separate NWB file that is linked to by the file being used for processing and analysis.""",
json_schema_extra={
"linkml_meta": {"any_of": [{"range": "NWBDataInterface"}, {"range": "DynamicTable"}]}
},
)
analysis: Optional[List[Union[DynamicTable, NWBContainer]]] = Field(
analysis: Optional[Dict[str, Union[DynamicTable, NWBContainer]]] = Field(
None,
description="""Lab-specific and custom scientific analysis of data. There is no defined format for the content of this group - the format is up to the individual user/lab. To facilitate sharing analysis data between labs, the contents here should be stored in standard types (e.g., neurodata_types) and appropriately documented. The file can store lab-specific and custom data analysis without restriction on its form or schema, reducing data formatting restrictions on end users. Such data should be placed in the analysis group. The analysis data should be documented so that it could be shared with other labs.""",
json_schema_extra={
"linkml_meta": {"any_of": [{"range": "NWBContainer"}, {"range": "DynamicTable"}]}
},
)
scratch: Optional[List[Union[DynamicTable, NWBContainer]]] = Field(
scratch: Optional[Dict[str, Union[DynamicTable, NWBContainer]]] = Field(
None,
description="""A place to store one-off analysis results. Data placed here is not intended for sharing. By placing data here, users acknowledge that there is no guarantee that their data meets any standard.""",
json_schema_extra={
"linkml_meta": {"any_of": [{"range": "NWBContainer"}, {"range": "DynamicTable"}]}
},
)
processing: Optional[List[ProcessingModule]] = Field(
processing: Optional[Dict[str, ProcessingModule]] = Field(
None,
description="""The home for ProcessingModules. These modules perform intermediate analysis of data that is necessary to perform before scientific analysis. Examples include spike clustering, extracting position from tracking data, stitching together image slices. ProcessingModules can be large and express many data sets from relatively complex analysis (e.g., spike detection and clustering) or small, representing extraction of position information from tracking video, or even binary lick/no-lick decisions. Common software tools (e.g., klustakwik, MClust) are expected to read/write data here. 'Processing' refers to intermediate analysis of the acquired data to make it more amenable to scientific analysis.""",
json_schema_extra={"linkml_meta": {"any_of": [{"range": "ProcessingModule"}]}},
@ -250,6 +250,9 @@ class NWBFile(NWBContainer):
description="""Experimental intervals, whether that be logically distinct sub-experiments having a particular scientific goal, trials (see trials subgroup) during an experiment, or epochs (see epochs subgroup) deriving from analysis of data.""",
)
units: Optional[Units] = Field(None, description="""Data about sorted spike units.""")
specifications: Optional[dict] = Field(
None, description="""Nested dictionary of schema specifications"""
)
class NWBFileStimulus(ConfiguredBaseModel):
@ -265,12 +268,12 @@ class NWBFileStimulus(ConfiguredBaseModel):
"linkml_meta": {"equals_string": "stimulus", "ifabsent": "string(stimulus)"}
},
)
presentation: Optional[List[TimeSeries]] = Field(
presentation: Optional[Dict[str, TimeSeries]] = Field(
None,
description="""Stimuli presented during the experiment.""",
json_schema_extra={"linkml_meta": {"any_of": [{"range": "TimeSeries"}]}},
)
templates: Optional[List[TimeSeries]] = Field(
templates: Optional[Dict[str, TimeSeries]] = Field(
None,
description="""Template stimuli. Timestamps in templates are based on stimulus design and are relative to the beginning of the stimulus. When templates are used, the stimulus instances must convert presentation times to the experiment`s time reference frame.""",
json_schema_extra={"linkml_meta": {"any_of": [{"range": "TimeSeries"}]}},
@ -348,11 +351,11 @@ class NWBFileGeneral(ConfiguredBaseModel):
None,
description="""Information about virus(es) used in experiments, including virus ID, source, date made, injection location, volume, etc.""",
)
nwb_container: Optional[List[NWBContainer]] = Field(
nwb_container: Optional[Dict[str, NWBContainer]] = Field(
None,
description="""Place-holder than can be extended so that lab-specific meta-data can be placed in /general.""",
)
devices: Optional[List[Device]] = Field(
devices: Optional[Dict[str, Device]] = Field(
None,
description="""Description of hardware devices used during experiment, e.g., monitors, ADC boards, microscopes, etc.""",
json_schema_extra={"linkml_meta": {"any_of": [{"range": "Device"}]}},
@ -367,12 +370,12 @@ class NWBFileGeneral(ConfiguredBaseModel):
intracellular_ephys: Optional[GeneralIntracellularEphys] = Field(
None, description="""Metadata related to intracellular electrophysiology."""
)
optogenetics: Optional[List[OptogeneticStimulusSite]] = Field(
optogenetics: Optional[Dict[str, OptogeneticStimulusSite]] = Field(
None,
description="""Metadata describing optogenetic stimuluation.""",
json_schema_extra={"linkml_meta": {"any_of": [{"range": "OptogeneticStimulusSite"}]}},
)
optophysiology: Optional[List[ImagingPlane]] = Field(
optophysiology: Optional[Dict[str, ImagingPlane]] = Field(
None,
description="""Metadata related to optophysiology.""",
json_schema_extra={"linkml_meta": {"any_of": [{"range": "ImagingPlane"}]}},
@ -450,12 +453,12 @@ class GeneralExtracellularEphys(ConfiguredBaseModel):
}
},
)
electrode_group: Optional[List[ElectrodeGroup]] = Field(
None, description="""Physical group of electrodes."""
)
electrodes: Optional[ExtracellularEphysElectrodes] = Field(
None, description="""A table of all electrodes (i.e. channels) used for recording."""
)
value: Optional[Dict[str, ElectrodeGroup]] = Field(
None, description="""Physical group of electrodes."""
)
class ExtracellularEphysElectrodes(DynamicTable):
@ -525,8 +528,14 @@ class ExtracellularEphysElectrodes(DynamicTable):
}
},
)
group: List[ElectrodeGroup] = Field(
..., description="""Reference to the ElectrodeGroup this electrode is a part of."""
group: VectorData[NDArray[Any, ElectrodeGroup]] = Field(
...,
description="""Reference to the ElectrodeGroup this electrode is a part of.""",
json_schema_extra={
"linkml_meta": {
"array": {"maximum_number_dimensions": False, "minimum_number_dimensions": 1}
}
},
)
group_name: VectorData[NDArray[Any, str]] = Field(
...,
@ -583,9 +592,6 @@ class ExtracellularEphysElectrodes(DynamicTable):
description="""Array of unique identifiers for the rows of this dynamic table.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_rows"}]}}},
)
vector_index: Optional[List[VectorIndex]] = Field(
None, description="""Indices for the vector columns of this dynamic table."""
)
class GeneralIntracellularEphys(ConfiguredBaseModel):
@ -608,12 +614,12 @@ class GeneralIntracellularEphys(ConfiguredBaseModel):
None,
description="""Description of filtering used. Includes filtering type and parameters, frequency fall-off, etc. If this changes between TimeSeries, filter description should be stored as a text attribute for each TimeSeries.""",
)
intracellular_electrode: Optional[List[IntracellularElectrode]] = Field(
None, description="""An intracellular electrode."""
)
sweep_table: Optional[SweepTable] = Field(
None, description="""The table which groups different PatchClampSeries together."""
)
value: Optional[Dict[str, IntracellularElectrode]] = Field(
None, description="""An intracellular electrode."""
)
class NWBFileIntervals(ConfiguredBaseModel):
@ -639,7 +645,7 @@ class NWBFileIntervals(ConfiguredBaseModel):
invalid_times: Optional[TimeIntervals] = Field(
None, description="""Time intervals that should be removed from analysis."""
)
time_intervals: Optional[List[TimeIntervals]] = Field(
value: Optional[Dict[str, TimeIntervals]] = Field(
None,
description="""Optional additional table(s) for describing other experimental time intervals.""",
)

133
nwb_models/src/nwb_models/models/pydantic/core/v2_2_0/core_nwb_icephys.py generated
View file

@ -262,11 +262,21 @@ class PatchClampSeriesData(ConfiguredBaseModel):
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: str = Field(
...,
description="""Base unit of measurement for working with the data. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
)
value: Optional[NDArray[Shape["* num_times"], float]] = Field(
value: Optional[NDArray[Shape["* num_times"], float | int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
@ -281,12 +291,12 @@ class CurrentClampSeries(PatchClampSeries):
)
name: str = Field(...)
data: CurrentClampSeriesData = Field(..., description="""Recorded voltage.""")
bias_current: Optional[float] = Field(None, description="""Bias current, in amps.""")
bridge_balance: Optional[float] = Field(None, description="""Bridge balance, in ohms.""")
capacitance_compensation: Optional[float] = Field(
None, description="""Capacitance compensation, in farads."""
)
data: CurrentClampSeriesData = Field(..., description="""Recorded voltage.""")
stimulus_description: str = Field(
..., description="""Protocol/stimulus name for this patch-clamp dataset."""
)
@ -354,12 +364,24 @@ class CurrentClampSeriesData(ConfiguredBaseModel):
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["volts"] = Field(
"volts",
description="""Base unit of measurement for working with the data. which is fixed to 'volts'. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
json_schema_extra={"linkml_meta": {"equals_string": "volts", "ifabsent": "string(volts)"}},
)
value: Any = Field(...)
value: Optional[NDArray[Shape["* num_times"], float | int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
class IZeroClampSeries(CurrentClampSeries):
@ -512,6 +534,16 @@ class CurrentClampStimulusSeriesData(ConfiguredBaseModel):
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["amperes"] = Field(
"amperes",
description="""Base unit of measurement for working with the data. which is fixed to 'amperes'. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
@ -519,7 +551,9 @@ class CurrentClampStimulusSeriesData(ConfiguredBaseModel):
"linkml_meta": {"equals_string": "amperes", "ifabsent": "string(amperes)"}
},
)
value: Any = Field(...)
value: Optional[NDArray[Shape["* num_times"], float | int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
class VoltageClampSeries(PatchClampSeries):
@ -532,13 +566,13 @@ class VoltageClampSeries(PatchClampSeries):
)
name: str = Field(...)
data: VoltageClampSeriesData = Field(..., description="""Recorded current.""")
capacitance_fast: Optional[VoltageClampSeriesCapacitanceFast] = Field(
None, description="""Fast capacitance, in farads."""
)
capacitance_slow: Optional[VoltageClampSeriesCapacitanceSlow] = Field(
None, description="""Slow capacitance, in farads."""
)
data: VoltageClampSeriesData = Field(..., description="""Recorded current.""")
resistance_comp_bandwidth: Optional[VoltageClampSeriesResistanceCompBandwidth] = Field(
None, description="""Resistance compensation bandwidth, in hertz."""
)
@ -610,27 +644,6 @@ class VoltageClampSeries(PatchClampSeries):
)
class VoltageClampSeriesData(ConfiguredBaseModel):
"""
Recorded current.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.icephys"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
unit: Literal["amperes"] = Field(
"amperes",
description="""Base unit of measurement for working with the data. which is fixed to 'amperes'. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
json_schema_extra={
"linkml_meta": {"equals_string": "amperes", "ifabsent": "string(amperes)"}
},
)
value: Any = Field(...)
class VoltageClampSeriesCapacitanceFast(ConfiguredBaseModel):
"""
Fast capacitance, in farads.
@ -683,6 +696,39 @@ class VoltageClampSeriesCapacitanceSlow(ConfiguredBaseModel):
value: float = Field(...)
class VoltageClampSeriesData(ConfiguredBaseModel):
"""
Recorded current.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.icephys"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["amperes"] = Field(
"amperes",
description="""Base unit of measurement for working with the data. which is fixed to 'amperes'. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
json_schema_extra={
"linkml_meta": {"equals_string": "amperes", "ifabsent": "string(amperes)"}
},
)
value: Optional[NDArray[Shape["* num_times"], float | int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
class VoltageClampSeriesResistanceCompBandwidth(ConfiguredBaseModel):
"""
Resistance compensation bandwidth, in hertz.
@ -887,12 +933,24 @@ class VoltageClampStimulusSeriesData(ConfiguredBaseModel):
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["volts"] = Field(
"volts",
description="""Base unit of measurement for working with the data. which is fixed to 'volts'. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
json_schema_extra={"linkml_meta": {"equals_string": "volts", "ifabsent": "string(volts)"}},
)
value: Any = Field(...)
value: Optional[NDArray[Shape["* num_times"], float | int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
class IntracellularElectrode(NWBContainer):
@ -942,18 +1000,15 @@ class SweepTable(DynamicTable):
)
name: str = Field(...)
sweep_number: VectorData[NDArray[Any, int]] = Field(
series: VectorData[NDArray[Any, PatchClampSeries]] = Field(
...,
description="""Sweep number of the PatchClampSeries in that row.""",
description="""The PatchClampSeries with the sweep number in that row.""",
json_schema_extra={
"linkml_meta": {
"array": {"maximum_number_dimensions": False, "minimum_number_dimensions": 1}
}
},
)
series: List[PatchClampSeries] = Field(
..., description="""The PatchClampSeries with the sweep number in that row."""
)
series_index: Named[VectorIndex] = Field(
...,
description="""Index for series.""",
@ -966,6 +1021,15 @@ class SweepTable(DynamicTable):
}
},
)
sweep_number: VectorData[NDArray[Any, int]] = Field(
...,
description="""Sweep number of the PatchClampSeries in that row.""",
json_schema_extra={
"linkml_meta": {
"array": {"maximum_number_dimensions": False, "minimum_number_dimensions": 1}
}
},
)
colnames: List[str] = Field(
...,
description="""The names of the columns in this table. This should be used to specify an order to the columns.""",
@ -976,9 +1040,6 @@ class SweepTable(DynamicTable):
description="""Array of unique identifiers for the rows of this dynamic table.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_rows"}]}}},
)
vector_index: Optional[List[VectorIndex]] = Field(
None, description="""Indices for the vector columns of this dynamic table."""
)
# Model rebuild
@ -991,9 +1052,9 @@ IZeroClampSeries.model_rebuild()
CurrentClampStimulusSeries.model_rebuild()
CurrentClampStimulusSeriesData.model_rebuild()
VoltageClampSeries.model_rebuild()
VoltageClampSeriesData.model_rebuild()
VoltageClampSeriesCapacitanceFast.model_rebuild()
VoltageClampSeriesCapacitanceSlow.model_rebuild()
VoltageClampSeriesData.model_rebuild()
VoltageClampSeriesResistanceCompBandwidth.model_rebuild()
VoltageClampSeriesResistanceCompCorrection.model_rebuild()
VoltageClampSeriesResistanceCompPrediction.model_rebuild()

113
nwb_models/src/nwb_models/models/pydantic/core/v2_2_0/core_nwb_image.py generated
View file

@ -152,7 +152,7 @@ class GrayscaleImage(Image):
)
name: str = Field(...)
value: Optional[NDArray[Shape["* x, * y"], float]] = Field(
value: Optional[NDArray[Shape["* x, * y"], float | int]] = Field(
None,
json_schema_extra={
"linkml_meta": {"array": {"dimensions": [{"alias": "x"}, {"alias": "y"}]}}
@ -174,7 +174,7 @@ class RGBImage(Image):
)
name: str = Field(...)
value: Optional[NDArray[Shape["* x, * y, 3 r_g_b"], float]] = Field(
value: Optional[NDArray[Shape["* x, * y, 3 r_g_b"], float | int]] = Field(
None,
json_schema_extra={
"linkml_meta": {
@ -204,7 +204,7 @@ class RGBAImage(Image):
)
name: str = Field(...)
value: Optional[NDArray[Shape["* x, * y, 4 r_g_b_a"], float]] = Field(
value: Optional[NDArray[Shape["* x, * y, 4 r_g_b_a"], float | int]] = Field(
None,
json_schema_extra={
"linkml_meta": {
@ -234,12 +234,9 @@ class ImageSeries(TimeSeries):
)
name: str = Field(...)
data: Optional[
Union[
NDArray[Shape["* frame, * x, * y"], float],
NDArray[Shape["* frame, * x, * y, * z"], float],
]
] = Field(None, description="""Binary data representing images across frames.""")
data: Optional[ImageSeriesData] = Field(
None, description="""Binary data representing images across frames."""
)
dimension: Optional[NDArray[Shape["* rank"], int]] = Field(
None,
description="""Number of pixels on x, y, (and z) axes.""",
@ -250,8 +247,9 @@ class ImageSeries(TimeSeries):
description="""Paths to one or more external file(s). The field is only present if format='external'. This is only relevant if the image series is stored in the file system as one or more image file(s). This field should NOT be used if the image is stored in another NWB file and that file is linked to this file.""",
)
format: Optional[str] = Field(
None,
"raw",
description="""Format of image. If this is 'external', then the attribute 'external_file' contains the path information to the image files. If this is 'raw', then the raw (single-channel) binary data is stored in the 'data' dataset. If this attribute is not present, then the default format='raw' case is assumed.""",
json_schema_extra={"linkml_meta": {"ifabsent": "string(raw)"}},
)
description: Optional[str] = Field(
"no description",
@ -290,6 +288,39 @@ class ImageSeries(TimeSeries):
)
class ImageSeriesData(ConfiguredBaseModel):
"""
Binary data representing images across frames.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.image"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: str = Field(
...,
description="""Base unit of measurement for working with the data. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
)
value: Optional[
Union[
NDArray[Shape["* frame, * x, * y"], float | int],
NDArray[Shape["* frame, * x, * y, * z"], float | int],
]
] = Field(None)
class ImageSeriesExternalFile(ConfiguredBaseModel):
"""
Paths to one or more external file(s). The field is only present if format='external'. This is only relevant if the image series is stored in the file system as one or more image file(s). This field should NOT be used if the image is stored in another NWB file and that file is linked to this file.
@ -331,12 +362,9 @@ class ImageMaskSeries(ImageSeries):
}
},
)
data: Optional[
Union[
NDArray[Shape["* frame, * x, * y"], float],
NDArray[Shape["* frame, * x, * y, * z"], float],
]
] = Field(None, description="""Binary data representing images across frames.""")
data: Optional[ImageSeriesData] = Field(
None, description="""Binary data representing images across frames."""
)
dimension: Optional[NDArray[Shape["* rank"], int]] = Field(
None,
description="""Number of pixels on x, y, (and z) axes.""",
@ -347,8 +375,9 @@ class ImageMaskSeries(ImageSeries):
description="""Paths to one or more external file(s). The field is only present if format='external'. This is only relevant if the image series is stored in the file system as one or more image file(s). This field should NOT be used if the image is stored in another NWB file and that file is linked to this file.""",
)
format: Optional[str] = Field(
None,
"raw",
description="""Format of image. If this is 'external', then the attribute 'external_file' contains the path information to the image files. If this is 'raw', then the raw (single-channel) binary data is stored in the 'data' dataset. If this attribute is not present, then the default format='raw' case is assumed.""",
json_schema_extra={"linkml_meta": {"ifabsent": "string(raw)"}},
)
description: Optional[str] = Field(
"no description",
@ -409,12 +438,9 @@ class OpticalSeries(ImageSeries):
None,
description="""Description of image relative to some reference frame (e.g., which way is up). Must also specify frame of reference.""",
)
data: Optional[
Union[
NDArray[Shape["* frame, * x, * y"], float],
NDArray[Shape["* frame, * x, * y, * z"], float],
]
] = Field(None, description="""Binary data representing images across frames.""")
data: Optional[ImageSeriesData] = Field(
None, description="""Binary data representing images across frames."""
)
dimension: Optional[NDArray[Shape["* rank"], int]] = Field(
None,
description="""Number of pixels on x, y, (and z) axes.""",
@ -425,8 +451,9 @@ class OpticalSeries(ImageSeries):
description="""Paths to one or more external file(s). The field is only present if format='external'. This is only relevant if the image series is stored in the file system as one or more image file(s). This field should NOT be used if the image is stored in another NWB file and that file is linked to this file.""",
)
format: Optional[str] = Field(
None,
"raw",
description="""Format of image. If this is 'external', then the attribute 'external_file' contains the path information to the image files. If this is 'raw', then the raw (single-channel) binary data is stored in the 'data' dataset. If this attribute is not present, then the default format='raw' case is assumed.""",
json_schema_extra={"linkml_meta": {"ifabsent": "string(raw)"}},
)
description: Optional[str] = Field(
"no description",
@ -475,10 +502,8 @@ class IndexSeries(TimeSeries):
)
name: str = Field(...)
data: NDArray[Shape["* num_times"], int] = Field(
...,
description="""Index of the frame in the referenced ImageSeries.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}},
data: IndexSeriesData = Field(
..., description="""Index of the frame in the referenced ImageSeries."""
)
indexed_timeseries: Union[ImageSeries, str] = Field(
...,
@ -526,13 +551,45 @@ class IndexSeries(TimeSeries):
)
class IndexSeriesData(ConfiguredBaseModel):
"""
Index of the frame in the referenced ImageSeries.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.image"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: str = Field(
...,
description="""Base unit of measurement for working with the data. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
)
value: Optional[NDArray[Shape["* num_times"], int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
# Model rebuild
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
GrayscaleImage.model_rebuild()
RGBImage.model_rebuild()
RGBAImage.model_rebuild()
ImageSeries.model_rebuild()
ImageSeriesData.model_rebuild()
ImageSeriesExternalFile.model_rebuild()
ImageMaskSeries.model_rebuild()
OpticalSeries.model_rebuild()
IndexSeries.model_rebuild()
IndexSeriesData.model_rebuild()

236
nwb_models/src/nwb_models/models/pydantic/core/v2_2_0/core_nwb_misc.py generated
View file

@ -251,6 +251,16 @@ class AbstractFeatureSeriesData(ConfiguredBaseModel):
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Optional[str] = Field(
"see ",
description="""Since there can be different units for different features, store the units in 'feature_units'. The default value for this attribute is \"see 'feature_units'\".""",
@ -258,8 +268,8 @@ class AbstractFeatureSeriesData(ConfiguredBaseModel):
)
value: Optional[
Union[
NDArray[Shape["* num_times"], float],
NDArray[Shape["* num_times, * num_features"], float],
NDArray[Shape["* num_times"], float | int],
NDArray[Shape["* num_times, * num_features"], float | int],
]
] = Field(None)
@ -274,10 +284,8 @@ class AnnotationSeries(TimeSeries):
)
name: str = Field(...)
data: NDArray[Shape["* num_times"], str] = Field(
...,
description="""Annotations made during an experiment.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}},
data: AnnotationSeriesData = Field(
..., description="""Annotations made during an experiment."""
)
description: Optional[str] = Field(
"no description",
@ -316,6 +324,39 @@ class AnnotationSeries(TimeSeries):
)
class AnnotationSeriesData(ConfiguredBaseModel):
"""
Annotations made during an experiment.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.misc"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: float = Field(
-1.0,
description="""Smallest meaningful difference between values in data. Annotations have no units, so the value is fixed to -1.0.""",
le=-1,
ge=-1,
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["n/a"] = Field(
"n/a",
description="""Base unit of measurement for working with the data. Annotations have no units, so the value is fixed to 'n/a'.""",
json_schema_extra={"linkml_meta": {"equals_string": "n/a", "ifabsent": "string(n/a)"}},
)
value: Optional[NDArray[Shape["* num_times"], str]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
class IntervalSeries(TimeSeries):
"""
Stores intervals of data. The timestamps field stores the beginning and end of intervals. The data field stores whether the interval just started (>0 value) or ended (<0 value). Different interval types can be represented in the same series by using multiple key values (eg, 1 for feature A, 2 for feature B, 3 for feature C, etc). The field data stores an 8-bit integer. This is largely an alias of a standard TimeSeries but that is identifiable as representing time intervals in a machine-readable way.
@ -326,10 +367,8 @@ class IntervalSeries(TimeSeries):
)
name: str = Field(...)
data: NDArray[Shape["* num_times"], int] = Field(
...,
description="""Use values >0 if interval started, <0 if interval ended.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}},
data: IntervalSeriesData = Field(
..., description="""Use values >0 if interval started, <0 if interval ended."""
)
description: Optional[str] = Field(
"no description",
@ -368,6 +407,39 @@ class IntervalSeries(TimeSeries):
)
class IntervalSeriesData(ConfiguredBaseModel):
"""
Use values >0 if interval started, <0 if interval ended.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.misc"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: float = Field(
-1.0,
description="""Smallest meaningful difference between values in data. Annotations have no units, so the value is fixed to -1.0.""",
le=-1,
ge=-1,
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["n/a"] = Field(
"n/a",
description="""Base unit of measurement for working with the data. Annotations have no units, so the value is fixed to 'n/a'.""",
json_schema_extra={"linkml_meta": {"equals_string": "n/a", "ifabsent": "string(n/a)"}},
)
value: Optional[NDArray[Shape["* num_times"], int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
class DecompositionSeries(TimeSeries):
"""
Spectral analysis of a time series, e.g. of an LFP or a speech signal.
@ -443,24 +515,36 @@ class DecompositionSeriesData(ConfiguredBaseModel):
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: str = Field(
"no unit",
description="""Base unit of measurement for working with the data. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
json_schema_extra={"linkml_meta": {"ifabsent": "string(no unit)"}},
)
value: Optional[NDArray[Shape["* num_times, * num_channels, * num_bands"], float]] = Field(
None,
json_schema_extra={
"linkml_meta": {
"array": {
"dimensions": [
{"alias": "num_times"},
{"alias": "num_channels"},
{"alias": "num_bands"},
]
value: Optional[NDArray[Shape["* num_times, * num_channels, * num_bands"], float | int]] = (
Field(
None,
json_schema_extra={
"linkml_meta": {
"array": {
"dimensions": [
{"alias": "num_times"},
{"alias": "num_channels"},
{"alias": "num_bands"},
]
}
}
}
},
},
)
)
@ -518,9 +602,6 @@ class DecompositionSeriesBands(DynamicTable):
description="""Array of unique identifiers for the rows of this dynamic table.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_rows"}]}}},
)
vector_index: Optional[List[VectorIndex]] = Field(
None, description="""Indices for the vector columns of this dynamic table."""
)
class Units(DynamicTable):
@ -533,9 +614,18 @@ class Units(DynamicTable):
)
name: str = Field("Units", json_schema_extra={"linkml_meta": {"ifabsent": "string(Units)"}})
spike_times_index: Optional[Named[VectorIndex]] = Field(
electrode_group: Optional[VectorData[NDArray[Any, ElectrodeGroup]]] = Field(
None,
description="""Index into the spike_times dataset.""",
description="""Electrode group that each spike unit came from.""",
json_schema_extra={
"linkml_meta": {
"array": {"maximum_number_dimensions": False, "minimum_number_dimensions": 1}
}
},
)
electrodes: Optional[Named[DynamicTableRegion]] = Field(
None,
description="""Electrode that each spike unit came from, specified using a DynamicTableRegion.""",
json_schema_extra={
"linkml_meta": {
"annotations": {
@ -545,12 +635,9 @@ class Units(DynamicTable):
}
},
)
spike_times: Optional[UnitsSpikeTimes] = Field(
None, description="""Spike times for each unit."""
)
obs_intervals_index: Optional[Named[VectorIndex]] = Field(
electrodes_index: Optional[Named[VectorIndex]] = Field(
None,
description="""Index into the obs_intervals dataset.""",
description="""Index into electrodes.""",
json_schema_extra={
"linkml_meta": {
"annotations": {
@ -576,9 +663,9 @@ class Units(DynamicTable):
},
)
)
electrodes_index: Optional[Named[VectorIndex]] = Field(
obs_intervals_index: Optional[Named[VectorIndex]] = Field(
None,
description="""Index into electrodes.""",
description="""Index into the obs_intervals dataset.""",
json_schema_extra={
"linkml_meta": {
"annotations": {
@ -588,9 +675,12 @@ class Units(DynamicTable):
}
},
)
electrodes: Optional[Named[DynamicTableRegion]] = Field(
spike_times: Optional[UnitsSpikeTimes] = Field(
None, description="""Spike times for each unit."""
)
spike_times_index: Optional[Named[VectorIndex]] = Field(
None,
description="""Electrode that each spike unit came from, specified using a DynamicTableRegion.""",
description="""Index into the spike_times dataset.""",
json_schema_extra={
"linkml_meta": {
"annotations": {
@ -600,25 +690,12 @@ class Units(DynamicTable):
}
},
)
electrode_group: Optional[List[ElectrodeGroup]] = Field(
None, description="""Electrode group that each spike unit came from."""
waveform_mean: Optional[UnitsWaveformMean] = Field(
None, description="""Spike waveform mean for each spike unit."""
)
waveform_sd: Optional[UnitsWaveformSd] = Field(
None, description="""Spike waveform standard deviation for each spike unit."""
)
waveform_mean: Optional[
VectorData[
Union[
NDArray[Shape["* num_units, * num_samples"], float],
NDArray[Shape["* num_units, * num_samples, * num_electrodes"], float],
]
]
] = Field(None, description="""Spike waveform mean for each spike unit.""")
waveform_sd: Optional[
VectorData[
Union[
NDArray[Shape["* num_units, * num_samples"], float],
NDArray[Shape["* num_units, * num_samples, * num_electrodes"], float],
]
]
] = Field(None, description="""Spike waveform standard deviation for each spike unit.""")
colnames: List[str] = Field(
...,
description="""The names of the columns in this table. This should be used to specify an order to the columns.""",
@ -629,9 +706,6 @@ class Units(DynamicTable):
description="""Array of unique identifiers for the rows of this dynamic table.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_rows"}]}}},
)
vector_index: Optional[List[VectorIndex]] = Field(
None, description="""Indices for the vector columns of this dynamic table."""
)
class UnitsSpikeTimes(VectorData):
@ -654,14 +728,62 @@ class UnitsSpikeTimes(VectorData):
description: str = Field(..., description="""Description of what these vectors represent.""")
class UnitsWaveformMean(VectorData):
"""
Spike waveform mean for each spike unit.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.misc"})
name: Literal["waveform_mean"] = Field(
"waveform_mean",
json_schema_extra={
"linkml_meta": {"equals_string": "waveform_mean", "ifabsent": "string(waveform_mean)"}
},
)
sampling_rate: Optional[float] = Field(None, description="""Sampling rate, in hertz.""")
unit: Optional[Literal["volts"]] = Field(
"volts",
description="""Unit of measurement. This value is fixed to 'volts'.""",
json_schema_extra={"linkml_meta": {"equals_string": "volts", "ifabsent": "string(volts)"}},
)
description: str = Field(..., description="""Description of what these vectors represent.""")
class UnitsWaveformSd(VectorData):
"""
Spike waveform standard deviation for each spike unit.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.misc"})
name: Literal["waveform_sd"] = Field(
"waveform_sd",
json_schema_extra={
"linkml_meta": {"equals_string": "waveform_sd", "ifabsent": "string(waveform_sd)"}
},
)
sampling_rate: Optional[float] = Field(None, description="""Sampling rate, in hertz.""")
unit: Optional[Literal["volts"]] = Field(
"volts",
description="""Unit of measurement. This value is fixed to 'volts'.""",
json_schema_extra={"linkml_meta": {"equals_string": "volts", "ifabsent": "string(volts)"}},
)
description: str = Field(..., description="""Description of what these vectors represent.""")
# Model rebuild
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
AbstractFeatureSeries.model_rebuild()
AbstractFeatureSeriesData.model_rebuild()
AnnotationSeries.model_rebuild()
AnnotationSeriesData.model_rebuild()
IntervalSeries.model_rebuild()
IntervalSeriesData.model_rebuild()
DecompositionSeries.model_rebuild()
DecompositionSeriesData.model_rebuild()
DecompositionSeriesBands.model_rebuild()
Units.model_rebuild()
UnitsSpikeTimes.model_rebuild()
UnitsWaveformMean.model_rebuild()
UnitsWaveformSd.model_rebuild()

38
nwb_models/src/nwb_models/models/pydantic/core/v2_2_0/core_nwb_ogen.py generated
View file

@ -158,10 +158,8 @@ class OptogeneticSeries(TimeSeries):
)
name: str = Field(...)
data: NDArray[Shape["* num_times"], float] = Field(
...,
description="""Applied power for optogenetic stimulus, in watts.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}},
data: OptogeneticSeriesData = Field(
..., description="""Applied power for optogenetic stimulus, in watts."""
)
site: Union[OptogeneticStimulusSite, str] = Field(
...,
@ -209,6 +207,37 @@ class OptogeneticSeries(TimeSeries):
)
class OptogeneticSeriesData(ConfiguredBaseModel):
"""
Applied power for optogenetic stimulus, in watts.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.ogen"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["watts"] = Field(
"watts",
description="""Unit of measurement for data, which is fixed to 'watts'.""",
json_schema_extra={"linkml_meta": {"equals_string": "watts", "ifabsent": "string(watts)"}},
)
value: Optional[NDArray[Shape["* num_times"], float | int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
class OptogeneticStimulusSite(NWBContainer):
"""
A site of optogenetic stimulation.
@ -239,4 +268,5 @@ class OptogeneticStimulusSite(NWBContainer):
# Model rebuild
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
OptogeneticSeries.model_rebuild()
OptogeneticSeriesData.model_rebuild()
OptogeneticStimulusSite.model_rebuild()

76
nwb_models/src/nwb_models/models/pydantic/core/v2_2_0/core_nwb_ophys.py generated
View file

@ -28,7 +28,7 @@ from ...core.v2_2_0.core_nwb_base import (
TimeSeriesSync,
)
from ...core.v2_2_0.core_nwb_device import Device
from ...core.v2_2_0.core_nwb_image import ImageSeries, ImageSeriesExternalFile
from ...core.v2_2_0.core_nwb_image import ImageSeries, ImageSeriesData, ImageSeriesExternalFile
from ...hdmf_common.v1_1_0.hdmf_common_table import DynamicTable, DynamicTableRegion
@ -209,12 +209,9 @@ class TwoPhotonSeries(ImageSeries):
}
},
)
data: Optional[
Union[
NDArray[Shape["* frame, * x, * y"], float],
NDArray[Shape["* frame, * x, * y, * z"], float],
]
] = Field(None, description="""Binary data representing images across frames.""")
data: Optional[ImageSeriesData] = Field(
None, description="""Binary data representing images across frames."""
)
dimension: Optional[NDArray[Shape["* rank"], int]] = Field(
None,
description="""Number of pixels on x, y, (and z) axes.""",
@ -225,8 +222,9 @@ class TwoPhotonSeries(ImageSeries):
description="""Paths to one or more external file(s). The field is only present if format='external'. This is only relevant if the image series is stored in the file system as one or more image file(s). This field should NOT be used if the image is stored in another NWB file and that file is linked to this file.""",
)
format: Optional[str] = Field(
None,
"raw",
description="""Format of image. If this is 'external', then the attribute 'external_file' contains the path information to the image files. If this is 'raw', then the raw (single-channel) binary data is stored in the 'data' dataset. If this attribute is not present, then the default format='raw' case is assumed.""",
json_schema_extra={"linkml_meta": {"ifabsent": "string(raw)"}},
)
description: Optional[str] = Field(
"no description",
@ -275,9 +273,7 @@ class RoiResponseSeries(TimeSeries):
)
name: str = Field(...)
data: Union[
NDArray[Shape["* num_times"], float], NDArray[Shape["* num_times, * num_rois"], float]
] = Field(..., description="""Signals from ROIs.""")
data: RoiResponseSeriesData = Field(..., description="""Signals from ROIs.""")
rois: Named[DynamicTableRegion] = Field(
...,
description="""DynamicTableRegion referencing into an ROITable containing information on the ROIs stored in this timeseries.""",
@ -327,6 +323,39 @@ class RoiResponseSeries(TimeSeries):
)
class RoiResponseSeriesData(ConfiguredBaseModel):
"""
Signals from ROIs.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.ophys"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: str = Field(
...,
description="""Base unit of measurement for working with the data. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
)
value: Optional[
Union[
NDArray[Shape["* num_times"], float | int],
NDArray[Shape["* num_times, * num_rois"], float | int],
]
] = Field(None)
class DfOverF(NWBDataInterface):
"""
dF/F information about a region of interest (ROI). Storage hierarchy of dF/F should be the same as for segmentation (i.e., same names for ROIs and for image planes).
@ -336,10 +365,10 @@ class DfOverF(NWBDataInterface):
{"from_schema": "core.nwb.ophys", "tree_root": True}
)
value: Optional[List[RoiResponseSeries]] = Field(
name: str = Field("DfOverF", json_schema_extra={"linkml_meta": {"ifabsent": "string(DfOverF)"}})
value: Optional[Dict[str, RoiResponseSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "RoiResponseSeries"}]}}
)
name: str = Field(...)
class Fluorescence(NWBDataInterface):
@ -351,10 +380,12 @@ class Fluorescence(NWBDataInterface):
{"from_schema": "core.nwb.ophys", "tree_root": True}
)
value: Optional[List[RoiResponseSeries]] = Field(
name: str = Field(
"Fluorescence", json_schema_extra={"linkml_meta": {"ifabsent": "string(Fluorescence)"}}
)
value: Optional[Dict[str, RoiResponseSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "RoiResponseSeries"}]}}
)
name: str = Field(...)
class ImageSegmentation(NWBDataInterface):
@ -366,10 +397,13 @@ class ImageSegmentation(NWBDataInterface):
{"from_schema": "core.nwb.ophys", "tree_root": True}
)
value: Optional[List[DynamicTable]] = Field(
name: str = Field(
"ImageSegmentation",
json_schema_extra={"linkml_meta": {"ifabsent": "string(ImageSegmentation)"}},
)
value: Optional[Dict[str, DynamicTable]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "DynamicTable"}]}}
)
name: str = Field(...)
class ImagingPlane(NWBContainer):
@ -538,16 +572,20 @@ class MotionCorrection(NWBDataInterface):
{"from_schema": "core.nwb.ophys", "tree_root": True}
)
value: Optional[List[NWBDataInterface]] = Field(
name: str = Field(
"MotionCorrection",
json_schema_extra={"linkml_meta": {"ifabsent": "string(MotionCorrection)"}},
)
value: Optional[Dict[str, NWBDataInterface]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "NWBDataInterface"}]}}
)
name: str = Field(...)
# Model rebuild
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
TwoPhotonSeries.model_rebuild()
RoiResponseSeries.model_rebuild()
RoiResponseSeriesData.model_rebuild()
DfOverF.model_rebuild()
Fluorescence.model_rebuild()
ImageSegmentation.model_rebuild()

4
nwb_models/src/nwb_models/models/pydantic/core/v2_2_0/core_nwb_retinotopy.py generated
View file

@ -235,7 +235,7 @@ class RetinotopyImage(GrayscaleImage):
)
field_of_view: List[float] = Field(..., description="""Size of viewing area, in meters.""")
format: str = Field(..., description="""Format of image. Right now only 'raw' is supported.""")
value: Optional[NDArray[Shape["* x, * y"], float]] = Field(
value: Optional[NDArray[Shape["* x, * y"], float | int]] = Field(
None,
json_schema_extra={
"linkml_meta": {"array": {"dimensions": [{"alias": "x"}, {"alias": "y"}]}}
@ -374,7 +374,7 @@ class ImagingRetinotopyFocalDepthImage(RetinotopyImage):
)
field_of_view: List[float] = Field(..., description="""Size of viewing area, in meters.""")
format: str = Field(..., description="""Format of image. Right now only 'raw' is supported.""")
value: Optional[NDArray[Shape["* x, * y"], float]] = Field(
value: Optional[NDArray[Shape["* x, * y"], float | int]] = Field(
None,
json_schema_extra={
"linkml_meta": {"array": {"dimensions": [{"alias": "x"}, {"alias": "y"}]}}

15
nwb_models/src/nwb_models/models/pydantic/core/v2_2_0/namespace.py generated
View file

@ -38,6 +38,7 @@ from ...core.v2_2_0.core_nwb_ecephys import (
ClusterWaveforms,
Clustering,
ElectricalSeries,
ElectricalSeriesData,
ElectrodeGroup,
ElectrodeGroupPosition,
EventDetection,
@ -46,6 +47,7 @@ from ...core.v2_2_0.core_nwb_ecephys import (
FilteredEphys,
LFP,
SpikeEventSeries,
SpikeEventSeriesData,
)
from ...core.v2_2_0.core_nwb_epoch import TimeIntervals, TimeIntervalsTimeseries
from ...core.v2_2_0.core_nwb_file import (
@ -85,8 +87,10 @@ from ...core.v2_2_0.core_nwb_image import (
GrayscaleImage,
ImageMaskSeries,
ImageSeries,
ImageSeriesData,
ImageSeriesExternalFile,
IndexSeries,
IndexSeriesData,
OpticalSeries,
RGBAImage,
RGBImage,
@ -95,14 +99,22 @@ from ...core.v2_2_0.core_nwb_misc import (
AbstractFeatureSeries,
AbstractFeatureSeriesData,
AnnotationSeries,
AnnotationSeriesData,
DecompositionSeries,
DecompositionSeriesBands,
DecompositionSeriesData,
IntervalSeries,
IntervalSeriesData,
Units,
UnitsSpikeTimes,
UnitsWaveformMean,
UnitsWaveformSd,
)
from ...core.v2_2_0.core_nwb_ogen import (
OptogeneticSeries,
OptogeneticSeriesData,
OptogeneticStimulusSite,
)
from ...core.v2_2_0.core_nwb_ogen import OptogeneticSeries, OptogeneticStimulusSite
from ...core.v2_2_0.core_nwb_ophys import (
DfOverF,
Fluorescence,
@ -114,6 +126,7 @@ from ...core.v2_2_0.core_nwb_ophys import (
MotionCorrection,
OpticalChannel,
RoiResponseSeries,
RoiResponseSeriesData,
TwoPhotonSeries,
)
from ...core.v2_2_0.core_nwb_retinotopy import (

15
nwb_models/src/nwb_models/models/pydantic/core/v2_2_1/core_nwb_base.py generated
View file

@ -170,9 +170,9 @@ class Image(NWBData):
description: Optional[str] = Field(None, description="""Description of the image.""")
value: Optional[
Union[
NDArray[Shape["* x, * y"], float],
NDArray[Shape["* x, * y, 3 r_g_b"], float],
NDArray[Shape["* x, * y, 4 r_g_b_a"], float],
NDArray[Shape["* x, * y"], float | int],
NDArray[Shape["* x, * y, 3 r_g_b"], float | int],
NDArray[Shape["* x, * y, 4 r_g_b_a"], float | int],
]
] = Field(None)
@ -333,13 +333,16 @@ class ProcessingModule(NWBContainer):
{"from_schema": "core.nwb.base", "tree_root": True}
)
value: Optional[List[Union[DynamicTable, NWBDataInterface]]] = Field(
name: str = Field(...)
description: str = Field(
..., description="""Description of this collection of processed data."""
)
value: Optional[Dict[str, Union[DynamicTable, NWBDataInterface]]] = Field(
None,
json_schema_extra={
"linkml_meta": {"any_of": [{"range": "NWBDataInterface"}, {"range": "DynamicTable"}]}
},
)
name: str = Field(...)
class Images(NWBDataInterface):
@ -353,7 +356,7 @@ class Images(NWBDataInterface):
name: str = Field("Images", json_schema_extra={"linkml_meta": {"ifabsent": "string(Images)"}})
description: str = Field(..., description="""Description of this collection of images.""")
image: List[Image] = Field(..., description="""Images stored in this collection.""")
image: List[str] = Field(..., description="""Images stored in this collection.""")
# Model rebuild

60
nwb_models/src/nwb_models/models/pydantic/core/v2_2_1/core_nwb_behavior.py generated
View file

@ -213,6 +213,16 @@ class SpatialSeriesData(ConfiguredBaseModel):
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Optional[str] = Field(
"meters",
description="""Base unit of measurement for working with the data. The default value is 'meters'. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
@ -220,8 +230,8 @@ class SpatialSeriesData(ConfiguredBaseModel):
)
value: Optional[
Union[
NDArray[Shape["* num_times"], float],
NDArray[Shape["* num_times, * num_features"], float],
NDArray[Shape["* num_times"], float | int],
NDArray[Shape["* num_times, * num_features"], float | int],
]
] = Field(None)
@ -235,10 +245,13 @@ class BehavioralEpochs(NWBDataInterface):
{"from_schema": "core.nwb.behavior", "tree_root": True}
)
value: Optional[List[IntervalSeries]] = Field(
name: str = Field(
"BehavioralEpochs",
json_schema_extra={"linkml_meta": {"ifabsent": "string(BehavioralEpochs)"}},
)
value: Optional[Dict[str, IntervalSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "IntervalSeries"}]}}
)
name: str = Field(...)
class BehavioralEvents(NWBDataInterface):
@ -250,10 +263,13 @@ class BehavioralEvents(NWBDataInterface):
{"from_schema": "core.nwb.behavior", "tree_root": True}
)
value: Optional[List[TimeSeries]] = Field(
name: str = Field(
"BehavioralEvents",
json_schema_extra={"linkml_meta": {"ifabsent": "string(BehavioralEvents)"}},
)
value: Optional[Dict[str, TimeSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "TimeSeries"}]}}
)
name: str = Field(...)
class BehavioralTimeSeries(NWBDataInterface):
@ -265,10 +281,13 @@ class BehavioralTimeSeries(NWBDataInterface):
{"from_schema": "core.nwb.behavior", "tree_root": True}
)
value: Optional[List[TimeSeries]] = Field(
name: str = Field(
"BehavioralTimeSeries",
json_schema_extra={"linkml_meta": {"ifabsent": "string(BehavioralTimeSeries)"}},
)
value: Optional[Dict[str, TimeSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "TimeSeries"}]}}
)
name: str = Field(...)
class PupilTracking(NWBDataInterface):
@ -280,10 +299,12 @@ class PupilTracking(NWBDataInterface):
{"from_schema": "core.nwb.behavior", "tree_root": True}
)
value: Optional[List[TimeSeries]] = Field(
name: str = Field(
"PupilTracking", json_schema_extra={"linkml_meta": {"ifabsent": "string(PupilTracking)"}}
)
value: Optional[Dict[str, TimeSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "TimeSeries"}]}}
)
name: str = Field(...)
class EyeTracking(NWBDataInterface):
@ -295,10 +316,12 @@ class EyeTracking(NWBDataInterface):
{"from_schema": "core.nwb.behavior", "tree_root": True}
)
value: Optional[List[SpatialSeries]] = Field(
name: str = Field(
"EyeTracking", json_schema_extra={"linkml_meta": {"ifabsent": "string(EyeTracking)"}}
)
value: Optional[Dict[str, SpatialSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "SpatialSeries"}]}}
)
name: str = Field(...)
class CompassDirection(NWBDataInterface):
@ -310,10 +333,13 @@ class CompassDirection(NWBDataInterface):
{"from_schema": "core.nwb.behavior", "tree_root": True}
)
value: Optional[List[SpatialSeries]] = Field(
name: str = Field(
"CompassDirection",
json_schema_extra={"linkml_meta": {"ifabsent": "string(CompassDirection)"}},
)
value: Optional[Dict[str, SpatialSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "SpatialSeries"}]}}
)
name: str = Field(...)
class Position(NWBDataInterface):
@ -325,10 +351,12 @@ class Position(NWBDataInterface):
{"from_schema": "core.nwb.behavior", "tree_root": True}
)
value: Optional[List[SpatialSeries]] = Field(
name: str = Field(
"Position", json_schema_extra={"linkml_meta": {"ifabsent": "string(Position)"}}
)
value: Optional[Dict[str, SpatialSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "SpatialSeries"}]}}
)
name: str = Field(...)
# Model rebuild

118
nwb_models/src/nwb_models/models/pydantic/core/v2_2_1/core_nwb_ecephys.py generated
View file

@ -190,11 +190,12 @@ class ElectricalSeries(TimeSeries):
)
name: str = Field(...)
data: Union[
NDArray[Shape["* num_times"], float],
NDArray[Shape["* num_times, * num_channels"], float],
NDArray[Shape["* num_times, * num_channels, * num_samples"], float],
] = Field(..., description="""Recorded voltage data.""")
channel_conversion: Optional[NDArray[Shape["* num_channels"], float]] = Field(
None,
description="""Channel-specific conversion factor. Multiply the data in the 'data' dataset by these values along the channel axis (as indicated by axis attribute) AND by the global conversion factor in the 'conversion' attribute of 'data' to get the data values in Volts, i.e, data in Volts = data * data.conversion * channel_conversion. This approach allows for both global and per-channel data conversion factors needed to support the storage of electrical recordings as native values generated by data acquisition systems. If this dataset is not present, then there is no channel-specific conversion factor, i.e. it is 1 for all channels.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_channels"}]}}},
)
data: ElectricalSeriesData = Field(..., description="""Recorded voltage data.""")
electrodes: Named[DynamicTableRegion] = Field(
...,
description="""DynamicTableRegion pointer to the electrodes that this time series was generated from.""",
@ -207,11 +208,6 @@ class ElectricalSeries(TimeSeries):
}
},
)
channel_conversion: Optional[NDArray[Shape["* num_channels"], float]] = Field(
None,
description="""Channel-specific conversion factor. Multiply the data in the 'data' dataset by these values along the channel axis (as indicated by axis attribute) AND by the global conversion factor in the 'conversion' attribute of 'data' to get the data values in Volts, i.e, data in Volts = data * data.conversion * channel_conversion. This approach allows for both global and per-channel data conversion factors needed to support the storage of electrical recordings as native values generated by data acquisition systems. If this dataset is not present, then there is no channel-specific conversion factor, i.e. it is 1 for all channels.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_channels"}]}}},
)
description: Optional[str] = Field(
"no description",
description="""Description of the time series.""",
@ -249,6 +245,41 @@ class ElectricalSeries(TimeSeries):
)
class ElectricalSeriesData(ConfiguredBaseModel):
"""
Recorded voltage data.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.ecephys"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["volts"] = Field(
"volts",
description="""Base unit of measurement for working with the data. This value is fixed to 'volts'. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion' and 'channel_conversion' (if present).""",
json_schema_extra={"linkml_meta": {"equals_string": "volts", "ifabsent": "string(volts)"}},
)
value: Optional[
Union[
NDArray[Shape["* num_times"], float | int],
NDArray[Shape["* num_times, * num_channels"], float | int],
NDArray[Shape["* num_times, * num_channels, * num_samples"], float | int],
]
] = Field(None)
class SpikeEventSeries(ElectricalSeries):
"""
Stores snapshots/snippets of recorded spike events (i.e., threshold crossings). This may also be raw data, as reported by ephys hardware. If so, the TimeSeries::description field should describe how events were detected. All SpikeEventSeries should reside in a module (under EventWaveform interface) even if the spikes were reported and stored by hardware. All events span the same recording channels and store snapshots of equal duration. TimeSeries::data array structure: [num events] [num channels] [num samples] (or [num events] [num samples] for single electrode).
@ -259,15 +290,17 @@ class SpikeEventSeries(ElectricalSeries):
)
name: str = Field(...)
data: Union[
NDArray[Shape["* num_events, * num_samples"], float],
NDArray[Shape["* num_events, * num_channels, * num_samples"], float],
] = Field(..., description="""Spike waveforms.""")
data: SpikeEventSeriesData = Field(..., description="""Spike waveforms.""")
timestamps: NDArray[Shape["* num_times"], float] = Field(
...,
description="""Timestamps for samples stored in data, in seconds, relative to the common experiment master-clock stored in NWBFile.timestamps_reference_time. Timestamps are required for the events. Unlike for TimeSeries, timestamps are required for SpikeEventSeries and are thus re-specified here.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}},
)
channel_conversion: Optional[NDArray[Shape["* num_channels"], float]] = Field(
None,
description="""Channel-specific conversion factor. Multiply the data in the 'data' dataset by these values along the channel axis (as indicated by axis attribute) AND by the global conversion factor in the 'conversion' attribute of 'data' to get the data values in Volts, i.e, data in Volts = data * data.conversion * channel_conversion. This approach allows for both global and per-channel data conversion factors needed to support the storage of electrical recordings as native values generated by data acquisition systems. If this dataset is not present, then there is no channel-specific conversion factor, i.e. it is 1 for all channels.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_channels"}]}}},
)
electrodes: Named[DynamicTableRegion] = Field(
...,
description="""DynamicTableRegion pointer to the electrodes that this time series was generated from.""",
@ -280,11 +313,6 @@ class SpikeEventSeries(ElectricalSeries):
}
},
)
channel_conversion: Optional[NDArray[Shape["* num_channels"], float]] = Field(
None,
description="""Channel-specific conversion factor. Multiply the data in the 'data' dataset by these values along the channel axis (as indicated by axis attribute) AND by the global conversion factor in the 'conversion' attribute of 'data' to get the data values in Volts, i.e, data in Volts = data * data.conversion * channel_conversion. This approach allows for both global and per-channel data conversion factors needed to support the storage of electrical recordings as native values generated by data acquisition systems. If this dataset is not present, then there is no channel-specific conversion factor, i.e. it is 1 for all channels.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_channels"}]}}},
)
description: Optional[str] = Field(
"no description",
description="""Description of the time series.""",
@ -317,6 +345,40 @@ class SpikeEventSeries(ElectricalSeries):
)
class SpikeEventSeriesData(ConfiguredBaseModel):
"""
Spike waveforms.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.ecephys"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["volts"] = Field(
"volts",
description="""Unit of measurement for waveforms, which is fixed to 'volts'.""",
json_schema_extra={"linkml_meta": {"equals_string": "volts", "ifabsent": "string(volts)"}},
)
value: Optional[
Union[
NDArray[Shape["* num_events, * num_samples"], float | int],
NDArray[Shape["* num_events, * num_channels, * num_samples"], float | int],
]
] = Field(None)
class FeatureExtraction(NWBDataInterface):
"""
Features, such as PC1 and PC2, that are extracted from signals stored in a SpikeEventSeries or other source.
@ -415,10 +477,12 @@ class EventWaveform(NWBDataInterface):
{"from_schema": "core.nwb.ecephys", "tree_root": True}
)
value: Optional[List[SpikeEventSeries]] = Field(
name: str = Field(
"EventWaveform", json_schema_extra={"linkml_meta": {"ifabsent": "string(EventWaveform)"}}
)
value: Optional[Dict[str, SpikeEventSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "SpikeEventSeries"}]}}
)
name: str = Field(...)
class FilteredEphys(NWBDataInterface):
@ -430,10 +494,12 @@ class FilteredEphys(NWBDataInterface):
{"from_schema": "core.nwb.ecephys", "tree_root": True}
)
value: Optional[List[ElectricalSeries]] = Field(
name: str = Field(
"FilteredEphys", json_schema_extra={"linkml_meta": {"ifabsent": "string(FilteredEphys)"}}
)
value: Optional[Dict[str, ElectricalSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "ElectricalSeries"}]}}
)
name: str = Field(...)
class LFP(NWBDataInterface):
@ -445,10 +511,10 @@ class LFP(NWBDataInterface):
{"from_schema": "core.nwb.ecephys", "tree_root": True}
)
value: Optional[List[ElectricalSeries]] = Field(
name: str = Field("LFP", json_schema_extra={"linkml_meta": {"ifabsent": "string(LFP)"}})
value: Optional[Dict[str, ElectricalSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "ElectricalSeries"}]}}
)
name: str = Field(...)
class ElectrodeGroup(NWBContainer):
@ -591,7 +657,9 @@ class Clustering(NWBDataInterface):
# Model rebuild
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
ElectricalSeries.model_rebuild()
ElectricalSeriesData.model_rebuild()
SpikeEventSeries.model_rebuild()
SpikeEventSeriesData.model_rebuild()
FeatureExtraction.model_rebuild()
EventDetection.model_rebuild()
EventWaveform.model_rebuild()

3
nwb_models/src/nwb_models/models/pydantic/core/v2_2_1/core_nwb_epoch.py generated
View file

@ -247,9 +247,6 @@ class TimeIntervals(DynamicTable):
description="""Array of unique identifiers for the rows of this dynamic table.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_rows"}]}}},
)
vector_index: Optional[List[VectorIndex]] = Field(
None, description="""Indices for the vector columns of this dynamic table."""
)
class TimeIntervalsTimeseries(VectorData):

50
nwb_models/src/nwb_models/models/pydantic/core/v2_2_1/core_nwb_file.py generated
View file

@ -211,28 +211,28 @@ class NWBFile(NWBContainer):
...,
description="""Date and time corresponding to time zero of all timestamps. The date is stored in UTC with local timezone offset as ISO 8601 extended formatted string: 2018-09-28T14:43:54.123+02:00. Dates stored in UTC end in \"Z\" with no timezone offset. Date accuracy is up to milliseconds. All times stored in the file use this time as reference (i.e., time zero).""",
)
acquisition: Optional[List[Union[DynamicTable, NWBDataInterface]]] = Field(
acquisition: Optional[Dict[str, Union[DynamicTable, NWBDataInterface]]] = Field(
None,
description="""Data streams recorded from the system, including ephys, ophys, tracking, etc. This group should be read-only after the experiment is completed and timestamps are corrected to a common timebase. The data stored here may be links to raw data stored in external NWB files. This will allow keeping bulky raw data out of the file while preserving the option of keeping some/all in the file. Acquired data includes tracking and experimental data streams (i.e., everything measured from the system). If bulky data is stored in the /acquisition group, the data can exist in a separate NWB file that is linked to by the file being used for processing and analysis.""",
json_schema_extra={
"linkml_meta": {"any_of": [{"range": "NWBDataInterface"}, {"range": "DynamicTable"}]}
},
)
analysis: Optional[List[Union[DynamicTable, NWBContainer]]] = Field(
analysis: Optional[Dict[str, Union[DynamicTable, NWBContainer]]] = Field(
None,
description="""Lab-specific and custom scientific analysis of data. There is no defined format for the content of this group - the format is up to the individual user/lab. To facilitate sharing analysis data between labs, the contents here should be stored in standard types (e.g., neurodata_types) and appropriately documented. The file can store lab-specific and custom data analysis without restriction on its form or schema, reducing data formatting restrictions on end users. Such data should be placed in the analysis group. The analysis data should be documented so that it could be shared with other labs.""",
json_schema_extra={
"linkml_meta": {"any_of": [{"range": "NWBContainer"}, {"range": "DynamicTable"}]}
},
)
scratch: Optional[List[Union[DynamicTable, NWBContainer]]] = Field(
scratch: Optional[Dict[str, Union[DynamicTable, NWBContainer]]] = Field(
None,
description="""A place to store one-off analysis results. Data placed here is not intended for sharing. By placing data here, users acknowledge that there is no guarantee that their data meets any standard.""",
json_schema_extra={
"linkml_meta": {"any_of": [{"range": "NWBContainer"}, {"range": "DynamicTable"}]}
},
)
processing: Optional[List[ProcessingModule]] = Field(
processing: Optional[Dict[str, ProcessingModule]] = Field(
None,
description="""The home for ProcessingModules. These modules perform intermediate analysis of data that is necessary to perform before scientific analysis. Examples include spike clustering, extracting position from tracking data, stitching together image slices. ProcessingModules can be large and express many data sets from relatively complex analysis (e.g., spike detection and clustering) or small, representing extraction of position information from tracking video, or even binary lick/no-lick decisions. Common software tools (e.g., klustakwik, MClust) are expected to read/write data here. 'Processing' refers to intermediate analysis of the acquired data to make it more amenable to scientific analysis.""",
json_schema_extra={"linkml_meta": {"any_of": [{"range": "ProcessingModule"}]}},
@ -250,6 +250,9 @@ class NWBFile(NWBContainer):
description="""Experimental intervals, whether that be logically distinct sub-experiments having a particular scientific goal, trials (see trials subgroup) during an experiment, or epochs (see epochs subgroup) deriving from analysis of data.""",
)
units: Optional[Units] = Field(None, description="""Data about sorted spike units.""")
specifications: Optional[dict] = Field(
None, description="""Nested dictionary of schema specifications"""
)
class NWBFileStimulus(ConfiguredBaseModel):
@ -265,12 +268,12 @@ class NWBFileStimulus(ConfiguredBaseModel):
"linkml_meta": {"equals_string": "stimulus", "ifabsent": "string(stimulus)"}
},
)
presentation: Optional[List[TimeSeries]] = Field(
presentation: Optional[Dict[str, TimeSeries]] = Field(
None,
description="""Stimuli presented during the experiment.""",
json_schema_extra={"linkml_meta": {"any_of": [{"range": "TimeSeries"}]}},
)
templates: Optional[List[TimeSeries]] = Field(
templates: Optional[Dict[str, TimeSeries]] = Field(
None,
description="""Template stimuli. Timestamps in templates are based on stimulus design and are relative to the beginning of the stimulus. When templates are used, the stimulus instances must convert presentation times to the experiment`s time reference frame.""",
json_schema_extra={"linkml_meta": {"any_of": [{"range": "TimeSeries"}]}},
@ -348,11 +351,11 @@ class NWBFileGeneral(ConfiguredBaseModel):
None,
description="""Information about virus(es) used in experiments, including virus ID, source, date made, injection location, volume, etc.""",
)
nwb_container: Optional[List[NWBContainer]] = Field(
nwb_container: Optional[Dict[str, NWBContainer]] = Field(
None,
description="""Place-holder than can be extended so that lab-specific meta-data can be placed in /general.""",
)
devices: Optional[List[Device]] = Field(
devices: Optional[Dict[str, Device]] = Field(
None,
description="""Description of hardware devices used during experiment, e.g., monitors, ADC boards, microscopes, etc.""",
json_schema_extra={"linkml_meta": {"any_of": [{"range": "Device"}]}},
@ -367,12 +370,12 @@ class NWBFileGeneral(ConfiguredBaseModel):
intracellular_ephys: Optional[GeneralIntracellularEphys] = Field(
None, description="""Metadata related to intracellular electrophysiology."""
)
optogenetics: Optional[List[OptogeneticStimulusSite]] = Field(
optogenetics: Optional[Dict[str, OptogeneticStimulusSite]] = Field(
None,
description="""Metadata describing optogenetic stimuluation.""",
json_schema_extra={"linkml_meta": {"any_of": [{"range": "OptogeneticStimulusSite"}]}},
)
optophysiology: Optional[List[ImagingPlane]] = Field(
optophysiology: Optional[Dict[str, ImagingPlane]] = Field(
None,
description="""Metadata related to optophysiology.""",
json_schema_extra={"linkml_meta": {"any_of": [{"range": "ImagingPlane"}]}},
@ -450,12 +453,12 @@ class GeneralExtracellularEphys(ConfiguredBaseModel):
}
},
)
electrode_group: Optional[List[ElectrodeGroup]] = Field(
None, description="""Physical group of electrodes."""
)
electrodes: Optional[ExtracellularEphysElectrodes] = Field(
None, description="""A table of all electrodes (i.e. channels) used for recording."""
)
value: Optional[Dict[str, ElectrodeGroup]] = Field(
None, description="""Physical group of electrodes."""
)
class ExtracellularEphysElectrodes(DynamicTable):
@ -525,8 +528,14 @@ class ExtracellularEphysElectrodes(DynamicTable):
}
},
)
group: List[ElectrodeGroup] = Field(
..., description="""Reference to the ElectrodeGroup this electrode is a part of."""
group: VectorData[NDArray[Any, ElectrodeGroup]] = Field(
...,
description="""Reference to the ElectrodeGroup this electrode is a part of.""",
json_schema_extra={
"linkml_meta": {
"array": {"maximum_number_dimensions": False, "minimum_number_dimensions": 1}
}
},
)
group_name: VectorData[NDArray[Any, str]] = Field(
...,
@ -583,9 +592,6 @@ class ExtracellularEphysElectrodes(DynamicTable):
description="""Array of unique identifiers for the rows of this dynamic table.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_rows"}]}}},
)
vector_index: Optional[List[VectorIndex]] = Field(
None, description="""Indices for the vector columns of this dynamic table."""
)
class GeneralIntracellularEphys(ConfiguredBaseModel):
@ -608,12 +614,12 @@ class GeneralIntracellularEphys(ConfiguredBaseModel):
None,
description="""Description of filtering used. Includes filtering type and parameters, frequency fall-off, etc. If this changes between TimeSeries, filter description should be stored as a text attribute for each TimeSeries.""",
)
intracellular_electrode: Optional[List[IntracellularElectrode]] = Field(
None, description="""An intracellular electrode."""
)
sweep_table: Optional[SweepTable] = Field(
None, description="""The table which groups different PatchClampSeries together."""
)
value: Optional[Dict[str, IntracellularElectrode]] = Field(
None, description="""An intracellular electrode."""
)
class NWBFileIntervals(ConfiguredBaseModel):
@ -639,7 +645,7 @@ class NWBFileIntervals(ConfiguredBaseModel):
invalid_times: Optional[TimeIntervals] = Field(
None, description="""Time intervals that should be removed from analysis."""
)
time_intervals: Optional[List[TimeIntervals]] = Field(
value: Optional[Dict[str, TimeIntervals]] = Field(
None,
description="""Optional additional table(s) for describing other experimental time intervals.""",
)

133
nwb_models/src/nwb_models/models/pydantic/core/v2_2_1/core_nwb_icephys.py generated
View file

@ -262,11 +262,21 @@ class PatchClampSeriesData(ConfiguredBaseModel):
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: str = Field(
...,
description="""Base unit of measurement for working with the data. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
)
value: Optional[NDArray[Shape["* num_times"], float]] = Field(
value: Optional[NDArray[Shape["* num_times"], float | int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
@ -281,12 +291,12 @@ class CurrentClampSeries(PatchClampSeries):
)
name: str = Field(...)
data: CurrentClampSeriesData = Field(..., description="""Recorded voltage.""")
bias_current: Optional[float] = Field(None, description="""Bias current, in amps.""")
bridge_balance: Optional[float] = Field(None, description="""Bridge balance, in ohms.""")
capacitance_compensation: Optional[float] = Field(
None, description="""Capacitance compensation, in farads."""
)
data: CurrentClampSeriesData = Field(..., description="""Recorded voltage.""")
stimulus_description: str = Field(
..., description="""Protocol/stimulus name for this patch-clamp dataset."""
)
@ -354,12 +364,24 @@ class CurrentClampSeriesData(ConfiguredBaseModel):
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["volts"] = Field(
"volts",
description="""Base unit of measurement for working with the data. which is fixed to 'volts'. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
json_schema_extra={"linkml_meta": {"equals_string": "volts", "ifabsent": "string(volts)"}},
)
value: Any = Field(...)
value: Optional[NDArray[Shape["* num_times"], float | int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
class IZeroClampSeries(CurrentClampSeries):
@ -512,6 +534,16 @@ class CurrentClampStimulusSeriesData(ConfiguredBaseModel):
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["amperes"] = Field(
"amperes",
description="""Base unit of measurement for working with the data. which is fixed to 'amperes'. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
@ -519,7 +551,9 @@ class CurrentClampStimulusSeriesData(ConfiguredBaseModel):
"linkml_meta": {"equals_string": "amperes", "ifabsent": "string(amperes)"}
},
)
value: Any = Field(...)
value: Optional[NDArray[Shape["* num_times"], float | int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
class VoltageClampSeries(PatchClampSeries):
@ -532,13 +566,13 @@ class VoltageClampSeries(PatchClampSeries):
)
name: str = Field(...)
data: VoltageClampSeriesData = Field(..., description="""Recorded current.""")
capacitance_fast: Optional[VoltageClampSeriesCapacitanceFast] = Field(
None, description="""Fast capacitance, in farads."""
)
capacitance_slow: Optional[VoltageClampSeriesCapacitanceSlow] = Field(
None, description="""Slow capacitance, in farads."""
)
data: VoltageClampSeriesData = Field(..., description="""Recorded current.""")
resistance_comp_bandwidth: Optional[VoltageClampSeriesResistanceCompBandwidth] = Field(
None, description="""Resistance compensation bandwidth, in hertz."""
)
@ -610,27 +644,6 @@ class VoltageClampSeries(PatchClampSeries):
)
class VoltageClampSeriesData(ConfiguredBaseModel):
"""
Recorded current.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.icephys"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
unit: Literal["amperes"] = Field(
"amperes",
description="""Base unit of measurement for working with the data. which is fixed to 'amperes'. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
json_schema_extra={
"linkml_meta": {"equals_string": "amperes", "ifabsent": "string(amperes)"}
},
)
value: Any = Field(...)
class VoltageClampSeriesCapacitanceFast(ConfiguredBaseModel):
"""
Fast capacitance, in farads.
@ -683,6 +696,39 @@ class VoltageClampSeriesCapacitanceSlow(ConfiguredBaseModel):
value: float = Field(...)
class VoltageClampSeriesData(ConfiguredBaseModel):
"""
Recorded current.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.icephys"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["amperes"] = Field(
"amperes",
description="""Base unit of measurement for working with the data. which is fixed to 'amperes'. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
json_schema_extra={
"linkml_meta": {"equals_string": "amperes", "ifabsent": "string(amperes)"}
},
)
value: Optional[NDArray[Shape["* num_times"], float | int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
class VoltageClampSeriesResistanceCompBandwidth(ConfiguredBaseModel):
"""
Resistance compensation bandwidth, in hertz.
@ -887,12 +933,24 @@ class VoltageClampStimulusSeriesData(ConfiguredBaseModel):
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["volts"] = Field(
"volts",
description="""Base unit of measurement for working with the data. which is fixed to 'volts'. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
json_schema_extra={"linkml_meta": {"equals_string": "volts", "ifabsent": "string(volts)"}},
)
value: Any = Field(...)
value: Optional[NDArray[Shape["* num_times"], float | int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
class IntracellularElectrode(NWBContainer):
@ -942,18 +1000,15 @@ class SweepTable(DynamicTable):
)
name: str = Field(...)
sweep_number: VectorData[NDArray[Any, int]] = Field(
series: VectorData[NDArray[Any, PatchClampSeries]] = Field(
...,
description="""Sweep number of the PatchClampSeries in that row.""",
description="""The PatchClampSeries with the sweep number in that row.""",
json_schema_extra={
"linkml_meta": {
"array": {"maximum_number_dimensions": False, "minimum_number_dimensions": 1}
}
},
)
series: List[PatchClampSeries] = Field(
..., description="""The PatchClampSeries with the sweep number in that row."""
)
series_index: Named[VectorIndex] = Field(
...,
description="""Index for series.""",
@ -966,6 +1021,15 @@ class SweepTable(DynamicTable):
}
},
)
sweep_number: VectorData[NDArray[Any, int]] = Field(
...,
description="""Sweep number of the PatchClampSeries in that row.""",
json_schema_extra={
"linkml_meta": {
"array": {"maximum_number_dimensions": False, "minimum_number_dimensions": 1}
}
},
)
colnames: List[str] = Field(
...,
description="""The names of the columns in this table. This should be used to specify an order to the columns.""",
@ -976,9 +1040,6 @@ class SweepTable(DynamicTable):
description="""Array of unique identifiers for the rows of this dynamic table.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_rows"}]}}},
)
vector_index: Optional[List[VectorIndex]] = Field(
None, description="""Indices for the vector columns of this dynamic table."""
)
# Model rebuild
@ -991,9 +1052,9 @@ IZeroClampSeries.model_rebuild()
CurrentClampStimulusSeries.model_rebuild()
CurrentClampStimulusSeriesData.model_rebuild()
VoltageClampSeries.model_rebuild()
VoltageClampSeriesData.model_rebuild()
VoltageClampSeriesCapacitanceFast.model_rebuild()
VoltageClampSeriesCapacitanceSlow.model_rebuild()
VoltageClampSeriesData.model_rebuild()
VoltageClampSeriesResistanceCompBandwidth.model_rebuild()
VoltageClampSeriesResistanceCompCorrection.model_rebuild()
VoltageClampSeriesResistanceCompPrediction.model_rebuild()

113
nwb_models/src/nwb_models/models/pydantic/core/v2_2_1/core_nwb_image.py generated
View file

@ -152,7 +152,7 @@ class GrayscaleImage(Image):
)
name: str = Field(...)
value: Optional[NDArray[Shape["* x, * y"], float]] = Field(
value: Optional[NDArray[Shape["* x, * y"], float | int]] = Field(
None,
json_schema_extra={
"linkml_meta": {"array": {"dimensions": [{"alias": "x"}, {"alias": "y"}]}}
@ -174,7 +174,7 @@ class RGBImage(Image):
)
name: str = Field(...)
value: Optional[NDArray[Shape["* x, * y, 3 r_g_b"], float]] = Field(
value: Optional[NDArray[Shape["* x, * y, 3 r_g_b"], float | int]] = Field(
None,
json_schema_extra={
"linkml_meta": {
@ -204,7 +204,7 @@ class RGBAImage(Image):
)
name: str = Field(...)
value: Optional[NDArray[Shape["* x, * y, 4 r_g_b_a"], float]] = Field(
value: Optional[NDArray[Shape["* x, * y, 4 r_g_b_a"], float | int]] = Field(
None,
json_schema_extra={
"linkml_meta": {
@ -234,12 +234,9 @@ class ImageSeries(TimeSeries):
)
name: str = Field(...)
data: Optional[
Union[
NDArray[Shape["* frame, * x, * y"], float],
NDArray[Shape["* frame, * x, * y, * z"], float],
]
] = Field(None, description="""Binary data representing images across frames.""")
data: Optional[ImageSeriesData] = Field(
None, description="""Binary data representing images across frames."""
)
dimension: Optional[NDArray[Shape["* rank"], int]] = Field(
None,
description="""Number of pixels on x, y, (and z) axes.""",
@ -250,8 +247,9 @@ class ImageSeries(TimeSeries):
description="""Paths to one or more external file(s). The field is only present if format='external'. This is only relevant if the image series is stored in the file system as one or more image file(s). This field should NOT be used if the image is stored in another NWB file and that file is linked to this file.""",
)
format: Optional[str] = Field(
None,
"raw",
description="""Format of image. If this is 'external', then the attribute 'external_file' contains the path information to the image files. If this is 'raw', then the raw (single-channel) binary data is stored in the 'data' dataset. If this attribute is not present, then the default format='raw' case is assumed.""",
json_schema_extra={"linkml_meta": {"ifabsent": "string(raw)"}},
)
description: Optional[str] = Field(
"no description",
@ -290,6 +288,39 @@ class ImageSeries(TimeSeries):
)
class ImageSeriesData(ConfiguredBaseModel):
"""
Binary data representing images across frames.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.image"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: str = Field(
...,
description="""Base unit of measurement for working with the data. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
)
value: Optional[
Union[
NDArray[Shape["* frame, * x, * y"], float | int],
NDArray[Shape["* frame, * x, * y, * z"], float | int],
]
] = Field(None)
class ImageSeriesExternalFile(ConfiguredBaseModel):
"""
Paths to one or more external file(s). The field is only present if format='external'. This is only relevant if the image series is stored in the file system as one or more image file(s). This field should NOT be used if the image is stored in another NWB file and that file is linked to this file.
@ -331,12 +362,9 @@ class ImageMaskSeries(ImageSeries):
}
},
)
data: Optional[
Union[
NDArray[Shape["* frame, * x, * y"], float],
NDArray[Shape["* frame, * x, * y, * z"], float],
]
] = Field(None, description="""Binary data representing images across frames.""")
data: Optional[ImageSeriesData] = Field(
None, description="""Binary data representing images across frames."""
)
dimension: Optional[NDArray[Shape["* rank"], int]] = Field(
None,
description="""Number of pixels on x, y, (and z) axes.""",
@ -347,8 +375,9 @@ class ImageMaskSeries(ImageSeries):
description="""Paths to one or more external file(s). The field is only present if format='external'. This is only relevant if the image series is stored in the file system as one or more image file(s). This field should NOT be used if the image is stored in another NWB file and that file is linked to this file.""",
)
format: Optional[str] = Field(
None,
"raw",
description="""Format of image. If this is 'external', then the attribute 'external_file' contains the path information to the image files. If this is 'raw', then the raw (single-channel) binary data is stored in the 'data' dataset. If this attribute is not present, then the default format='raw' case is assumed.""",
json_schema_extra={"linkml_meta": {"ifabsent": "string(raw)"}},
)
description: Optional[str] = Field(
"no description",
@ -409,12 +438,9 @@ class OpticalSeries(ImageSeries):
None,
description="""Description of image relative to some reference frame (e.g., which way is up). Must also specify frame of reference.""",
)
data: Optional[
Union[
NDArray[Shape["* frame, * x, * y"], float],
NDArray[Shape["* frame, * x, * y, * z"], float],
]
] = Field(None, description="""Binary data representing images across frames.""")
data: Optional[ImageSeriesData] = Field(
None, description="""Binary data representing images across frames."""
)
dimension: Optional[NDArray[Shape["* rank"], int]] = Field(
None,
description="""Number of pixels on x, y, (and z) axes.""",
@ -425,8 +451,9 @@ class OpticalSeries(ImageSeries):
description="""Paths to one or more external file(s). The field is only present if format='external'. This is only relevant if the image series is stored in the file system as one or more image file(s). This field should NOT be used if the image is stored in another NWB file and that file is linked to this file.""",
)
format: Optional[str] = Field(
None,
"raw",
description="""Format of image. If this is 'external', then the attribute 'external_file' contains the path information to the image files. If this is 'raw', then the raw (single-channel) binary data is stored in the 'data' dataset. If this attribute is not present, then the default format='raw' case is assumed.""",
json_schema_extra={"linkml_meta": {"ifabsent": "string(raw)"}},
)
description: Optional[str] = Field(
"no description",
@ -475,10 +502,8 @@ class IndexSeries(TimeSeries):
)
name: str = Field(...)
data: NDArray[Shape["* num_times"], int] = Field(
...,
description="""Index of the frame in the referenced ImageSeries.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}},
data: IndexSeriesData = Field(
..., description="""Index of the frame in the referenced ImageSeries."""
)
indexed_timeseries: Union[ImageSeries, str] = Field(
...,
@ -526,13 +551,45 @@ class IndexSeries(TimeSeries):
)
class IndexSeriesData(ConfiguredBaseModel):
"""
Index of the frame in the referenced ImageSeries.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.image"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: str = Field(
...,
description="""Base unit of measurement for working with the data. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
)
value: Optional[NDArray[Shape["* num_times"], int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
# Model rebuild
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
GrayscaleImage.model_rebuild()
RGBImage.model_rebuild()
RGBAImage.model_rebuild()
ImageSeries.model_rebuild()
ImageSeriesData.model_rebuild()
ImageSeriesExternalFile.model_rebuild()
ImageMaskSeries.model_rebuild()
OpticalSeries.model_rebuild()
IndexSeries.model_rebuild()
IndexSeriesData.model_rebuild()

236
nwb_models/src/nwb_models/models/pydantic/core/v2_2_1/core_nwb_misc.py generated
View file

@ -251,6 +251,16 @@ class AbstractFeatureSeriesData(ConfiguredBaseModel):
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Optional[str] = Field(
"see ",
description="""Since there can be different units for different features, store the units in 'feature_units'. The default value for this attribute is \"see 'feature_units'\".""",
@ -258,8 +268,8 @@ class AbstractFeatureSeriesData(ConfiguredBaseModel):
)
value: Optional[
Union[
NDArray[Shape["* num_times"], float],
NDArray[Shape["* num_times, * num_features"], float],
NDArray[Shape["* num_times"], float | int],
NDArray[Shape["* num_times, * num_features"], float | int],
]
] = Field(None)
@ -274,10 +284,8 @@ class AnnotationSeries(TimeSeries):
)
name: str = Field(...)
data: NDArray[Shape["* num_times"], str] = Field(
...,
description="""Annotations made during an experiment.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}},
data: AnnotationSeriesData = Field(
..., description="""Annotations made during an experiment."""
)
description: Optional[str] = Field(
"no description",
@ -316,6 +324,39 @@ class AnnotationSeries(TimeSeries):
)
class AnnotationSeriesData(ConfiguredBaseModel):
"""
Annotations made during an experiment.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.misc"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: float = Field(
-1.0,
description="""Smallest meaningful difference between values in data. Annotations have no units, so the value is fixed to -1.0.""",
le=-1,
ge=-1,
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["n/a"] = Field(
"n/a",
description="""Base unit of measurement for working with the data. Annotations have no units, so the value is fixed to 'n/a'.""",
json_schema_extra={"linkml_meta": {"equals_string": "n/a", "ifabsent": "string(n/a)"}},
)
value: Optional[NDArray[Shape["* num_times"], str]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
class IntervalSeries(TimeSeries):
"""
Stores intervals of data. The timestamps field stores the beginning and end of intervals. The data field stores whether the interval just started (>0 value) or ended (<0 value). Different interval types can be represented in the same series by using multiple key values (eg, 1 for feature A, 2 for feature B, 3 for feature C, etc). The field data stores an 8-bit integer. This is largely an alias of a standard TimeSeries but that is identifiable as representing time intervals in a machine-readable way.
@ -326,10 +367,8 @@ class IntervalSeries(TimeSeries):
)
name: str = Field(...)
data: NDArray[Shape["* num_times"], int] = Field(
...,
description="""Use values >0 if interval started, <0 if interval ended.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}},
data: IntervalSeriesData = Field(
..., description="""Use values >0 if interval started, <0 if interval ended."""
)
description: Optional[str] = Field(
"no description",
@ -368,6 +407,39 @@ class IntervalSeries(TimeSeries):
)
class IntervalSeriesData(ConfiguredBaseModel):
"""
Use values >0 if interval started, <0 if interval ended.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.misc"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: float = Field(
-1.0,
description="""Smallest meaningful difference between values in data. Annotations have no units, so the value is fixed to -1.0.""",
le=-1,
ge=-1,
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["n/a"] = Field(
"n/a",
description="""Base unit of measurement for working with the data. Annotations have no units, so the value is fixed to 'n/a'.""",
json_schema_extra={"linkml_meta": {"equals_string": "n/a", "ifabsent": "string(n/a)"}},
)
value: Optional[NDArray[Shape["* num_times"], int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
class DecompositionSeries(TimeSeries):
"""
Spectral analysis of a time series, e.g. of an LFP or a speech signal.
@ -443,24 +515,36 @@ class DecompositionSeriesData(ConfiguredBaseModel):
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: str = Field(
"no unit",
description="""Base unit of measurement for working with the data. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
json_schema_extra={"linkml_meta": {"ifabsent": "string(no unit)"}},
)
value: Optional[NDArray[Shape["* num_times, * num_channels, * num_bands"], float]] = Field(
None,
json_schema_extra={
"linkml_meta": {
"array": {
"dimensions": [
{"alias": "num_times"},
{"alias": "num_channels"},
{"alias": "num_bands"},
]
value: Optional[NDArray[Shape["* num_times, * num_channels, * num_bands"], float | int]] = (
Field(
None,
json_schema_extra={
"linkml_meta": {
"array": {
"dimensions": [
{"alias": "num_times"},
{"alias": "num_channels"},
{"alias": "num_bands"},
]
}
}
}
},
},
)
)
@ -518,9 +602,6 @@ class DecompositionSeriesBands(DynamicTable):
description="""Array of unique identifiers for the rows of this dynamic table.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_rows"}]}}},
)
vector_index: Optional[List[VectorIndex]] = Field(
None, description="""Indices for the vector columns of this dynamic table."""
)
class Units(DynamicTable):
@ -533,9 +614,18 @@ class Units(DynamicTable):
)
name: str = Field("Units", json_schema_extra={"linkml_meta": {"ifabsent": "string(Units)"}})
spike_times_index: Optional[Named[VectorIndex]] = Field(
electrode_group: Optional[VectorData[NDArray[Any, ElectrodeGroup]]] = Field(
None,
description="""Index into the spike_times dataset.""",
description="""Electrode group that each spike unit came from.""",
json_schema_extra={
"linkml_meta": {
"array": {"maximum_number_dimensions": False, "minimum_number_dimensions": 1}
}
},
)
electrodes: Optional[Named[DynamicTableRegion]] = Field(
None,
description="""Electrode that each spike unit came from, specified using a DynamicTableRegion.""",
json_schema_extra={
"linkml_meta": {
"annotations": {
@ -545,12 +635,9 @@ class Units(DynamicTable):
}
},
)
spike_times: Optional[UnitsSpikeTimes] = Field(
None, description="""Spike times for each unit."""
)
obs_intervals_index: Optional[Named[VectorIndex]] = Field(
electrodes_index: Optional[Named[VectorIndex]] = Field(
None,
description="""Index into the obs_intervals dataset.""",
description="""Index into electrodes.""",
json_schema_extra={
"linkml_meta": {
"annotations": {
@ -576,9 +663,9 @@ class Units(DynamicTable):
},
)
)
electrodes_index: Optional[Named[VectorIndex]] = Field(
obs_intervals_index: Optional[Named[VectorIndex]] = Field(
None,
description="""Index into electrodes.""",
description="""Index into the obs_intervals dataset.""",
json_schema_extra={
"linkml_meta": {
"annotations": {
@ -588,9 +675,12 @@ class Units(DynamicTable):
}
},
)
electrodes: Optional[Named[DynamicTableRegion]] = Field(
spike_times: Optional[UnitsSpikeTimes] = Field(
None, description="""Spike times for each unit."""
)
spike_times_index: Optional[Named[VectorIndex]] = Field(
None,
description="""Electrode that each spike unit came from, specified using a DynamicTableRegion.""",
description="""Index into the spike_times dataset.""",
json_schema_extra={
"linkml_meta": {
"annotations": {
@ -600,25 +690,12 @@ class Units(DynamicTable):
}
},
)
electrode_group: Optional[List[ElectrodeGroup]] = Field(
None, description="""Electrode group that each spike unit came from."""
waveform_mean: Optional[UnitsWaveformMean] = Field(
None, description="""Spike waveform mean for each spike unit."""
)
waveform_sd: Optional[UnitsWaveformSd] = Field(
None, description="""Spike waveform standard deviation for each spike unit."""
)
waveform_mean: Optional[
VectorData[
Union[
NDArray[Shape["* num_units, * num_samples"], float],
NDArray[Shape["* num_units, * num_samples, * num_electrodes"], float],
]
]
] = Field(None, description="""Spike waveform mean for each spike unit.""")
waveform_sd: Optional[
VectorData[
Union[
NDArray[Shape["* num_units, * num_samples"], float],
NDArray[Shape["* num_units, * num_samples, * num_electrodes"], float],
]
]
] = Field(None, description="""Spike waveform standard deviation for each spike unit.""")
colnames: List[str] = Field(
...,
description="""The names of the columns in this table. This should be used to specify an order to the columns.""",
@ -629,9 +706,6 @@ class Units(DynamicTable):
description="""Array of unique identifiers for the rows of this dynamic table.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_rows"}]}}},
)
vector_index: Optional[List[VectorIndex]] = Field(
None, description="""Indices for the vector columns of this dynamic table."""
)
class UnitsSpikeTimes(VectorData):
@ -654,14 +728,62 @@ class UnitsSpikeTimes(VectorData):
description: str = Field(..., description="""Description of what these vectors represent.""")
class UnitsWaveformMean(VectorData):
"""
Spike waveform mean for each spike unit.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.misc"})
name: Literal["waveform_mean"] = Field(
"waveform_mean",
json_schema_extra={
"linkml_meta": {"equals_string": "waveform_mean", "ifabsent": "string(waveform_mean)"}
},
)
sampling_rate: Optional[float] = Field(None, description="""Sampling rate, in hertz.""")
unit: Optional[Literal["volts"]] = Field(
"volts",
description="""Unit of measurement. This value is fixed to 'volts'.""",
json_schema_extra={"linkml_meta": {"equals_string": "volts", "ifabsent": "string(volts)"}},
)
description: str = Field(..., description="""Description of what these vectors represent.""")
class UnitsWaveformSd(VectorData):
"""
Spike waveform standard deviation for each spike unit.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.misc"})
name: Literal["waveform_sd"] = Field(
"waveform_sd",
json_schema_extra={
"linkml_meta": {"equals_string": "waveform_sd", "ifabsent": "string(waveform_sd)"}
},
)
sampling_rate: Optional[float] = Field(None, description="""Sampling rate, in hertz.""")
unit: Optional[Literal["volts"]] = Field(
"volts",
description="""Unit of measurement. This value is fixed to 'volts'.""",
json_schema_extra={"linkml_meta": {"equals_string": "volts", "ifabsent": "string(volts)"}},
)
description: str = Field(..., description="""Description of what these vectors represent.""")
# Model rebuild
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
AbstractFeatureSeries.model_rebuild()
AbstractFeatureSeriesData.model_rebuild()
AnnotationSeries.model_rebuild()
AnnotationSeriesData.model_rebuild()
IntervalSeries.model_rebuild()
IntervalSeriesData.model_rebuild()
DecompositionSeries.model_rebuild()
DecompositionSeriesData.model_rebuild()
DecompositionSeriesBands.model_rebuild()
Units.model_rebuild()
UnitsSpikeTimes.model_rebuild()
UnitsWaveformMean.model_rebuild()
UnitsWaveformSd.model_rebuild()

38
nwb_models/src/nwb_models/models/pydantic/core/v2_2_1/core_nwb_ogen.py generated
View file

@ -158,10 +158,8 @@ class OptogeneticSeries(TimeSeries):
)
name: str = Field(...)
data: NDArray[Shape["* num_times"], float] = Field(
...,
description="""Applied power for optogenetic stimulus, in watts.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}},
data: OptogeneticSeriesData = Field(
..., description="""Applied power for optogenetic stimulus, in watts."""
)
site: Union[OptogeneticStimulusSite, str] = Field(
...,
@ -209,6 +207,37 @@ class OptogeneticSeries(TimeSeries):
)
class OptogeneticSeriesData(ConfiguredBaseModel):
"""
Applied power for optogenetic stimulus, in watts.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.ogen"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["watts"] = Field(
"watts",
description="""Unit of measurement for data, which is fixed to 'watts'.""",
json_schema_extra={"linkml_meta": {"equals_string": "watts", "ifabsent": "string(watts)"}},
)
value: Optional[NDArray[Shape["* num_times"], float | int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
class OptogeneticStimulusSite(NWBContainer):
"""
A site of optogenetic stimulation.
@ -239,4 +268,5 @@ class OptogeneticStimulusSite(NWBContainer):
# Model rebuild
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
OptogeneticSeries.model_rebuild()
OptogeneticSeriesData.model_rebuild()
OptogeneticStimulusSite.model_rebuild()

76
nwb_models/src/nwb_models/models/pydantic/core/v2_2_1/core_nwb_ophys.py generated
View file

@ -28,7 +28,7 @@ from ...core.v2_2_1.core_nwb_base import (
TimeSeriesSync,
)
from ...core.v2_2_1.core_nwb_device import Device
from ...core.v2_2_1.core_nwb_image import ImageSeries, ImageSeriesExternalFile
from ...core.v2_2_1.core_nwb_image import ImageSeries, ImageSeriesData, ImageSeriesExternalFile
from ...hdmf_common.v1_1_2.hdmf_common_table import DynamicTable, DynamicTableRegion
@ -209,12 +209,9 @@ class TwoPhotonSeries(ImageSeries):
}
},
)
data: Optional[
Union[
NDArray[Shape["* frame, * x, * y"], float],
NDArray[Shape["* frame, * x, * y, * z"], float],
]
] = Field(None, description="""Binary data representing images across frames.""")
data: Optional[ImageSeriesData] = Field(
None, description="""Binary data representing images across frames."""
)
dimension: Optional[NDArray[Shape["* rank"], int]] = Field(
None,
description="""Number of pixels on x, y, (and z) axes.""",
@ -225,8 +222,9 @@ class TwoPhotonSeries(ImageSeries):
description="""Paths to one or more external file(s). The field is only present if format='external'. This is only relevant if the image series is stored in the file system as one or more image file(s). This field should NOT be used if the image is stored in another NWB file and that file is linked to this file.""",
)
format: Optional[str] = Field(
None,
"raw",
description="""Format of image. If this is 'external', then the attribute 'external_file' contains the path information to the image files. If this is 'raw', then the raw (single-channel) binary data is stored in the 'data' dataset. If this attribute is not present, then the default format='raw' case is assumed.""",
json_schema_extra={"linkml_meta": {"ifabsent": "string(raw)"}},
)
description: Optional[str] = Field(
"no description",
@ -275,9 +273,7 @@ class RoiResponseSeries(TimeSeries):
)
name: str = Field(...)
data: Union[
NDArray[Shape["* num_times"], float], NDArray[Shape["* num_times, * num_rois"], float]
] = Field(..., description="""Signals from ROIs.""")
data: RoiResponseSeriesData = Field(..., description="""Signals from ROIs.""")
rois: Named[DynamicTableRegion] = Field(
...,
description="""DynamicTableRegion referencing into an ROITable containing information on the ROIs stored in this timeseries.""",
@ -327,6 +323,39 @@ class RoiResponseSeries(TimeSeries):
)
class RoiResponseSeriesData(ConfiguredBaseModel):
"""
Signals from ROIs.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.ophys"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: str = Field(
...,
description="""Base unit of measurement for working with the data. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
)
value: Optional[
Union[
NDArray[Shape["* num_times"], float | int],
NDArray[Shape["* num_times, * num_rois"], float | int],
]
] = Field(None)
class DfOverF(NWBDataInterface):
"""
dF/F information about a region of interest (ROI). Storage hierarchy of dF/F should be the same as for segmentation (i.e., same names for ROIs and for image planes).
@ -336,10 +365,10 @@ class DfOverF(NWBDataInterface):
{"from_schema": "core.nwb.ophys", "tree_root": True}
)
value: Optional[List[RoiResponseSeries]] = Field(
name: str = Field("DfOverF", json_schema_extra={"linkml_meta": {"ifabsent": "string(DfOverF)"}})
value: Optional[Dict[str, RoiResponseSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "RoiResponseSeries"}]}}
)
name: str = Field(...)
class Fluorescence(NWBDataInterface):
@ -351,10 +380,12 @@ class Fluorescence(NWBDataInterface):
{"from_schema": "core.nwb.ophys", "tree_root": True}
)
value: Optional[List[RoiResponseSeries]] = Field(
name: str = Field(
"Fluorescence", json_schema_extra={"linkml_meta": {"ifabsent": "string(Fluorescence)"}}
)
value: Optional[Dict[str, RoiResponseSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "RoiResponseSeries"}]}}
)
name: str = Field(...)
class ImageSegmentation(NWBDataInterface):
@ -366,10 +397,13 @@ class ImageSegmentation(NWBDataInterface):
{"from_schema": "core.nwb.ophys", "tree_root": True}
)
value: Optional[List[DynamicTable]] = Field(
name: str = Field(
"ImageSegmentation",
json_schema_extra={"linkml_meta": {"ifabsent": "string(ImageSegmentation)"}},
)
value: Optional[Dict[str, DynamicTable]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "DynamicTable"}]}}
)
name: str = Field(...)
class ImagingPlane(NWBContainer):
@ -538,16 +572,20 @@ class MotionCorrection(NWBDataInterface):
{"from_schema": "core.nwb.ophys", "tree_root": True}
)
value: Optional[List[NWBDataInterface]] = Field(
name: str = Field(
"MotionCorrection",
json_schema_extra={"linkml_meta": {"ifabsent": "string(MotionCorrection)"}},
)
value: Optional[Dict[str, NWBDataInterface]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "NWBDataInterface"}]}}
)
name: str = Field(...)
# Model rebuild
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
TwoPhotonSeries.model_rebuild()
RoiResponseSeries.model_rebuild()
RoiResponseSeriesData.model_rebuild()
DfOverF.model_rebuild()
Fluorescence.model_rebuild()
ImageSegmentation.model_rebuild()

4
nwb_models/src/nwb_models/models/pydantic/core/v2_2_1/core_nwb_retinotopy.py generated
View file

@ -235,7 +235,7 @@ class RetinotopyImage(GrayscaleImage):
)
field_of_view: List[float] = Field(..., description="""Size of viewing area, in meters.""")
format: str = Field(..., description="""Format of image. Right now only 'raw' is supported.""")
value: Optional[NDArray[Shape["* x, * y"], float]] = Field(
value: Optional[NDArray[Shape["* x, * y"], float | int]] = Field(
None,
json_schema_extra={
"linkml_meta": {"array": {"dimensions": [{"alias": "x"}, {"alias": "y"}]}}
@ -374,7 +374,7 @@ class ImagingRetinotopyFocalDepthImage(RetinotopyImage):
)
field_of_view: List[float] = Field(..., description="""Size of viewing area, in meters.""")
format: str = Field(..., description="""Format of image. Right now only 'raw' is supported.""")
value: Optional[NDArray[Shape["* x, * y"], float]] = Field(
value: Optional[NDArray[Shape["* x, * y"], float | int]] = Field(
None,
json_schema_extra={
"linkml_meta": {"array": {"dimensions": [{"alias": "x"}, {"alias": "y"}]}}

15
nwb_models/src/nwb_models/models/pydantic/core/v2_2_1/namespace.py generated
View file

@ -38,6 +38,7 @@ from ...core.v2_2_1.core_nwb_ecephys import (
ClusterWaveforms,
Clustering,
ElectricalSeries,
ElectricalSeriesData,
ElectrodeGroup,
ElectrodeGroupPosition,
EventDetection,
@ -46,6 +47,7 @@ from ...core.v2_2_1.core_nwb_ecephys import (
FilteredEphys,
LFP,
SpikeEventSeries,
SpikeEventSeriesData,
)
from ...core.v2_2_1.core_nwb_epoch import TimeIntervals, TimeIntervalsTimeseries
from ...core.v2_2_1.core_nwb_file import (
@ -85,8 +87,10 @@ from ...core.v2_2_1.core_nwb_image import (
GrayscaleImage,
ImageMaskSeries,
ImageSeries,
ImageSeriesData,
ImageSeriesExternalFile,
IndexSeries,
IndexSeriesData,
OpticalSeries,
RGBAImage,
RGBImage,
@ -95,14 +99,22 @@ from ...core.v2_2_1.core_nwb_misc import (
AbstractFeatureSeries,
AbstractFeatureSeriesData,
AnnotationSeries,
AnnotationSeriesData,
DecompositionSeries,
DecompositionSeriesBands,
DecompositionSeriesData,
IntervalSeries,
IntervalSeriesData,
Units,
UnitsSpikeTimes,
UnitsWaveformMean,
UnitsWaveformSd,
)
from ...core.v2_2_1.core_nwb_ogen import (
OptogeneticSeries,
OptogeneticSeriesData,
OptogeneticStimulusSite,
)
from ...core.v2_2_1.core_nwb_ogen import OptogeneticSeries, OptogeneticStimulusSite
from ...core.v2_2_1.core_nwb_ophys import (
DfOverF,
Fluorescence,
@ -114,6 +126,7 @@ from ...core.v2_2_1.core_nwb_ophys import (
MotionCorrection,
OpticalChannel,
RoiResponseSeries,
RoiResponseSeriesData,
TwoPhotonSeries,
)
from ...core.v2_2_1.core_nwb_retinotopy import (

15
nwb_models/src/nwb_models/models/pydantic/core/v2_2_2/core_nwb_base.py generated
View file

@ -170,9 +170,9 @@ class Image(NWBData):
description: Optional[str] = Field(None, description="""Description of the image.""")
value: Optional[
Union[
NDArray[Shape["* x, * y"], float],
NDArray[Shape["* x, * y, 3 r_g_b"], float],
NDArray[Shape["* x, * y, 4 r_g_b_a"], float],
NDArray[Shape["* x, * y"], float | int],
NDArray[Shape["* x, * y, 3 r_g_b"], float | int],
NDArray[Shape["* x, * y, 4 r_g_b_a"], float | int],
]
] = Field(None)
@ -333,13 +333,16 @@ class ProcessingModule(NWBContainer):
{"from_schema": "core.nwb.base", "tree_root": True}
)
value: Optional[List[Union[DynamicTable, NWBDataInterface]]] = Field(
name: str = Field(...)
description: str = Field(
..., description="""Description of this collection of processed data."""
)
value: Optional[Dict[str, Union[DynamicTable, NWBDataInterface]]] = Field(
None,
json_schema_extra={
"linkml_meta": {"any_of": [{"range": "NWBDataInterface"}, {"range": "DynamicTable"}]}
},
)
name: str = Field(...)
class Images(NWBDataInterface):
@ -353,7 +356,7 @@ class Images(NWBDataInterface):
name: str = Field("Images", json_schema_extra={"linkml_meta": {"ifabsent": "string(Images)"}})
description: str = Field(..., description="""Description of this collection of images.""")
image: List[Image] = Field(..., description="""Images stored in this collection.""")
image: List[str] = Field(..., description="""Images stored in this collection.""")
# Model rebuild

60
nwb_models/src/nwb_models/models/pydantic/core/v2_2_2/core_nwb_behavior.py generated
View file

@ -213,6 +213,16 @@ class SpatialSeriesData(ConfiguredBaseModel):
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Optional[str] = Field(
"meters",
description="""Base unit of measurement for working with the data. The default value is 'meters'. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
@ -220,8 +230,8 @@ class SpatialSeriesData(ConfiguredBaseModel):
)
value: Optional[
Union[
NDArray[Shape["* num_times"], float],
NDArray[Shape["* num_times, * num_features"], float],
NDArray[Shape["* num_times"], float | int],
NDArray[Shape["* num_times, * num_features"], float | int],
]
] = Field(None)
@ -235,10 +245,13 @@ class BehavioralEpochs(NWBDataInterface):
{"from_schema": "core.nwb.behavior", "tree_root": True}
)
value: Optional[List[IntervalSeries]] = Field(
name: str = Field(
"BehavioralEpochs",
json_schema_extra={"linkml_meta": {"ifabsent": "string(BehavioralEpochs)"}},
)
value: Optional[Dict[str, IntervalSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "IntervalSeries"}]}}
)
name: str = Field(...)
class BehavioralEvents(NWBDataInterface):
@ -250,10 +263,13 @@ class BehavioralEvents(NWBDataInterface):
{"from_schema": "core.nwb.behavior", "tree_root": True}
)
value: Optional[List[TimeSeries]] = Field(
name: str = Field(
"BehavioralEvents",
json_schema_extra={"linkml_meta": {"ifabsent": "string(BehavioralEvents)"}},
)
value: Optional[Dict[str, TimeSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "TimeSeries"}]}}
)
name: str = Field(...)
class BehavioralTimeSeries(NWBDataInterface):
@ -265,10 +281,13 @@ class BehavioralTimeSeries(NWBDataInterface):
{"from_schema": "core.nwb.behavior", "tree_root": True}
)
value: Optional[List[TimeSeries]] = Field(
name: str = Field(
"BehavioralTimeSeries",
json_schema_extra={"linkml_meta": {"ifabsent": "string(BehavioralTimeSeries)"}},
)
value: Optional[Dict[str, TimeSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "TimeSeries"}]}}
)
name: str = Field(...)
class PupilTracking(NWBDataInterface):
@ -280,10 +299,12 @@ class PupilTracking(NWBDataInterface):
{"from_schema": "core.nwb.behavior", "tree_root": True}
)
value: Optional[List[TimeSeries]] = Field(
name: str = Field(
"PupilTracking", json_schema_extra={"linkml_meta": {"ifabsent": "string(PupilTracking)"}}
)
value: Optional[Dict[str, TimeSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "TimeSeries"}]}}
)
name: str = Field(...)
class EyeTracking(NWBDataInterface):
@ -295,10 +316,12 @@ class EyeTracking(NWBDataInterface):
{"from_schema": "core.nwb.behavior", "tree_root": True}
)
value: Optional[List[SpatialSeries]] = Field(
name: str = Field(
"EyeTracking", json_schema_extra={"linkml_meta": {"ifabsent": "string(EyeTracking)"}}
)
value: Optional[Dict[str, SpatialSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "SpatialSeries"}]}}
)
name: str = Field(...)
class CompassDirection(NWBDataInterface):
@ -310,10 +333,13 @@ class CompassDirection(NWBDataInterface):
{"from_schema": "core.nwb.behavior", "tree_root": True}
)
value: Optional[List[SpatialSeries]] = Field(
name: str = Field(
"CompassDirection",
json_schema_extra={"linkml_meta": {"ifabsent": "string(CompassDirection)"}},
)
value: Optional[Dict[str, SpatialSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "SpatialSeries"}]}}
)
name: str = Field(...)
class Position(NWBDataInterface):
@ -325,10 +351,12 @@ class Position(NWBDataInterface):
{"from_schema": "core.nwb.behavior", "tree_root": True}
)
value: Optional[List[SpatialSeries]] = Field(
name: str = Field(
"Position", json_schema_extra={"linkml_meta": {"ifabsent": "string(Position)"}}
)
value: Optional[Dict[str, SpatialSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "SpatialSeries"}]}}
)
name: str = Field(...)
# Model rebuild

118
nwb_models/src/nwb_models/models/pydantic/core/v2_2_2/core_nwb_ecephys.py generated
View file

@ -190,11 +190,12 @@ class ElectricalSeries(TimeSeries):
)
name: str = Field(...)
data: Union[
NDArray[Shape["* num_times"], float],
NDArray[Shape["* num_times, * num_channels"], float],
NDArray[Shape["* num_times, * num_channels, * num_samples"], float],
] = Field(..., description="""Recorded voltage data.""")
channel_conversion: Optional[NDArray[Shape["* num_channels"], float]] = Field(
None,
description="""Channel-specific conversion factor. Multiply the data in the 'data' dataset by these values along the channel axis (as indicated by axis attribute) AND by the global conversion factor in the 'conversion' attribute of 'data' to get the data values in Volts, i.e, data in Volts = data * data.conversion * channel_conversion. This approach allows for both global and per-channel data conversion factors needed to support the storage of electrical recordings as native values generated by data acquisition systems. If this dataset is not present, then there is no channel-specific conversion factor, i.e. it is 1 for all channels.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_channels"}]}}},
)
data: ElectricalSeriesData = Field(..., description="""Recorded voltage data.""")
electrodes: Named[DynamicTableRegion] = Field(
...,
description="""DynamicTableRegion pointer to the electrodes that this time series was generated from.""",
@ -207,11 +208,6 @@ class ElectricalSeries(TimeSeries):
}
},
)
channel_conversion: Optional[NDArray[Shape["* num_channels"], float]] = Field(
None,
description="""Channel-specific conversion factor. Multiply the data in the 'data' dataset by these values along the channel axis (as indicated by axis attribute) AND by the global conversion factor in the 'conversion' attribute of 'data' to get the data values in Volts, i.e, data in Volts = data * data.conversion * channel_conversion. This approach allows for both global and per-channel data conversion factors needed to support the storage of electrical recordings as native values generated by data acquisition systems. If this dataset is not present, then there is no channel-specific conversion factor, i.e. it is 1 for all channels.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_channels"}]}}},
)
description: Optional[str] = Field(
"no description",
description="""Description of the time series.""",
@ -249,6 +245,41 @@ class ElectricalSeries(TimeSeries):
)
class ElectricalSeriesData(ConfiguredBaseModel):
"""
Recorded voltage data.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.ecephys"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["volts"] = Field(
"volts",
description="""Base unit of measurement for working with the data. This value is fixed to 'volts'. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion' and 'channel_conversion' (if present).""",
json_schema_extra={"linkml_meta": {"equals_string": "volts", "ifabsent": "string(volts)"}},
)
value: Optional[
Union[
NDArray[Shape["* num_times"], float | int],
NDArray[Shape["* num_times, * num_channels"], float | int],
NDArray[Shape["* num_times, * num_channels, * num_samples"], float | int],
]
] = Field(None)
class SpikeEventSeries(ElectricalSeries):
"""
Stores snapshots/snippets of recorded spike events (i.e., threshold crossings). This may also be raw data, as reported by ephys hardware. If so, the TimeSeries::description field should describe how events were detected. All SpikeEventSeries should reside in a module (under EventWaveform interface) even if the spikes were reported and stored by hardware. All events span the same recording channels and store snapshots of equal duration. TimeSeries::data array structure: [num events] [num channels] [num samples] (or [num events] [num samples] for single electrode).
@ -259,15 +290,17 @@ class SpikeEventSeries(ElectricalSeries):
)
name: str = Field(...)
data: Union[
NDArray[Shape["* num_events, * num_samples"], float],
NDArray[Shape["* num_events, * num_channels, * num_samples"], float],
] = Field(..., description="""Spike waveforms.""")
data: SpikeEventSeriesData = Field(..., description="""Spike waveforms.""")
timestamps: NDArray[Shape["* num_times"], float] = Field(
...,
description="""Timestamps for samples stored in data, in seconds, relative to the common experiment master-clock stored in NWBFile.timestamps_reference_time. Timestamps are required for the events. Unlike for TimeSeries, timestamps are required for SpikeEventSeries and are thus re-specified here.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}},
)
channel_conversion: Optional[NDArray[Shape["* num_channels"], float]] = Field(
None,
description="""Channel-specific conversion factor. Multiply the data in the 'data' dataset by these values along the channel axis (as indicated by axis attribute) AND by the global conversion factor in the 'conversion' attribute of 'data' to get the data values in Volts, i.e, data in Volts = data * data.conversion * channel_conversion. This approach allows for both global and per-channel data conversion factors needed to support the storage of electrical recordings as native values generated by data acquisition systems. If this dataset is not present, then there is no channel-specific conversion factor, i.e. it is 1 for all channels.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_channels"}]}}},
)
electrodes: Named[DynamicTableRegion] = Field(
...,
description="""DynamicTableRegion pointer to the electrodes that this time series was generated from.""",
@ -280,11 +313,6 @@ class SpikeEventSeries(ElectricalSeries):
}
},
)
channel_conversion: Optional[NDArray[Shape["* num_channels"], float]] = Field(
None,
description="""Channel-specific conversion factor. Multiply the data in the 'data' dataset by these values along the channel axis (as indicated by axis attribute) AND by the global conversion factor in the 'conversion' attribute of 'data' to get the data values in Volts, i.e, data in Volts = data * data.conversion * channel_conversion. This approach allows for both global and per-channel data conversion factors needed to support the storage of electrical recordings as native values generated by data acquisition systems. If this dataset is not present, then there is no channel-specific conversion factor, i.e. it is 1 for all channels.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_channels"}]}}},
)
description: Optional[str] = Field(
"no description",
description="""Description of the time series.""",
@ -317,6 +345,40 @@ class SpikeEventSeries(ElectricalSeries):
)
class SpikeEventSeriesData(ConfiguredBaseModel):
"""
Spike waveforms.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.ecephys"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["volts"] = Field(
"volts",
description="""Unit of measurement for waveforms, which is fixed to 'volts'.""",
json_schema_extra={"linkml_meta": {"equals_string": "volts", "ifabsent": "string(volts)"}},
)
value: Optional[
Union[
NDArray[Shape["* num_events, * num_samples"], float | int],
NDArray[Shape["* num_events, * num_channels, * num_samples"], float | int],
]
] = Field(None)
class FeatureExtraction(NWBDataInterface):
"""
Features, such as PC1 and PC2, that are extracted from signals stored in a SpikeEventSeries or other source.
@ -415,10 +477,12 @@ class EventWaveform(NWBDataInterface):
{"from_schema": "core.nwb.ecephys", "tree_root": True}
)
value: Optional[List[SpikeEventSeries]] = Field(
name: str = Field(
"EventWaveform", json_schema_extra={"linkml_meta": {"ifabsent": "string(EventWaveform)"}}
)
value: Optional[Dict[str, SpikeEventSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "SpikeEventSeries"}]}}
)
name: str = Field(...)
class FilteredEphys(NWBDataInterface):
@ -430,10 +494,12 @@ class FilteredEphys(NWBDataInterface):
{"from_schema": "core.nwb.ecephys", "tree_root": True}
)
value: Optional[List[ElectricalSeries]] = Field(
name: str = Field(
"FilteredEphys", json_schema_extra={"linkml_meta": {"ifabsent": "string(FilteredEphys)"}}
)
value: Optional[Dict[str, ElectricalSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "ElectricalSeries"}]}}
)
name: str = Field(...)
class LFP(NWBDataInterface):
@ -445,10 +511,10 @@ class LFP(NWBDataInterface):
{"from_schema": "core.nwb.ecephys", "tree_root": True}
)
value: Optional[List[ElectricalSeries]] = Field(
name: str = Field("LFP", json_schema_extra={"linkml_meta": {"ifabsent": "string(LFP)"}})
value: Optional[Dict[str, ElectricalSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "ElectricalSeries"}]}}
)
name: str = Field(...)
class ElectrodeGroup(NWBContainer):
@ -591,7 +657,9 @@ class Clustering(NWBDataInterface):
# Model rebuild
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
ElectricalSeries.model_rebuild()
ElectricalSeriesData.model_rebuild()
SpikeEventSeries.model_rebuild()
SpikeEventSeriesData.model_rebuild()
FeatureExtraction.model_rebuild()
EventDetection.model_rebuild()
EventWaveform.model_rebuild()

3
nwb_models/src/nwb_models/models/pydantic/core/v2_2_2/core_nwb_epoch.py generated
View file

@ -247,9 +247,6 @@ class TimeIntervals(DynamicTable):
description="""Array of unique identifiers for the rows of this dynamic table.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_rows"}]}}},
)
vector_index: Optional[List[VectorIndex]] = Field(
None, description="""Indices for the vector columns of this dynamic table."""
)
class TimeIntervalsTimeseries(VectorData):

50
nwb_models/src/nwb_models/models/pydantic/core/v2_2_2/core_nwb_file.py generated
View file

@ -211,28 +211,28 @@ class NWBFile(NWBContainer):
...,
description="""Date and time corresponding to time zero of all timestamps. The date is stored in UTC with local timezone offset as ISO 8601 extended formatted string: 2018-09-28T14:43:54.123+02:00. Dates stored in UTC end in \"Z\" with no timezone offset. Date accuracy is up to milliseconds. All times stored in the file use this time as reference (i.e., time zero).""",
)
acquisition: Optional[List[Union[DynamicTable, NWBDataInterface]]] = Field(
acquisition: Optional[Dict[str, Union[DynamicTable, NWBDataInterface]]] = Field(
None,
description="""Data streams recorded from the system, including ephys, ophys, tracking, etc. This group should be read-only after the experiment is completed and timestamps are corrected to a common timebase. The data stored here may be links to raw data stored in external NWB files. This will allow keeping bulky raw data out of the file while preserving the option of keeping some/all in the file. Acquired data includes tracking and experimental data streams (i.e., everything measured from the system). If bulky data is stored in the /acquisition group, the data can exist in a separate NWB file that is linked to by the file being used for processing and analysis.""",
json_schema_extra={
"linkml_meta": {"any_of": [{"range": "NWBDataInterface"}, {"range": "DynamicTable"}]}
},
)
analysis: Optional[List[Union[DynamicTable, NWBContainer]]] = Field(
analysis: Optional[Dict[str, Union[DynamicTable, NWBContainer]]] = Field(
None,
description="""Lab-specific and custom scientific analysis of data. There is no defined format for the content of this group - the format is up to the individual user/lab. To facilitate sharing analysis data between labs, the contents here should be stored in standard types (e.g., neurodata_types) and appropriately documented. The file can store lab-specific and custom data analysis without restriction on its form or schema, reducing data formatting restrictions on end users. Such data should be placed in the analysis group. The analysis data should be documented so that it could be shared with other labs.""",
json_schema_extra={
"linkml_meta": {"any_of": [{"range": "NWBContainer"}, {"range": "DynamicTable"}]}
},
)
scratch: Optional[List[Union[DynamicTable, NWBContainer]]] = Field(
scratch: Optional[Dict[str, Union[DynamicTable, NWBContainer]]] = Field(
None,
description="""A place to store one-off analysis results. Data placed here is not intended for sharing. By placing data here, users acknowledge that there is no guarantee that their data meets any standard.""",
json_schema_extra={
"linkml_meta": {"any_of": [{"range": "NWBContainer"}, {"range": "DynamicTable"}]}
},
)
processing: Optional[List[ProcessingModule]] = Field(
processing: Optional[Dict[str, ProcessingModule]] = Field(
None,
description="""The home for ProcessingModules. These modules perform intermediate analysis of data that is necessary to perform before scientific analysis. Examples include spike clustering, extracting position from tracking data, stitching together image slices. ProcessingModules can be large and express many data sets from relatively complex analysis (e.g., spike detection and clustering) or small, representing extraction of position information from tracking video, or even binary lick/no-lick decisions. Common software tools (e.g., klustakwik, MClust) are expected to read/write data here. 'Processing' refers to intermediate analysis of the acquired data to make it more amenable to scientific analysis.""",
json_schema_extra={"linkml_meta": {"any_of": [{"range": "ProcessingModule"}]}},
@ -250,6 +250,9 @@ class NWBFile(NWBContainer):
description="""Experimental intervals, whether that be logically distinct sub-experiments having a particular scientific goal, trials (see trials subgroup) during an experiment, or epochs (see epochs subgroup) deriving from analysis of data.""",
)
units: Optional[Units] = Field(None, description="""Data about sorted spike units.""")
specifications: Optional[dict] = Field(
None, description="""Nested dictionary of schema specifications"""
)
class NWBFileStimulus(ConfiguredBaseModel):
@ -265,12 +268,12 @@ class NWBFileStimulus(ConfiguredBaseModel):
"linkml_meta": {"equals_string": "stimulus", "ifabsent": "string(stimulus)"}
},
)
presentation: Optional[List[TimeSeries]] = Field(
presentation: Optional[Dict[str, TimeSeries]] = Field(
None,
description="""Stimuli presented during the experiment.""",
json_schema_extra={"linkml_meta": {"any_of": [{"range": "TimeSeries"}]}},
)
templates: Optional[List[TimeSeries]] = Field(
templates: Optional[Dict[str, TimeSeries]] = Field(
None,
description="""Template stimuli. Timestamps in templates are based on stimulus design and are relative to the beginning of the stimulus. When templates are used, the stimulus instances must convert presentation times to the experiment`s time reference frame.""",
json_schema_extra={"linkml_meta": {"any_of": [{"range": "TimeSeries"}]}},
@ -348,11 +351,11 @@ class NWBFileGeneral(ConfiguredBaseModel):
None,
description="""Information about virus(es) used in experiments, including virus ID, source, date made, injection location, volume, etc.""",
)
nwb_container: Optional[List[NWBContainer]] = Field(
nwb_container: Optional[Dict[str, NWBContainer]] = Field(
None,
description="""Place-holder than can be extended so that lab-specific meta-data can be placed in /general.""",
)
devices: Optional[List[Device]] = Field(
devices: Optional[Dict[str, Device]] = Field(
None,
description="""Description of hardware devices used during experiment, e.g., monitors, ADC boards, microscopes, etc.""",
json_schema_extra={"linkml_meta": {"any_of": [{"range": "Device"}]}},
@ -367,12 +370,12 @@ class NWBFileGeneral(ConfiguredBaseModel):
intracellular_ephys: Optional[GeneralIntracellularEphys] = Field(
None, description="""Metadata related to intracellular electrophysiology."""
)
optogenetics: Optional[List[OptogeneticStimulusSite]] = Field(
optogenetics: Optional[Dict[str, OptogeneticStimulusSite]] = Field(
None,
description="""Metadata describing optogenetic stimuluation.""",
json_schema_extra={"linkml_meta": {"any_of": [{"range": "OptogeneticStimulusSite"}]}},
)
optophysiology: Optional[List[ImagingPlane]] = Field(
optophysiology: Optional[Dict[str, ImagingPlane]] = Field(
None,
description="""Metadata related to optophysiology.""",
json_schema_extra={"linkml_meta": {"any_of": [{"range": "ImagingPlane"}]}},
@ -450,12 +453,12 @@ class GeneralExtracellularEphys(ConfiguredBaseModel):
}
},
)
electrode_group: Optional[List[ElectrodeGroup]] = Field(
None, description="""Physical group of electrodes."""
)
electrodes: Optional[ExtracellularEphysElectrodes] = Field(
None, description="""A table of all electrodes (i.e. channels) used for recording."""
)
value: Optional[Dict[str, ElectrodeGroup]] = Field(
None, description="""Physical group of electrodes."""
)
class ExtracellularEphysElectrodes(DynamicTable):
@ -525,8 +528,14 @@ class ExtracellularEphysElectrodes(DynamicTable):
}
},
)
group: List[ElectrodeGroup] = Field(
..., description="""Reference to the ElectrodeGroup this electrode is a part of."""
group: VectorData[NDArray[Any, ElectrodeGroup]] = Field(
...,
description="""Reference to the ElectrodeGroup this electrode is a part of.""",
json_schema_extra={
"linkml_meta": {
"array": {"maximum_number_dimensions": False, "minimum_number_dimensions": 1}
}
},
)
group_name: VectorData[NDArray[Any, str]] = Field(
...,
@ -583,9 +592,6 @@ class ExtracellularEphysElectrodes(DynamicTable):
description="""Array of unique identifiers for the rows of this dynamic table.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_rows"}]}}},
)
vector_index: Optional[List[VectorIndex]] = Field(
None, description="""Indices for the vector columns of this dynamic table."""
)
class GeneralIntracellularEphys(ConfiguredBaseModel):
@ -608,12 +614,12 @@ class GeneralIntracellularEphys(ConfiguredBaseModel):
None,
description="""Description of filtering used. Includes filtering type and parameters, frequency fall-off, etc. If this changes between TimeSeries, filter description should be stored as a text attribute for each TimeSeries.""",
)
intracellular_electrode: Optional[List[IntracellularElectrode]] = Field(
None, description="""An intracellular electrode."""
)
sweep_table: Optional[SweepTable] = Field(
None, description="""The table which groups different PatchClampSeries together."""
)
value: Optional[Dict[str, IntracellularElectrode]] = Field(
None, description="""An intracellular electrode."""
)
class NWBFileIntervals(ConfiguredBaseModel):
@ -639,7 +645,7 @@ class NWBFileIntervals(ConfiguredBaseModel):
invalid_times: Optional[TimeIntervals] = Field(
None, description="""Time intervals that should be removed from analysis."""
)
time_intervals: Optional[List[TimeIntervals]] = Field(
value: Optional[Dict[str, TimeIntervals]] = Field(
None,
description="""Optional additional table(s) for describing other experimental time intervals.""",
)

133
nwb_models/src/nwb_models/models/pydantic/core/v2_2_2/core_nwb_icephys.py generated
View file

@ -262,11 +262,21 @@ class PatchClampSeriesData(ConfiguredBaseModel):
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: str = Field(
...,
description="""Base unit of measurement for working with the data. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
)
value: Optional[NDArray[Shape["* num_times"], float]] = Field(
value: Optional[NDArray[Shape["* num_times"], float | int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
@ -281,12 +291,12 @@ class CurrentClampSeries(PatchClampSeries):
)
name: str = Field(...)
data: CurrentClampSeriesData = Field(..., description="""Recorded voltage.""")
bias_current: Optional[float] = Field(None, description="""Bias current, in amps.""")
bridge_balance: Optional[float] = Field(None, description="""Bridge balance, in ohms.""")
capacitance_compensation: Optional[float] = Field(
None, description="""Capacitance compensation, in farads."""
)
data: CurrentClampSeriesData = Field(..., description="""Recorded voltage.""")
stimulus_description: str = Field(
..., description="""Protocol/stimulus name for this patch-clamp dataset."""
)
@ -354,12 +364,24 @@ class CurrentClampSeriesData(ConfiguredBaseModel):
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["volts"] = Field(
"volts",
description="""Base unit of measurement for working with the data. which is fixed to 'volts'. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
json_schema_extra={"linkml_meta": {"equals_string": "volts", "ifabsent": "string(volts)"}},
)
value: Any = Field(...)
value: Optional[NDArray[Shape["* num_times"], float | int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
class IZeroClampSeries(CurrentClampSeries):
@ -512,6 +534,16 @@ class CurrentClampStimulusSeriesData(ConfiguredBaseModel):
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["amperes"] = Field(
"amperes",
description="""Base unit of measurement for working with the data. which is fixed to 'amperes'. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
@ -519,7 +551,9 @@ class CurrentClampStimulusSeriesData(ConfiguredBaseModel):
"linkml_meta": {"equals_string": "amperes", "ifabsent": "string(amperes)"}
},
)
value: Any = Field(...)
value: Optional[NDArray[Shape["* num_times"], float | int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
class VoltageClampSeries(PatchClampSeries):
@ -532,13 +566,13 @@ class VoltageClampSeries(PatchClampSeries):
)
name: str = Field(...)
data: VoltageClampSeriesData = Field(..., description="""Recorded current.""")
capacitance_fast: Optional[VoltageClampSeriesCapacitanceFast] = Field(
None, description="""Fast capacitance, in farads."""
)
capacitance_slow: Optional[VoltageClampSeriesCapacitanceSlow] = Field(
None, description="""Slow capacitance, in farads."""
)
data: VoltageClampSeriesData = Field(..., description="""Recorded current.""")
resistance_comp_bandwidth: Optional[VoltageClampSeriesResistanceCompBandwidth] = Field(
None, description="""Resistance compensation bandwidth, in hertz."""
)
@ -610,27 +644,6 @@ class VoltageClampSeries(PatchClampSeries):
)
class VoltageClampSeriesData(ConfiguredBaseModel):
"""
Recorded current.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.icephys"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
unit: Literal["amperes"] = Field(
"amperes",
description="""Base unit of measurement for working with the data. which is fixed to 'amperes'. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
json_schema_extra={
"linkml_meta": {"equals_string": "amperes", "ifabsent": "string(amperes)"}
},
)
value: Any = Field(...)
class VoltageClampSeriesCapacitanceFast(ConfiguredBaseModel):
"""
Fast capacitance, in farads.
@ -683,6 +696,39 @@ class VoltageClampSeriesCapacitanceSlow(ConfiguredBaseModel):
value: float = Field(...)
class VoltageClampSeriesData(ConfiguredBaseModel):
"""
Recorded current.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.icephys"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["amperes"] = Field(
"amperes",
description="""Base unit of measurement for working with the data. which is fixed to 'amperes'. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
json_schema_extra={
"linkml_meta": {"equals_string": "amperes", "ifabsent": "string(amperes)"}
},
)
value: Optional[NDArray[Shape["* num_times"], float | int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
class VoltageClampSeriesResistanceCompBandwidth(ConfiguredBaseModel):
"""
Resistance compensation bandwidth, in hertz.
@ -887,12 +933,24 @@ class VoltageClampStimulusSeriesData(ConfiguredBaseModel):
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["volts"] = Field(
"volts",
description="""Base unit of measurement for working with the data. which is fixed to 'volts'. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
json_schema_extra={"linkml_meta": {"equals_string": "volts", "ifabsent": "string(volts)"}},
)
value: Any = Field(...)
value: Optional[NDArray[Shape["* num_times"], float | int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
class IntracellularElectrode(NWBContainer):
@ -942,18 +1000,15 @@ class SweepTable(DynamicTable):
)
name: str = Field(...)
sweep_number: VectorData[NDArray[Any, int]] = Field(
series: VectorData[NDArray[Any, PatchClampSeries]] = Field(
...,
description="""Sweep number of the PatchClampSeries in that row.""",
description="""The PatchClampSeries with the sweep number in that row.""",
json_schema_extra={
"linkml_meta": {
"array": {"maximum_number_dimensions": False, "minimum_number_dimensions": 1}
}
},
)
series: List[PatchClampSeries] = Field(
..., description="""The PatchClampSeries with the sweep number in that row."""
)
series_index: Named[VectorIndex] = Field(
...,
description="""Index for series.""",
@ -966,6 +1021,15 @@ class SweepTable(DynamicTable):
}
},
)
sweep_number: VectorData[NDArray[Any, int]] = Field(
...,
description="""Sweep number of the PatchClampSeries in that row.""",
json_schema_extra={
"linkml_meta": {
"array": {"maximum_number_dimensions": False, "minimum_number_dimensions": 1}
}
},
)
colnames: List[str] = Field(
...,
description="""The names of the columns in this table. This should be used to specify an order to the columns.""",
@ -976,9 +1040,6 @@ class SweepTable(DynamicTable):
description="""Array of unique identifiers for the rows of this dynamic table.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_rows"}]}}},
)
vector_index: Optional[List[VectorIndex]] = Field(
None, description="""Indices for the vector columns of this dynamic table."""
)
# Model rebuild
@ -991,9 +1052,9 @@ IZeroClampSeries.model_rebuild()
CurrentClampStimulusSeries.model_rebuild()
CurrentClampStimulusSeriesData.model_rebuild()
VoltageClampSeries.model_rebuild()
VoltageClampSeriesData.model_rebuild()
VoltageClampSeriesCapacitanceFast.model_rebuild()
VoltageClampSeriesCapacitanceSlow.model_rebuild()
VoltageClampSeriesData.model_rebuild()
VoltageClampSeriesResistanceCompBandwidth.model_rebuild()
VoltageClampSeriesResistanceCompCorrection.model_rebuild()
VoltageClampSeriesResistanceCompPrediction.model_rebuild()

145
nwb_models/src/nwb_models/models/pydantic/core/v2_2_2/core_nwb_image.py generated
View file

@ -152,7 +152,7 @@ class GrayscaleImage(Image):
)
name: str = Field(...)
value: Optional[NDArray[Shape["* x, * y"], float]] = Field(
value: Optional[NDArray[Shape["* x, * y"], float | int]] = Field(
None,
json_schema_extra={
"linkml_meta": {"array": {"dimensions": [{"alias": "x"}, {"alias": "y"}]}}
@ -174,7 +174,7 @@ class RGBImage(Image):
)
name: str = Field(...)
value: Optional[NDArray[Shape["* x, * y, 3 r_g_b"], float]] = Field(
value: Optional[NDArray[Shape["* x, * y, 3 r_g_b"], float | int]] = Field(
None,
json_schema_extra={
"linkml_meta": {
@ -204,7 +204,7 @@ class RGBAImage(Image):
)
name: str = Field(...)
value: Optional[NDArray[Shape["* x, * y, 4 r_g_b_a"], float]] = Field(
value: Optional[NDArray[Shape["* x, * y, 4 r_g_b_a"], float | int]] = Field(
None,
json_schema_extra={
"linkml_meta": {
@ -234,12 +234,9 @@ class ImageSeries(TimeSeries):
)
name: str = Field(...)
data: Optional[
Union[
NDArray[Shape["* frame, * x, * y"], float],
NDArray[Shape["* frame, * x, * y, * z"], float],
]
] = Field(None, description="""Binary data representing images across frames.""")
data: Optional[ImageSeriesData] = Field(
None, description="""Binary data representing images across frames."""
)
dimension: Optional[NDArray[Shape["* rank"], int]] = Field(
None,
description="""Number of pixels on x, y, (and z) axes.""",
@ -250,8 +247,9 @@ class ImageSeries(TimeSeries):
description="""Paths to one or more external file(s). The field is only present if format='external'. This is only relevant if the image series is stored in the file system as one or more image file(s). This field should NOT be used if the image is stored in another NWB file and that file is linked to this file.""",
)
format: Optional[str] = Field(
None,
"raw",
description="""Format of image. If this is 'external', then the attribute 'external_file' contains the path information to the image files. If this is 'raw', then the raw (single-channel) binary data is stored in the 'data' dataset. If this attribute is not present, then the default format='raw' case is assumed.""",
json_schema_extra={"linkml_meta": {"ifabsent": "string(raw)"}},
)
description: Optional[str] = Field(
"no description",
@ -290,6 +288,39 @@ class ImageSeries(TimeSeries):
)
class ImageSeriesData(ConfiguredBaseModel):
"""
Binary data representing images across frames.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.image"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: str = Field(
...,
description="""Base unit of measurement for working with the data. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
)
value: Optional[
Union[
NDArray[Shape["* frame, * x, * y"], float | int],
NDArray[Shape["* frame, * x, * y, * z"], float | int],
]
] = Field(None)
class ImageSeriesExternalFile(ConfiguredBaseModel):
"""
Paths to one or more external file(s). The field is only present if format='external'. This is only relevant if the image series is stored in the file system as one or more image file(s). This field should NOT be used if the image is stored in another NWB file and that file is linked to this file.
@ -331,12 +362,9 @@ class ImageMaskSeries(ImageSeries):
}
},
)
data: Optional[
Union[
NDArray[Shape["* frame, * x, * y"], float],
NDArray[Shape["* frame, * x, * y, * z"], float],
]
] = Field(None, description="""Binary data representing images across frames.""")
data: Optional[ImageSeriesData] = Field(
None, description="""Binary data representing images across frames."""
)
dimension: Optional[NDArray[Shape["* rank"], int]] = Field(
None,
description="""Number of pixels on x, y, (and z) axes.""",
@ -347,8 +375,9 @@ class ImageMaskSeries(ImageSeries):
description="""Paths to one or more external file(s). The field is only present if format='external'. This is only relevant if the image series is stored in the file system as one or more image file(s). This field should NOT be used if the image is stored in another NWB file and that file is linked to this file.""",
)
format: Optional[str] = Field(
None,
"raw",
description="""Format of image. If this is 'external', then the attribute 'external_file' contains the path information to the image files. If this is 'raw', then the raw (single-channel) binary data is stored in the 'data' dataset. If this attribute is not present, then the default format='raw' case is assumed.""",
json_schema_extra={"linkml_meta": {"ifabsent": "string(raw)"}},
)
description: Optional[str] = Field(
"no description",
@ -397,6 +426,9 @@ class OpticalSeries(ImageSeries):
)
name: str = Field(...)
data: OpticalSeriesData = Field(
..., description="""Images presented to subject, either grayscale or RGB"""
)
distance: Optional[float] = Field(
None, description="""Distance from camera/monitor to target/eye."""
)
@ -405,10 +437,6 @@ class OpticalSeries(ImageSeries):
NDArray[Shape["2 width_height"], float], NDArray[Shape["3 width_height_depth"], float]
]
] = Field(None, description="""Width, height and depth of image, or imaged area, in meters.""")
data: Union[
NDArray[Shape["* frame, * x, * y"], float],
NDArray[Shape["* frame, * x, * y, 3 r_g_b"], float],
] = Field(..., description="""Images presented to subject, either grayscale or RGB""")
orientation: Optional[str] = Field(
None,
description="""Description of image relative to some reference frame (e.g., which way is up). Must also specify frame of reference.""",
@ -423,8 +451,9 @@ class OpticalSeries(ImageSeries):
description="""Paths to one or more external file(s). The field is only present if format='external'. This is only relevant if the image series is stored in the file system as one or more image file(s). This field should NOT be used if the image is stored in another NWB file and that file is linked to this file.""",
)
format: Optional[str] = Field(
None,
"raw",
description="""Format of image. If this is 'external', then the attribute 'external_file' contains the path information to the image files. If this is 'raw', then the raw (single-channel) binary data is stored in the 'data' dataset. If this attribute is not present, then the default format='raw' case is assumed.""",
json_schema_extra={"linkml_meta": {"ifabsent": "string(raw)"}},
)
description: Optional[str] = Field(
"no description",
@ -463,6 +492,39 @@ class OpticalSeries(ImageSeries):
)
class OpticalSeriesData(ConfiguredBaseModel):
"""
Images presented to subject, either grayscale or RGB
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.image"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: str = Field(
...,
description="""Base unit of measurement for working with the data. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
)
value: Optional[
Union[
NDArray[Shape["* frame, * x, * y"], float | int],
NDArray[Shape["* frame, * x, * y, 3 r_g_b"], float | int],
]
] = Field(None)
class IndexSeries(TimeSeries):
"""
Stores indices to image frames stored in an ImageSeries. The purpose of the ImageIndexSeries is to allow a static image stack to be stored somewhere, and the images in the stack to be referenced out-of-order. This can be for the display of individual images, or of movie segments (as a movie is simply a series of images). The data field stores the index of the frame in the referenced ImageSeries, and the timestamps array indicates when that image was displayed.
@ -473,10 +535,8 @@ class IndexSeries(TimeSeries):
)
name: str = Field(...)
data: NDArray[Shape["* num_times"], int] = Field(
...,
description="""Index of the frame in the referenced ImageSeries.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}},
data: IndexSeriesData = Field(
..., description="""Index of the frame in the referenced ImageSeries."""
)
indexed_timeseries: Union[ImageSeries, str] = Field(
...,
@ -524,13 +584,46 @@ class IndexSeries(TimeSeries):
)
class IndexSeriesData(ConfiguredBaseModel):
"""
Index of the frame in the referenced ImageSeries.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.image"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: str = Field(
...,
description="""Base unit of measurement for working with the data. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
)
value: Optional[NDArray[Shape["* num_times"], int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
# Model rebuild
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
GrayscaleImage.model_rebuild()
RGBImage.model_rebuild()
RGBAImage.model_rebuild()
ImageSeries.model_rebuild()
ImageSeriesData.model_rebuild()
ImageSeriesExternalFile.model_rebuild()
ImageMaskSeries.model_rebuild()
OpticalSeries.model_rebuild()
OpticalSeriesData.model_rebuild()
IndexSeries.model_rebuild()
IndexSeriesData.model_rebuild()

252
nwb_models/src/nwb_models/models/pydantic/core/v2_2_2/core_nwb_misc.py generated
View file

@ -251,6 +251,16 @@ class AbstractFeatureSeriesData(ConfiguredBaseModel):
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Optional[str] = Field(
"see ",
description="""Since there can be different units for different features, store the units in 'feature_units'. The default value for this attribute is \"see 'feature_units'\".""",
@ -258,8 +268,8 @@ class AbstractFeatureSeriesData(ConfiguredBaseModel):
)
value: Optional[
Union[
NDArray[Shape["* num_times"], float],
NDArray[Shape["* num_times, * num_features"], float],
NDArray[Shape["* num_times"], float | int],
NDArray[Shape["* num_times, * num_features"], float | int],
]
] = Field(None)
@ -274,10 +284,8 @@ class AnnotationSeries(TimeSeries):
)
name: str = Field(...)
data: NDArray[Shape["* num_times"], str] = Field(
...,
description="""Annotations made during an experiment.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}},
data: AnnotationSeriesData = Field(
..., description="""Annotations made during an experiment."""
)
description: Optional[str] = Field(
"no description",
@ -316,6 +324,39 @@ class AnnotationSeries(TimeSeries):
)
class AnnotationSeriesData(ConfiguredBaseModel):
"""
Annotations made during an experiment.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.misc"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: float = Field(
-1.0,
description="""Smallest meaningful difference between values in data. Annotations have no units, so the value is fixed to -1.0.""",
le=-1,
ge=-1,
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["n/a"] = Field(
"n/a",
description="""Base unit of measurement for working with the data. Annotations have no units, so the value is fixed to 'n/a'.""",
json_schema_extra={"linkml_meta": {"equals_string": "n/a", "ifabsent": "string(n/a)"}},
)
value: Optional[NDArray[Shape["* num_times"], str]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
class IntervalSeries(TimeSeries):
"""
Stores intervals of data. The timestamps field stores the beginning and end of intervals. The data field stores whether the interval just started (>0 value) or ended (<0 value). Different interval types can be represented in the same series by using multiple key values (eg, 1 for feature A, 2 for feature B, 3 for feature C, etc). The field data stores an 8-bit integer. This is largely an alias of a standard TimeSeries but that is identifiable as representing time intervals in a machine-readable way.
@ -326,10 +367,8 @@ class IntervalSeries(TimeSeries):
)
name: str = Field(...)
data: NDArray[Shape["* num_times"], int] = Field(
...,
description="""Use values >0 if interval started, <0 if interval ended.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}},
data: IntervalSeriesData = Field(
..., description="""Use values >0 if interval started, <0 if interval ended."""
)
description: Optional[str] = Field(
"no description",
@ -368,6 +407,39 @@ class IntervalSeries(TimeSeries):
)
class IntervalSeriesData(ConfiguredBaseModel):
"""
Use values >0 if interval started, <0 if interval ended.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.misc"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: float = Field(
-1.0,
description="""Smallest meaningful difference between values in data. Annotations have no units, so the value is fixed to -1.0.""",
le=-1,
ge=-1,
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["n/a"] = Field(
"n/a",
description="""Base unit of measurement for working with the data. Annotations have no units, so the value is fixed to 'n/a'.""",
json_schema_extra={"linkml_meta": {"equals_string": "n/a", "ifabsent": "string(n/a)"}},
)
value: Optional[NDArray[Shape["* num_times"], int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
class DecompositionSeries(TimeSeries):
"""
Spectral analysis of a time series, e.g. of an LFP or a speech signal.
@ -443,24 +515,36 @@ class DecompositionSeriesData(ConfiguredBaseModel):
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: str = Field(
"no unit",
description="""Base unit of measurement for working with the data. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
json_schema_extra={"linkml_meta": {"ifabsent": "string(no unit)"}},
)
value: Optional[NDArray[Shape["* num_times, * num_channels, * num_bands"], float]] = Field(
None,
json_schema_extra={
"linkml_meta": {
"array": {
"dimensions": [
{"alias": "num_times"},
{"alias": "num_channels"},
{"alias": "num_bands"},
]
value: Optional[NDArray[Shape["* num_times, * num_channels, * num_bands"], float | int]] = (
Field(
None,
json_schema_extra={
"linkml_meta": {
"array": {
"dimensions": [
{"alias": "num_times"},
{"alias": "num_channels"},
{"alias": "num_bands"},
]
}
}
}
},
},
)
)
@ -518,9 +602,6 @@ class DecompositionSeriesBands(DynamicTable):
description="""Array of unique identifiers for the rows of this dynamic table.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_rows"}]}}},
)
vector_index: Optional[List[VectorIndex]] = Field(
None, description="""Indices for the vector columns of this dynamic table."""
)
class Units(DynamicTable):
@ -533,9 +614,18 @@ class Units(DynamicTable):
)
name: str = Field("Units", json_schema_extra={"linkml_meta": {"ifabsent": "string(Units)"}})
spike_times_index: Optional[Named[VectorIndex]] = Field(
electrode_group: Optional[VectorData[NDArray[Any, ElectrodeGroup]]] = Field(
None,
description="""Index into the spike_times dataset.""",
description="""Electrode group that each spike unit came from.""",
json_schema_extra={
"linkml_meta": {
"array": {"maximum_number_dimensions": False, "minimum_number_dimensions": 1}
}
},
)
electrodes: Optional[Named[DynamicTableRegion]] = Field(
None,
description="""Electrode that each spike unit came from, specified using a DynamicTableRegion.""",
json_schema_extra={
"linkml_meta": {
"annotations": {
@ -545,12 +635,9 @@ class Units(DynamicTable):
}
},
)
spike_times: Optional[UnitsSpikeTimes] = Field(
None, description="""Spike times for each unit."""
)
obs_intervals_index: Optional[Named[VectorIndex]] = Field(
electrodes_index: Optional[Named[VectorIndex]] = Field(
None,
description="""Index into the obs_intervals dataset.""",
description="""Index into electrodes.""",
json_schema_extra={
"linkml_meta": {
"annotations": {
@ -576,9 +663,9 @@ class Units(DynamicTable):
},
)
)
electrodes_index: Optional[Named[VectorIndex]] = Field(
obs_intervals_index: Optional[Named[VectorIndex]] = Field(
None,
description="""Index into electrodes.""",
description="""Index into the obs_intervals dataset.""",
json_schema_extra={
"linkml_meta": {
"annotations": {
@ -588,9 +675,12 @@ class Units(DynamicTable):
}
},
)
electrodes: Optional[Named[DynamicTableRegion]] = Field(
spike_times: Optional[UnitsSpikeTimes] = Field(
None, description="""Spike times for each unit."""
)
spike_times_index: Optional[Named[VectorIndex]] = Field(
None,
description="""Electrode that each spike unit came from, specified using a DynamicTableRegion.""",
description="""Index into the spike_times dataset.""",
json_schema_extra={
"linkml_meta": {
"annotations": {
@ -600,25 +690,12 @@ class Units(DynamicTable):
}
},
)
electrode_group: Optional[List[ElectrodeGroup]] = Field(
None, description="""Electrode group that each spike unit came from."""
waveform_mean: Optional[UnitsWaveformMean] = Field(
None, description="""Spike waveform mean for each spike unit."""
)
waveform_sd: Optional[UnitsWaveformSd] = Field(
None, description="""Spike waveform standard deviation for each spike unit."""
)
waveform_mean: Optional[
VectorData[
Union[
NDArray[Shape["* num_units, * num_samples"], float],
NDArray[Shape["* num_units, * num_samples, * num_electrodes"], float],
]
]
] = Field(None, description="""Spike waveform mean for each spike unit.""")
waveform_sd: Optional[
VectorData[
Union[
NDArray[Shape["* num_units, * num_samples"], float],
NDArray[Shape["* num_units, * num_samples, * num_electrodes"], float],
]
]
] = Field(None, description="""Spike waveform standard deviation for each spike unit.""")
colnames: List[str] = Field(
...,
description="""The names of the columns in this table. This should be used to specify an order to the columns.""",
@ -629,9 +706,6 @@ class Units(DynamicTable):
description="""Array of unique identifiers for the rows of this dynamic table.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_rows"}]}}},
)
vector_index: Optional[List[VectorIndex]] = Field(
None, description="""Indices for the vector columns of this dynamic table."""
)
class UnitsSpikeTimes(VectorData):
@ -662,14 +736,78 @@ class UnitsSpikeTimes(VectorData):
] = Field(None)
class UnitsWaveformMean(VectorData):
"""
Spike waveform mean for each spike unit.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.misc"})
name: Literal["waveform_mean"] = Field(
"waveform_mean",
json_schema_extra={
"linkml_meta": {"equals_string": "waveform_mean", "ifabsent": "string(waveform_mean)"}
},
)
sampling_rate: Optional[float] = Field(None, description="""Sampling rate, in hertz.""")
unit: Optional[Literal["volts"]] = Field(
"volts",
description="""Unit of measurement. This value is fixed to 'volts'.""",
json_schema_extra={"linkml_meta": {"equals_string": "volts", "ifabsent": "string(volts)"}},
)
description: str = Field(..., description="""Description of what these vectors represent.""")
value: Optional[
Union[
NDArray[Shape["* dim0"], Any],
NDArray[Shape["* dim0, * dim1"], Any],
NDArray[Shape["* dim0, * dim1, * dim2"], Any],
NDArray[Shape["* dim0, * dim1, * dim2, * dim3"], Any],
]
] = Field(None)
class UnitsWaveformSd(VectorData):
"""
Spike waveform standard deviation for each spike unit.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.misc"})
name: Literal["waveform_sd"] = Field(
"waveform_sd",
json_schema_extra={
"linkml_meta": {"equals_string": "waveform_sd", "ifabsent": "string(waveform_sd)"}
},
)
sampling_rate: Optional[float] = Field(None, description="""Sampling rate, in hertz.""")
unit: Optional[Literal["volts"]] = Field(
"volts",
description="""Unit of measurement. This value is fixed to 'volts'.""",
json_schema_extra={"linkml_meta": {"equals_string": "volts", "ifabsent": "string(volts)"}},
)
description: str = Field(..., description="""Description of what these vectors represent.""")
value: Optional[
Union[
NDArray[Shape["* dim0"], Any],
NDArray[Shape["* dim0, * dim1"], Any],
NDArray[Shape["* dim0, * dim1, * dim2"], Any],
NDArray[Shape["* dim0, * dim1, * dim2, * dim3"], Any],
]
] = Field(None)
# Model rebuild
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
AbstractFeatureSeries.model_rebuild()
AbstractFeatureSeriesData.model_rebuild()
AnnotationSeries.model_rebuild()
AnnotationSeriesData.model_rebuild()
IntervalSeries.model_rebuild()
IntervalSeriesData.model_rebuild()
DecompositionSeries.model_rebuild()
DecompositionSeriesData.model_rebuild()
DecompositionSeriesBands.model_rebuild()
Units.model_rebuild()
UnitsSpikeTimes.model_rebuild()
UnitsWaveformMean.model_rebuild()
UnitsWaveformSd.model_rebuild()

38
nwb_models/src/nwb_models/models/pydantic/core/v2_2_2/core_nwb_ogen.py generated
View file

@ -158,10 +158,8 @@ class OptogeneticSeries(TimeSeries):
)
name: str = Field(...)
data: NDArray[Shape["* num_times"], float] = Field(
...,
description="""Applied power for optogenetic stimulus, in watts.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}},
data: OptogeneticSeriesData = Field(
..., description="""Applied power for optogenetic stimulus, in watts."""
)
site: Union[OptogeneticStimulusSite, str] = Field(
...,
@ -209,6 +207,37 @@ class OptogeneticSeries(TimeSeries):
)
class OptogeneticSeriesData(ConfiguredBaseModel):
"""
Applied power for optogenetic stimulus, in watts.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.ogen"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["watts"] = Field(
"watts",
description="""Unit of measurement for data, which is fixed to 'watts'.""",
json_schema_extra={"linkml_meta": {"equals_string": "watts", "ifabsent": "string(watts)"}},
)
value: Optional[NDArray[Shape["* num_times"], float | int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
class OptogeneticStimulusSite(NWBContainer):
"""
A site of optogenetic stimulation.
@ -239,4 +268,5 @@ class OptogeneticStimulusSite(NWBContainer):
# Model rebuild
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
OptogeneticSeries.model_rebuild()
OptogeneticSeriesData.model_rebuild()
OptogeneticStimulusSite.model_rebuild()

76
nwb_models/src/nwb_models/models/pydantic/core/v2_2_2/core_nwb_ophys.py generated
View file

@ -28,7 +28,7 @@ from ...core.v2_2_2.core_nwb_base import (
TimeSeriesSync,
)
from ...core.v2_2_2.core_nwb_device import Device
from ...core.v2_2_2.core_nwb_image import ImageSeries, ImageSeriesExternalFile
from ...core.v2_2_2.core_nwb_image import ImageSeries, ImageSeriesData, ImageSeriesExternalFile
from ...hdmf_common.v1_1_3.hdmf_common_table import DynamicTable, DynamicTableRegion
@ -209,12 +209,9 @@ class TwoPhotonSeries(ImageSeries):
}
},
)
data: Optional[
Union[
NDArray[Shape["* frame, * x, * y"], float],
NDArray[Shape["* frame, * x, * y, * z"], float],
]
] = Field(None, description="""Binary data representing images across frames.""")
data: Optional[ImageSeriesData] = Field(
None, description="""Binary data representing images across frames."""
)
dimension: Optional[NDArray[Shape["* rank"], int]] = Field(
None,
description="""Number of pixels on x, y, (and z) axes.""",
@ -225,8 +222,9 @@ class TwoPhotonSeries(ImageSeries):
description="""Paths to one or more external file(s). The field is only present if format='external'. This is only relevant if the image series is stored in the file system as one or more image file(s). This field should NOT be used if the image is stored in another NWB file and that file is linked to this file.""",
)
format: Optional[str] = Field(
None,
"raw",
description="""Format of image. If this is 'external', then the attribute 'external_file' contains the path information to the image files. If this is 'raw', then the raw (single-channel) binary data is stored in the 'data' dataset. If this attribute is not present, then the default format='raw' case is assumed.""",
json_schema_extra={"linkml_meta": {"ifabsent": "string(raw)"}},
)
description: Optional[str] = Field(
"no description",
@ -275,9 +273,7 @@ class RoiResponseSeries(TimeSeries):
)
name: str = Field(...)
data: Union[
NDArray[Shape["* num_times"], float], NDArray[Shape["* num_times, * num_rois"], float]
] = Field(..., description="""Signals from ROIs.""")
data: RoiResponseSeriesData = Field(..., description="""Signals from ROIs.""")
rois: Named[DynamicTableRegion] = Field(
...,
description="""DynamicTableRegion referencing into an ROITable containing information on the ROIs stored in this timeseries.""",
@ -327,6 +323,39 @@ class RoiResponseSeries(TimeSeries):
)
class RoiResponseSeriesData(ConfiguredBaseModel):
"""
Signals from ROIs.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.ophys"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: str = Field(
...,
description="""Base unit of measurement for working with the data. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
)
value: Optional[
Union[
NDArray[Shape["* num_times"], float | int],
NDArray[Shape["* num_times, * num_rois"], float | int],
]
] = Field(None)
class DfOverF(NWBDataInterface):
"""
dF/F information about a region of interest (ROI). Storage hierarchy of dF/F should be the same as for segmentation (i.e., same names for ROIs and for image planes).
@ -336,10 +365,10 @@ class DfOverF(NWBDataInterface):
{"from_schema": "core.nwb.ophys", "tree_root": True}
)
value: Optional[List[RoiResponseSeries]] = Field(
name: str = Field("DfOverF", json_schema_extra={"linkml_meta": {"ifabsent": "string(DfOverF)"}})
value: Optional[Dict[str, RoiResponseSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "RoiResponseSeries"}]}}
)
name: str = Field(...)
class Fluorescence(NWBDataInterface):
@ -351,10 +380,12 @@ class Fluorescence(NWBDataInterface):
{"from_schema": "core.nwb.ophys", "tree_root": True}
)
value: Optional[List[RoiResponseSeries]] = Field(
name: str = Field(
"Fluorescence", json_schema_extra={"linkml_meta": {"ifabsent": "string(Fluorescence)"}}
)
value: Optional[Dict[str, RoiResponseSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "RoiResponseSeries"}]}}
)
name: str = Field(...)
class ImageSegmentation(NWBDataInterface):
@ -366,10 +397,13 @@ class ImageSegmentation(NWBDataInterface):
{"from_schema": "core.nwb.ophys", "tree_root": True}
)
value: Optional[List[DynamicTable]] = Field(
name: str = Field(
"ImageSegmentation",
json_schema_extra={"linkml_meta": {"ifabsent": "string(ImageSegmentation)"}},
)
value: Optional[Dict[str, DynamicTable]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "DynamicTable"}]}}
)
name: str = Field(...)
class ImagingPlane(NWBContainer):
@ -538,16 +572,20 @@ class MotionCorrection(NWBDataInterface):
{"from_schema": "core.nwb.ophys", "tree_root": True}
)
value: Optional[List[NWBDataInterface]] = Field(
name: str = Field(
"MotionCorrection",
json_schema_extra={"linkml_meta": {"ifabsent": "string(MotionCorrection)"}},
)
value: Optional[Dict[str, NWBDataInterface]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "NWBDataInterface"}]}}
)
name: str = Field(...)
# Model rebuild
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
TwoPhotonSeries.model_rebuild()
RoiResponseSeries.model_rebuild()
RoiResponseSeriesData.model_rebuild()
DfOverF.model_rebuild()
Fluorescence.model_rebuild()
ImageSegmentation.model_rebuild()

16
nwb_models/src/nwb_models/models/pydantic/core/v2_2_2/namespace.py generated
View file

@ -38,6 +38,7 @@ from ...core.v2_2_2.core_nwb_ecephys import (
ClusterWaveforms,
Clustering,
ElectricalSeries,
ElectricalSeriesData,
ElectrodeGroup,
ElectrodeGroupPosition,
EventDetection,
@ -46,6 +47,7 @@ from ...core.v2_2_2.core_nwb_ecephys import (
FilteredEphys,
LFP,
SpikeEventSeries,
SpikeEventSeriesData,
)
from ...core.v2_2_2.core_nwb_epoch import TimeIntervals, TimeIntervalsTimeseries
from ...core.v2_2_2.core_nwb_file import (
@ -85,9 +87,12 @@ from ...core.v2_2_2.core_nwb_image import (
GrayscaleImage,
ImageMaskSeries,
ImageSeries,
ImageSeriesData,
ImageSeriesExternalFile,
IndexSeries,
IndexSeriesData,
OpticalSeries,
OpticalSeriesData,
RGBAImage,
RGBImage,
)
@ -95,14 +100,22 @@ from ...core.v2_2_2.core_nwb_misc import (
AbstractFeatureSeries,
AbstractFeatureSeriesData,
AnnotationSeries,
AnnotationSeriesData,
DecompositionSeries,
DecompositionSeriesBands,
DecompositionSeriesData,
IntervalSeries,
IntervalSeriesData,
Units,
UnitsSpikeTimes,
UnitsWaveformMean,
UnitsWaveformSd,
)
from ...core.v2_2_2.core_nwb_ogen import (
OptogeneticSeries,
OptogeneticSeriesData,
OptogeneticStimulusSite,
)
from ...core.v2_2_2.core_nwb_ogen import OptogeneticSeries, OptogeneticStimulusSite
from ...core.v2_2_2.core_nwb_ophys import (
DfOverF,
Fluorescence,
@ -114,6 +127,7 @@ from ...core.v2_2_2.core_nwb_ophys import (
MotionCorrection,
OpticalChannel,
RoiResponseSeries,
RoiResponseSeriesData,
TwoPhotonSeries,
)
from ...core.v2_2_2.core_nwb_retinotopy import (

15
nwb_models/src/nwb_models/models/pydantic/core/v2_2_4/core_nwb_base.py generated
View file

@ -170,9 +170,9 @@ class Image(NWBData):
description: Optional[str] = Field(None, description="""Description of the image.""")
value: Optional[
Union[
NDArray[Shape["* x, * y"], float],
NDArray[Shape["* x, * y, 3 r_g_b"], float],
NDArray[Shape["* x, * y, 4 r_g_b_a"], float],
NDArray[Shape["* x, * y"], float | int],
NDArray[Shape["* x, * y, 3 r_g_b"], float | int],
NDArray[Shape["* x, * y, 4 r_g_b_a"], float | int],
]
] = Field(None)
@ -333,13 +333,16 @@ class ProcessingModule(NWBContainer):
{"from_schema": "core.nwb.base", "tree_root": True}
)
value: Optional[List[Union[DynamicTable, NWBDataInterface]]] = Field(
name: str = Field(...)
description: str = Field(
..., description="""Description of this collection of processed data."""
)
value: Optional[Dict[str, Union[DynamicTable, NWBDataInterface]]] = Field(
None,
json_schema_extra={
"linkml_meta": {"any_of": [{"range": "NWBDataInterface"}, {"range": "DynamicTable"}]}
},
)
name: str = Field(...)
class Images(NWBDataInterface):
@ -353,7 +356,7 @@ class Images(NWBDataInterface):
name: str = Field("Images", json_schema_extra={"linkml_meta": {"ifabsent": "string(Images)"}})
description: str = Field(..., description="""Description of this collection of images.""")
image: List[Image] = Field(..., description="""Images stored in this collection.""")
image: List[str] = Field(..., description="""Images stored in this collection.""")
# Model rebuild

60
nwb_models/src/nwb_models/models/pydantic/core/v2_2_4/core_nwb_behavior.py generated
View file

@ -213,6 +213,16 @@ class SpatialSeriesData(ConfiguredBaseModel):
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Optional[str] = Field(
"meters",
description="""Base unit of measurement for working with the data. The default value is 'meters'. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
@ -220,8 +230,8 @@ class SpatialSeriesData(ConfiguredBaseModel):
)
value: Optional[
Union[
NDArray[Shape["* num_times"], float],
NDArray[Shape["* num_times, * num_features"], float],
NDArray[Shape["* num_times"], float | int],
NDArray[Shape["* num_times, * num_features"], float | int],
]
] = Field(None)
@ -235,10 +245,13 @@ class BehavioralEpochs(NWBDataInterface):
{"from_schema": "core.nwb.behavior", "tree_root": True}
)
value: Optional[List[IntervalSeries]] = Field(
name: str = Field(
"BehavioralEpochs",
json_schema_extra={"linkml_meta": {"ifabsent": "string(BehavioralEpochs)"}},
)
value: Optional[Dict[str, IntervalSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "IntervalSeries"}]}}
)
name: str = Field(...)
class BehavioralEvents(NWBDataInterface):
@ -250,10 +263,13 @@ class BehavioralEvents(NWBDataInterface):
{"from_schema": "core.nwb.behavior", "tree_root": True}
)
value: Optional[List[TimeSeries]] = Field(
name: str = Field(
"BehavioralEvents",
json_schema_extra={"linkml_meta": {"ifabsent": "string(BehavioralEvents)"}},
)
value: Optional[Dict[str, TimeSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "TimeSeries"}]}}
)
name: str = Field(...)
class BehavioralTimeSeries(NWBDataInterface):
@ -265,10 +281,13 @@ class BehavioralTimeSeries(NWBDataInterface):
{"from_schema": "core.nwb.behavior", "tree_root": True}
)
value: Optional[List[TimeSeries]] = Field(
name: str = Field(
"BehavioralTimeSeries",
json_schema_extra={"linkml_meta": {"ifabsent": "string(BehavioralTimeSeries)"}},
)
value: Optional[Dict[str, TimeSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "TimeSeries"}]}}
)
name: str = Field(...)
class PupilTracking(NWBDataInterface):
@ -280,10 +299,12 @@ class PupilTracking(NWBDataInterface):
{"from_schema": "core.nwb.behavior", "tree_root": True}
)
value: Optional[List[TimeSeries]] = Field(
name: str = Field(
"PupilTracking", json_schema_extra={"linkml_meta": {"ifabsent": "string(PupilTracking)"}}
)
value: Optional[Dict[str, TimeSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "TimeSeries"}]}}
)
name: str = Field(...)
class EyeTracking(NWBDataInterface):
@ -295,10 +316,12 @@ class EyeTracking(NWBDataInterface):
{"from_schema": "core.nwb.behavior", "tree_root": True}
)
value: Optional[List[SpatialSeries]] = Field(
name: str = Field(
"EyeTracking", json_schema_extra={"linkml_meta": {"ifabsent": "string(EyeTracking)"}}
)
value: Optional[Dict[str, SpatialSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "SpatialSeries"}]}}
)
name: str = Field(...)
class CompassDirection(NWBDataInterface):
@ -310,10 +333,13 @@ class CompassDirection(NWBDataInterface):
{"from_schema": "core.nwb.behavior", "tree_root": True}
)
value: Optional[List[SpatialSeries]] = Field(
name: str = Field(
"CompassDirection",
json_schema_extra={"linkml_meta": {"ifabsent": "string(CompassDirection)"}},
)
value: Optional[Dict[str, SpatialSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "SpatialSeries"}]}}
)
name: str = Field(...)
class Position(NWBDataInterface):
@ -325,10 +351,12 @@ class Position(NWBDataInterface):
{"from_schema": "core.nwb.behavior", "tree_root": True}
)
value: Optional[List[SpatialSeries]] = Field(
name: str = Field(
"Position", json_schema_extra={"linkml_meta": {"ifabsent": "string(Position)"}}
)
value: Optional[Dict[str, SpatialSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "SpatialSeries"}]}}
)
name: str = Field(...)
# Model rebuild

118
nwb_models/src/nwb_models/models/pydantic/core/v2_2_4/core_nwb_ecephys.py generated
View file

@ -190,11 +190,12 @@ class ElectricalSeries(TimeSeries):
)
name: str = Field(...)
data: Union[
NDArray[Shape["* num_times"], float],
NDArray[Shape["* num_times, * num_channels"], float],
NDArray[Shape["* num_times, * num_channels, * num_samples"], float],
] = Field(..., description="""Recorded voltage data.""")
channel_conversion: Optional[NDArray[Shape["* num_channels"], float]] = Field(
None,
description="""Channel-specific conversion factor. Multiply the data in the 'data' dataset by these values along the channel axis (as indicated by axis attribute) AND by the global conversion factor in the 'conversion' attribute of 'data' to get the data values in Volts, i.e, data in Volts = data * data.conversion * channel_conversion. This approach allows for both global and per-channel data conversion factors needed to support the storage of electrical recordings as native values generated by data acquisition systems. If this dataset is not present, then there is no channel-specific conversion factor, i.e. it is 1 for all channels.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_channels"}]}}},
)
data: ElectricalSeriesData = Field(..., description="""Recorded voltage data.""")
electrodes: Named[DynamicTableRegion] = Field(
...,
description="""DynamicTableRegion pointer to the electrodes that this time series was generated from.""",
@ -207,11 +208,6 @@ class ElectricalSeries(TimeSeries):
}
},
)
channel_conversion: Optional[NDArray[Shape["* num_channels"], float]] = Field(
None,
description="""Channel-specific conversion factor. Multiply the data in the 'data' dataset by these values along the channel axis (as indicated by axis attribute) AND by the global conversion factor in the 'conversion' attribute of 'data' to get the data values in Volts, i.e, data in Volts = data * data.conversion * channel_conversion. This approach allows for both global and per-channel data conversion factors needed to support the storage of electrical recordings as native values generated by data acquisition systems. If this dataset is not present, then there is no channel-specific conversion factor, i.e. it is 1 for all channels.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_channels"}]}}},
)
description: Optional[str] = Field(
"no description",
description="""Description of the time series.""",
@ -249,6 +245,41 @@ class ElectricalSeries(TimeSeries):
)
class ElectricalSeriesData(ConfiguredBaseModel):
"""
Recorded voltage data.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.ecephys"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["volts"] = Field(
"volts",
description="""Base unit of measurement for working with the data. This value is fixed to 'volts'. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion' and 'channel_conversion' (if present).""",
json_schema_extra={"linkml_meta": {"equals_string": "volts", "ifabsent": "string(volts)"}},
)
value: Optional[
Union[
NDArray[Shape["* num_times"], float | int],
NDArray[Shape["* num_times, * num_channels"], float | int],
NDArray[Shape["* num_times, * num_channels, * num_samples"], float | int],
]
] = Field(None)
class SpikeEventSeries(ElectricalSeries):
"""
Stores snapshots/snippets of recorded spike events (i.e., threshold crossings). This may also be raw data, as reported by ephys hardware. If so, the TimeSeries::description field should describe how events were detected. All SpikeEventSeries should reside in a module (under EventWaveform interface) even if the spikes were reported and stored by hardware. All events span the same recording channels and store snapshots of equal duration. TimeSeries::data array structure: [num events] [num channels] [num samples] (or [num events] [num samples] for single electrode).
@ -259,15 +290,17 @@ class SpikeEventSeries(ElectricalSeries):
)
name: str = Field(...)
data: Union[
NDArray[Shape["* num_events, * num_samples"], float],
NDArray[Shape["* num_events, * num_channels, * num_samples"], float],
] = Field(..., description="""Spike waveforms.""")
data: SpikeEventSeriesData = Field(..., description="""Spike waveforms.""")
timestamps: NDArray[Shape["* num_times"], float] = Field(
...,
description="""Timestamps for samples stored in data, in seconds, relative to the common experiment master-clock stored in NWBFile.timestamps_reference_time. Timestamps are required for the events. Unlike for TimeSeries, timestamps are required for SpikeEventSeries and are thus re-specified here.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}},
)
channel_conversion: Optional[NDArray[Shape["* num_channels"], float]] = Field(
None,
description="""Channel-specific conversion factor. Multiply the data in the 'data' dataset by these values along the channel axis (as indicated by axis attribute) AND by the global conversion factor in the 'conversion' attribute of 'data' to get the data values in Volts, i.e, data in Volts = data * data.conversion * channel_conversion. This approach allows for both global and per-channel data conversion factors needed to support the storage of electrical recordings as native values generated by data acquisition systems. If this dataset is not present, then there is no channel-specific conversion factor, i.e. it is 1 for all channels.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_channels"}]}}},
)
electrodes: Named[DynamicTableRegion] = Field(
...,
description="""DynamicTableRegion pointer to the electrodes that this time series was generated from.""",
@ -280,11 +313,6 @@ class SpikeEventSeries(ElectricalSeries):
}
},
)
channel_conversion: Optional[NDArray[Shape["* num_channels"], float]] = Field(
None,
description="""Channel-specific conversion factor. Multiply the data in the 'data' dataset by these values along the channel axis (as indicated by axis attribute) AND by the global conversion factor in the 'conversion' attribute of 'data' to get the data values in Volts, i.e, data in Volts = data * data.conversion * channel_conversion. This approach allows for both global and per-channel data conversion factors needed to support the storage of electrical recordings as native values generated by data acquisition systems. If this dataset is not present, then there is no channel-specific conversion factor, i.e. it is 1 for all channels.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_channels"}]}}},
)
description: Optional[str] = Field(
"no description",
description="""Description of the time series.""",
@ -317,6 +345,40 @@ class SpikeEventSeries(ElectricalSeries):
)
class SpikeEventSeriesData(ConfiguredBaseModel):
"""
Spike waveforms.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.ecephys"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["volts"] = Field(
"volts",
description="""Unit of measurement for waveforms, which is fixed to 'volts'.""",
json_schema_extra={"linkml_meta": {"equals_string": "volts", "ifabsent": "string(volts)"}},
)
value: Optional[
Union[
NDArray[Shape["* num_events, * num_samples"], float | int],
NDArray[Shape["* num_events, * num_channels, * num_samples"], float | int],
]
] = Field(None)
class FeatureExtraction(NWBDataInterface):
"""
Features, such as PC1 and PC2, that are extracted from signals stored in a SpikeEventSeries or other source.
@ -415,10 +477,12 @@ class EventWaveform(NWBDataInterface):
{"from_schema": "core.nwb.ecephys", "tree_root": True}
)
value: Optional[List[SpikeEventSeries]] = Field(
name: str = Field(
"EventWaveform", json_schema_extra={"linkml_meta": {"ifabsent": "string(EventWaveform)"}}
)
value: Optional[Dict[str, SpikeEventSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "SpikeEventSeries"}]}}
)
name: str = Field(...)
class FilteredEphys(NWBDataInterface):
@ -430,10 +494,12 @@ class FilteredEphys(NWBDataInterface):
{"from_schema": "core.nwb.ecephys", "tree_root": True}
)
value: Optional[List[ElectricalSeries]] = Field(
name: str = Field(
"FilteredEphys", json_schema_extra={"linkml_meta": {"ifabsent": "string(FilteredEphys)"}}
)
value: Optional[Dict[str, ElectricalSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "ElectricalSeries"}]}}
)
name: str = Field(...)
class LFP(NWBDataInterface):
@ -445,10 +511,10 @@ class LFP(NWBDataInterface):
{"from_schema": "core.nwb.ecephys", "tree_root": True}
)
value: Optional[List[ElectricalSeries]] = Field(
name: str = Field("LFP", json_schema_extra={"linkml_meta": {"ifabsent": "string(LFP)"}})
value: Optional[Dict[str, ElectricalSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "ElectricalSeries"}]}}
)
name: str = Field(...)
class ElectrodeGroup(NWBContainer):
@ -591,7 +657,9 @@ class Clustering(NWBDataInterface):
# Model rebuild
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
ElectricalSeries.model_rebuild()
ElectricalSeriesData.model_rebuild()
SpikeEventSeries.model_rebuild()
SpikeEventSeriesData.model_rebuild()
FeatureExtraction.model_rebuild()
EventDetection.model_rebuild()
EventWaveform.model_rebuild()

3
nwb_models/src/nwb_models/models/pydantic/core/v2_2_4/core_nwb_epoch.py generated
View file

@ -247,9 +247,6 @@ class TimeIntervals(DynamicTable):
description="""Array of unique identifiers for the rows of this dynamic table.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_rows"}]}}},
)
vector_index: Optional[List[VectorIndex]] = Field(
None, description="""Indices for the vector columns of this dynamic table."""
)
class TimeIntervalsTimeseries(VectorData):

56
nwb_models/src/nwb_models/models/pydantic/core/v2_2_4/core_nwb_file.py generated
View file

@ -225,28 +225,28 @@ class NWBFile(NWBContainer):
...,
description="""Date and time corresponding to time zero of all timestamps. The date is stored in UTC with local timezone offset as ISO 8601 extended formatted string: 2018-09-28T14:43:54.123+02:00. Dates stored in UTC end in \"Z\" with no timezone offset. Date accuracy is up to milliseconds. All times stored in the file use this time as reference (i.e., time zero).""",
)
acquisition: Optional[List[Union[DynamicTable, NWBDataInterface]]] = Field(
acquisition: Optional[Dict[str, Union[DynamicTable, NWBDataInterface]]] = Field(
None,
description="""Data streams recorded from the system, including ephys, ophys, tracking, etc. This group should be read-only after the experiment is completed and timestamps are corrected to a common timebase. The data stored here may be links to raw data stored in external NWB files. This will allow keeping bulky raw data out of the file while preserving the option of keeping some/all in the file. Acquired data includes tracking and experimental data streams (i.e., everything measured from the system). If bulky data is stored in the /acquisition group, the data can exist in a separate NWB file that is linked to by the file being used for processing and analysis.""",
json_schema_extra={
"linkml_meta": {"any_of": [{"range": "NWBDataInterface"}, {"range": "DynamicTable"}]}
},
)
analysis: Optional[List[Union[DynamicTable, NWBContainer]]] = Field(
analysis: Optional[Dict[str, Union[DynamicTable, NWBContainer]]] = Field(
None,
description="""Lab-specific and custom scientific analysis of data. There is no defined format for the content of this group - the format is up to the individual user/lab. To facilitate sharing analysis data between labs, the contents here should be stored in standard types (e.g., neurodata_types) and appropriately documented. The file can store lab-specific and custom data analysis without restriction on its form or schema, reducing data formatting restrictions on end users. Such data should be placed in the analysis group. The analysis data should be documented so that it could be shared with other labs.""",
json_schema_extra={
"linkml_meta": {"any_of": [{"range": "NWBContainer"}, {"range": "DynamicTable"}]}
},
)
scratch: Optional[List[Union[DynamicTable, NWBContainer]]] = Field(
scratch: Optional[Dict[str, Union[DynamicTable, NWBContainer]]] = Field(
None,
description="""A place to store one-off analysis results. Data placed here is not intended for sharing. By placing data here, users acknowledge that there is no guarantee that their data meets any standard.""",
json_schema_extra={
"linkml_meta": {"any_of": [{"range": "NWBContainer"}, {"range": "DynamicTable"}]}
},
)
processing: Optional[List[ProcessingModule]] = Field(
processing: Optional[Dict[str, ProcessingModule]] = Field(
None,
description="""The home for ProcessingModules. These modules perform intermediate analysis of data that is necessary to perform before scientific analysis. Examples include spike clustering, extracting position from tracking data, stitching together image slices. ProcessingModules can be large and express many data sets from relatively complex analysis (e.g., spike detection and clustering) or small, representing extraction of position information from tracking video, or even binary lick/no-lick decisions. Common software tools (e.g., klustakwik, MClust) are expected to read/write data here. 'Processing' refers to intermediate analysis of the acquired data to make it more amenable to scientific analysis.""",
json_schema_extra={"linkml_meta": {"any_of": [{"range": "ProcessingModule"}]}},
@ -264,6 +264,9 @@ class NWBFile(NWBContainer):
description="""Experimental intervals, whether that be logically distinct sub-experiments having a particular scientific goal, trials (see trials subgroup) during an experiment, or epochs (see epochs subgroup) deriving from analysis of data.""",
)
units: Optional[Units] = Field(None, description="""Data about sorted spike units.""")
specifications: Optional[dict] = Field(
None, description="""Nested dictionary of schema specifications"""
)
class NWBFileStimulus(ConfiguredBaseModel):
@ -279,12 +282,12 @@ class NWBFileStimulus(ConfiguredBaseModel):
"linkml_meta": {"equals_string": "stimulus", "ifabsent": "string(stimulus)"}
},
)
presentation: Optional[List[TimeSeries]] = Field(
presentation: Optional[Dict[str, TimeSeries]] = Field(
None,
description="""Stimuli presented during the experiment.""",
json_schema_extra={"linkml_meta": {"any_of": [{"range": "TimeSeries"}]}},
)
templates: Optional[List[TimeSeries]] = Field(
templates: Optional[Dict[str, TimeSeries]] = Field(
None,
description="""Template stimuli. Timestamps in templates are based on stimulus design and are relative to the beginning of the stimulus. When templates are used, the stimulus instances must convert presentation times to the experiment`s time reference frame.""",
json_schema_extra={"linkml_meta": {"any_of": [{"range": "TimeSeries"}]}},
@ -362,11 +365,7 @@ class NWBFileGeneral(ConfiguredBaseModel):
None,
description="""Information about virus(es) used in experiments, including virus ID, source, date made, injection location, volume, etc.""",
)
lab_meta_data: Optional[List[LabMetaData]] = Field(
None,
description="""Place-holder than can be extended so that lab-specific meta-data can be placed in /general.""",
)
devices: Optional[List[Device]] = Field(
devices: Optional[Dict[str, Device]] = Field(
None,
description="""Description of hardware devices used during experiment, e.g., monitors, ADC boards, microscopes, etc.""",
json_schema_extra={"linkml_meta": {"any_of": [{"range": "Device"}]}},
@ -381,16 +380,20 @@ class NWBFileGeneral(ConfiguredBaseModel):
intracellular_ephys: Optional[GeneralIntracellularEphys] = Field(
None, description="""Metadata related to intracellular electrophysiology."""
)
optogenetics: Optional[List[OptogeneticStimulusSite]] = Field(
optogenetics: Optional[Dict[str, OptogeneticStimulusSite]] = Field(
None,
description="""Metadata describing optogenetic stimuluation.""",
json_schema_extra={"linkml_meta": {"any_of": [{"range": "OptogeneticStimulusSite"}]}},
)
optophysiology: Optional[List[ImagingPlane]] = Field(
optophysiology: Optional[Dict[str, ImagingPlane]] = Field(
None,
description="""Metadata related to optophysiology.""",
json_schema_extra={"linkml_meta": {"any_of": [{"range": "ImagingPlane"}]}},
)
value: Optional[Dict[str, LabMetaData]] = Field(
None,
description="""Place-holder than can be extended so that lab-specific meta-data can be placed in /general.""",
)
class GeneralSourceScript(ConfiguredBaseModel):
@ -426,12 +429,12 @@ class GeneralExtracellularEphys(ConfiguredBaseModel):
}
},
)
electrode_group: Optional[List[ElectrodeGroup]] = Field(
None, description="""Physical group of electrodes."""
)
electrodes: Optional[ExtracellularEphysElectrodes] = Field(
None, description="""A table of all electrodes (i.e. channels) used for recording."""
)
value: Optional[Dict[str, ElectrodeGroup]] = Field(
None, description="""Physical group of electrodes."""
)
class ExtracellularEphysElectrodes(DynamicTable):
@ -501,8 +504,14 @@ class ExtracellularEphysElectrodes(DynamicTable):
}
},
)
group: List[ElectrodeGroup] = Field(
..., description="""Reference to the ElectrodeGroup this electrode is a part of."""
group: VectorData[NDArray[Any, ElectrodeGroup]] = Field(
...,
description="""Reference to the ElectrodeGroup this electrode is a part of.""",
json_schema_extra={
"linkml_meta": {
"array": {"maximum_number_dimensions": False, "minimum_number_dimensions": 1}
}
},
)
group_name: VectorData[NDArray[Any, str]] = Field(
...,
@ -559,9 +568,6 @@ class ExtracellularEphysElectrodes(DynamicTable):
description="""Array of unique identifiers for the rows of this dynamic table.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_rows"}]}}},
)
vector_index: Optional[List[VectorIndex]] = Field(
None, description="""Indices for the vector columns of this dynamic table."""
)
class GeneralIntracellularEphys(ConfiguredBaseModel):
@ -584,12 +590,12 @@ class GeneralIntracellularEphys(ConfiguredBaseModel):
None,
description="""Description of filtering used. Includes filtering type and parameters, frequency fall-off, etc. If this changes between TimeSeries, filter description should be stored as a text attribute for each TimeSeries.""",
)
intracellular_electrode: Optional[List[IntracellularElectrode]] = Field(
None, description="""An intracellular electrode."""
)
sweep_table: Optional[SweepTable] = Field(
None, description="""The table which groups different PatchClampSeries together."""
)
value: Optional[Dict[str, IntracellularElectrode]] = Field(
None, description="""An intracellular electrode."""
)
class NWBFileIntervals(ConfiguredBaseModel):
@ -615,7 +621,7 @@ class NWBFileIntervals(ConfiguredBaseModel):
invalid_times: Optional[TimeIntervals] = Field(
None, description="""Time intervals that should be removed from analysis."""
)
time_intervals: Optional[List[TimeIntervals]] = Field(
value: Optional[Dict[str, TimeIntervals]] = Field(
None,
description="""Optional additional table(s) for describing other experimental time intervals.""",
)

133
nwb_models/src/nwb_models/models/pydantic/core/v2_2_4/core_nwb_icephys.py generated
View file

@ -262,11 +262,21 @@ class PatchClampSeriesData(ConfiguredBaseModel):
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: str = Field(
...,
description="""Base unit of measurement for working with the data. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
)
value: Optional[NDArray[Shape["* num_times"], float]] = Field(
value: Optional[NDArray[Shape["* num_times"], float | int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
@ -281,12 +291,12 @@ class CurrentClampSeries(PatchClampSeries):
)
name: str = Field(...)
data: CurrentClampSeriesData = Field(..., description="""Recorded voltage.""")
bias_current: Optional[float] = Field(None, description="""Bias current, in amps.""")
bridge_balance: Optional[float] = Field(None, description="""Bridge balance, in ohms.""")
capacitance_compensation: Optional[float] = Field(
None, description="""Capacitance compensation, in farads."""
)
data: CurrentClampSeriesData = Field(..., description="""Recorded voltage.""")
stimulus_description: str = Field(
..., description="""Protocol/stimulus name for this patch-clamp dataset."""
)
@ -354,12 +364,24 @@ class CurrentClampSeriesData(ConfiguredBaseModel):
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["volts"] = Field(
"volts",
description="""Base unit of measurement for working with the data. which is fixed to 'volts'. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
json_schema_extra={"linkml_meta": {"equals_string": "volts", "ifabsent": "string(volts)"}},
)
value: Any = Field(...)
value: Optional[NDArray[Shape["* num_times"], float | int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
class IZeroClampSeries(CurrentClampSeries):
@ -512,6 +534,16 @@ class CurrentClampStimulusSeriesData(ConfiguredBaseModel):
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["amperes"] = Field(
"amperes",
description="""Base unit of measurement for working with the data. which is fixed to 'amperes'. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
@ -519,7 +551,9 @@ class CurrentClampStimulusSeriesData(ConfiguredBaseModel):
"linkml_meta": {"equals_string": "amperes", "ifabsent": "string(amperes)"}
},
)
value: Any = Field(...)
value: Optional[NDArray[Shape["* num_times"], float | int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
class VoltageClampSeries(PatchClampSeries):
@ -532,13 +566,13 @@ class VoltageClampSeries(PatchClampSeries):
)
name: str = Field(...)
data: VoltageClampSeriesData = Field(..., description="""Recorded current.""")
capacitance_fast: Optional[VoltageClampSeriesCapacitanceFast] = Field(
None, description="""Fast capacitance, in farads."""
)
capacitance_slow: Optional[VoltageClampSeriesCapacitanceSlow] = Field(
None, description="""Slow capacitance, in farads."""
)
data: VoltageClampSeriesData = Field(..., description="""Recorded current.""")
resistance_comp_bandwidth: Optional[VoltageClampSeriesResistanceCompBandwidth] = Field(
None, description="""Resistance compensation bandwidth, in hertz."""
)
@ -610,27 +644,6 @@ class VoltageClampSeries(PatchClampSeries):
)
class VoltageClampSeriesData(ConfiguredBaseModel):
"""
Recorded current.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.icephys"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
unit: Literal["amperes"] = Field(
"amperes",
description="""Base unit of measurement for working with the data. which is fixed to 'amperes'. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
json_schema_extra={
"linkml_meta": {"equals_string": "amperes", "ifabsent": "string(amperes)"}
},
)
value: Any = Field(...)
class VoltageClampSeriesCapacitanceFast(ConfiguredBaseModel):
"""
Fast capacitance, in farads.
@ -683,6 +696,39 @@ class VoltageClampSeriesCapacitanceSlow(ConfiguredBaseModel):
value: float = Field(...)
class VoltageClampSeriesData(ConfiguredBaseModel):
"""
Recorded current.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.icephys"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["amperes"] = Field(
"amperes",
description="""Base unit of measurement for working with the data. which is fixed to 'amperes'. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
json_schema_extra={
"linkml_meta": {"equals_string": "amperes", "ifabsent": "string(amperes)"}
},
)
value: Optional[NDArray[Shape["* num_times"], float | int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
class VoltageClampSeriesResistanceCompBandwidth(ConfiguredBaseModel):
"""
Resistance compensation bandwidth, in hertz.
@ -887,12 +933,24 @@ class VoltageClampStimulusSeriesData(ConfiguredBaseModel):
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["volts"] = Field(
"volts",
description="""Base unit of measurement for working with the data. which is fixed to 'volts'. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
json_schema_extra={"linkml_meta": {"equals_string": "volts", "ifabsent": "string(volts)"}},
)
value: Any = Field(...)
value: Optional[NDArray[Shape["* num_times"], float | int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
class IntracellularElectrode(NWBContainer):
@ -942,18 +1000,15 @@ class SweepTable(DynamicTable):
)
name: str = Field(...)
sweep_number: VectorData[NDArray[Any, int]] = Field(
series: VectorData[NDArray[Any, PatchClampSeries]] = Field(
...,
description="""Sweep number of the PatchClampSeries in that row.""",
description="""The PatchClampSeries with the sweep number in that row.""",
json_schema_extra={
"linkml_meta": {
"array": {"maximum_number_dimensions": False, "minimum_number_dimensions": 1}
}
},
)
series: List[PatchClampSeries] = Field(
..., description="""The PatchClampSeries with the sweep number in that row."""
)
series_index: Named[VectorIndex] = Field(
...,
description="""Index for series.""",
@ -966,6 +1021,15 @@ class SweepTable(DynamicTable):
}
},
)
sweep_number: VectorData[NDArray[Any, int]] = Field(
...,
description="""Sweep number of the PatchClampSeries in that row.""",
json_schema_extra={
"linkml_meta": {
"array": {"maximum_number_dimensions": False, "minimum_number_dimensions": 1}
}
},
)
colnames: List[str] = Field(
...,
description="""The names of the columns in this table. This should be used to specify an order to the columns.""",
@ -976,9 +1040,6 @@ class SweepTable(DynamicTable):
description="""Array of unique identifiers for the rows of this dynamic table.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_rows"}]}}},
)
vector_index: Optional[List[VectorIndex]] = Field(
None, description="""Indices for the vector columns of this dynamic table."""
)
# Model rebuild
@ -991,9 +1052,9 @@ IZeroClampSeries.model_rebuild()
CurrentClampStimulusSeries.model_rebuild()
CurrentClampStimulusSeriesData.model_rebuild()
VoltageClampSeries.model_rebuild()
VoltageClampSeriesData.model_rebuild()
VoltageClampSeriesCapacitanceFast.model_rebuild()
VoltageClampSeriesCapacitanceSlow.model_rebuild()
VoltageClampSeriesData.model_rebuild()
VoltageClampSeriesResistanceCompBandwidth.model_rebuild()
VoltageClampSeriesResistanceCompCorrection.model_rebuild()
VoltageClampSeriesResistanceCompPrediction.model_rebuild()

145
nwb_models/src/nwb_models/models/pydantic/core/v2_2_4/core_nwb_image.py generated
View file

@ -152,7 +152,7 @@ class GrayscaleImage(Image):
)
name: str = Field(...)
value: Optional[NDArray[Shape["* x, * y"], float]] = Field(
value: Optional[NDArray[Shape["* x, * y"], float | int]] = Field(
None,
json_schema_extra={
"linkml_meta": {"array": {"dimensions": [{"alias": "x"}, {"alias": "y"}]}}
@ -174,7 +174,7 @@ class RGBImage(Image):
)
name: str = Field(...)
value: Optional[NDArray[Shape["* x, * y, 3 r_g_b"], float]] = Field(
value: Optional[NDArray[Shape["* x, * y, 3 r_g_b"], float | int]] = Field(
None,
json_schema_extra={
"linkml_meta": {
@ -204,7 +204,7 @@ class RGBAImage(Image):
)
name: str = Field(...)
value: Optional[NDArray[Shape["* x, * y, 4 r_g_b_a"], float]] = Field(
value: Optional[NDArray[Shape["* x, * y, 4 r_g_b_a"], float | int]] = Field(
None,
json_schema_extra={
"linkml_meta": {
@ -234,12 +234,9 @@ class ImageSeries(TimeSeries):
)
name: str = Field(...)
data: Optional[
Union[
NDArray[Shape["* frame, * x, * y"], float],
NDArray[Shape["* frame, * x, * y, * z"], float],
]
] = Field(None, description="""Binary data representing images across frames.""")
data: Optional[ImageSeriesData] = Field(
None, description="""Binary data representing images across frames."""
)
dimension: Optional[NDArray[Shape["* rank"], int]] = Field(
None,
description="""Number of pixels on x, y, (and z) axes.""",
@ -250,8 +247,9 @@ class ImageSeries(TimeSeries):
description="""Paths to one or more external file(s). The field is only present if format='external'. This is only relevant if the image series is stored in the file system as one or more image file(s). This field should NOT be used if the image is stored in another NWB file and that file is linked to this file.""",
)
format: Optional[str] = Field(
None,
"raw",
description="""Format of image. If this is 'external', then the attribute 'external_file' contains the path information to the image files. If this is 'raw', then the raw (single-channel) binary data is stored in the 'data' dataset. If this attribute is not present, then the default format='raw' case is assumed.""",
json_schema_extra={"linkml_meta": {"ifabsent": "string(raw)"}},
)
description: Optional[str] = Field(
"no description",
@ -290,6 +288,39 @@ class ImageSeries(TimeSeries):
)
class ImageSeriesData(ConfiguredBaseModel):
"""
Binary data representing images across frames.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.image"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: str = Field(
...,
description="""Base unit of measurement for working with the data. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
)
value: Optional[
Union[
NDArray[Shape["* frame, * x, * y"], float | int],
NDArray[Shape["* frame, * x, * y, * z"], float | int],
]
] = Field(None)
class ImageSeriesExternalFile(ConfiguredBaseModel):
"""
Paths to one or more external file(s). The field is only present if format='external'. This is only relevant if the image series is stored in the file system as one or more image file(s). This field should NOT be used if the image is stored in another NWB file and that file is linked to this file.
@ -331,12 +362,9 @@ class ImageMaskSeries(ImageSeries):
}
},
)
data: Optional[
Union[
NDArray[Shape["* frame, * x, * y"], float],
NDArray[Shape["* frame, * x, * y, * z"], float],
]
] = Field(None, description="""Binary data representing images across frames.""")
data: Optional[ImageSeriesData] = Field(
None, description="""Binary data representing images across frames."""
)
dimension: Optional[NDArray[Shape["* rank"], int]] = Field(
None,
description="""Number of pixels on x, y, (and z) axes.""",
@ -347,8 +375,9 @@ class ImageMaskSeries(ImageSeries):
description="""Paths to one or more external file(s). The field is only present if format='external'. This is only relevant if the image series is stored in the file system as one or more image file(s). This field should NOT be used if the image is stored in another NWB file and that file is linked to this file.""",
)
format: Optional[str] = Field(
None,
"raw",
description="""Format of image. If this is 'external', then the attribute 'external_file' contains the path information to the image files. If this is 'raw', then the raw (single-channel) binary data is stored in the 'data' dataset. If this attribute is not present, then the default format='raw' case is assumed.""",
json_schema_extra={"linkml_meta": {"ifabsent": "string(raw)"}},
)
description: Optional[str] = Field(
"no description",
@ -397,6 +426,9 @@ class OpticalSeries(ImageSeries):
)
name: str = Field(...)
data: OpticalSeriesData = Field(
..., description="""Images presented to subject, either grayscale or RGB"""
)
distance: Optional[float] = Field(
None, description="""Distance from camera/monitor to target/eye."""
)
@ -405,10 +437,6 @@ class OpticalSeries(ImageSeries):
NDArray[Shape["2 width_height"], float], NDArray[Shape["3 width_height_depth"], float]
]
] = Field(None, description="""Width, height and depth of image, or imaged area, in meters.""")
data: Union[
NDArray[Shape["* frame, * x, * y"], float],
NDArray[Shape["* frame, * x, * y, 3 r_g_b"], float],
] = Field(..., description="""Images presented to subject, either grayscale or RGB""")
orientation: Optional[str] = Field(
None,
description="""Description of image relative to some reference frame (e.g., which way is up). Must also specify frame of reference.""",
@ -423,8 +451,9 @@ class OpticalSeries(ImageSeries):
description="""Paths to one or more external file(s). The field is only present if format='external'. This is only relevant if the image series is stored in the file system as one or more image file(s). This field should NOT be used if the image is stored in another NWB file and that file is linked to this file.""",
)
format: Optional[str] = Field(
None,
"raw",
description="""Format of image. If this is 'external', then the attribute 'external_file' contains the path information to the image files. If this is 'raw', then the raw (single-channel) binary data is stored in the 'data' dataset. If this attribute is not present, then the default format='raw' case is assumed.""",
json_schema_extra={"linkml_meta": {"ifabsent": "string(raw)"}},
)
description: Optional[str] = Field(
"no description",
@ -463,6 +492,39 @@ class OpticalSeries(ImageSeries):
)
class OpticalSeriesData(ConfiguredBaseModel):
"""
Images presented to subject, either grayscale or RGB
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.image"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: str = Field(
...,
description="""Base unit of measurement for working with the data. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
)
value: Optional[
Union[
NDArray[Shape["* frame, * x, * y"], float | int],
NDArray[Shape["* frame, * x, * y, 3 r_g_b"], float | int],
]
] = Field(None)
class IndexSeries(TimeSeries):
"""
Stores indices to image frames stored in an ImageSeries. The purpose of the ImageIndexSeries is to allow a static image stack to be stored somewhere, and the images in the stack to be referenced out-of-order. This can be for the display of individual images, or of movie segments (as a movie is simply a series of images). The data field stores the index of the frame in the referenced ImageSeries, and the timestamps array indicates when that image was displayed.
@ -473,10 +535,8 @@ class IndexSeries(TimeSeries):
)
name: str = Field(...)
data: NDArray[Shape["* num_times"], int] = Field(
...,
description="""Index of the frame in the referenced ImageSeries.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}},
data: IndexSeriesData = Field(
..., description="""Index of the frame in the referenced ImageSeries."""
)
indexed_timeseries: Union[ImageSeries, str] = Field(
...,
@ -524,13 +584,46 @@ class IndexSeries(TimeSeries):
)
class IndexSeriesData(ConfiguredBaseModel):
"""
Index of the frame in the referenced ImageSeries.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.image"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: str = Field(
...,
description="""Base unit of measurement for working with the data. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
)
value: Optional[NDArray[Shape["* num_times"], int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
# Model rebuild
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
GrayscaleImage.model_rebuild()
RGBImage.model_rebuild()
RGBAImage.model_rebuild()
ImageSeries.model_rebuild()
ImageSeriesData.model_rebuild()
ImageSeriesExternalFile.model_rebuild()
ImageMaskSeries.model_rebuild()
OpticalSeries.model_rebuild()
OpticalSeriesData.model_rebuild()
IndexSeries.model_rebuild()
IndexSeriesData.model_rebuild()

252
nwb_models/src/nwb_models/models/pydantic/core/v2_2_4/core_nwb_misc.py generated
View file

@ -251,6 +251,16 @@ class AbstractFeatureSeriesData(ConfiguredBaseModel):
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Optional[str] = Field(
"see ",
description="""Since there can be different units for different features, store the units in 'feature_units'. The default value for this attribute is \"see 'feature_units'\".""",
@ -258,8 +268,8 @@ class AbstractFeatureSeriesData(ConfiguredBaseModel):
)
value: Optional[
Union[
NDArray[Shape["* num_times"], float],
NDArray[Shape["* num_times, * num_features"], float],
NDArray[Shape["* num_times"], float | int],
NDArray[Shape["* num_times, * num_features"], float | int],
]
] = Field(None)
@ -274,10 +284,8 @@ class AnnotationSeries(TimeSeries):
)
name: str = Field(...)
data: NDArray[Shape["* num_times"], str] = Field(
...,
description="""Annotations made during an experiment.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}},
data: AnnotationSeriesData = Field(
..., description="""Annotations made during an experiment."""
)
description: Optional[str] = Field(
"no description",
@ -316,6 +324,39 @@ class AnnotationSeries(TimeSeries):
)
class AnnotationSeriesData(ConfiguredBaseModel):
"""
Annotations made during an experiment.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.misc"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: float = Field(
-1.0,
description="""Smallest meaningful difference between values in data. Annotations have no units, so the value is fixed to -1.0.""",
le=-1,
ge=-1,
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["n/a"] = Field(
"n/a",
description="""Base unit of measurement for working with the data. Annotations have no units, so the value is fixed to 'n/a'.""",
json_schema_extra={"linkml_meta": {"equals_string": "n/a", "ifabsent": "string(n/a)"}},
)
value: Optional[NDArray[Shape["* num_times"], str]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
class IntervalSeries(TimeSeries):
"""
Stores intervals of data. The timestamps field stores the beginning and end of intervals. The data field stores whether the interval just started (>0 value) or ended (<0 value). Different interval types can be represented in the same series by using multiple key values (eg, 1 for feature A, 2 for feature B, 3 for feature C, etc). The field data stores an 8-bit integer. This is largely an alias of a standard TimeSeries but that is identifiable as representing time intervals in a machine-readable way.
@ -326,10 +367,8 @@ class IntervalSeries(TimeSeries):
)
name: str = Field(...)
data: NDArray[Shape["* num_times"], int] = Field(
...,
description="""Use values >0 if interval started, <0 if interval ended.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}},
data: IntervalSeriesData = Field(
..., description="""Use values >0 if interval started, <0 if interval ended."""
)
description: Optional[str] = Field(
"no description",
@ -368,6 +407,39 @@ class IntervalSeries(TimeSeries):
)
class IntervalSeriesData(ConfiguredBaseModel):
"""
Use values >0 if interval started, <0 if interval ended.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.misc"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: float = Field(
-1.0,
description="""Smallest meaningful difference between values in data. Annotations have no units, so the value is fixed to -1.0.""",
le=-1,
ge=-1,
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["n/a"] = Field(
"n/a",
description="""Base unit of measurement for working with the data. Annotations have no units, so the value is fixed to 'n/a'.""",
json_schema_extra={"linkml_meta": {"equals_string": "n/a", "ifabsent": "string(n/a)"}},
)
value: Optional[NDArray[Shape["* num_times"], int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
class DecompositionSeries(TimeSeries):
"""
Spectral analysis of a time series, e.g. of an LFP or a speech signal.
@ -443,24 +515,36 @@ class DecompositionSeriesData(ConfiguredBaseModel):
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: str = Field(
"no unit",
description="""Base unit of measurement for working with the data. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
json_schema_extra={"linkml_meta": {"ifabsent": "string(no unit)"}},
)
value: Optional[NDArray[Shape["* num_times, * num_channels, * num_bands"], float]] = Field(
None,
json_schema_extra={
"linkml_meta": {
"array": {
"dimensions": [
{"alias": "num_times"},
{"alias": "num_channels"},
{"alias": "num_bands"},
]
value: Optional[NDArray[Shape["* num_times, * num_channels, * num_bands"], float | int]] = (
Field(
None,
json_schema_extra={
"linkml_meta": {
"array": {
"dimensions": [
{"alias": "num_times"},
{"alias": "num_channels"},
{"alias": "num_bands"},
]
}
}
}
},
},
)
)
@ -518,9 +602,6 @@ class DecompositionSeriesBands(DynamicTable):
description="""Array of unique identifiers for the rows of this dynamic table.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_rows"}]}}},
)
vector_index: Optional[List[VectorIndex]] = Field(
None, description="""Indices for the vector columns of this dynamic table."""
)
class Units(DynamicTable):
@ -533,9 +614,18 @@ class Units(DynamicTable):
)
name: str = Field("Units", json_schema_extra={"linkml_meta": {"ifabsent": "string(Units)"}})
spike_times_index: Optional[Named[VectorIndex]] = Field(
electrode_group: Optional[VectorData[NDArray[Any, ElectrodeGroup]]] = Field(
None,
description="""Index into the spike_times dataset.""",
description="""Electrode group that each spike unit came from.""",
json_schema_extra={
"linkml_meta": {
"array": {"maximum_number_dimensions": False, "minimum_number_dimensions": 1}
}
},
)
electrodes: Optional[Named[DynamicTableRegion]] = Field(
None,
description="""Electrode that each spike unit came from, specified using a DynamicTableRegion.""",
json_schema_extra={
"linkml_meta": {
"annotations": {
@ -545,12 +635,9 @@ class Units(DynamicTable):
}
},
)
spike_times: Optional[UnitsSpikeTimes] = Field(
None, description="""Spike times for each unit."""
)
obs_intervals_index: Optional[Named[VectorIndex]] = Field(
electrodes_index: Optional[Named[VectorIndex]] = Field(
None,
description="""Index into the obs_intervals dataset.""",
description="""Index into electrodes.""",
json_schema_extra={
"linkml_meta": {
"annotations": {
@ -576,9 +663,9 @@ class Units(DynamicTable):
},
)
)
electrodes_index: Optional[Named[VectorIndex]] = Field(
obs_intervals_index: Optional[Named[VectorIndex]] = Field(
None,
description="""Index into electrodes.""",
description="""Index into the obs_intervals dataset.""",
json_schema_extra={
"linkml_meta": {
"annotations": {
@ -588,9 +675,12 @@ class Units(DynamicTable):
}
},
)
electrodes: Optional[Named[DynamicTableRegion]] = Field(
spike_times: Optional[UnitsSpikeTimes] = Field(
None, description="""Spike times for each unit."""
)
spike_times_index: Optional[Named[VectorIndex]] = Field(
None,
description="""Electrode that each spike unit came from, specified using a DynamicTableRegion.""",
description="""Index into the spike_times dataset.""",
json_schema_extra={
"linkml_meta": {
"annotations": {
@ -600,25 +690,12 @@ class Units(DynamicTable):
}
},
)
electrode_group: Optional[List[ElectrodeGroup]] = Field(
None, description="""Electrode group that each spike unit came from."""
waveform_mean: Optional[UnitsWaveformMean] = Field(
None, description="""Spike waveform mean for each spike unit."""
)
waveform_sd: Optional[UnitsWaveformSd] = Field(
None, description="""Spike waveform standard deviation for each spike unit."""
)
waveform_mean: Optional[
VectorData[
Union[
NDArray[Shape["* num_units, * num_samples"], float],
NDArray[Shape["* num_units, * num_samples, * num_electrodes"], float],
]
]
] = Field(None, description="""Spike waveform mean for each spike unit.""")
waveform_sd: Optional[
VectorData[
Union[
NDArray[Shape["* num_units, * num_samples"], float],
NDArray[Shape["* num_units, * num_samples, * num_electrodes"], float],
]
]
] = Field(None, description="""Spike waveform standard deviation for each spike unit.""")
colnames: List[str] = Field(
...,
description="""The names of the columns in this table. This should be used to specify an order to the columns.""",
@ -629,9 +706,6 @@ class Units(DynamicTable):
description="""Array of unique identifiers for the rows of this dynamic table.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_rows"}]}}},
)
vector_index: Optional[List[VectorIndex]] = Field(
None, description="""Indices for the vector columns of this dynamic table."""
)
class UnitsSpikeTimes(VectorData):
@ -662,14 +736,78 @@ class UnitsSpikeTimes(VectorData):
] = Field(None)
class UnitsWaveformMean(VectorData):
"""
Spike waveform mean for each spike unit.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.misc"})
name: Literal["waveform_mean"] = Field(
"waveform_mean",
json_schema_extra={
"linkml_meta": {"equals_string": "waveform_mean", "ifabsent": "string(waveform_mean)"}
},
)
sampling_rate: Optional[float] = Field(None, description="""Sampling rate, in hertz.""")
unit: Optional[Literal["volts"]] = Field(
"volts",
description="""Unit of measurement. This value is fixed to 'volts'.""",
json_schema_extra={"linkml_meta": {"equals_string": "volts", "ifabsent": "string(volts)"}},
)
description: str = Field(..., description="""Description of what these vectors represent.""")
value: Optional[
Union[
NDArray[Shape["* dim0"], Any],
NDArray[Shape["* dim0, * dim1"], Any],
NDArray[Shape["* dim0, * dim1, * dim2"], Any],
NDArray[Shape["* dim0, * dim1, * dim2, * dim3"], Any],
]
] = Field(None)
class UnitsWaveformSd(VectorData):
"""
Spike waveform standard deviation for each spike unit.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.misc"})
name: Literal["waveform_sd"] = Field(
"waveform_sd",
json_schema_extra={
"linkml_meta": {"equals_string": "waveform_sd", "ifabsent": "string(waveform_sd)"}
},
)
sampling_rate: Optional[float] = Field(None, description="""Sampling rate, in hertz.""")
unit: Optional[Literal["volts"]] = Field(
"volts",
description="""Unit of measurement. This value is fixed to 'volts'.""",
json_schema_extra={"linkml_meta": {"equals_string": "volts", "ifabsent": "string(volts)"}},
)
description: str = Field(..., description="""Description of what these vectors represent.""")
value: Optional[
Union[
NDArray[Shape["* dim0"], Any],
NDArray[Shape["* dim0, * dim1"], Any],
NDArray[Shape["* dim0, * dim1, * dim2"], Any],
NDArray[Shape["* dim0, * dim1, * dim2, * dim3"], Any],
]
] = Field(None)
# Model rebuild
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
AbstractFeatureSeries.model_rebuild()
AbstractFeatureSeriesData.model_rebuild()
AnnotationSeries.model_rebuild()
AnnotationSeriesData.model_rebuild()
IntervalSeries.model_rebuild()
IntervalSeriesData.model_rebuild()
DecompositionSeries.model_rebuild()
DecompositionSeriesData.model_rebuild()
DecompositionSeriesBands.model_rebuild()
Units.model_rebuild()
UnitsSpikeTimes.model_rebuild()
UnitsWaveformMean.model_rebuild()
UnitsWaveformSd.model_rebuild()

38
nwb_models/src/nwb_models/models/pydantic/core/v2_2_4/core_nwb_ogen.py generated
View file

@ -158,10 +158,8 @@ class OptogeneticSeries(TimeSeries):
)
name: str = Field(...)
data: NDArray[Shape["* num_times"], float] = Field(
...,
description="""Applied power for optogenetic stimulus, in watts.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}},
data: OptogeneticSeriesData = Field(
..., description="""Applied power for optogenetic stimulus, in watts."""
)
site: Union[OptogeneticStimulusSite, str] = Field(
...,
@ -209,6 +207,37 @@ class OptogeneticSeries(TimeSeries):
)
class OptogeneticSeriesData(ConfiguredBaseModel):
"""
Applied power for optogenetic stimulus, in watts.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.ogen"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["watts"] = Field(
"watts",
description="""Unit of measurement for data, which is fixed to 'watts'.""",
json_schema_extra={"linkml_meta": {"equals_string": "watts", "ifabsent": "string(watts)"}},
)
value: Optional[NDArray[Shape["* num_times"], float | int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
class OptogeneticStimulusSite(NWBContainer):
"""
A site of optogenetic stimulation.
@ -239,4 +268,5 @@ class OptogeneticStimulusSite(NWBContainer):
# Model rebuild
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
OptogeneticSeries.model_rebuild()
OptogeneticSeriesData.model_rebuild()
OptogeneticStimulusSite.model_rebuild()

129
nwb_models/src/nwb_models/models/pydantic/core/v2_2_4/core_nwb_ophys.py generated
View file

@ -28,7 +28,7 @@ from ...core.v2_2_4.core_nwb_base import (
TimeSeriesSync,
)
from ...core.v2_2_4.core_nwb_device import Device
from ...core.v2_2_4.core_nwb_image import ImageSeries, ImageSeriesExternalFile
from ...core.v2_2_4.core_nwb_image import ImageSeries, ImageSeriesData, ImageSeriesExternalFile
from ...hdmf_common.v1_1_3.hdmf_common_table import (
DynamicTable,
DynamicTableRegion,
@ -215,12 +215,9 @@ class TwoPhotonSeries(ImageSeries):
}
},
)
data: Optional[
Union[
NDArray[Shape["* frame, * x, * y"], float],
NDArray[Shape["* frame, * x, * y, * z"], float],
]
] = Field(None, description="""Binary data representing images across frames.""")
data: Optional[ImageSeriesData] = Field(
None, description="""Binary data representing images across frames."""
)
dimension: Optional[NDArray[Shape["* rank"], int]] = Field(
None,
description="""Number of pixels on x, y, (and z) axes.""",
@ -231,8 +228,9 @@ class TwoPhotonSeries(ImageSeries):
description="""Paths to one or more external file(s). The field is only present if format='external'. This is only relevant if the image series is stored in the file system as one or more image file(s). This field should NOT be used if the image is stored in another NWB file and that file is linked to this file.""",
)
format: Optional[str] = Field(
None,
"raw",
description="""Format of image. If this is 'external', then the attribute 'external_file' contains the path information to the image files. If this is 'raw', then the raw (single-channel) binary data is stored in the 'data' dataset. If this attribute is not present, then the default format='raw' case is assumed.""",
json_schema_extra={"linkml_meta": {"ifabsent": "string(raw)"}},
)
description: Optional[str] = Field(
"no description",
@ -281,9 +279,7 @@ class RoiResponseSeries(TimeSeries):
)
name: str = Field(...)
data: Union[
NDArray[Shape["* num_times"], float], NDArray[Shape["* num_times, * num_rois"], float]
] = Field(..., description="""Signals from ROIs.""")
data: RoiResponseSeriesData = Field(..., description="""Signals from ROIs.""")
rois: Named[DynamicTableRegion] = Field(
...,
description="""DynamicTableRegion referencing into an ROITable containing information on the ROIs stored in this timeseries.""",
@ -333,6 +329,39 @@ class RoiResponseSeries(TimeSeries):
)
class RoiResponseSeriesData(ConfiguredBaseModel):
"""
Signals from ROIs.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.ophys"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: str = Field(
...,
description="""Base unit of measurement for working with the data. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
)
value: Optional[
Union[
NDArray[Shape["* num_times"], float | int],
NDArray[Shape["* num_times, * num_rois"], float | int],
]
] = Field(None)
class DfOverF(NWBDataInterface):
"""
dF/F information about a region of interest (ROI). Storage hierarchy of dF/F should be the same as for segmentation (i.e., same names for ROIs and for image planes).
@ -342,10 +371,10 @@ class DfOverF(NWBDataInterface):
{"from_schema": "core.nwb.ophys", "tree_root": True}
)
value: Optional[List[RoiResponseSeries]] = Field(
name: str = Field("DfOverF", json_schema_extra={"linkml_meta": {"ifabsent": "string(DfOverF)"}})
value: Optional[Dict[str, RoiResponseSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "RoiResponseSeries"}]}}
)
name: str = Field(...)
class Fluorescence(NWBDataInterface):
@ -357,10 +386,12 @@ class Fluorescence(NWBDataInterface):
{"from_schema": "core.nwb.ophys", "tree_root": True}
)
value: Optional[List[RoiResponseSeries]] = Field(
name: str = Field(
"Fluorescence", json_schema_extra={"linkml_meta": {"ifabsent": "string(Fluorescence)"}}
)
value: Optional[Dict[str, RoiResponseSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "RoiResponseSeries"}]}}
)
name: str = Field(...)
class ImageSegmentation(NWBDataInterface):
@ -372,10 +403,13 @@ class ImageSegmentation(NWBDataInterface):
{"from_schema": "core.nwb.ophys", "tree_root": True}
)
value: Optional[List[PlaneSegmentation]] = Field(
name: str = Field(
"ImageSegmentation",
json_schema_extra={"linkml_meta": {"ifabsent": "string(ImageSegmentation)"}},
)
value: Optional[Dict[str, PlaneSegmentation]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "PlaneSegmentation"}]}}
)
name: str = Field(...)
class PlaneSegmentation(DynamicTable):
@ -388,10 +422,21 @@ class PlaneSegmentation(DynamicTable):
)
name: str = Field(...)
image_mask: Optional[PlaneSegmentationImageMask] = Field(
image_mask: Optional[
VectorData[
Union[
NDArray[Shape["* num_roi, * num_x, * num_y"], Any],
NDArray[Shape["* num_roi, * num_x, * num_y, * num_z"], Any],
]
]
] = Field(
None,
description="""ROI masks for each ROI. Each image mask is the size of the original imaging plane (or volume) and members of the ROI are finite non-zero.""",
)
pixel_mask: Optional[PlaneSegmentationPixelMask] = Field(
None,
description="""Pixel masks for each ROI: a list of indices and weights for the ROI. Pixel masks are concatenated and parsing of this dataset is maintained by the PlaneSegmentation""",
)
pixel_mask_index: Optional[Named[VectorIndex]] = Field(
None,
description="""Index into pixel_mask.""",
@ -404,9 +449,9 @@ class PlaneSegmentation(DynamicTable):
}
},
)
pixel_mask: Optional[PlaneSegmentationPixelMask] = Field(
voxel_mask: Optional[PlaneSegmentationVoxelMask] = Field(
None,
description="""Pixel masks for each ROI: a list of indices and weights for the ROI. Pixel masks are concatenated and parsing of this dataset is maintained by the PlaneSegmentation""",
description="""Voxel masks for each ROI: a list of indices and weights for the ROI. Voxel masks are concatenated and parsing of this dataset is maintained by the PlaneSegmentation""",
)
voxel_mask_index: Optional[Named[VectorIndex]] = Field(
None,
@ -420,11 +465,7 @@ class PlaneSegmentation(DynamicTable):
}
},
)
voxel_mask: Optional[PlaneSegmentationVoxelMask] = Field(
None,
description="""Voxel masks for each ROI: a list of indices and weights for the ROI. Voxel masks are concatenated and parsing of this dataset is maintained by the PlaneSegmentation""",
)
reference_images: Optional[List[ImageSeries]] = Field(
reference_images: Optional[Dict[str, ImageSeries]] = Field(
None,
description="""Image stacks that the segmentation masks apply to.""",
json_schema_extra={"linkml_meta": {"any_of": [{"range": "ImageSeries"}]}},
@ -448,33 +489,6 @@ class PlaneSegmentation(DynamicTable):
description="""Array of unique identifiers for the rows of this dynamic table.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_rows"}]}}},
)
vector_index: Optional[List[VectorIndex]] = Field(
None, description="""Indices for the vector columns of this dynamic table."""
)
class PlaneSegmentationImageMask(VectorData):
"""
ROI masks for each ROI. Each image mask is the size of the original imaging plane (or volume) and members of the ROI are finite non-zero.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.ophys"})
name: Literal["image_mask"] = Field(
"image_mask",
json_schema_extra={
"linkml_meta": {"equals_string": "image_mask", "ifabsent": "string(image_mask)"}
},
)
description: str = Field(..., description="""Description of what these vectors represent.""")
value: Optional[
Union[
NDArray[Shape["* dim0"], Any],
NDArray[Shape["* dim0, * dim1"], Any],
NDArray[Shape["* dim0, * dim1, * dim2"], Any],
NDArray[Shape["* dim0, * dim1, * dim2, * dim3"], Any],
]
] = Field(None)
class PlaneSegmentationPixelMask(VectorData):
@ -597,7 +611,7 @@ class ImagingPlane(NWBContainer):
None,
description="""Describes reference frame of origin_coords and grid_spacing. For example, this can be a text description of the anatomical location and orientation of the grid defined by origin_coords and grid_spacing or the vectors needed to transform or rotate the grid to a common anatomical axis (e.g., AP/DV/ML). This field is necessary to interpret origin_coords and grid_spacing. If origin_coords and grid_spacing are not present, then this field is not required. For example, if the microscope takes 10 x 10 x 2 images, where the first value of the data matrix (index (0, 0, 0)) corresponds to (-1.2, -0.6, -2) mm relative to bregma, the spacing between pixels is 0.2 mm in x, 0.2 mm in y and 0.5 mm in z, and larger numbers in x means more anterior, larger numbers in y means more rightward, and larger numbers in z means more ventral, then enter the following -- origin_coords = (-1.2, -0.6, -2) grid_spacing = (0.2, 0.2, 0.5) reference_frame = \"Origin coordinates are relative to bregma. First dimension corresponds to anterior-posterior axis (larger index = more anterior). Second dimension corresponds to medial-lateral axis (larger index = more rightward). Third dimension corresponds to dorsal-ventral axis (larger index = more ventral).\"""",
)
optical_channel: List[OpticalChannel] = Field(
optical_channel: Dict[str, OpticalChannel] = Field(
..., description="""An optical channel used to record from an imaging plane."""
)
device: Union[Device, str] = Field(
@ -731,10 +745,13 @@ class MotionCorrection(NWBDataInterface):
{"from_schema": "core.nwb.ophys", "tree_root": True}
)
value: Optional[List[CorrectedImageStack]] = Field(
name: str = Field(
"MotionCorrection",
json_schema_extra={"linkml_meta": {"ifabsent": "string(MotionCorrection)"}},
)
value: Optional[Dict[str, CorrectedImageStack]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "CorrectedImageStack"}]}}
)
name: str = Field(...)
class CorrectedImageStack(NWBDataInterface):
@ -769,11 +786,11 @@ class CorrectedImageStack(NWBDataInterface):
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
TwoPhotonSeries.model_rebuild()
RoiResponseSeries.model_rebuild()
RoiResponseSeriesData.model_rebuild()
DfOverF.model_rebuild()
Fluorescence.model_rebuild()
ImageSegmentation.model_rebuild()
PlaneSegmentation.model_rebuild()
PlaneSegmentationImageMask.model_rebuild()
PlaneSegmentationPixelMask.model_rebuild()
PlaneSegmentationVoxelMask.model_rebuild()
ImagingPlane.model_rebuild()

17
nwb_models/src/nwb_models/models/pydantic/core/v2_2_4/namespace.py generated
View file

@ -38,6 +38,7 @@ from ...core.v2_2_4.core_nwb_ecephys import (
ClusterWaveforms,
Clustering,
ElectricalSeries,
ElectricalSeriesData,
ElectrodeGroup,
ElectrodeGroupPosition,
EventDetection,
@ -46,6 +47,7 @@ from ...core.v2_2_4.core_nwb_ecephys import (
FilteredEphys,
LFP,
SpikeEventSeries,
SpikeEventSeriesData,
)
from ...core.v2_2_4.core_nwb_epoch import TimeIntervals, TimeIntervalsTimeseries
from ...core.v2_2_4.core_nwb_file import (
@ -87,9 +89,12 @@ from ...core.v2_2_4.core_nwb_image import (
GrayscaleImage,
ImageMaskSeries,
ImageSeries,
ImageSeriesData,
ImageSeriesExternalFile,
IndexSeries,
IndexSeriesData,
OpticalSeries,
OpticalSeriesData,
RGBAImage,
RGBImage,
)
@ -97,14 +102,22 @@ from ...core.v2_2_4.core_nwb_misc import (
AbstractFeatureSeries,
AbstractFeatureSeriesData,
AnnotationSeries,
AnnotationSeriesData,
DecompositionSeries,
DecompositionSeriesBands,
DecompositionSeriesData,
IntervalSeries,
IntervalSeriesData,
Units,
UnitsSpikeTimes,
UnitsWaveformMean,
UnitsWaveformSd,
)
from ...core.v2_2_4.core_nwb_ogen import (
OptogeneticSeries,
OptogeneticSeriesData,
OptogeneticStimulusSite,
)
from ...core.v2_2_4.core_nwb_ogen import OptogeneticSeries, OptogeneticStimulusSite
from ...core.v2_2_4.core_nwb_ophys import (
CorrectedImageStack,
DfOverF,
@ -117,10 +130,10 @@ from ...core.v2_2_4.core_nwb_ophys import (
MotionCorrection,
OpticalChannel,
PlaneSegmentation,
PlaneSegmentationImageMask,
PlaneSegmentationPixelMask,
PlaneSegmentationVoxelMask,
RoiResponseSeries,
RoiResponseSeriesData,
TwoPhotonSeries,
)
from ...core.v2_2_4.core_nwb_retinotopy import (

15
nwb_models/src/nwb_models/models/pydantic/core/v2_2_5/core_nwb_base.py generated
View file

@ -170,9 +170,9 @@ class Image(NWBData):
description: Optional[str] = Field(None, description="""Description of the image.""")
value: Optional[
Union[
NDArray[Shape["* x, * y"], float],
NDArray[Shape["* x, * y, 3 r_g_b"], float],
NDArray[Shape["* x, * y, 4 r_g_b_a"], float],
NDArray[Shape["* x, * y"], float | int],
NDArray[Shape["* x, * y, 3 r_g_b"], float | int],
NDArray[Shape["* x, * y, 4 r_g_b_a"], float | int],
]
] = Field(None)
@ -333,13 +333,16 @@ class ProcessingModule(NWBContainer):
{"from_schema": "core.nwb.base", "tree_root": True}
)
value: Optional[List[Union[DynamicTable, NWBDataInterface]]] = Field(
name: str = Field(...)
description: str = Field(
..., description="""Description of this collection of processed data."""
)
value: Optional[Dict[str, Union[DynamicTable, NWBDataInterface]]] = Field(
None,
json_schema_extra={
"linkml_meta": {"any_of": [{"range": "NWBDataInterface"}, {"range": "DynamicTable"}]}
},
)
name: str = Field(...)
class Images(NWBDataInterface):
@ -353,7 +356,7 @@ class Images(NWBDataInterface):
name: str = Field("Images", json_schema_extra={"linkml_meta": {"ifabsent": "string(Images)"}})
description: str = Field(..., description="""Description of this collection of images.""")
image: List[Image] = Field(..., description="""Images stored in this collection.""")
image: List[str] = Field(..., description="""Images stored in this collection.""")
# Model rebuild

60
nwb_models/src/nwb_models/models/pydantic/core/v2_2_5/core_nwb_behavior.py generated
View file

@ -213,6 +213,16 @@ class SpatialSeriesData(ConfiguredBaseModel):
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Optional[str] = Field(
"meters",
description="""Base unit of measurement for working with the data. The default value is 'meters'. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
@ -220,8 +230,8 @@ class SpatialSeriesData(ConfiguredBaseModel):
)
value: Optional[
Union[
NDArray[Shape["* num_times"], float],
NDArray[Shape["* num_times, * num_features"], float],
NDArray[Shape["* num_times"], float | int],
NDArray[Shape["* num_times, * num_features"], float | int],
]
] = Field(None)
@ -235,10 +245,13 @@ class BehavioralEpochs(NWBDataInterface):
{"from_schema": "core.nwb.behavior", "tree_root": True}
)
value: Optional[List[IntervalSeries]] = Field(
name: str = Field(
"BehavioralEpochs",
json_schema_extra={"linkml_meta": {"ifabsent": "string(BehavioralEpochs)"}},
)
value: Optional[Dict[str, IntervalSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "IntervalSeries"}]}}
)
name: str = Field(...)
class BehavioralEvents(NWBDataInterface):
@ -250,10 +263,13 @@ class BehavioralEvents(NWBDataInterface):
{"from_schema": "core.nwb.behavior", "tree_root": True}
)
value: Optional[List[TimeSeries]] = Field(
name: str = Field(
"BehavioralEvents",
json_schema_extra={"linkml_meta": {"ifabsent": "string(BehavioralEvents)"}},
)
value: Optional[Dict[str, TimeSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "TimeSeries"}]}}
)
name: str = Field(...)
class BehavioralTimeSeries(NWBDataInterface):
@ -265,10 +281,13 @@ class BehavioralTimeSeries(NWBDataInterface):
{"from_schema": "core.nwb.behavior", "tree_root": True}
)
value: Optional[List[TimeSeries]] = Field(
name: str = Field(
"BehavioralTimeSeries",
json_schema_extra={"linkml_meta": {"ifabsent": "string(BehavioralTimeSeries)"}},
)
value: Optional[Dict[str, TimeSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "TimeSeries"}]}}
)
name: str = Field(...)
class PupilTracking(NWBDataInterface):
@ -280,10 +299,12 @@ class PupilTracking(NWBDataInterface):
{"from_schema": "core.nwb.behavior", "tree_root": True}
)
value: Optional[List[TimeSeries]] = Field(
name: str = Field(
"PupilTracking", json_schema_extra={"linkml_meta": {"ifabsent": "string(PupilTracking)"}}
)
value: Optional[Dict[str, TimeSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "TimeSeries"}]}}
)
name: str = Field(...)
class EyeTracking(NWBDataInterface):
@ -295,10 +316,12 @@ class EyeTracking(NWBDataInterface):
{"from_schema": "core.nwb.behavior", "tree_root": True}
)
value: Optional[List[SpatialSeries]] = Field(
name: str = Field(
"EyeTracking", json_schema_extra={"linkml_meta": {"ifabsent": "string(EyeTracking)"}}
)
value: Optional[Dict[str, SpatialSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "SpatialSeries"}]}}
)
name: str = Field(...)
class CompassDirection(NWBDataInterface):
@ -310,10 +333,13 @@ class CompassDirection(NWBDataInterface):
{"from_schema": "core.nwb.behavior", "tree_root": True}
)
value: Optional[List[SpatialSeries]] = Field(
name: str = Field(
"CompassDirection",
json_schema_extra={"linkml_meta": {"ifabsent": "string(CompassDirection)"}},
)
value: Optional[Dict[str, SpatialSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "SpatialSeries"}]}}
)
name: str = Field(...)
class Position(NWBDataInterface):
@ -325,10 +351,12 @@ class Position(NWBDataInterface):
{"from_schema": "core.nwb.behavior", "tree_root": True}
)
value: Optional[List[SpatialSeries]] = Field(
name: str = Field(
"Position", json_schema_extra={"linkml_meta": {"ifabsent": "string(Position)"}}
)
value: Optional[Dict[str, SpatialSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "SpatialSeries"}]}}
)
name: str = Field(...)
# Model rebuild

118
nwb_models/src/nwb_models/models/pydantic/core/v2_2_5/core_nwb_ecephys.py generated
View file

@ -190,11 +190,12 @@ class ElectricalSeries(TimeSeries):
)
name: str = Field(...)
data: Union[
NDArray[Shape["* num_times"], float],
NDArray[Shape["* num_times, * num_channels"], float],
NDArray[Shape["* num_times, * num_channels, * num_samples"], float],
] = Field(..., description="""Recorded voltage data.""")
channel_conversion: Optional[NDArray[Shape["* num_channels"], float]] = Field(
None,
description="""Channel-specific conversion factor. Multiply the data in the 'data' dataset by these values along the channel axis (as indicated by axis attribute) AND by the global conversion factor in the 'conversion' attribute of 'data' to get the data values in Volts, i.e, data in Volts = data * data.conversion * channel_conversion. This approach allows for both global and per-channel data conversion factors needed to support the storage of electrical recordings as native values generated by data acquisition systems. If this dataset is not present, then there is no channel-specific conversion factor, i.e. it is 1 for all channels.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_channels"}]}}},
)
data: ElectricalSeriesData = Field(..., description="""Recorded voltage data.""")
electrodes: Named[DynamicTableRegion] = Field(
...,
description="""DynamicTableRegion pointer to the electrodes that this time series was generated from.""",
@ -207,11 +208,6 @@ class ElectricalSeries(TimeSeries):
}
},
)
channel_conversion: Optional[NDArray[Shape["* num_channels"], float]] = Field(
None,
description="""Channel-specific conversion factor. Multiply the data in the 'data' dataset by these values along the channel axis (as indicated by axis attribute) AND by the global conversion factor in the 'conversion' attribute of 'data' to get the data values in Volts, i.e, data in Volts = data * data.conversion * channel_conversion. This approach allows for both global and per-channel data conversion factors needed to support the storage of electrical recordings as native values generated by data acquisition systems. If this dataset is not present, then there is no channel-specific conversion factor, i.e. it is 1 for all channels.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_channels"}]}}},
)
description: Optional[str] = Field(
"no description",
description="""Description of the time series.""",
@ -249,6 +245,41 @@ class ElectricalSeries(TimeSeries):
)
class ElectricalSeriesData(ConfiguredBaseModel):
"""
Recorded voltage data.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.ecephys"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["volts"] = Field(
"volts",
description="""Base unit of measurement for working with the data. This value is fixed to 'volts'. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion' and 'channel_conversion' (if present).""",
json_schema_extra={"linkml_meta": {"equals_string": "volts", "ifabsent": "string(volts)"}},
)
value: Optional[
Union[
NDArray[Shape["* num_times"], float | int],
NDArray[Shape["* num_times, * num_channels"], float | int],
NDArray[Shape["* num_times, * num_channels, * num_samples"], float | int],
]
] = Field(None)
class SpikeEventSeries(ElectricalSeries):
"""
Stores snapshots/snippets of recorded spike events (i.e., threshold crossings). This may also be raw data, as reported by ephys hardware. If so, the TimeSeries::description field should describe how events were detected. All SpikeEventSeries should reside in a module (under EventWaveform interface) even if the spikes were reported and stored by hardware. All events span the same recording channels and store snapshots of equal duration. TimeSeries::data array structure: [num events] [num channels] [num samples] (or [num events] [num samples] for single electrode).
@ -259,15 +290,17 @@ class SpikeEventSeries(ElectricalSeries):
)
name: str = Field(...)
data: Union[
NDArray[Shape["* num_events, * num_samples"], float],
NDArray[Shape["* num_events, * num_channels, * num_samples"], float],
] = Field(..., description="""Spike waveforms.""")
data: SpikeEventSeriesData = Field(..., description="""Spike waveforms.""")
timestamps: NDArray[Shape["* num_times"], float] = Field(
...,
description="""Timestamps for samples stored in data, in seconds, relative to the common experiment master-clock stored in NWBFile.timestamps_reference_time. Timestamps are required for the events. Unlike for TimeSeries, timestamps are required for SpikeEventSeries and are thus re-specified here.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}},
)
channel_conversion: Optional[NDArray[Shape["* num_channels"], float]] = Field(
None,
description="""Channel-specific conversion factor. Multiply the data in the 'data' dataset by these values along the channel axis (as indicated by axis attribute) AND by the global conversion factor in the 'conversion' attribute of 'data' to get the data values in Volts, i.e, data in Volts = data * data.conversion * channel_conversion. This approach allows for both global and per-channel data conversion factors needed to support the storage of electrical recordings as native values generated by data acquisition systems. If this dataset is not present, then there is no channel-specific conversion factor, i.e. it is 1 for all channels.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_channels"}]}}},
)
electrodes: Named[DynamicTableRegion] = Field(
...,
description="""DynamicTableRegion pointer to the electrodes that this time series was generated from.""",
@ -280,11 +313,6 @@ class SpikeEventSeries(ElectricalSeries):
}
},
)
channel_conversion: Optional[NDArray[Shape["* num_channels"], float]] = Field(
None,
description="""Channel-specific conversion factor. Multiply the data in the 'data' dataset by these values along the channel axis (as indicated by axis attribute) AND by the global conversion factor in the 'conversion' attribute of 'data' to get the data values in Volts, i.e, data in Volts = data * data.conversion * channel_conversion. This approach allows for both global and per-channel data conversion factors needed to support the storage of electrical recordings as native values generated by data acquisition systems. If this dataset is not present, then there is no channel-specific conversion factor, i.e. it is 1 for all channels.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_channels"}]}}},
)
description: Optional[str] = Field(
"no description",
description="""Description of the time series.""",
@ -317,6 +345,40 @@ class SpikeEventSeries(ElectricalSeries):
)
class SpikeEventSeriesData(ConfiguredBaseModel):
"""
Spike waveforms.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.ecephys"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["volts"] = Field(
"volts",
description="""Unit of measurement for waveforms, which is fixed to 'volts'.""",
json_schema_extra={"linkml_meta": {"equals_string": "volts", "ifabsent": "string(volts)"}},
)
value: Optional[
Union[
NDArray[Shape["* num_events, * num_samples"], float | int],
NDArray[Shape["* num_events, * num_channels, * num_samples"], float | int],
]
] = Field(None)
class FeatureExtraction(NWBDataInterface):
"""
Features, such as PC1 and PC2, that are extracted from signals stored in a SpikeEventSeries or other source.
@ -415,10 +477,12 @@ class EventWaveform(NWBDataInterface):
{"from_schema": "core.nwb.ecephys", "tree_root": True}
)
value: Optional[List[SpikeEventSeries]] = Field(
name: str = Field(
"EventWaveform", json_schema_extra={"linkml_meta": {"ifabsent": "string(EventWaveform)"}}
)
value: Optional[Dict[str, SpikeEventSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "SpikeEventSeries"}]}}
)
name: str = Field(...)
class FilteredEphys(NWBDataInterface):
@ -430,10 +494,12 @@ class FilteredEphys(NWBDataInterface):
{"from_schema": "core.nwb.ecephys", "tree_root": True}
)
value: Optional[List[ElectricalSeries]] = Field(
name: str = Field(
"FilteredEphys", json_schema_extra={"linkml_meta": {"ifabsent": "string(FilteredEphys)"}}
)
value: Optional[Dict[str, ElectricalSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "ElectricalSeries"}]}}
)
name: str = Field(...)
class LFP(NWBDataInterface):
@ -445,10 +511,10 @@ class LFP(NWBDataInterface):
{"from_schema": "core.nwb.ecephys", "tree_root": True}
)
value: Optional[List[ElectricalSeries]] = Field(
name: str = Field("LFP", json_schema_extra={"linkml_meta": {"ifabsent": "string(LFP)"}})
value: Optional[Dict[str, ElectricalSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "ElectricalSeries"}]}}
)
name: str = Field(...)
class ElectrodeGroup(NWBContainer):
@ -591,7 +657,9 @@ class Clustering(NWBDataInterface):
# Model rebuild
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
ElectricalSeries.model_rebuild()
ElectricalSeriesData.model_rebuild()
SpikeEventSeries.model_rebuild()
SpikeEventSeriesData.model_rebuild()
FeatureExtraction.model_rebuild()
EventDetection.model_rebuild()
EventWaveform.model_rebuild()

3
nwb_models/src/nwb_models/models/pydantic/core/v2_2_5/core_nwb_epoch.py generated
View file

@ -247,9 +247,6 @@ class TimeIntervals(DynamicTable):
description="""Array of unique identifiers for the rows of this dynamic table.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_rows"}]}}},
)
vector_index: Optional[List[VectorIndex]] = Field(
None, description="""Indices for the vector columns of this dynamic table."""
)
class TimeIntervalsTimeseries(VectorData):

56
nwb_models/src/nwb_models/models/pydantic/core/v2_2_5/core_nwb_file.py generated
View file

@ -225,28 +225,28 @@ class NWBFile(NWBContainer):
...,
description="""Date and time corresponding to time zero of all timestamps. The date is stored in UTC with local timezone offset as ISO 8601 extended formatted string: 2018-09-28T14:43:54.123+02:00. Dates stored in UTC end in \"Z\" with no timezone offset. Date accuracy is up to milliseconds. All times stored in the file use this time as reference (i.e., time zero).""",
)
acquisition: Optional[List[Union[DynamicTable, NWBDataInterface]]] = Field(
acquisition: Optional[Dict[str, Union[DynamicTable, NWBDataInterface]]] = Field(
None,
description="""Data streams recorded from the system, including ephys, ophys, tracking, etc. This group should be read-only after the experiment is completed and timestamps are corrected to a common timebase. The data stored here may be links to raw data stored in external NWB files. This will allow keeping bulky raw data out of the file while preserving the option of keeping some/all in the file. Acquired data includes tracking and experimental data streams (i.e., everything measured from the system). If bulky data is stored in the /acquisition group, the data can exist in a separate NWB file that is linked to by the file being used for processing and analysis.""",
json_schema_extra={
"linkml_meta": {"any_of": [{"range": "NWBDataInterface"}, {"range": "DynamicTable"}]}
},
)
analysis: Optional[List[Union[DynamicTable, NWBContainer]]] = Field(
analysis: Optional[Dict[str, Union[DynamicTable, NWBContainer]]] = Field(
None,
description="""Lab-specific and custom scientific analysis of data. There is no defined format for the content of this group - the format is up to the individual user/lab. To facilitate sharing analysis data between labs, the contents here should be stored in standard types (e.g., neurodata_types) and appropriately documented. The file can store lab-specific and custom data analysis without restriction on its form or schema, reducing data formatting restrictions on end users. Such data should be placed in the analysis group. The analysis data should be documented so that it could be shared with other labs.""",
json_schema_extra={
"linkml_meta": {"any_of": [{"range": "NWBContainer"}, {"range": "DynamicTable"}]}
},
)
scratch: Optional[List[Union[DynamicTable, NWBContainer]]] = Field(
scratch: Optional[Dict[str, Union[DynamicTable, NWBContainer]]] = Field(
None,
description="""A place to store one-off analysis results. Data placed here is not intended for sharing. By placing data here, users acknowledge that there is no guarantee that their data meets any standard.""",
json_schema_extra={
"linkml_meta": {"any_of": [{"range": "NWBContainer"}, {"range": "DynamicTable"}]}
},
)
processing: Optional[List[ProcessingModule]] = Field(
processing: Optional[Dict[str, ProcessingModule]] = Field(
None,
description="""The home for ProcessingModules. These modules perform intermediate analysis of data that is necessary to perform before scientific analysis. Examples include spike clustering, extracting position from tracking data, stitching together image slices. ProcessingModules can be large and express many data sets from relatively complex analysis (e.g., spike detection and clustering) or small, representing extraction of position information from tracking video, or even binary lick/no-lick decisions. Common software tools (e.g., klustakwik, MClust) are expected to read/write data here. 'Processing' refers to intermediate analysis of the acquired data to make it more amenable to scientific analysis.""",
json_schema_extra={"linkml_meta": {"any_of": [{"range": "ProcessingModule"}]}},
@ -264,6 +264,9 @@ class NWBFile(NWBContainer):
description="""Experimental intervals, whether that be logically distinct sub-experiments having a particular scientific goal, trials (see trials subgroup) during an experiment, or epochs (see epochs subgroup) deriving from analysis of data.""",
)
units: Optional[Units] = Field(None, description="""Data about sorted spike units.""")
specifications: Optional[dict] = Field(
None, description="""Nested dictionary of schema specifications"""
)
class NWBFileStimulus(ConfiguredBaseModel):
@ -279,12 +282,12 @@ class NWBFileStimulus(ConfiguredBaseModel):
"linkml_meta": {"equals_string": "stimulus", "ifabsent": "string(stimulus)"}
},
)
presentation: Optional[List[TimeSeries]] = Field(
presentation: Optional[Dict[str, TimeSeries]] = Field(
None,
description="""Stimuli presented during the experiment.""",
json_schema_extra={"linkml_meta": {"any_of": [{"range": "TimeSeries"}]}},
)
templates: Optional[List[TimeSeries]] = Field(
templates: Optional[Dict[str, TimeSeries]] = Field(
None,
description="""Template stimuli. Timestamps in templates are based on stimulus design and are relative to the beginning of the stimulus. When templates are used, the stimulus instances must convert presentation times to the experiment`s time reference frame.""",
json_schema_extra={"linkml_meta": {"any_of": [{"range": "TimeSeries"}]}},
@ -362,11 +365,7 @@ class NWBFileGeneral(ConfiguredBaseModel):
None,
description="""Information about virus(es) used in experiments, including virus ID, source, date made, injection location, volume, etc.""",
)
lab_meta_data: Optional[List[LabMetaData]] = Field(
None,
description="""Place-holder than can be extended so that lab-specific meta-data can be placed in /general.""",
)
devices: Optional[List[Device]] = Field(
devices: Optional[Dict[str, Device]] = Field(
None,
description="""Description of hardware devices used during experiment, e.g., monitors, ADC boards, microscopes, etc.""",
json_schema_extra={"linkml_meta": {"any_of": [{"range": "Device"}]}},
@ -381,16 +380,20 @@ class NWBFileGeneral(ConfiguredBaseModel):
intracellular_ephys: Optional[GeneralIntracellularEphys] = Field(
None, description="""Metadata related to intracellular electrophysiology."""
)
optogenetics: Optional[List[OptogeneticStimulusSite]] = Field(
optogenetics: Optional[Dict[str, OptogeneticStimulusSite]] = Field(
None,
description="""Metadata describing optogenetic stimuluation.""",
json_schema_extra={"linkml_meta": {"any_of": [{"range": "OptogeneticStimulusSite"}]}},
)
optophysiology: Optional[List[ImagingPlane]] = Field(
optophysiology: Optional[Dict[str, ImagingPlane]] = Field(
None,
description="""Metadata related to optophysiology.""",
json_schema_extra={"linkml_meta": {"any_of": [{"range": "ImagingPlane"}]}},
)
value: Optional[Dict[str, LabMetaData]] = Field(
None,
description="""Place-holder than can be extended so that lab-specific meta-data can be placed in /general.""",
)
class GeneralSourceScript(ConfiguredBaseModel):
@ -426,12 +429,12 @@ class GeneralExtracellularEphys(ConfiguredBaseModel):
}
},
)
electrode_group: Optional[List[ElectrodeGroup]] = Field(
None, description="""Physical group of electrodes."""
)
electrodes: Optional[ExtracellularEphysElectrodes] = Field(
None, description="""A table of all electrodes (i.e. channels) used for recording."""
)
value: Optional[Dict[str, ElectrodeGroup]] = Field(
None, description="""Physical group of electrodes."""
)
class ExtracellularEphysElectrodes(DynamicTable):
@ -501,8 +504,14 @@ class ExtracellularEphysElectrodes(DynamicTable):
}
},
)
group: List[ElectrodeGroup] = Field(
..., description="""Reference to the ElectrodeGroup this electrode is a part of."""
group: VectorData[NDArray[Any, ElectrodeGroup]] = Field(
...,
description="""Reference to the ElectrodeGroup this electrode is a part of.""",
json_schema_extra={
"linkml_meta": {
"array": {"maximum_number_dimensions": False, "minimum_number_dimensions": 1}
}
},
)
group_name: VectorData[NDArray[Any, str]] = Field(
...,
@ -559,9 +568,6 @@ class ExtracellularEphysElectrodes(DynamicTable):
description="""Array of unique identifiers for the rows of this dynamic table.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_rows"}]}}},
)
vector_index: Optional[List[VectorIndex]] = Field(
None, description="""Indices for the vector columns of this dynamic table."""
)
class GeneralIntracellularEphys(ConfiguredBaseModel):
@ -584,12 +590,12 @@ class GeneralIntracellularEphys(ConfiguredBaseModel):
None,
description="""Description of filtering used. Includes filtering type and parameters, frequency fall-off, etc. If this changes between TimeSeries, filter description should be stored as a text attribute for each TimeSeries.""",
)
intracellular_electrode: Optional[List[IntracellularElectrode]] = Field(
None, description="""An intracellular electrode."""
)
sweep_table: Optional[SweepTable] = Field(
None, description="""The table which groups different PatchClampSeries together."""
)
value: Optional[Dict[str, IntracellularElectrode]] = Field(
None, description="""An intracellular electrode."""
)
class NWBFileIntervals(ConfiguredBaseModel):
@ -615,7 +621,7 @@ class NWBFileIntervals(ConfiguredBaseModel):
invalid_times: Optional[TimeIntervals] = Field(
None, description="""Time intervals that should be removed from analysis."""
)
time_intervals: Optional[List[TimeIntervals]] = Field(
value: Optional[Dict[str, TimeIntervals]] = Field(
None,
description="""Optional additional table(s) for describing other experimental time intervals.""",
)

133
nwb_models/src/nwb_models/models/pydantic/core/v2_2_5/core_nwb_icephys.py generated
View file

@ -262,11 +262,21 @@ class PatchClampSeriesData(ConfiguredBaseModel):
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: str = Field(
...,
description="""Base unit of measurement for working with the data. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
)
value: Optional[NDArray[Shape["* num_times"], float]] = Field(
value: Optional[NDArray[Shape["* num_times"], float | int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
@ -281,12 +291,12 @@ class CurrentClampSeries(PatchClampSeries):
)
name: str = Field(...)
data: CurrentClampSeriesData = Field(..., description="""Recorded voltage.""")
bias_current: Optional[float] = Field(None, description="""Bias current, in amps.""")
bridge_balance: Optional[float] = Field(None, description="""Bridge balance, in ohms.""")
capacitance_compensation: Optional[float] = Field(
None, description="""Capacitance compensation, in farads."""
)
data: CurrentClampSeriesData = Field(..., description="""Recorded voltage.""")
stimulus_description: str = Field(
..., description="""Protocol/stimulus name for this patch-clamp dataset."""
)
@ -354,12 +364,24 @@ class CurrentClampSeriesData(ConfiguredBaseModel):
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["volts"] = Field(
"volts",
description="""Base unit of measurement for working with the data. which is fixed to 'volts'. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
json_schema_extra={"linkml_meta": {"equals_string": "volts", "ifabsent": "string(volts)"}},
)
value: Any = Field(...)
value: Optional[NDArray[Shape["* num_times"], float | int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
class IZeroClampSeries(CurrentClampSeries):
@ -512,6 +534,16 @@ class CurrentClampStimulusSeriesData(ConfiguredBaseModel):
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["amperes"] = Field(
"amperes",
description="""Base unit of measurement for working with the data. which is fixed to 'amperes'. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
@ -519,7 +551,9 @@ class CurrentClampStimulusSeriesData(ConfiguredBaseModel):
"linkml_meta": {"equals_string": "amperes", "ifabsent": "string(amperes)"}
},
)
value: Any = Field(...)
value: Optional[NDArray[Shape["* num_times"], float | int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
class VoltageClampSeries(PatchClampSeries):
@ -532,13 +566,13 @@ class VoltageClampSeries(PatchClampSeries):
)
name: str = Field(...)
data: VoltageClampSeriesData = Field(..., description="""Recorded current.""")
capacitance_fast: Optional[VoltageClampSeriesCapacitanceFast] = Field(
None, description="""Fast capacitance, in farads."""
)
capacitance_slow: Optional[VoltageClampSeriesCapacitanceSlow] = Field(
None, description="""Slow capacitance, in farads."""
)
data: VoltageClampSeriesData = Field(..., description="""Recorded current.""")
resistance_comp_bandwidth: Optional[VoltageClampSeriesResistanceCompBandwidth] = Field(
None, description="""Resistance compensation bandwidth, in hertz."""
)
@ -610,27 +644,6 @@ class VoltageClampSeries(PatchClampSeries):
)
class VoltageClampSeriesData(ConfiguredBaseModel):
"""
Recorded current.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.icephys"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
unit: Literal["amperes"] = Field(
"amperes",
description="""Base unit of measurement for working with the data. which is fixed to 'amperes'. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
json_schema_extra={
"linkml_meta": {"equals_string": "amperes", "ifabsent": "string(amperes)"}
},
)
value: Any = Field(...)
class VoltageClampSeriesCapacitanceFast(ConfiguredBaseModel):
"""
Fast capacitance, in farads.
@ -683,6 +696,39 @@ class VoltageClampSeriesCapacitanceSlow(ConfiguredBaseModel):
value: float = Field(...)
class VoltageClampSeriesData(ConfiguredBaseModel):
"""
Recorded current.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.icephys"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["amperes"] = Field(
"amperes",
description="""Base unit of measurement for working with the data. which is fixed to 'amperes'. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
json_schema_extra={
"linkml_meta": {"equals_string": "amperes", "ifabsent": "string(amperes)"}
},
)
value: Optional[NDArray[Shape["* num_times"], float | int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
class VoltageClampSeriesResistanceCompBandwidth(ConfiguredBaseModel):
"""
Resistance compensation bandwidth, in hertz.
@ -887,12 +933,24 @@ class VoltageClampStimulusSeriesData(ConfiguredBaseModel):
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["volts"] = Field(
"volts",
description="""Base unit of measurement for working with the data. which is fixed to 'volts'. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
json_schema_extra={"linkml_meta": {"equals_string": "volts", "ifabsent": "string(volts)"}},
)
value: Any = Field(...)
value: Optional[NDArray[Shape["* num_times"], float | int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
class IntracellularElectrode(NWBContainer):
@ -942,18 +1000,15 @@ class SweepTable(DynamicTable):
)
name: str = Field(...)
sweep_number: VectorData[NDArray[Any, int]] = Field(
series: VectorData[NDArray[Any, PatchClampSeries]] = Field(
...,
description="""Sweep number of the PatchClampSeries in that row.""",
description="""The PatchClampSeries with the sweep number in that row.""",
json_schema_extra={
"linkml_meta": {
"array": {"maximum_number_dimensions": False, "minimum_number_dimensions": 1}
}
},
)
series: List[PatchClampSeries] = Field(
..., description="""The PatchClampSeries with the sweep number in that row."""
)
series_index: Named[VectorIndex] = Field(
...,
description="""Index for series.""",
@ -966,6 +1021,15 @@ class SweepTable(DynamicTable):
}
},
)
sweep_number: VectorData[NDArray[Any, int]] = Field(
...,
description="""Sweep number of the PatchClampSeries in that row.""",
json_schema_extra={
"linkml_meta": {
"array": {"maximum_number_dimensions": False, "minimum_number_dimensions": 1}
}
},
)
colnames: List[str] = Field(
...,
description="""The names of the columns in this table. This should be used to specify an order to the columns.""",
@ -976,9 +1040,6 @@ class SweepTable(DynamicTable):
description="""Array of unique identifiers for the rows of this dynamic table.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_rows"}]}}},
)
vector_index: Optional[List[VectorIndex]] = Field(
None, description="""Indices for the vector columns of this dynamic table."""
)
# Model rebuild
@ -991,9 +1052,9 @@ IZeroClampSeries.model_rebuild()
CurrentClampStimulusSeries.model_rebuild()
CurrentClampStimulusSeriesData.model_rebuild()
VoltageClampSeries.model_rebuild()
VoltageClampSeriesData.model_rebuild()
VoltageClampSeriesCapacitanceFast.model_rebuild()
VoltageClampSeriesCapacitanceSlow.model_rebuild()
VoltageClampSeriesData.model_rebuild()
VoltageClampSeriesResistanceCompBandwidth.model_rebuild()
VoltageClampSeriesResistanceCompCorrection.model_rebuild()
VoltageClampSeriesResistanceCompPrediction.model_rebuild()

145
nwb_models/src/nwb_models/models/pydantic/core/v2_2_5/core_nwb_image.py generated
View file

@ -152,7 +152,7 @@ class GrayscaleImage(Image):
)
name: str = Field(...)
value: Optional[NDArray[Shape["* x, * y"], float]] = Field(
value: Optional[NDArray[Shape["* x, * y"], float | int]] = Field(
None,
json_schema_extra={
"linkml_meta": {"array": {"dimensions": [{"alias": "x"}, {"alias": "y"}]}}
@ -174,7 +174,7 @@ class RGBImage(Image):
)
name: str = Field(...)
value: Optional[NDArray[Shape["* x, * y, 3 r_g_b"], float]] = Field(
value: Optional[NDArray[Shape["* x, * y, 3 r_g_b"], float | int]] = Field(
None,
json_schema_extra={
"linkml_meta": {
@ -204,7 +204,7 @@ class RGBAImage(Image):
)
name: str = Field(...)
value: Optional[NDArray[Shape["* x, * y, 4 r_g_b_a"], float]] = Field(
value: Optional[NDArray[Shape["* x, * y, 4 r_g_b_a"], float | int]] = Field(
None,
json_schema_extra={
"linkml_meta": {
@ -234,12 +234,9 @@ class ImageSeries(TimeSeries):
)
name: str = Field(...)
data: Optional[
Union[
NDArray[Shape["* frame, * x, * y"], float],
NDArray[Shape["* frame, * x, * y, * z"], float],
]
] = Field(None, description="""Binary data representing images across frames.""")
data: Optional[ImageSeriesData] = Field(
None, description="""Binary data representing images across frames."""
)
dimension: Optional[NDArray[Shape["* rank"], int]] = Field(
None,
description="""Number of pixels on x, y, (and z) axes.""",
@ -250,8 +247,9 @@ class ImageSeries(TimeSeries):
description="""Paths to one or more external file(s). The field is only present if format='external'. This is only relevant if the image series is stored in the file system as one or more image file(s). This field should NOT be used if the image is stored in another NWB file and that file is linked to this file.""",
)
format: Optional[str] = Field(
None,
"raw",
description="""Format of image. If this is 'external', then the attribute 'external_file' contains the path information to the image files. If this is 'raw', then the raw (single-channel) binary data is stored in the 'data' dataset. If this attribute is not present, then the default format='raw' case is assumed.""",
json_schema_extra={"linkml_meta": {"ifabsent": "string(raw)"}},
)
description: Optional[str] = Field(
"no description",
@ -290,6 +288,39 @@ class ImageSeries(TimeSeries):
)
class ImageSeriesData(ConfiguredBaseModel):
"""
Binary data representing images across frames.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.image"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: str = Field(
...,
description="""Base unit of measurement for working with the data. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
)
value: Optional[
Union[
NDArray[Shape["* frame, * x, * y"], float | int],
NDArray[Shape["* frame, * x, * y, * z"], float | int],
]
] = Field(None)
class ImageSeriesExternalFile(ConfiguredBaseModel):
"""
Paths to one or more external file(s). The field is only present if format='external'. This is only relevant if the image series is stored in the file system as one or more image file(s). This field should NOT be used if the image is stored in another NWB file and that file is linked to this file.
@ -331,12 +362,9 @@ class ImageMaskSeries(ImageSeries):
}
},
)
data: Optional[
Union[
NDArray[Shape["* frame, * x, * y"], float],
NDArray[Shape["* frame, * x, * y, * z"], float],
]
] = Field(None, description="""Binary data representing images across frames.""")
data: Optional[ImageSeriesData] = Field(
None, description="""Binary data representing images across frames."""
)
dimension: Optional[NDArray[Shape["* rank"], int]] = Field(
None,
description="""Number of pixels on x, y, (and z) axes.""",
@ -347,8 +375,9 @@ class ImageMaskSeries(ImageSeries):
description="""Paths to one or more external file(s). The field is only present if format='external'. This is only relevant if the image series is stored in the file system as one or more image file(s). This field should NOT be used if the image is stored in another NWB file and that file is linked to this file.""",
)
format: Optional[str] = Field(
None,
"raw",
description="""Format of image. If this is 'external', then the attribute 'external_file' contains the path information to the image files. If this is 'raw', then the raw (single-channel) binary data is stored in the 'data' dataset. If this attribute is not present, then the default format='raw' case is assumed.""",
json_schema_extra={"linkml_meta": {"ifabsent": "string(raw)"}},
)
description: Optional[str] = Field(
"no description",
@ -397,6 +426,9 @@ class OpticalSeries(ImageSeries):
)
name: str = Field(...)
data: OpticalSeriesData = Field(
..., description="""Images presented to subject, either grayscale or RGB"""
)
distance: Optional[float] = Field(
None, description="""Distance from camera/monitor to target/eye."""
)
@ -405,10 +437,6 @@ class OpticalSeries(ImageSeries):
NDArray[Shape["2 width_height"], float], NDArray[Shape["3 width_height_depth"], float]
]
] = Field(None, description="""Width, height and depth of image, or imaged area, in meters.""")
data: Union[
NDArray[Shape["* frame, * x, * y"], float],
NDArray[Shape["* frame, * x, * y, 3 r_g_b"], float],
] = Field(..., description="""Images presented to subject, either grayscale or RGB""")
orientation: Optional[str] = Field(
None,
description="""Description of image relative to some reference frame (e.g., which way is up). Must also specify frame of reference.""",
@ -423,8 +451,9 @@ class OpticalSeries(ImageSeries):
description="""Paths to one or more external file(s). The field is only present if format='external'. This is only relevant if the image series is stored in the file system as one or more image file(s). This field should NOT be used if the image is stored in another NWB file and that file is linked to this file.""",
)
format: Optional[str] = Field(
None,
"raw",
description="""Format of image. If this is 'external', then the attribute 'external_file' contains the path information to the image files. If this is 'raw', then the raw (single-channel) binary data is stored in the 'data' dataset. If this attribute is not present, then the default format='raw' case is assumed.""",
json_schema_extra={"linkml_meta": {"ifabsent": "string(raw)"}},
)
description: Optional[str] = Field(
"no description",
@ -463,6 +492,39 @@ class OpticalSeries(ImageSeries):
)
class OpticalSeriesData(ConfiguredBaseModel):
"""
Images presented to subject, either grayscale or RGB
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.image"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: str = Field(
...,
description="""Base unit of measurement for working with the data. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
)
value: Optional[
Union[
NDArray[Shape["* frame, * x, * y"], float | int],
NDArray[Shape["* frame, * x, * y, 3 r_g_b"], float | int],
]
] = Field(None)
class IndexSeries(TimeSeries):
"""
Stores indices to image frames stored in an ImageSeries. The purpose of the ImageIndexSeries is to allow a static image stack to be stored somewhere, and the images in the stack to be referenced out-of-order. This can be for the display of individual images, or of movie segments (as a movie is simply a series of images). The data field stores the index of the frame in the referenced ImageSeries, and the timestamps array indicates when that image was displayed.
@ -473,10 +535,8 @@ class IndexSeries(TimeSeries):
)
name: str = Field(...)
data: NDArray[Shape["* num_times"], int] = Field(
...,
description="""Index of the frame in the referenced ImageSeries.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}},
data: IndexSeriesData = Field(
..., description="""Index of the frame in the referenced ImageSeries."""
)
indexed_timeseries: Union[ImageSeries, str] = Field(
...,
@ -524,13 +584,46 @@ class IndexSeries(TimeSeries):
)
class IndexSeriesData(ConfiguredBaseModel):
"""
Index of the frame in the referenced ImageSeries.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.image"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: str = Field(
...,
description="""Base unit of measurement for working with the data. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
)
value: Optional[NDArray[Shape["* num_times"], int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
# Model rebuild
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
GrayscaleImage.model_rebuild()
RGBImage.model_rebuild()
RGBAImage.model_rebuild()
ImageSeries.model_rebuild()
ImageSeriesData.model_rebuild()
ImageSeriesExternalFile.model_rebuild()
ImageMaskSeries.model_rebuild()
OpticalSeries.model_rebuild()
OpticalSeriesData.model_rebuild()
IndexSeries.model_rebuild()
IndexSeriesData.model_rebuild()

252
nwb_models/src/nwb_models/models/pydantic/core/v2_2_5/core_nwb_misc.py generated
View file

@ -251,6 +251,16 @@ class AbstractFeatureSeriesData(ConfiguredBaseModel):
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Optional[str] = Field(
"see ",
description="""Since there can be different units for different features, store the units in 'feature_units'. The default value for this attribute is \"see 'feature_units'\".""",
@ -258,8 +268,8 @@ class AbstractFeatureSeriesData(ConfiguredBaseModel):
)
value: Optional[
Union[
NDArray[Shape["* num_times"], float],
NDArray[Shape["* num_times, * num_features"], float],
NDArray[Shape["* num_times"], float | int],
NDArray[Shape["* num_times, * num_features"], float | int],
]
] = Field(None)
@ -274,10 +284,8 @@ class AnnotationSeries(TimeSeries):
)
name: str = Field(...)
data: NDArray[Shape["* num_times"], str] = Field(
...,
description="""Annotations made during an experiment.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}},
data: AnnotationSeriesData = Field(
..., description="""Annotations made during an experiment."""
)
description: Optional[str] = Field(
"no description",
@ -316,6 +324,39 @@ class AnnotationSeries(TimeSeries):
)
class AnnotationSeriesData(ConfiguredBaseModel):
"""
Annotations made during an experiment.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.misc"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: float = Field(
-1.0,
description="""Smallest meaningful difference between values in data. Annotations have no units, so the value is fixed to -1.0.""",
le=-1,
ge=-1,
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["n/a"] = Field(
"n/a",
description="""Base unit of measurement for working with the data. Annotations have no units, so the value is fixed to 'n/a'.""",
json_schema_extra={"linkml_meta": {"equals_string": "n/a", "ifabsent": "string(n/a)"}},
)
value: Optional[NDArray[Shape["* num_times"], str]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
class IntervalSeries(TimeSeries):
"""
Stores intervals of data. The timestamps field stores the beginning and end of intervals. The data field stores whether the interval just started (>0 value) or ended (<0 value). Different interval types can be represented in the same series by using multiple key values (eg, 1 for feature A, 2 for feature B, 3 for feature C, etc). The field data stores an 8-bit integer. This is largely an alias of a standard TimeSeries but that is identifiable as representing time intervals in a machine-readable way.
@ -326,10 +367,8 @@ class IntervalSeries(TimeSeries):
)
name: str = Field(...)
data: NDArray[Shape["* num_times"], int] = Field(
...,
description="""Use values >0 if interval started, <0 if interval ended.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}},
data: IntervalSeriesData = Field(
..., description="""Use values >0 if interval started, <0 if interval ended."""
)
description: Optional[str] = Field(
"no description",
@ -368,6 +407,39 @@ class IntervalSeries(TimeSeries):
)
class IntervalSeriesData(ConfiguredBaseModel):
"""
Use values >0 if interval started, <0 if interval ended.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.misc"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: float = Field(
-1.0,
description="""Smallest meaningful difference between values in data. Annotations have no units, so the value is fixed to -1.0.""",
le=-1,
ge=-1,
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["n/a"] = Field(
"n/a",
description="""Base unit of measurement for working with the data. Annotations have no units, so the value is fixed to 'n/a'.""",
json_schema_extra={"linkml_meta": {"equals_string": "n/a", "ifabsent": "string(n/a)"}},
)
value: Optional[NDArray[Shape["* num_times"], int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
class DecompositionSeries(TimeSeries):
"""
Spectral analysis of a time series, e.g. of an LFP or a speech signal.
@ -443,24 +515,36 @@ class DecompositionSeriesData(ConfiguredBaseModel):
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: str = Field(
"no unit",
description="""Base unit of measurement for working with the data. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
json_schema_extra={"linkml_meta": {"ifabsent": "string(no unit)"}},
)
value: Optional[NDArray[Shape["* num_times, * num_channels, * num_bands"], float]] = Field(
None,
json_schema_extra={
"linkml_meta": {
"array": {
"dimensions": [
{"alias": "num_times"},
{"alias": "num_channels"},
{"alias": "num_bands"},
]
value: Optional[NDArray[Shape["* num_times, * num_channels, * num_bands"], float | int]] = (
Field(
None,
json_schema_extra={
"linkml_meta": {
"array": {
"dimensions": [
{"alias": "num_times"},
{"alias": "num_channels"},
{"alias": "num_bands"},
]
}
}
}
},
},
)
)
@ -518,9 +602,6 @@ class DecompositionSeriesBands(DynamicTable):
description="""Array of unique identifiers for the rows of this dynamic table.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_rows"}]}}},
)
vector_index: Optional[List[VectorIndex]] = Field(
None, description="""Indices for the vector columns of this dynamic table."""
)
class Units(DynamicTable):
@ -533,9 +614,18 @@ class Units(DynamicTable):
)
name: str = Field("Units", json_schema_extra={"linkml_meta": {"ifabsent": "string(Units)"}})
spike_times_index: Optional[Named[VectorIndex]] = Field(
electrode_group: Optional[VectorData[NDArray[Any, ElectrodeGroup]]] = Field(
None,
description="""Index into the spike_times dataset.""",
description="""Electrode group that each spike unit came from.""",
json_schema_extra={
"linkml_meta": {
"array": {"maximum_number_dimensions": False, "minimum_number_dimensions": 1}
}
},
)
electrodes: Optional[Named[DynamicTableRegion]] = Field(
None,
description="""Electrode that each spike unit came from, specified using a DynamicTableRegion.""",
json_schema_extra={
"linkml_meta": {
"annotations": {
@ -545,12 +635,9 @@ class Units(DynamicTable):
}
},
)
spike_times: Optional[UnitsSpikeTimes] = Field(
None, description="""Spike times for each unit."""
)
obs_intervals_index: Optional[Named[VectorIndex]] = Field(
electrodes_index: Optional[Named[VectorIndex]] = Field(
None,
description="""Index into the obs_intervals dataset.""",
description="""Index into electrodes.""",
json_schema_extra={
"linkml_meta": {
"annotations": {
@ -576,9 +663,9 @@ class Units(DynamicTable):
},
)
)
electrodes_index: Optional[Named[VectorIndex]] = Field(
obs_intervals_index: Optional[Named[VectorIndex]] = Field(
None,
description="""Index into electrodes.""",
description="""Index into the obs_intervals dataset.""",
json_schema_extra={
"linkml_meta": {
"annotations": {
@ -588,9 +675,12 @@ class Units(DynamicTable):
}
},
)
electrodes: Optional[Named[DynamicTableRegion]] = Field(
spike_times: Optional[UnitsSpikeTimes] = Field(
None, description="""Spike times for each unit."""
)
spike_times_index: Optional[Named[VectorIndex]] = Field(
None,
description="""Electrode that each spike unit came from, specified using a DynamicTableRegion.""",
description="""Index into the spike_times dataset.""",
json_schema_extra={
"linkml_meta": {
"annotations": {
@ -600,25 +690,12 @@ class Units(DynamicTable):
}
},
)
electrode_group: Optional[List[ElectrodeGroup]] = Field(
None, description="""Electrode group that each spike unit came from."""
waveform_mean: Optional[UnitsWaveformMean] = Field(
None, description="""Spike waveform mean for each spike unit."""
)
waveform_sd: Optional[UnitsWaveformSd] = Field(
None, description="""Spike waveform standard deviation for each spike unit."""
)
waveform_mean: Optional[
VectorData[
Union[
NDArray[Shape["* num_units, * num_samples"], float],
NDArray[Shape["* num_units, * num_samples, * num_electrodes"], float],
]
]
] = Field(None, description="""Spike waveform mean for each spike unit.""")
waveform_sd: Optional[
VectorData[
Union[
NDArray[Shape["* num_units, * num_samples"], float],
NDArray[Shape["* num_units, * num_samples, * num_electrodes"], float],
]
]
] = Field(None, description="""Spike waveform standard deviation for each spike unit.""")
colnames: List[str] = Field(
...,
description="""The names of the columns in this table. This should be used to specify an order to the columns.""",
@ -629,9 +706,6 @@ class Units(DynamicTable):
description="""Array of unique identifiers for the rows of this dynamic table.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_rows"}]}}},
)
vector_index: Optional[List[VectorIndex]] = Field(
None, description="""Indices for the vector columns of this dynamic table."""
)
class UnitsSpikeTimes(VectorData):
@ -662,14 +736,78 @@ class UnitsSpikeTimes(VectorData):
] = Field(None)
class UnitsWaveformMean(VectorData):
"""
Spike waveform mean for each spike unit.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.misc"})
name: Literal["waveform_mean"] = Field(
"waveform_mean",
json_schema_extra={
"linkml_meta": {"equals_string": "waveform_mean", "ifabsent": "string(waveform_mean)"}
},
)
sampling_rate: Optional[float] = Field(None, description="""Sampling rate, in hertz.""")
unit: Optional[Literal["volts"]] = Field(
"volts",
description="""Unit of measurement. This value is fixed to 'volts'.""",
json_schema_extra={"linkml_meta": {"equals_string": "volts", "ifabsent": "string(volts)"}},
)
description: str = Field(..., description="""Description of what these vectors represent.""")
value: Optional[
Union[
NDArray[Shape["* dim0"], Any],
NDArray[Shape["* dim0, * dim1"], Any],
NDArray[Shape["* dim0, * dim1, * dim2"], Any],
NDArray[Shape["* dim0, * dim1, * dim2, * dim3"], Any],
]
] = Field(None)
class UnitsWaveformSd(VectorData):
"""
Spike waveform standard deviation for each spike unit.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.misc"})
name: Literal["waveform_sd"] = Field(
"waveform_sd",
json_schema_extra={
"linkml_meta": {"equals_string": "waveform_sd", "ifabsent": "string(waveform_sd)"}
},
)
sampling_rate: Optional[float] = Field(None, description="""Sampling rate, in hertz.""")
unit: Optional[Literal["volts"]] = Field(
"volts",
description="""Unit of measurement. This value is fixed to 'volts'.""",
json_schema_extra={"linkml_meta": {"equals_string": "volts", "ifabsent": "string(volts)"}},
)
description: str = Field(..., description="""Description of what these vectors represent.""")
value: Optional[
Union[
NDArray[Shape["* dim0"], Any],
NDArray[Shape["* dim0, * dim1"], Any],
NDArray[Shape["* dim0, * dim1, * dim2"], Any],
NDArray[Shape["* dim0, * dim1, * dim2, * dim3"], Any],
]
] = Field(None)
# Model rebuild
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
AbstractFeatureSeries.model_rebuild()
AbstractFeatureSeriesData.model_rebuild()
AnnotationSeries.model_rebuild()
AnnotationSeriesData.model_rebuild()
IntervalSeries.model_rebuild()
IntervalSeriesData.model_rebuild()
DecompositionSeries.model_rebuild()
DecompositionSeriesData.model_rebuild()
DecompositionSeriesBands.model_rebuild()
Units.model_rebuild()
UnitsSpikeTimes.model_rebuild()
UnitsWaveformMean.model_rebuild()
UnitsWaveformSd.model_rebuild()

38
nwb_models/src/nwb_models/models/pydantic/core/v2_2_5/core_nwb_ogen.py generated
View file

@ -158,10 +158,8 @@ class OptogeneticSeries(TimeSeries):
)
name: str = Field(...)
data: NDArray[Shape["* num_times"], float] = Field(
...,
description="""Applied power for optogenetic stimulus, in watts.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}},
data: OptogeneticSeriesData = Field(
..., description="""Applied power for optogenetic stimulus, in watts."""
)
site: Union[OptogeneticStimulusSite, str] = Field(
...,
@ -209,6 +207,37 @@ class OptogeneticSeries(TimeSeries):
)
class OptogeneticSeriesData(ConfiguredBaseModel):
"""
Applied power for optogenetic stimulus, in watts.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.ogen"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: Literal["watts"] = Field(
"watts",
description="""Unit of measurement for data, which is fixed to 'watts'.""",
json_schema_extra={"linkml_meta": {"equals_string": "watts", "ifabsent": "string(watts)"}},
)
value: Optional[NDArray[Shape["* num_times"], float | int]] = Field(
None, json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_times"}]}}}
)
class OptogeneticStimulusSite(NWBContainer):
"""
A site of optogenetic stimulation.
@ -239,4 +268,5 @@ class OptogeneticStimulusSite(NWBContainer):
# Model rebuild
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
OptogeneticSeries.model_rebuild()
OptogeneticSeriesData.model_rebuild()
OptogeneticStimulusSite.model_rebuild()

129
nwb_models/src/nwb_models/models/pydantic/core/v2_2_5/core_nwb_ophys.py generated
View file

@ -28,7 +28,7 @@ from ...core.v2_2_5.core_nwb_base import (
TimeSeriesSync,
)
from ...core.v2_2_5.core_nwb_device import Device
from ...core.v2_2_5.core_nwb_image import ImageSeries, ImageSeriesExternalFile
from ...core.v2_2_5.core_nwb_image import ImageSeries, ImageSeriesData, ImageSeriesExternalFile
from ...hdmf_common.v1_1_3.hdmf_common_table import (
DynamicTable,
DynamicTableRegion,
@ -217,12 +217,9 @@ class TwoPhotonSeries(ImageSeries):
}
},
)
data: Optional[
Union[
NDArray[Shape["* frame, * x, * y"], float],
NDArray[Shape["* frame, * x, * y, * z"], float],
]
] = Field(None, description="""Binary data representing images across frames.""")
data: Optional[ImageSeriesData] = Field(
None, description="""Binary data representing images across frames."""
)
dimension: Optional[NDArray[Shape["* rank"], int]] = Field(
None,
description="""Number of pixels on x, y, (and z) axes.""",
@ -233,8 +230,9 @@ class TwoPhotonSeries(ImageSeries):
description="""Paths to one or more external file(s). The field is only present if format='external'. This is only relevant if the image series is stored in the file system as one or more image file(s). This field should NOT be used if the image is stored in another NWB file and that file is linked to this file.""",
)
format: Optional[str] = Field(
None,
"raw",
description="""Format of image. If this is 'external', then the attribute 'external_file' contains the path information to the image files. If this is 'raw', then the raw (single-channel) binary data is stored in the 'data' dataset. If this attribute is not present, then the default format='raw' case is assumed.""",
json_schema_extra={"linkml_meta": {"ifabsent": "string(raw)"}},
)
description: Optional[str] = Field(
"no description",
@ -283,9 +281,7 @@ class RoiResponseSeries(TimeSeries):
)
name: str = Field(...)
data: Union[
NDArray[Shape["* num_times"], float], NDArray[Shape["* num_times, * num_rois"], float]
] = Field(..., description="""Signals from ROIs.""")
data: RoiResponseSeriesData = Field(..., description="""Signals from ROIs.""")
rois: Named[DynamicTableRegion] = Field(
...,
description="""DynamicTableRegion referencing into an ROITable containing information on the ROIs stored in this timeseries.""",
@ -335,6 +331,39 @@ class RoiResponseSeries(TimeSeries):
)
class RoiResponseSeriesData(ConfiguredBaseModel):
"""
Signals from ROIs.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.ophys"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
)
conversion: Optional[float] = Field(
1.0,
description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(1.0)"}},
)
resolution: Optional[float] = Field(
-1.0,
description="""Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.""",
json_schema_extra={"linkml_meta": {"ifabsent": "float(-1.0)"}},
)
unit: str = Field(
...,
description="""Base unit of measurement for working with the data. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.""",
)
value: Optional[
Union[
NDArray[Shape["* num_times"], float | int],
NDArray[Shape["* num_times, * num_rois"], float | int],
]
] = Field(None)
class DfOverF(NWBDataInterface):
"""
dF/F information about a region of interest (ROI). Storage hierarchy of dF/F should be the same as for segmentation (i.e., same names for ROIs and for image planes).
@ -344,10 +373,10 @@ class DfOverF(NWBDataInterface):
{"from_schema": "core.nwb.ophys", "tree_root": True}
)
value: Optional[List[RoiResponseSeries]] = Field(
name: str = Field("DfOverF", json_schema_extra={"linkml_meta": {"ifabsent": "string(DfOverF)"}})
value: Optional[Dict[str, RoiResponseSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "RoiResponseSeries"}]}}
)
name: str = Field(...)
class Fluorescence(NWBDataInterface):
@ -359,10 +388,12 @@ class Fluorescence(NWBDataInterface):
{"from_schema": "core.nwb.ophys", "tree_root": True}
)
value: Optional[List[RoiResponseSeries]] = Field(
name: str = Field(
"Fluorescence", json_schema_extra={"linkml_meta": {"ifabsent": "string(Fluorescence)"}}
)
value: Optional[Dict[str, RoiResponseSeries]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "RoiResponseSeries"}]}}
)
name: str = Field(...)
class ImageSegmentation(NWBDataInterface):
@ -374,10 +405,13 @@ class ImageSegmentation(NWBDataInterface):
{"from_schema": "core.nwb.ophys", "tree_root": True}
)
value: Optional[List[PlaneSegmentation]] = Field(
name: str = Field(
"ImageSegmentation",
json_schema_extra={"linkml_meta": {"ifabsent": "string(ImageSegmentation)"}},
)
value: Optional[Dict[str, PlaneSegmentation]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "PlaneSegmentation"}]}}
)
name: str = Field(...)
class PlaneSegmentation(DynamicTable):
@ -390,10 +424,21 @@ class PlaneSegmentation(DynamicTable):
)
name: str = Field(...)
image_mask: Optional[PlaneSegmentationImageMask] = Field(
image_mask: Optional[
VectorData[
Union[
NDArray[Shape["* num_roi, * num_x, * num_y"], Any],
NDArray[Shape["* num_roi, * num_x, * num_y, * num_z"], Any],
]
]
] = Field(
None,
description="""ROI masks for each ROI. Each image mask is the size of the original imaging plane (or volume) and members of the ROI are finite non-zero.""",
)
pixel_mask: Optional[PlaneSegmentationPixelMask] = Field(
None,
description="""Pixel masks for each ROI: a list of indices and weights for the ROI. Pixel masks are concatenated and parsing of this dataset is maintained by the PlaneSegmentation""",
)
pixel_mask_index: Optional[Named[VectorIndex]] = Field(
None,
description="""Index into pixel_mask.""",
@ -406,9 +451,9 @@ class PlaneSegmentation(DynamicTable):
}
},
)
pixel_mask: Optional[PlaneSegmentationPixelMask] = Field(
voxel_mask: Optional[PlaneSegmentationVoxelMask] = Field(
None,
description="""Pixel masks for each ROI: a list of indices and weights for the ROI. Pixel masks are concatenated and parsing of this dataset is maintained by the PlaneSegmentation""",
description="""Voxel masks for each ROI: a list of indices and weights for the ROI. Voxel masks are concatenated and parsing of this dataset is maintained by the PlaneSegmentation""",
)
voxel_mask_index: Optional[Named[VectorIndex]] = Field(
None,
@ -422,11 +467,7 @@ class PlaneSegmentation(DynamicTable):
}
},
)
voxel_mask: Optional[PlaneSegmentationVoxelMask] = Field(
None,
description="""Voxel masks for each ROI: a list of indices and weights for the ROI. Voxel masks are concatenated and parsing of this dataset is maintained by the PlaneSegmentation""",
)
reference_images: Optional[List[ImageSeries]] = Field(
reference_images: Optional[Dict[str, ImageSeries]] = Field(
None,
description="""Image stacks that the segmentation masks apply to.""",
json_schema_extra={"linkml_meta": {"any_of": [{"range": "ImageSeries"}]}},
@ -450,33 +491,6 @@ class PlaneSegmentation(DynamicTable):
description="""Array of unique identifiers for the rows of this dynamic table.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_rows"}]}}},
)
vector_index: Optional[List[VectorIndex]] = Field(
None, description="""Indices for the vector columns of this dynamic table."""
)
class PlaneSegmentationImageMask(VectorData):
"""
ROI masks for each ROI. Each image mask is the size of the original imaging plane (or volume) and members of the ROI are finite non-zero.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "core.nwb.ophys"})
name: Literal["image_mask"] = Field(
"image_mask",
json_schema_extra={
"linkml_meta": {"equals_string": "image_mask", "ifabsent": "string(image_mask)"}
},
)
description: str = Field(..., description="""Description of what these vectors represent.""")
value: Optional[
Union[
NDArray[Shape["* dim0"], Any],
NDArray[Shape["* dim0, * dim1"], Any],
NDArray[Shape["* dim0, * dim1, * dim2"], Any],
NDArray[Shape["* dim0, * dim1, * dim2, * dim3"], Any],
]
] = Field(None)
class PlaneSegmentationPixelMask(VectorData):
@ -599,7 +613,7 @@ class ImagingPlane(NWBContainer):
None,
description="""Describes reference frame of origin_coords and grid_spacing. For example, this can be a text description of the anatomical location and orientation of the grid defined by origin_coords and grid_spacing or the vectors needed to transform or rotate the grid to a common anatomical axis (e.g., AP/DV/ML). This field is necessary to interpret origin_coords and grid_spacing. If origin_coords and grid_spacing are not present, then this field is not required. For example, if the microscope takes 10 x 10 x 2 images, where the first value of the data matrix (index (0, 0, 0)) corresponds to (-1.2, -0.6, -2) mm relative to bregma, the spacing between pixels is 0.2 mm in x, 0.2 mm in y and 0.5 mm in z, and larger numbers in x means more anterior, larger numbers in y means more rightward, and larger numbers in z means more ventral, then enter the following -- origin_coords = (-1.2, -0.6, -2) grid_spacing = (0.2, 0.2, 0.5) reference_frame = \"Origin coordinates are relative to bregma. First dimension corresponds to anterior-posterior axis (larger index = more anterior). Second dimension corresponds to medial-lateral axis (larger index = more rightward). Third dimension corresponds to dorsal-ventral axis (larger index = more ventral).\"""",
)
optical_channel: List[OpticalChannel] = Field(
optical_channel: Dict[str, OpticalChannel] = Field(
..., description="""An optical channel used to record from an imaging plane."""
)
device: Union[Device, str] = Field(
@ -713,10 +727,13 @@ class MotionCorrection(NWBDataInterface):
{"from_schema": "core.nwb.ophys", "tree_root": True}
)
value: Optional[List[CorrectedImageStack]] = Field(
name: str = Field(
"MotionCorrection",
json_schema_extra={"linkml_meta": {"ifabsent": "string(MotionCorrection)"}},
)
value: Optional[Dict[str, CorrectedImageStack]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "CorrectedImageStack"}]}}
)
name: str = Field(...)
class CorrectedImageStack(NWBDataInterface):
@ -751,11 +768,11 @@ class CorrectedImageStack(NWBDataInterface):
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
TwoPhotonSeries.model_rebuild()
RoiResponseSeries.model_rebuild()
RoiResponseSeriesData.model_rebuild()
DfOverF.model_rebuild()
Fluorescence.model_rebuild()
ImageSegmentation.model_rebuild()
PlaneSegmentation.model_rebuild()
PlaneSegmentationImageMask.model_rebuild()
PlaneSegmentationPixelMask.model_rebuild()
PlaneSegmentationVoxelMask.model_rebuild()
ImagingPlane.model_rebuild()

17
nwb_models/src/nwb_models/models/pydantic/core/v2_2_5/namespace.py generated
View file

@ -38,6 +38,7 @@ from ...core.v2_2_5.core_nwb_ecephys import (
ClusterWaveforms,
Clustering,
ElectricalSeries,
ElectricalSeriesData,
ElectrodeGroup,
ElectrodeGroupPosition,
EventDetection,
@ -46,6 +47,7 @@ from ...core.v2_2_5.core_nwb_ecephys import (
FilteredEphys,
LFP,
SpikeEventSeries,
SpikeEventSeriesData,
)
from ...core.v2_2_5.core_nwb_epoch import TimeIntervals, TimeIntervalsTimeseries
from ...core.v2_2_5.core_nwb_file import (
@ -87,9 +89,12 @@ from ...core.v2_2_5.core_nwb_image import (
GrayscaleImage,
ImageMaskSeries,
ImageSeries,
ImageSeriesData,
ImageSeriesExternalFile,
IndexSeries,
IndexSeriesData,
OpticalSeries,
OpticalSeriesData,
RGBAImage,
RGBImage,
)
@ -97,14 +102,22 @@ from ...core.v2_2_5.core_nwb_misc import (
AbstractFeatureSeries,
AbstractFeatureSeriesData,
AnnotationSeries,
AnnotationSeriesData,
DecompositionSeries,
DecompositionSeriesBands,
DecompositionSeriesData,
IntervalSeries,
IntervalSeriesData,
Units,
UnitsSpikeTimes,
UnitsWaveformMean,
UnitsWaveformSd,
)
from ...core.v2_2_5.core_nwb_ogen import (
OptogeneticSeries,
OptogeneticSeriesData,
OptogeneticStimulusSite,
)
from ...core.v2_2_5.core_nwb_ogen import OptogeneticSeries, OptogeneticStimulusSite
from ...core.v2_2_5.core_nwb_ophys import (
CorrectedImageStack,
DfOverF,
@ -117,10 +130,10 @@ from ...core.v2_2_5.core_nwb_ophys import (
MotionCorrection,
OpticalChannel,
PlaneSegmentation,
PlaneSegmentationImageMask,
PlaneSegmentationPixelMask,
PlaneSegmentationVoxelMask,
RoiResponseSeries,
RoiResponseSeriesData,
TwoPhotonSeries,
)
from ...core.v2_2_5.core_nwb_retinotopy import (

9
nwb_models/src/nwb_models/models/pydantic/hdmf_common/v1_1_0/hdmf_common_table.py generated
View file

@ -989,12 +989,12 @@ class DynamicTableRegion(DynamicTableRegionMixin, VectorData):
)
name: str = Field(...)
table: DynamicTable = Field(
..., description="""Reference to the DynamicTable object that this region applies to."""
)
description: str = Field(
..., description="""Description of what this table region points to."""
)
table: DynamicTable = Field(
..., description="""Reference to the DynamicTable object that this region applies to."""
)
class Container(ConfiguredBaseModel):
@ -1029,9 +1029,6 @@ class DynamicTable(DynamicTableMixin):
description="""Array of unique identifiers for the rows of this dynamic table.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_rows"}]}}},
)
vector_index: Optional[List[VectorIndex]] = Field(
None, description="""Indices for the vector columns of this dynamic table."""
)
# Model rebuild

9
nwb_models/src/nwb_models/models/pydantic/hdmf_common/v1_1_2/hdmf_common_table.py generated
View file

@ -989,12 +989,12 @@ class DynamicTableRegion(DynamicTableRegionMixin, VectorData):
)
name: str = Field(...)
table: DynamicTable = Field(
..., description="""Reference to the DynamicTable object that this region applies to."""
)
description: str = Field(
..., description="""Description of what this table region points to."""
)
table: DynamicTable = Field(
..., description="""Reference to the DynamicTable object that this region applies to."""
)
class Container(ConfiguredBaseModel):
@ -1029,9 +1029,6 @@ class DynamicTable(DynamicTableMixin):
description="""Array of unique identifiers for the rows of this dynamic table.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_rows"}]}}},
)
vector_index: Optional[List[VectorIndex]] = Field(
None, description="""Indices for the vector columns of this dynamic table."""
)
# Model rebuild

9
nwb_models/src/nwb_models/models/pydantic/hdmf_common/v1_1_3/hdmf_common_table.py generated
View file

@ -993,12 +993,12 @@ class DynamicTableRegion(DynamicTableRegionMixin, VectorData):
)
name: str = Field(...)
table: DynamicTable = Field(
..., description="""Reference to the DynamicTable object that this region applies to."""
)
description: str = Field(
..., description="""Description of what this table region points to."""
)
table: DynamicTable = Field(
..., description="""Reference to the DynamicTable object that this region applies to."""
)
value: Optional[
Union[
NDArray[Shape["* dim0"], Any],
@ -1041,9 +1041,6 @@ class DynamicTable(DynamicTableMixin):
description="""Array of unique identifiers for the rows of this dynamic table.""",
json_schema_extra={"linkml_meta": {"array": {"dimensions": [{"alias": "num_rows"}]}}},
)
vector_index: Optional[List[VectorIndex]] = Field(
None, description="""Indices for the vector columns of this dynamic table."""
)
# Model rebuild

6
nwb_models/src/nwb_models/models/pydantic/hdmf_common/v1_2_0/hdmf_common_table.py generated
View file

@ -974,12 +974,12 @@ class DynamicTableRegion(DynamicTableRegionMixin, VectorData):
)
name: str = Field(...)
table: DynamicTable = Field(
..., description="""Reference to the DynamicTable object that this region applies to."""
)
description: str = Field(
..., description="""Description of what this table region points to."""
)
table: DynamicTable = Field(
..., description="""Reference to the DynamicTable object that this region applies to."""
)
value: Optional[
Union[
NDArray[Shape["* dim0"], Any],

4
nwb_models/src/nwb_models/models/pydantic/hdmf_common/v1_2_1/hdmf_common_base.py generated
View file

@ -171,10 +171,10 @@ class SimpleMultiContainer(Container):
{"from_schema": "hdmf-common.base", "tree_root": True}
)
value: Optional[List[Container]] = Field(
name: str = Field(...)
value: Optional[Dict[str, Container]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "Container"}]}}
)
name: str = Field(...)
# Model rebuild

6
nwb_models/src/nwb_models/models/pydantic/hdmf_common/v1_2_1/hdmf_common_table.py generated
View file

@ -974,12 +974,12 @@ class DynamicTableRegion(DynamicTableRegionMixin, VectorData):
)
name: str = Field(...)
table: DynamicTable = Field(
..., description="""Reference to the DynamicTable object that this region applies to."""
)
description: str = Field(
..., description="""Description of what this table region points to."""
)
table: DynamicTable = Field(
..., description="""Reference to the DynamicTable object that this region applies to."""
)
value: Optional[
Union[
NDArray[Shape["* dim0"], Any],

4
nwb_models/src/nwb_models/models/pydantic/hdmf_common/v1_3_0/hdmf_common_base.py generated
View file

@ -171,10 +171,10 @@ class SimpleMultiContainer(Container):
{"from_schema": "hdmf-common.base", "tree_root": True}
)
value: Optional[List[Container]] = Field(
name: str = Field(...)
value: Optional[Dict[str, Container]] = Field(
None, json_schema_extra={"linkml_meta": {"any_of": [{"range": "Container"}]}}
)
name: str = Field(...)
# Model rebuild

20
nwb_models/src/nwb_models/models/pydantic/hdmf_common/v1_3_0/hdmf_common_sparse.py generated
View file

@ -169,23 +169,15 @@ class CSRMatrix(Container):
"linkml_meta": {"array": {"dimensions": [{"alias": "number_of_rows_in_the_matrix_1"}]}}
},
)
data: CSRMatrixData = Field(..., description="""The non-zero values in the matrix.""")
class CSRMatrixData(ConfiguredBaseModel):
"""
The non-zero values in the matrix.
"""
linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta({"from_schema": "hdmf-common.sparse"})
name: Literal["data"] = Field(
"data",
json_schema_extra={"linkml_meta": {"equals_string": "data", "ifabsent": "string(data)"}},
data: NDArray[Shape["* number_of_non_zero_values"], Any] = Field(
...,
description="""The non-zero values in the matrix.""",
json_schema_extra={
"linkml_meta": {"array": {"dimensions": [{"alias": "number_of_non_zero_values"}]}}
},
)
# Model rebuild
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
CSRMatrix.model_rebuild()
CSRMatrixData.model_rebuild()

6
nwb_models/src/nwb_models/models/pydantic/hdmf_common/v1_3_0/hdmf_common_table.py generated
View file

@ -974,12 +974,12 @@ class DynamicTableRegion(DynamicTableRegionMixin, VectorData):
)
name: str = Field(...)
table: DynamicTable = Field(
..., description="""Reference to the DynamicTable object that this region applies to."""
)
description: str = Field(
..., description="""Description of what this table region points to."""
)
table: DynamicTable = Field(
..., description="""Reference to the DynamicTable object that this region applies to."""
)
value: Optional[
Union[
NDArray[Shape["* dim0"], Any],

2
nwb_models/src/nwb_models/models/pydantic/hdmf_common/v1_3_0/namespace.py generated
View file

@ -18,7 +18,7 @@ from ...hdmf_common.v1_3_0.hdmf_common_resources import (
ExternalResourcesObjects,
ExternalResourcesResources,
)
from ...hdmf_common.v1_3_0.hdmf_common_sparse import CSRMatrix, CSRMatrixData
from ...hdmf_common.v1_3_0.hdmf_common_sparse import CSRMatrix
from ...hdmf_common.v1_3_0.hdmf_common_table import (
DynamicTable,
DynamicTableRegion,

4
nwb_models/src/nwb_models/models/pydantic/hdmf_experimental/v0_1_0/hdmf_experimental_experimental.py generated
View file

@ -11,7 +11,7 @@ import numpy as np
from numpydantic import NDArray, Shape
from pydantic import BaseModel, ConfigDict, Field, RootModel, field_validator, model_validator
from ...hdmf_common.v1_4_0.hdmf_common_table import VectorData
from ...hdmf_common.v1_5_0.hdmf_common_table import VectorData
metamodel_version = "None"
@ -136,7 +136,7 @@ linkml_meta = LinkMLMeta(
},
"default_prefix": "hdmf-experimental.experimental/",
"id": "hdmf-experimental.experimental",
"imports": ["../../hdmf_common/v1_4_0/namespace", "hdmf-experimental.nwb.language"],
"imports": ["../../hdmf_common/v1_5_0/namespace", "hdmf-experimental.nwb.language"],
"name": "hdmf-experimental.experimental",
}
)

4
nwb_models/src/nwb_models/models/pydantic/hdmf_experimental/v0_1_0/hdmf_experimental_resources.py generated
View file

@ -11,7 +11,7 @@ import numpy as np
from numpydantic import NDArray, Shape
from pydantic import BaseModel, ConfigDict, Field, RootModel, field_validator, model_validator
from ...hdmf_common.v1_4_0.hdmf_common_base import Container, Data
from ...hdmf_common.v1_5_0.hdmf_common_base import Container, Data
metamodel_version = "None"
@ -136,7 +136,7 @@ linkml_meta = LinkMLMeta(
},
"default_prefix": "hdmf-experimental.resources/",
"id": "hdmf-experimental.resources",
"imports": ["../../hdmf_common/v1_4_0/namespace", "hdmf-experimental.nwb.language"],
"imports": ["../../hdmf_common/v1_5_0/namespace", "hdmf-experimental.nwb.language"],
"name": "hdmf-experimental.resources",
}
)

7
nwb_models/src/nwb_models/models/pydantic/hdmf_experimental/v0_1_0/namespace.py generated
View file

@ -10,9 +10,10 @@ from typing import Any, ClassVar, Dict, List, Literal, Optional, Union
import numpy as np
from pydantic import BaseModel, ConfigDict, Field, RootModel, field_validator, model_validator
from ...hdmf_common.v1_4_0.hdmf_common_base import Container, Data, SimpleMultiContainer
from ...hdmf_common.v1_4_0.hdmf_common_sparse import CSRMatrix
from ...hdmf_common.v1_4_0.hdmf_common_table import (
from ...hdmf_common.v1_5_0.hdmf_common_base import Container, Data, SimpleMultiContainer
from ...hdmf_common.v1_5_0.hdmf_common_sparse import CSRMatrix
from ...hdmf_common.v1_5_0.hdmf_common_table import (
AlignedDynamicTable,
DynamicTable,
DynamicTableRegion,
ElementIdentifiers,

43
nwb_models/src/nwb_models/schema/linkml/core/v2_2_0/core.nwb.base.yaml generated
View file

@ -20,6 +20,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
tree_root: true
@ -32,6 +33,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
resolution:
@ -73,6 +75,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
tree_root: true
@ -84,6 +87,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
tree_root: true
@ -94,6 +98,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
description:
@ -118,15 +123,14 @@ classes:
external file.
range: TimeSeries__data
required: true
multivalued: false
inlined: true
starting_time:
name: starting_time
description: Timestamp of the first sample in seconds. When timestamps are
uniformly spaced, the timestamp of the first sample can be specified and
all subsequent ones calculated from the sampling rate attribute.
range: TimeSeries__starting_time
required: false
multivalued: false
inlined: true
timestamps:
name: timestamps
description: Timestamps for samples stored in data, in seconds, relative to
@ -168,8 +172,8 @@ classes:
external to the NWB file, in files storing raw data. Once timestamp data
is calculated, the contents of 'sync' are mostly for archival purposes.
range: TimeSeries__sync
required: false
multivalued: false
inlined: true
inlined_as_list: true
tree_root: true
TimeSeries__data:
name: TimeSeries__data
@ -180,6 +184,7 @@ classes:
name:
name: name
ifabsent: string(data)
identifier: true
range: string
required: true
equals_string: data
@ -244,6 +249,7 @@ classes:
name:
name: name
ifabsent: string(starting_time)
identifier: true
range: string
required: true
equals_string: starting_time
@ -275,6 +281,7 @@ classes:
name:
name: name
ifabsent: string(sync)
identifier: true
range: string
required: true
equals_string: sync
@ -283,13 +290,24 @@ classes:
description: A collection of processed data.
is_a: NWBContainer
attributes:
- name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: NWBDataInterface
- range: DynamicTable
name:
name: name
identifier: true
range: string
required: true
description:
name: description
description: Description of this collection of processed data.
range: text
required: true
value:
name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: NWBDataInterface
- range: DynamicTable
tree_root: true
Images:
name: Images
@ -299,6 +317,7 @@ classes:
name:
name: name
ifabsent: string(Images)
identifier: true
range: string
required: true
description:

162
nwb_models/src/nwb_models/schema/linkml/core/v2_2_0/core.nwb.behavior.yaml generated
View file

@ -29,6 +29,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
data:
@ -37,13 +38,11 @@ classes:
reference frame.
range: SpatialSeries__data
required: true
multivalued: false
inlined: true
reference_frame:
name: reference_frame
description: Description defining what exactly 'straight-ahead' means.
range: text
required: false
multivalued: false
tree_root: true
SpatialSeries__data:
name: SpatialSeries__data
@ -53,9 +52,33 @@ classes:
name:
name: name
ifabsent: string(data)
identifier: true
range: string
required: true
equals_string: data
conversion:
name: conversion
description: Scalar to multiply each element in data to convert it to the
specified 'unit'. If the data are stored in acquisition system units or
other units that require a conversion to be interpretable, multiply the
data by 'conversion' to convert the data to the specified 'unit'. e.g. if
the data acquisition system stores values in this object as signed 16-bit
integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V
to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion'
multiplier to get from raw data acquisition values to recorded volts is
2.5/32768/8000 = 9.5367e-9.
ifabsent: float(1.0)
range: float32
required: false
resolution:
name: resolution
description: Smallest meaningful difference between values in data, stored
in the specified by unit, e.g., the change in value of the least significant
bit, or a larger number if signal noise is known to be present. If unknown,
use -1.0.
ifabsent: float(-1.0)
range: float32
required: false
unit:
name: unit
description: Base unit of measurement for working with the data. The default
@ -91,12 +114,19 @@ classes:
events. BehavioralTimeSeries is for continuous data.
is_a: NWBDataInterface
attributes:
- name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: IntervalSeries
name:
name: name
ifabsent: string(BehavioralEpochs)
identifier: true
range: string
required: true
value:
name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: IntervalSeries
tree_root: true
BehavioralEvents:
name: BehavioralEvents
@ -104,12 +134,19 @@ classes:
for more details.
is_a: NWBDataInterface
attributes:
- name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: TimeSeries
name:
name: name
ifabsent: string(BehavioralEvents)
identifier: true
range: string
required: true
value:
name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: TimeSeries
tree_root: true
BehavioralTimeSeries:
name: BehavioralTimeSeries
@ -117,36 +154,57 @@ classes:
of <a href="#BehavioralEpochs">BehavioralEpochs</a> for more details.
is_a: NWBDataInterface
attributes:
- name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: TimeSeries
name:
name: name
ifabsent: string(BehavioralTimeSeries)
identifier: true
range: string
required: true
value:
name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: TimeSeries
tree_root: true
PupilTracking:
name: PupilTracking
description: Eye-tracking data, representing pupil size.
is_a: NWBDataInterface
attributes:
- name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: TimeSeries
name:
name: name
ifabsent: string(PupilTracking)
identifier: true
range: string
required: true
value:
name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: TimeSeries
tree_root: true
EyeTracking:
name: EyeTracking
description: Eye-tracking data, representing direction of gaze.
is_a: NWBDataInterface
attributes:
- name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: SpatialSeries
name:
name: name
ifabsent: string(EyeTracking)
identifier: true
range: string
required: true
value:
name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: SpatialSeries
tree_root: true
CompassDirection:
name: CompassDirection
@ -157,22 +215,36 @@ classes:
be radians or degrees.
is_a: NWBDataInterface
attributes:
- name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: SpatialSeries
name:
name: name
ifabsent: string(CompassDirection)
identifier: true
range: string
required: true
value:
name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: SpatialSeries
tree_root: true
Position:
name: Position
description: Position data, whether along the x, x/y or x/y/z axis.
is_a: NWBDataInterface
attributes:
- name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: SpatialSeries
name:
name: name
ifabsent: string(Position)
identifier: true
range: string
required: true
value:
name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: SpatialSeries
tree_root: true

1
nwb_models/src/nwb_models/schema/linkml/core/v2_2_0/core.nwb.device.yaml generated
View file

@ -21,6 +21,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
description:

266
nwb_models/src/nwb_models/schema/linkml/core/v2_2_0/core.nwb.ecephys.yaml generated
View file

@ -25,41 +25,9 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
data:
name: data
description: Recorded voltage data.
range: numeric
required: true
multivalued: false
any_of:
- array:
dimensions:
- alias: num_times
- array:
dimensions:
- alias: num_times
- alias: num_channels
- array:
dimensions:
- alias: num_times
- alias: num_channels
- alias: num_samples
electrodes:
name: electrodes
annotations:
named:
tag: named
value: true
source_type:
tag: source_type
value: neurodata_type_inc
description: DynamicTableRegion pointer to the electrodes that this time series
was generated from.
range: DynamicTableRegion
required: true
multivalued: false
channel_conversion:
name: channel_conversion
description: Channel-specific conversion factor. Multiply the data in the
@ -77,7 +45,87 @@ classes:
range: float32
required: false
multivalued: false
data:
name: data
description: Recorded voltage data.
range: ElectricalSeries__data
required: true
inlined: true
electrodes:
name: electrodes
annotations:
named:
tag: named
value: true
source_type:
tag: source_type
value: neurodata_type_inc
description: DynamicTableRegion pointer to the electrodes that this time series
was generated from.
range: DynamicTableRegion
required: true
inlined: true
tree_root: true
ElectricalSeries__data:
name: ElectricalSeries__data
description: Recorded voltage data.
attributes:
name:
name: name
ifabsent: string(data)
identifier: true
range: string
required: true
equals_string: data
conversion:
name: conversion
description: Scalar to multiply each element in data to convert it to the
specified 'unit'. If the data are stored in acquisition system units or
other units that require a conversion to be interpretable, multiply the
data by 'conversion' to convert the data to the specified 'unit'. e.g. if
the data acquisition system stores values in this object as signed 16-bit
integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V
to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion'
multiplier to get from raw data acquisition values to recorded volts is
2.5/32768/8000 = 9.5367e-9.
ifabsent: float(1.0)
range: float32
required: false
resolution:
name: resolution
description: Smallest meaningful difference between values in data, stored
in the specified by unit, e.g., the change in value of the least significant
bit, or a larger number if signal noise is known to be present. If unknown,
use -1.0.
ifabsent: float(-1.0)
range: float32
required: false
unit:
name: unit
description: Base unit of measurement for working with the data. This value
is fixed to 'volts'. Actual stored values are not necessarily stored in
these units. To access the data in these units, multiply 'data' by 'conversion'
and 'channel_conversion' (if present).
ifabsent: string(volts)
range: text
required: true
equals_string: volts
value:
name: value
range: numeric
any_of:
- array:
dimensions:
- alias: num_times
- array:
dimensions:
- alias: num_times
- alias: num_channels
- array:
dimensions:
- alias: num_times
- alias: num_channels
- alias: num_samples
SpikeEventSeries:
name: SpikeEventSeries
description: 'Stores snapshots/snippets of recorded spike events (i.e., threshold
@ -92,24 +140,15 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
data:
name: data
description: Spike waveforms.
range: numeric
range: SpikeEventSeries__data
required: true
multivalued: false
any_of:
- array:
dimensions:
- alias: num_events
- alias: num_samples
- array:
dimensions:
- alias: num_events
- alias: num_channels
- alias: num_samples
inlined: true
timestamps:
name: timestamps
description: Timestamps for samples stored in data, in seconds, relative to
@ -123,6 +162,60 @@ classes:
required: true
multivalued: false
tree_root: true
SpikeEventSeries__data:
name: SpikeEventSeries__data
description: Spike waveforms.
attributes:
name:
name: name
ifabsent: string(data)
identifier: true
range: string
required: true
equals_string: data
conversion:
name: conversion
description: Scalar to multiply each element in data to convert it to the
specified 'unit'. If the data are stored in acquisition system units or
other units that require a conversion to be interpretable, multiply the
data by 'conversion' to convert the data to the specified 'unit'. e.g. if
the data acquisition system stores values in this object as signed 16-bit
integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V
to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion'
multiplier to get from raw data acquisition values to recorded volts is
2.5/32768/8000 = 9.5367e-9.
ifabsent: float(1.0)
range: float32
required: false
resolution:
name: resolution
description: Smallest meaningful difference between values in data, stored
in the specified by unit, e.g., the change in value of the least significant
bit, or a larger number if signal noise is known to be present. If unknown,
use -1.0.
ifabsent: float(-1.0)
range: float32
required: false
unit:
name: unit
description: Unit of measurement for waveforms, which is fixed to 'volts'.
ifabsent: string(volts)
range: text
required: true
equals_string: volts
value:
name: value
range: numeric
any_of:
- array:
dimensions:
- alias: num_events
- alias: num_samples
- array:
dimensions:
- alias: num_events
- alias: num_channels
- alias: num_samples
FeatureExtraction:
name: FeatureExtraction
description: Features, such as PC1 and PC2, that are extracted from signals stored
@ -132,6 +225,7 @@ classes:
name:
name: name
ifabsent: string(FeatureExtraction)
identifier: true
range: string
required: true
description:
@ -177,7 +271,7 @@ classes:
was generated from.
range: DynamicTableRegion
required: true
multivalued: false
inlined: true
tree_root: true
EventDetection:
name: EventDetection
@ -187,6 +281,7 @@ classes:
name:
name: name
ifabsent: string(EventDetection)
identifier: true
range: string
required: true
detection_method:
@ -195,7 +290,6 @@ classes:
or dV/dT threshold, as well as relevant values.
range: text
required: true
multivalued: false
source_idx:
name: source_idx
description: Indices (zero-based) into source ElectricalSeries::data array
@ -224,7 +318,7 @@ classes:
tag: source_type
value: link
required: true
multivalued: false
inlined: true
any_of:
- range: ElectricalSeries
- range: string
@ -236,12 +330,19 @@ classes:
during experiment acquisition.
is_a: NWBDataInterface
attributes:
- name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: SpikeEventSeries
name:
name: name
ifabsent: string(EventWaveform)
identifier: true
range: string
required: true
value:
name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: SpikeEventSeries
tree_root: true
FilteredEphys:
name: FilteredEphys
@ -258,12 +359,19 @@ classes:
the ElectricalSeries.
is_a: NWBDataInterface
attributes:
- name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: ElectricalSeries
name:
name: name
ifabsent: string(FilteredEphys)
identifier: true
range: string
required: true
value:
name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: ElectricalSeries
tree_root: true
LFP:
name: LFP
@ -272,12 +380,19 @@ classes:
properties should be noted in the ElectricalSeries description or comments field.
is_a: NWBDataInterface
attributes:
- name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: ElectricalSeries
name:
name: name
ifabsent: string(LFP)
identifier: true
range: string
required: true
value:
name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: ElectricalSeries
tree_root: true
ElectrodeGroup:
name: ElectrodeGroup
@ -286,6 +401,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
description:
@ -304,8 +420,7 @@ classes:
name: position
description: stereotaxic or common framework coordinates
range: ElectrodeGroup__position
required: false
multivalued: false
inlined: true
device:
name: device
annotations:
@ -313,7 +428,7 @@ classes:
tag: source_type
value: link
required: true
multivalued: false
inlined: true
any_of:
- range: Device
- range: string
@ -325,6 +440,7 @@ classes:
name:
name: name
ifabsent: string(position)
identifier: true
range: string
required: true
equals_string: position
@ -334,24 +450,18 @@ classes:
array:
exact_number_dimensions: 1
range: float32
required: false
multivalued: false
y:
name: y
description: y coordinate
array:
exact_number_dimensions: 1
range: float32
required: false
multivalued: false
z:
name: z
description: z coordinate
array:
exact_number_dimensions: 1
range: float32
required: false
multivalued: false
ClusterWaveforms:
name: ClusterWaveforms
description: DEPRECATED The mean waveform shape, including standard deviation,
@ -365,6 +475,7 @@ classes:
name:
name: name
ifabsent: string(ClusterWaveforms)
identifier: true
range: string
required: true
waveform_filtering:
@ -372,7 +483,6 @@ classes:
description: Filtering applied to data before generating mean/sd
range: text
required: true
multivalued: false
waveform_mean:
name: waveform_mean
description: The mean waveform for each cluster, using the same indices for
@ -404,7 +514,7 @@ classes:
tag: source_type
value: link
required: true
multivalued: false
inlined: true
any_of:
- range: Clustering
- range: string
@ -418,6 +528,7 @@ classes:
name:
name: name
ifabsent: string(Clustering)
identifier: true
range: string
required: true
description:
@ -426,7 +537,6 @@ classes:
clusters curated using Klusters, etc)
range: text
required: true
multivalued: false
num:
name: num
description: Cluster number of each event

18
nwb_models/src/nwb_models/schema/linkml/core/v2_2_0/core.nwb.epoch.yaml generated
View file

@ -22,6 +22,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
start_time:
@ -62,14 +63,12 @@ classes:
value: neurodata_type_inc
description: Index for tags.
range: VectorIndex
required: false
multivalued: false
inlined: true
timeseries:
name: timeseries
description: An index into a TimeSeries object.
range: TimeIntervals__timeseries
required: false
multivalued: false
inlined: true
timeseries_index:
name: timeseries_index
annotations:
@ -81,8 +80,7 @@ classes:
value: neurodata_type_inc
description: Index for timeseries.
range: VectorIndex
required: false
multivalued: false
inlined: true
tree_root: true
TimeIntervals__timeseries:
name: TimeIntervals__timeseries
@ -92,6 +90,7 @@ classes:
name:
name: name
ifabsent: string(timeseries)
identifier: true
range: string
required: true
equals_string: timeseries
@ -103,8 +102,6 @@ classes:
array:
exact_number_dimensions: 1
range: int32
required: false
multivalued: false
count:
name: count
description: Number of data samples available in this time series, during
@ -112,13 +109,10 @@ classes:
array:
exact_number_dimensions: 1
range: int32
required: false
multivalued: false
timeseries:
name: timeseries
description: the TimeSeries that this index applies to.
array:
exact_number_dimensions: 1
range: TimeSeries
required: false
multivalued: false
inlined: true

150
nwb_models/src/nwb_models/schema/linkml/core/v2_2_0/core.nwb.file.yaml generated
View file

@ -30,6 +30,7 @@ classes:
name:
name: name
ifabsent: string(root)
identifier: true
range: string
required: true
equals_string: root
@ -64,13 +65,11 @@ classes:
other files.
range: text
required: true
multivalued: false
session_description:
name: session_description
description: A description of the experimental session and data in the file.
range: text
required: true
multivalued: false
session_start_time:
name: session_start_time
description: 'Date and time of the experiment/session start. The date is stored
@ -79,7 +78,6 @@ classes:
offset. Date accuracy is up to milliseconds.'
range: isodatetime
required: true
multivalued: false
timestamps_reference_time:
name: timestamps_reference_time
description: 'Date and time corresponding to time zero of all timestamps.
@ -89,7 +87,6 @@ classes:
times stored in the file use this time as reference (i.e., time zero).'
range: isodatetime
required: true
multivalued: false
acquisition:
name: acquisition
description: Data streams recorded from the system, including ephys, ophys,
@ -168,7 +165,8 @@ classes:
can exist in the present file or can be linked to a remote library file.
range: NWBFile__stimulus
required: true
multivalued: false
inlined: true
inlined_as_list: true
general:
name: general
description: Experimental metadata, including protocol, notes and description
@ -188,7 +186,8 @@ classes:
should not be created unless there is data to store within them.
range: NWBFile__general
required: true
multivalued: false
inlined: true
inlined_as_list: true
intervals:
name: intervals
description: Experimental intervals, whether that be logically distinct sub-experiments
@ -196,14 +195,18 @@ classes:
an experiment, or epochs (see epochs subgroup) deriving from analysis of
data.
range: NWBFile__intervals
required: false
multivalued: false
inlined: true
inlined_as_list: true
units:
name: units
description: Data about sorted spike units.
range: Units
required: false
multivalued: false
inlined: true
inlined_as_list: false
specifications:
name: specifications
description: Nested dictionary of schema specifications
range: dict
tree_root: true
NWBFile__stimulus:
name: NWBFile__stimulus
@ -223,6 +226,7 @@ classes:
name:
name: name
ifabsent: string(stimulus)
identifier: true
range: string
required: true
equals_string: stimulus
@ -265,6 +269,7 @@ classes:
name:
name: name
ifabsent: string(general)
identifier: true
range: string
required: true
equals_string: general
@ -272,14 +277,10 @@ classes:
name: data_collection
description: Notes about data collection and analysis.
range: text
required: false
multivalued: false
experiment_description:
name: experiment_description
description: General description of the experiment.
range: text
required: false
multivalued: false
experimenter:
name: experimenter
description: Name of person(s) who performed the experiment. Can also specify
@ -294,8 +295,6 @@ classes:
name: institution
description: Institution(s) where experiment was performed.
range: text
required: false
multivalued: false
keywords:
name: keywords
description: Terms to search over.
@ -309,28 +308,20 @@ classes:
name: lab
description: Laboratory where experiment was performed.
range: text
required: false
multivalued: false
notes:
name: notes
description: Notes about the experiment.
range: text
required: false
multivalued: false
pharmacology:
name: pharmacology
description: Description of drugs used, including how and when they were administered.
Anesthesia(s), painkiller(s), etc., plus dosage, concentration, etc.
range: text
required: false
multivalued: false
protocol:
name: protocol
description: Experimental protocol, if applicable. e.g., include IACUC protocol
number.
range: text
required: false
multivalued: false
related_publications:
name: related_publications
description: Publication information. PMID, DOI, URL, etc.
@ -344,49 +335,39 @@ classes:
name: session_id
description: Lab-specific ID for the session.
range: text
required: false
multivalued: false
slices:
name: slices
description: Description of slices, including information about preparation
thickness, orientation, temperature, and bath solution.
range: text
required: false
multivalued: false
source_script:
name: source_script
description: Script file or link to public source code used to create this
NWB file.
range: general__source_script
required: false
multivalued: false
inlined: true
stimulus:
name: stimulus
description: Notes about stimuli, such as how and where they were presented.
range: text
required: false
multivalued: false
surgery:
name: surgery
description: Narrative description about surgery/surgeries, including date(s)
and who performed surgery.
range: text
required: false
multivalued: false
virus:
name: virus
description: Information about virus(es) used in experiments, including virus
ID, source, date made, injection location, volume, etc.
range: text
required: false
multivalued: false
nwb_container:
name: nwb_container
description: Place-holder than can be extended so that lab-specific meta-data
can be placed in /general.
range: NWBContainer
required: false
multivalued: true
inlined: true
inlined_as_list: false
devices:
name: devices
description: Description of hardware devices used during experiment, e.g.,
@ -401,20 +382,20 @@ classes:
description: Information about the animal or person from which the data was
measured.
range: Subject
required: false
multivalued: false
inlined: true
inlined_as_list: true
extracellular_ephys:
name: extracellular_ephys
description: Metadata related to extracellular electrophysiology.
range: general__extracellular_ephys
required: false
multivalued: false
inlined: true
inlined_as_list: true
intracellular_ephys:
name: intracellular_ephys
description: Metadata related to intracellular electrophysiology.
range: general__intracellular_ephys
required: false
multivalued: false
inlined: true
inlined_as_list: true
optogenetics:
name: optogenetics
description: Metadata describing optogenetic stimuluation.
@ -439,6 +420,7 @@ classes:
name:
name: name
ifabsent: string(source_script)
identifier: true
range: string
required: true
equals_string: source_script
@ -459,6 +441,7 @@ classes:
name:
name: name
ifabsent: string(subject)
identifier: true
range: string
required: true
equals_string: subject
@ -466,52 +449,36 @@ classes:
name: age
description: Age of subject. Can be supplied instead of 'date_of_birth'.
range: text
required: false
multivalued: false
date_of_birth:
name: date_of_birth
description: Date of birth of subject. Can be supplied instead of 'age'.
range: isodatetime
required: false
multivalued: false
description:
name: description
description: Description of subject and where subject came from (e.g., breeder,
if animal).
range: text
required: false
multivalued: false
genotype:
name: genotype
description: Genetic strain. If absent, assume Wild Type (WT).
range: text
required: false
multivalued: false
sex:
name: sex
description: Gender of subject.
range: text
required: false
multivalued: false
species:
name: species
description: Species of subject.
range: text
required: false
multivalued: false
subject_id:
name: subject_id
description: ID of animal/person used/participating in experiment (lab convention).
range: text
required: false
multivalued: false
weight:
name: weight
description: Weight at time of experiment, at time of surgery and at other
important times.
range: text
required: false
multivalued: false
general__extracellular_ephys:
name: general__extracellular_ephys
description: Metadata related to extracellular electrophysiology.
@ -519,21 +486,23 @@ classes:
name:
name: name
ifabsent: string(extracellular_ephys)
identifier: true
range: string
required: true
equals_string: extracellular_ephys
electrode_group:
name: electrode_group
description: Physical group of electrodes.
range: ElectrodeGroup
required: false
multivalued: true
electrodes:
name: electrodes
description: A table of all electrodes (i.e. channels) used for recording.
range: extracellular_ephys__electrodes
required: false
multivalued: false
inlined: true
inlined_as_list: true
value:
name: value
description: Physical group of electrodes.
range: ElectrodeGroup
multivalued: true
inlined: true
inlined_as_list: false
extracellular_ephys__electrodes:
name: extracellular_ephys__electrodes
description: A table of all electrodes (i.e. channels) used for recording.
@ -542,6 +511,7 @@ classes:
name:
name: name
ifabsent: string(electrodes)
identifier: true
range: string
required: true
equals_string: electrodes
@ -604,9 +574,13 @@ classes:
group:
name: group
description: Reference to the ElectrodeGroup this electrode is a part of.
array:
minimum_number_dimensions: 1
maximum_number_dimensions: false
range: ElectrodeGroup
required: true
multivalued: true
multivalued: false
inlined: true
group_name:
name: group_name
description: Name of the ElectrodeGroup this electrode is a part of.
@ -659,6 +633,7 @@ classes:
name:
name: name
ifabsent: string(intracellular_ephys)
identifier: true
range: string
required: true
equals_string: intracellular_ephys
@ -668,20 +643,19 @@ classes:
frequency fall-off, etc. If this changes between TimeSeries, filter description
should be stored as a text attribute for each TimeSeries.
range: text
required: false
multivalued: false
intracellular_electrode:
name: intracellular_electrode
description: An intracellular electrode.
range: IntracellularElectrode
required: false
multivalued: true
sweep_table:
name: sweep_table
description: The table which groups different PatchClampSeries together.
range: SweepTable
required: false
multivalued: false
inlined: true
inlined_as_list: false
value:
name: value
description: An intracellular electrode.
range: IntracellularElectrode
multivalued: true
inlined: true
inlined_as_list: false
NWBFile__intervals:
name: NWBFile__intervals
description: Experimental intervals, whether that be logically distinct sub-experiments
@ -691,6 +665,7 @@ classes:
name:
name: name
ifabsent: string(intervals)
identifier: true
range: string
required: true
equals_string: intervals
@ -699,24 +674,25 @@ classes:
description: Divisions in time marking experimental stages or sub-divisions
of a single recording session.
range: TimeIntervals
required: false
multivalued: false
inlined: true
inlined_as_list: false
trials:
name: trials
description: Repeated experimental events that have a logical grouping.
range: TimeIntervals
required: false
multivalued: false
inlined: true
inlined_as_list: false
invalid_times:
name: invalid_times
description: Time intervals that should be removed from analysis.
range: TimeIntervals
required: false
multivalued: false
time_intervals:
name: time_intervals
inlined: true
inlined_as_list: false
value:
name: value
description: Optional additional table(s) for describing other experimental
time intervals.
range: TimeIntervals
required: false
multivalued: true
inlined: true
inlined_as_list: false

324
nwb_models/src/nwb_models/schema/linkml/core/v2_2_0/core.nwb.icephys.yaml generated
View file

@ -23,6 +23,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
stimulus_description:
@ -40,14 +41,12 @@ classes:
description: Recorded voltage or current.
range: PatchClampSeries__data
required: true
multivalued: false
inlined: true
gain:
name: gain
description: Gain of the recording, in units Volt/Amp (v-clamp) or Volt/Volt
(c-clamp).
range: float32
required: false
multivalued: false
electrode:
name: electrode
annotations:
@ -55,7 +54,7 @@ classes:
tag: source_type
value: link
required: true
multivalued: false
inlined: true
any_of:
- range: IntracellularElectrode
- range: string
@ -67,9 +66,33 @@ classes:
name:
name: name
ifabsent: string(data)
identifier: true
range: string
required: true
equals_string: data
conversion:
name: conversion
description: Scalar to multiply each element in data to convert it to the
specified 'unit'. If the data are stored in acquisition system units or
other units that require a conversion to be interpretable, multiply the
data by 'conversion' to convert the data to the specified 'unit'. e.g. if
the data acquisition system stores values in this object as signed 16-bit
integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V
to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion'
multiplier to get from raw data acquisition values to recorded volts is
2.5/32768/8000 = 9.5367e-9.
ifabsent: float(1.0)
range: float32
required: false
resolution:
name: resolution
description: Smallest meaningful difference between values in data, stored
in the specified by unit, e.g., the change in value of the least significant
bit, or a larger number if signal noise is known to be present. If unknown,
use -1.0.
ifabsent: float(-1.0)
range: float32
required: false
unit:
name: unit
description: Base unit of measurement for working with the data. Actual stored
@ -92,32 +115,27 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
bias_current:
name: bias_current
description: Bias current, in amps.
range: float32
bridge_balance:
name: bridge_balance
description: Bridge balance, in ohms.
range: float32
capacitance_compensation:
name: capacitance_compensation
description: Capacitance compensation, in farads.
range: float32
data:
name: data
description: Recorded voltage.
range: CurrentClampSeries__data
required: true
multivalued: false
bias_current:
name: bias_current
description: Bias current, in amps.
range: float32
required: false
multivalued: false
bridge_balance:
name: bridge_balance
description: Bridge balance, in ohms.
range: float32
required: false
multivalued: false
capacitance_compensation:
name: capacitance_compensation
description: Capacitance compensation, in farads.
range: float32
required: false
multivalued: false
inlined: true
tree_root: true
CurrentClampSeries__data:
name: CurrentClampSeries__data
@ -126,9 +144,33 @@ classes:
name:
name: name
ifabsent: string(data)
identifier: true
range: string
required: true
equals_string: data
conversion:
name: conversion
description: Scalar to multiply each element in data to convert it to the
specified 'unit'. If the data are stored in acquisition system units or
other units that require a conversion to be interpretable, multiply the
data by 'conversion' to convert the data to the specified 'unit'. e.g. if
the data acquisition system stores values in this object as signed 16-bit
integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V
to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion'
multiplier to get from raw data acquisition values to recorded volts is
2.5/32768/8000 = 9.5367e-9.
ifabsent: float(1.0)
range: float32
required: false
resolution:
name: resolution
description: Smallest meaningful difference between values in data, stored
in the specified by unit, e.g., the change in value of the least significant
bit, or a larger number if signal noise is known to be present. If unknown,
use -1.0.
ifabsent: float(-1.0)
range: float32
required: false
unit:
name: unit
description: Base unit of measurement for working with the data. which is
@ -140,8 +182,10 @@ classes:
equals_string: volts
value:
name: value
range: AnyType
required: true
array:
dimensions:
- alias: num_times
range: numeric
IZeroClampSeries:
name: IZeroClampSeries
description: Voltage data from an intracellular recording when all current and
@ -152,6 +196,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
bias_current:
@ -159,19 +204,16 @@ classes:
description: Bias current, in amps, fixed to 0.0.
range: float32
required: true
multivalued: false
bridge_balance:
name: bridge_balance
description: Bridge balance, in ohms, fixed to 0.0.
range: float32
required: true
multivalued: false
capacitance_compensation:
name: capacitance_compensation
description: Capacitance compensation, in farads, fixed to 0.0.
range: float32
required: true
multivalued: false
tree_root: true
CurrentClampStimulusSeries:
name: CurrentClampStimulusSeries
@ -180,6 +222,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
data:
@ -187,7 +230,7 @@ classes:
description: Stimulus current applied.
range: CurrentClampStimulusSeries__data
required: true
multivalued: false
inlined: true
tree_root: true
CurrentClampStimulusSeries__data:
name: CurrentClampStimulusSeries__data
@ -196,9 +239,33 @@ classes:
name:
name: name
ifabsent: string(data)
identifier: true
range: string
required: true
equals_string: data
conversion:
name: conversion
description: Scalar to multiply each element in data to convert it to the
specified 'unit'. If the data are stored in acquisition system units or
other units that require a conversion to be interpretable, multiply the
data by 'conversion' to convert the data to the specified 'unit'. e.g. if
the data acquisition system stores values in this object as signed 16-bit
integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V
to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion'
multiplier to get from raw data acquisition values to recorded volts is
2.5/32768/8000 = 9.5367e-9.
ifabsent: float(1.0)
range: float32
required: false
resolution:
name: resolution
description: Smallest meaningful difference between values in data, stored
in the specified by unit, e.g., the change in value of the least significant
bit, or a larger number if signal noise is known to be present. If unknown,
use -1.0.
ifabsent: float(-1.0)
range: float32
required: false
unit:
name: unit
description: Base unit of measurement for working with the data. which is
@ -210,8 +277,10 @@ classes:
equals_string: amperes
value:
name: value
range: AnyType
required: true
array:
dimensions:
- alias: num_times
range: numeric
VoltageClampSeries:
name: VoltageClampSeries
description: Current data from an intracellular voltage-clamp recording. A corresponding
@ -221,80 +290,51 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
capacitance_fast:
name: capacitance_fast
description: Fast capacitance, in farads.
range: VoltageClampSeries__capacitance_fast
inlined: true
capacitance_slow:
name: capacitance_slow
description: Slow capacitance, in farads.
range: VoltageClampSeries__capacitance_slow
inlined: true
data:
name: data
description: Recorded current.
range: VoltageClampSeries__data
required: true
multivalued: false
capacitance_fast:
name: capacitance_fast
description: Fast capacitance, in farads.
range: VoltageClampSeries__capacitance_fast
required: false
multivalued: false
capacitance_slow:
name: capacitance_slow
description: Slow capacitance, in farads.
range: VoltageClampSeries__capacitance_slow
required: false
multivalued: false
inlined: true
resistance_comp_bandwidth:
name: resistance_comp_bandwidth
description: Resistance compensation bandwidth, in hertz.
range: VoltageClampSeries__resistance_comp_bandwidth
required: false
multivalued: false
inlined: true
resistance_comp_correction:
name: resistance_comp_correction
description: Resistance compensation correction, in percent.
range: VoltageClampSeries__resistance_comp_correction
required: false
multivalued: false
inlined: true
resistance_comp_prediction:
name: resistance_comp_prediction
description: Resistance compensation prediction, in percent.
range: VoltageClampSeries__resistance_comp_prediction
required: false
multivalued: false
inlined: true
whole_cell_capacitance_comp:
name: whole_cell_capacitance_comp
description: Whole cell capacitance compensation, in farads.
range: VoltageClampSeries__whole_cell_capacitance_comp
required: false
multivalued: false
inlined: true
whole_cell_series_resistance_comp:
name: whole_cell_series_resistance_comp
description: Whole cell series resistance compensation, in ohms.
range: VoltageClampSeries__whole_cell_series_resistance_comp
required: false
multivalued: false
inlined: true
tree_root: true
VoltageClampSeries__data:
name: VoltageClampSeries__data
description: Recorded current.
attributes:
name:
name: name
ifabsent: string(data)
range: string
required: true
equals_string: data
unit:
name: unit
description: Base unit of measurement for working with the data. which is
fixed to 'amperes'. Actual stored values are not necessarily stored in these
units. To access the data in these units, multiply 'data' by 'conversion'.
ifabsent: string(amperes)
range: text
required: true
equals_string: amperes
value:
name: value
range: AnyType
required: true
VoltageClampSeries__capacitance_fast:
name: VoltageClampSeries__capacitance_fast
description: Fast capacitance, in farads.
@ -302,6 +342,7 @@ classes:
name:
name: name
ifabsent: string(capacitance_fast)
identifier: true
range: string
required: true
equals_string: capacitance_fast
@ -323,6 +364,7 @@ classes:
name:
name: name
ifabsent: string(capacitance_slow)
identifier: true
range: string
required: true
equals_string: capacitance_slow
@ -337,6 +379,55 @@ classes:
name: value
range: float32
required: true
VoltageClampSeries__data:
name: VoltageClampSeries__data
description: Recorded current.
attributes:
name:
name: name
ifabsent: string(data)
identifier: true
range: string
required: true
equals_string: data
conversion:
name: conversion
description: Scalar to multiply each element in data to convert it to the
specified 'unit'. If the data are stored in acquisition system units or
other units that require a conversion to be interpretable, multiply the
data by 'conversion' to convert the data to the specified 'unit'. e.g. if
the data acquisition system stores values in this object as signed 16-bit
integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V
to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion'
multiplier to get from raw data acquisition values to recorded volts is
2.5/32768/8000 = 9.5367e-9.
ifabsent: float(1.0)
range: float32
required: false
resolution:
name: resolution
description: Smallest meaningful difference between values in data, stored
in the specified by unit, e.g., the change in value of the least significant
bit, or a larger number if signal noise is known to be present. If unknown,
use -1.0.
ifabsent: float(-1.0)
range: float32
required: false
unit:
name: unit
description: Base unit of measurement for working with the data. which is
fixed to 'amperes'. Actual stored values are not necessarily stored in these
units. To access the data in these units, multiply 'data' by 'conversion'.
ifabsent: string(amperes)
range: text
required: true
equals_string: amperes
value:
name: value
array:
dimensions:
- alias: num_times
range: numeric
VoltageClampSeries__resistance_comp_bandwidth:
name: VoltageClampSeries__resistance_comp_bandwidth
description: Resistance compensation bandwidth, in hertz.
@ -344,6 +435,7 @@ classes:
name:
name: name
ifabsent: string(resistance_comp_bandwidth)
identifier: true
range: string
required: true
equals_string: resistance_comp_bandwidth
@ -366,6 +458,7 @@ classes:
name:
name: name
ifabsent: string(resistance_comp_correction)
identifier: true
range: string
required: true
equals_string: resistance_comp_correction
@ -388,6 +481,7 @@ classes:
name:
name: name
ifabsent: string(resistance_comp_prediction)
identifier: true
range: string
required: true
equals_string: resistance_comp_prediction
@ -410,6 +504,7 @@ classes:
name:
name: name
ifabsent: string(whole_cell_capacitance_comp)
identifier: true
range: string
required: true
equals_string: whole_cell_capacitance_comp
@ -432,6 +527,7 @@ classes:
name:
name: name
ifabsent: string(whole_cell_series_resistance_comp)
identifier: true
range: string
required: true
equals_string: whole_cell_series_resistance_comp
@ -454,6 +550,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
data:
@ -461,7 +558,7 @@ classes:
description: Stimulus voltage applied.
range: VoltageClampStimulusSeries__data
required: true
multivalued: false
inlined: true
tree_root: true
VoltageClampStimulusSeries__data:
name: VoltageClampStimulusSeries__data
@ -470,9 +567,33 @@ classes:
name:
name: name
ifabsent: string(data)
identifier: true
range: string
required: true
equals_string: data
conversion:
name: conversion
description: Scalar to multiply each element in data to convert it to the
specified 'unit'. If the data are stored in acquisition system units or
other units that require a conversion to be interpretable, multiply the
data by 'conversion' to convert the data to the specified 'unit'. e.g. if
the data acquisition system stores values in this object as signed 16-bit
integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V
to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion'
multiplier to get from raw data acquisition values to recorded volts is
2.5/32768/8000 = 9.5367e-9.
ifabsent: float(1.0)
range: float32
required: false
resolution:
name: resolution
description: Smallest meaningful difference between values in data, stored
in the specified by unit, e.g., the change in value of the least significant
bit, or a larger number if signal noise is known to be present. If unknown,
use -1.0.
ifabsent: float(-1.0)
range: float32
required: false
unit:
name: unit
description: Base unit of measurement for working with the data. which is
@ -484,8 +605,10 @@ classes:
equals_string: volts
value:
name: value
range: AnyType
required: true
array:
dimensions:
- alias: num_times
range: numeric
IntracellularElectrode:
name: IntracellularElectrode
description: An intracellular electrode and its metadata.
@ -493,6 +616,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
description:
@ -500,45 +624,32 @@ classes:
description: Description of electrode (e.g., whole-cell, sharp, etc.).
range: text
required: true
multivalued: false
filtering:
name: filtering
description: Electrode specific filtering.
range: text
required: false
multivalued: false
initial_access_resistance:
name: initial_access_resistance
description: Initial access resistance.
range: text
required: false
multivalued: false
location:
name: location
description: Location of the electrode. Specify the area, layer, comments
on estimation of area/layer, stereotaxic coordinates if in vivo, etc. Use
standard atlas names for anatomical regions when possible.
range: text
required: false
multivalued: false
resistance:
name: resistance
description: Electrode resistance, in ohms.
range: text
required: false
multivalued: false
seal:
name: seal
description: Information about seal used for recording.
range: text
required: false
multivalued: false
slice:
name: slice
description: Information about slice used for recording.
range: text
required: false
multivalued: false
device:
name: device
annotations:
@ -546,7 +657,7 @@ classes:
tag: source_type
value: link
required: true
multivalued: false
inlined: true
any_of:
- range: Device
- range: string
@ -558,23 +669,19 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
sweep_number:
name: sweep_number
description: Sweep number of the PatchClampSeries in that row.
array:
minimum_number_dimensions: 1
maximum_number_dimensions: false
range: uint32
required: true
multivalued: false
series:
name: series
description: The PatchClampSeries with the sweep number in that row.
array:
minimum_number_dimensions: 1
maximum_number_dimensions: false
range: PatchClampSeries
required: true
multivalued: true
multivalued: false
inlined: true
series_index:
name: series_index
annotations:
@ -587,5 +694,14 @@ classes:
description: Index for series.
range: VectorIndex
required: true
inlined: true
sweep_number:
name: sweep_number
description: Sweep number of the PatchClampSeries in that row.
array:
minimum_number_dimensions: 1
maximum_number_dimensions: false
range: uint32
required: true
multivalued: false
tree_root: true

149
nwb_models/src/nwb_models/schema/linkml/core/v2_2_0/core.nwb.image.yaml generated
View file

@ -20,6 +20,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
value:
@ -37,6 +38,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
value:
@ -56,6 +58,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
value:
@ -80,26 +83,14 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
data:
name: data
description: Binary data representing images across frames.
range: numeric
required: false
multivalued: false
any_of:
- array:
dimensions:
- alias: frame
- alias: x
- alias: y
- array:
dimensions:
- alias: frame
- alias: x
- alias: y
- alias: z
range: ImageSeries__data
inlined: true
dimension:
name: dimension
description: Number of pixels on x, y, (and z) axes.
@ -117,18 +108,72 @@ classes:
used if the image is stored in another NWB file and that file is linked
to this file.
range: ImageSeries__external_file
required: false
multivalued: false
inlined: true
format:
name: format
description: Format of image. If this is 'external', then the attribute 'external_file'
contains the path information to the image files. If this is 'raw', then
the raw (single-channel) binary data is stored in the 'data' dataset. If
this attribute is not present, then the default format='raw' case is assumed.
ifabsent: string(raw)
range: text
required: false
multivalued: false
tree_root: true
ImageSeries__data:
name: ImageSeries__data
description: Binary data representing images across frames.
attributes:
name:
name: name
ifabsent: string(data)
identifier: true
range: string
required: true
equals_string: data
conversion:
name: conversion
description: Scalar to multiply each element in data to convert it to the
specified 'unit'. If the data are stored in acquisition system units or
other units that require a conversion to be interpretable, multiply the
data by 'conversion' to convert the data to the specified 'unit'. e.g. if
the data acquisition system stores values in this object as signed 16-bit
integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V
to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion'
multiplier to get from raw data acquisition values to recorded volts is
2.5/32768/8000 = 9.5367e-9.
ifabsent: float(1.0)
range: float32
required: false
resolution:
name: resolution
description: Smallest meaningful difference between values in data, stored
in the specified by unit, e.g., the change in value of the least significant
bit, or a larger number if signal noise is known to be present. If unknown,
use -1.0.
ifabsent: float(-1.0)
range: float32
required: false
unit:
name: unit
description: Base unit of measurement for working with the data. Actual stored
values are not necessarily stored in these units. To access the data in
these units, multiply 'data' by 'conversion'.
range: text
required: true
value:
name: value
range: numeric
any_of:
- array:
dimensions:
- alias: frame
- alias: x
- alias: y
- array:
dimensions:
- alias: frame
- alias: x
- alias: y
- alias: z
ImageSeries__external_file:
name: ImageSeries__external_file
description: Paths to one or more external file(s). The field is only present
@ -139,6 +184,7 @@ classes:
name:
name: name
ifabsent: string(external_file)
identifier: true
range: string
required: true
equals_string: external_file
@ -176,6 +222,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
masked_imageseries:
@ -185,7 +232,7 @@ classes:
tag: source_type
value: link
required: true
multivalued: false
inlined: true
any_of:
- range: ImageSeries
- range: string
@ -201,14 +248,13 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
distance:
name: distance
description: Distance from camera/monitor to target/eye.
range: float32
required: false
multivalued: false
field_of_view:
name: field_of_view
description: Width, height and depth of image, or imaged area, in meters.
@ -229,8 +275,6 @@ classes:
description: Description of image relative to some reference frame (e.g.,
which way is up). Must also specify frame of reference.
range: text
required: false
multivalued: false
tree_root: true
IndexSeries:
name: IndexSeries
@ -244,17 +288,15 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
data:
name: data
description: Index of the frame in the referenced ImageSeries.
array:
dimensions:
- alias: num_times
range: int32
range: IndexSeries__data
required: true
multivalued: false
inlined: true
indexed_timeseries:
name: indexed_timeseries
annotations:
@ -262,8 +304,55 @@ classes:
tag: source_type
value: link
required: true
multivalued: false
inlined: true
any_of:
- range: ImageSeries
- range: string
tree_root: true
IndexSeries__data:
name: IndexSeries__data
description: Index of the frame in the referenced ImageSeries.
attributes:
name:
name: name
ifabsent: string(data)
identifier: true
range: string
required: true
equals_string: data
conversion:
name: conversion
description: Scalar to multiply each element in data to convert it to the
specified 'unit'. If the data are stored in acquisition system units or
other units that require a conversion to be interpretable, multiply the
data by 'conversion' to convert the data to the specified 'unit'. e.g. if
the data acquisition system stores values in this object as signed 16-bit
integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V
to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion'
multiplier to get from raw data acquisition values to recorded volts is
2.5/32768/8000 = 9.5367e-9.
ifabsent: float(1.0)
range: float32
required: false
resolution:
name: resolution
description: Smallest meaningful difference between values in data, stored
in the specified by unit, e.g., the change in value of the least significant
bit, or a larger number if signal noise is known to be present. If unknown,
use -1.0.
ifabsent: float(-1.0)
range: float32
required: false
unit:
name: unit
description: Base unit of measurement for working with the data. Actual stored
values are not necessarily stored in these units. To access the data in
these units, multiply 'data' by 'conversion'.
range: text
required: true
value:
name: value
array:
dimensions:
- alias: num_times
range: int32

4
nwb_models/src/nwb_models/schema/linkml/core/v2_2_0/core.nwb.language.yaml generated
View file

@ -66,6 +66,7 @@ types:
numeric:
name: numeric
typeof: float
repr: float | int
text:
name: text
typeof: string
@ -87,6 +88,9 @@ types:
isodatetime:
name: isodatetime
typeof: datetime
dict:
name: dict
repr: dict
classes:
AnyType:
name: AnyType

335
nwb_models/src/nwb_models/schema/linkml/core/v2_2_0/core.nwb.misc.yaml generated
View file

@ -30,6 +30,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
data:
@ -37,7 +38,7 @@ classes:
description: Values of each feature at each time.
range: AbstractFeatureSeries__data
required: true
multivalued: false
inlined: true
feature_units:
name: feature_units
description: Units of each feature.
@ -64,9 +65,33 @@ classes:
name:
name: name
ifabsent: string(data)
identifier: true
range: string
required: true
equals_string: data
conversion:
name: conversion
description: Scalar to multiply each element in data to convert it to the
specified 'unit'. If the data are stored in acquisition system units or
other units that require a conversion to be interpretable, multiply the
data by 'conversion' to convert the data to the specified 'unit'. e.g. if
the data acquisition system stores values in this object as signed 16-bit
integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V
to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion'
multiplier to get from raw data acquisition values to recorded volts is
2.5/32768/8000 = 9.5367e-9.
ifabsent: float(1.0)
range: float32
required: false
resolution:
name: resolution
description: Smallest meaningful difference between values in data, stored
in the specified by unit, e.g., the change in value of the least significant
bit, or a larger number if signal noise is known to be present. If unknown,
use -1.0.
ifabsent: float(-1.0)
range: float32
required: false
unit:
name: unit
description: Since there can be different units for different features, store
@ -96,18 +121,63 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
data:
name: data
description: Annotations made during an experiment.
range: AnnotationSeries__data
required: true
inlined: true
tree_root: true
AnnotationSeries__data:
name: AnnotationSeries__data
description: Annotations made during an experiment.
attributes:
name:
name: name
ifabsent: string(data)
identifier: true
range: string
required: true
equals_string: data
conversion:
name: conversion
description: Scalar to multiply each element in data to convert it to the
specified 'unit'. If the data are stored in acquisition system units or
other units that require a conversion to be interpretable, multiply the
data by 'conversion' to convert the data to the specified 'unit'. e.g. if
the data acquisition system stores values in this object as signed 16-bit
integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V
to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion'
multiplier to get from raw data acquisition values to recorded volts is
2.5/32768/8000 = 9.5367e-9.
ifabsent: float(1.0)
range: float32
required: false
resolution:
name: resolution
description: Smallest meaningful difference between values in data. Annotations
have no units, so the value is fixed to -1.0.
ifabsent: float(-1.0)
range: float32
required: true
equals_number: -1
unit:
name: unit
description: Base unit of measurement for working with the data. Annotations
have no units, so the value is fixed to 'n/a'.
ifabsent: string(n/a)
range: text
required: true
equals_string: n/a
value:
name: value
array:
dimensions:
- alias: num_times
range: text
required: true
multivalued: false
tree_root: true
IntervalSeries:
name: IntervalSeries
description: Stores intervals of data. The timestamps field stores the beginning
@ -121,18 +191,63 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
data:
name: data
description: Use values >0 if interval started, <0 if interval ended.
range: IntervalSeries__data
required: true
inlined: true
tree_root: true
IntervalSeries__data:
name: IntervalSeries__data
description: Use values >0 if interval started, <0 if interval ended.
attributes:
name:
name: name
ifabsent: string(data)
identifier: true
range: string
required: true
equals_string: data
conversion:
name: conversion
description: Scalar to multiply each element in data to convert it to the
specified 'unit'. If the data are stored in acquisition system units or
other units that require a conversion to be interpretable, multiply the
data by 'conversion' to convert the data to the specified 'unit'. e.g. if
the data acquisition system stores values in this object as signed 16-bit
integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V
to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion'
multiplier to get from raw data acquisition values to recorded volts is
2.5/32768/8000 = 9.5367e-9.
ifabsent: float(1.0)
range: float32
required: false
resolution:
name: resolution
description: Smallest meaningful difference between values in data. Annotations
have no units, so the value is fixed to -1.0.
ifabsent: float(-1.0)
range: float32
required: true
equals_number: -1
unit:
name: unit
description: Base unit of measurement for working with the data. Annotations
have no units, so the value is fixed to 'n/a'.
ifabsent: string(n/a)
range: text
required: true
equals_string: n/a
value:
name: value
array:
dimensions:
- alias: num_times
range: int8
required: true
multivalued: false
tree_root: true
DecompositionSeries:
name: DecompositionSeries
description: Spectral analysis of a time series, e.g. of an LFP or a speech signal.
@ -140,6 +255,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
data:
@ -147,28 +263,27 @@ classes:
description: Data decomposed into frequency bands.
range: DecompositionSeries__data
required: true
multivalued: false
inlined: true
metric:
name: metric
description: The metric used, e.g. phase, amplitude, power.
range: text
required: true
multivalued: false
bands:
name: bands
description: Table for describing the bands that this series was generated
from. There should be one row in this table for each band.
range: DecompositionSeries__bands
required: true
multivalued: false
inlined: true
inlined_as_list: true
source_timeseries:
name: source_timeseries
annotations:
source_type:
tag: source_type
value: link
required: false
multivalued: false
inlined: true
any_of:
- range: TimeSeries
- range: string
@ -180,9 +295,33 @@ classes:
name:
name: name
ifabsent: string(data)
identifier: true
range: string
required: true
equals_string: data
conversion:
name: conversion
description: Scalar to multiply each element in data to convert it to the
specified 'unit'. If the data are stored in acquisition system units or
other units that require a conversion to be interpretable, multiply the
data by 'conversion' to convert the data to the specified 'unit'. e.g. if
the data acquisition system stores values in this object as signed 16-bit
integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V
to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion'
multiplier to get from raw data acquisition values to recorded volts is
2.5/32768/8000 = 9.5367e-9.
ifabsent: float(1.0)
range: float32
required: false
resolution:
name: resolution
description: Smallest meaningful difference between values in data, stored
in the specified by unit, e.g., the change in value of the least significant
bit, or a larger number if signal noise is known to be present. If unknown,
use -1.0.
ifabsent: float(-1.0)
range: float32
required: false
unit:
name: unit
description: Base unit of measurement for working with the data. Actual stored
@ -208,6 +347,7 @@ classes:
name:
name: name
ifabsent: string(bands)
identifier: true
range: string
required: true
equals_string: bands
@ -259,10 +399,21 @@ classes:
name:
name: name
ifabsent: string(Units)
identifier: true
range: string
required: true
spike_times_index:
name: spike_times_index
electrode_group:
name: electrode_group
description: Electrode group that each spike unit came from.
array:
minimum_number_dimensions: 1
maximum_number_dimensions: false
range: ElectrodeGroup
required: false
multivalued: false
inlined: true
electrodes:
name: electrodes
annotations:
named:
tag: named
@ -270,14 +421,30 @@ classes:
source_type:
tag: source_type
value: neurodata_type_inc
description: Index into the spike_times dataset.
description: Electrode that each spike unit came from, specified using a DynamicTableRegion.
range: DynamicTableRegion
inlined: true
electrodes_index:
name: electrodes_index
annotations:
named:
tag: named
value: true
source_type:
tag: source_type
value: neurodata_type_inc
description: Index into electrodes.
range: VectorIndex
required: false
multivalued: false
spike_times:
name: spike_times
description: Spike times for each unit.
range: Units__spike_times
inlined: true
obs_intervals:
name: obs_intervals
description: Observation intervals for each unit.
array:
dimensions:
- alias: num_intervals
- alias: start_end
exact_cardinality: 2
range: float64
required: false
multivalued: false
obs_intervals_index:
@ -291,21 +458,14 @@ classes:
value: neurodata_type_inc
description: Index into the obs_intervals dataset.
range: VectorIndex
required: false
multivalued: false
obs_intervals:
name: obs_intervals
description: Observation intervals for each unit.
array:
dimensions:
- alias: num_intervals
- alias: start_end
exact_cardinality: 2
range: float64
required: false
multivalued: false
electrodes_index:
name: electrodes_index
inlined: true
spike_times:
name: spike_times
description: Spike times for each unit.
range: Units__spike_times
inlined: true
spike_times_index:
name: spike_times_index
annotations:
named:
tag: named
@ -313,61 +473,19 @@ classes:
source_type:
tag: source_type
value: neurodata_type_inc
description: Index into electrodes.
description: Index into the spike_times dataset.
range: VectorIndex
required: false
multivalued: false
electrodes:
name: electrodes
annotations:
named:
tag: named
value: true
source_type:
tag: source_type
value: neurodata_type_inc
description: Electrode that each spike unit came from, specified using a DynamicTableRegion.
range: DynamicTableRegion
required: false
multivalued: false
electrode_group:
name: electrode_group
description: Electrode group that each spike unit came from.
range: ElectrodeGroup
required: false
multivalued: true
inlined: true
waveform_mean:
name: waveform_mean
description: Spike waveform mean for each spike unit.
range: float32
required: false
multivalued: false
any_of:
- array:
dimensions:
- alias: num_units
- alias: num_samples
- array:
dimensions:
- alias: num_units
- alias: num_samples
- alias: num_electrodes
range: Units__waveform_mean
inlined: true
waveform_sd:
name: waveform_sd
description: Spike waveform standard deviation for each spike unit.
range: float32
required: false
multivalued: false
any_of:
- array:
dimensions:
- alias: num_units
- alias: num_samples
- array:
dimensions:
- alias: num_units
- alias: num_samples
- alias: num_electrodes
range: Units__waveform_sd
inlined: true
tree_root: true
Units__spike_times:
name: Units__spike_times
@ -377,6 +495,7 @@ classes:
name:
name: name
ifabsent: string(spike_times)
identifier: true
range: string
required: true
equals_string: spike_times
@ -389,3 +508,51 @@ classes:
for the spike time to be between samples.
range: float64
required: false
Units__waveform_mean:
name: Units__waveform_mean
description: Spike waveform mean for each spike unit.
is_a: VectorData
attributes:
name:
name: name
ifabsent: string(waveform_mean)
identifier: true
range: string
required: true
equals_string: waveform_mean
sampling_rate:
name: sampling_rate
description: Sampling rate, in hertz.
range: float32
required: false
unit:
name: unit
description: Unit of measurement. This value is fixed to 'volts'.
ifabsent: string(volts)
range: text
required: false
equals_string: volts
Units__waveform_sd:
name: Units__waveform_sd
description: Spike waveform standard deviation for each spike unit.
is_a: VectorData
attributes:
name:
name: name
ifabsent: string(waveform_sd)
identifier: true
range: string
required: true
equals_string: waveform_sd
sampling_rate:
name: sampling_rate
description: Sampling rate, in hertz.
range: float32
required: false
unit:
name: unit
description: Unit of measurement. This value is fixed to 'volts'.
ifabsent: string(volts)
range: text
required: false
equals_string: volts

63
nwb_models/src/nwb_models/schema/linkml/core/v2_2_0/core.nwb.ogen.yaml generated
View file

@ -21,17 +21,15 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
data:
name: data
description: Applied power for optogenetic stimulus, in watts.
array:
dimensions:
- alias: num_times
range: numeric
range: OptogeneticSeries__data
required: true
multivalued: false
inlined: true
site:
name: site
annotations:
@ -39,11 +37,58 @@ classes:
tag: source_type
value: link
required: true
multivalued: false
inlined: true
any_of:
- range: OptogeneticStimulusSite
- range: string
tree_root: true
OptogeneticSeries__data:
name: OptogeneticSeries__data
description: Applied power for optogenetic stimulus, in watts.
attributes:
name:
name: name
ifabsent: string(data)
identifier: true
range: string
required: true
equals_string: data
conversion:
name: conversion
description: Scalar to multiply each element in data to convert it to the
specified 'unit'. If the data are stored in acquisition system units or
other units that require a conversion to be interpretable, multiply the
data by 'conversion' to convert the data to the specified 'unit'. e.g. if
the data acquisition system stores values in this object as signed 16-bit
integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V
to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion'
multiplier to get from raw data acquisition values to recorded volts is
2.5/32768/8000 = 9.5367e-9.
ifabsent: float(1.0)
range: float32
required: false
resolution:
name: resolution
description: Smallest meaningful difference between values in data, stored
in the specified by unit, e.g., the change in value of the least significant
bit, or a larger number if signal noise is known to be present. If unknown,
use -1.0.
ifabsent: float(-1.0)
range: float32
required: false
unit:
name: unit
description: Unit of measurement for data, which is fixed to 'watts'.
ifabsent: string(watts)
range: text
required: true
equals_string: watts
value:
name: value
array:
dimensions:
- alias: num_times
range: numeric
OptogeneticStimulusSite:
name: OptogeneticStimulusSite
description: A site of optogenetic stimulation.
@ -51,6 +96,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
description:
@ -58,13 +104,11 @@ classes:
description: Description of stimulation site.
range: text
required: true
multivalued: false
excitation_lambda:
name: excitation_lambda
description: Excitation wavelength, in nm.
range: float32
required: true
multivalued: false
location:
name: location
description: Location of the stimulation site. Specify the area, layer, comments
@ -72,7 +116,6 @@ classes:
standard atlas names for anatomical regions when possible.
range: text
required: true
multivalued: false
device:
name: device
annotations:
@ -80,7 +123,7 @@ classes:
tag: source_type
value: link
required: true
multivalued: false
inlined: true
any_of:
- range: Device
- range: string

175
nwb_models/src/nwb_models/schema/linkml/core/v2_2_0/core.nwb.ophys.yaml generated
View file

@ -23,6 +23,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
pmt_gain:
@ -59,7 +60,7 @@ classes:
tag: source_type
value: link
required: true
multivalued: false
inlined: true
any_of:
- range: ImagingPlane
- range: string
@ -72,22 +73,15 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
data:
name: data
description: Signals from ROIs.
range: numeric
range: RoiResponseSeries__data
required: true
multivalued: false
any_of:
- array:
dimensions:
- alias: num_times
- array:
dimensions:
- alias: num_times
- alias: num_rois
inlined: true
rois:
name: rois
annotations:
@ -101,8 +95,60 @@ classes:
on the ROIs stored in this timeseries.
range: DynamicTableRegion
required: true
multivalued: false
inlined: true
tree_root: true
RoiResponseSeries__data:
name: RoiResponseSeries__data
description: Signals from ROIs.
attributes:
name:
name: name
ifabsent: string(data)
identifier: true
range: string
required: true
equals_string: data
conversion:
name: conversion
description: Scalar to multiply each element in data to convert it to the
specified 'unit'. If the data are stored in acquisition system units or
other units that require a conversion to be interpretable, multiply the
data by 'conversion' to convert the data to the specified 'unit'. e.g. if
the data acquisition system stores values in this object as signed 16-bit
integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V
to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion'
multiplier to get from raw data acquisition values to recorded volts is
2.5/32768/8000 = 9.5367e-9.
ifabsent: float(1.0)
range: float32
required: false
resolution:
name: resolution
description: Smallest meaningful difference between values in data, stored
in the specified by unit, e.g., the change in value of the least significant
bit, or a larger number if signal noise is known to be present. If unknown,
use -1.0.
ifabsent: float(-1.0)
range: float32
required: false
unit:
name: unit
description: Base unit of measurement for working with the data. Actual stored
values are not necessarily stored in these units. To access the data in
these units, multiply 'data' by 'conversion'.
range: text
required: true
value:
name: value
range: numeric
any_of:
- array:
dimensions:
- alias: num_times
- array:
dimensions:
- alias: num_times
- alias: num_rois
DfOverF:
name: DfOverF
description: dF/F information about a region of interest (ROI). Storage hierarchy
@ -110,12 +156,19 @@ classes:
for image planes).
is_a: NWBDataInterface
attributes:
- name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: RoiResponseSeries
name:
name: name
ifabsent: string(DfOverF)
identifier: true
range: string
required: true
value:
name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: RoiResponseSeries
tree_root: true
Fluorescence:
name: Fluorescence
@ -124,12 +177,19 @@ classes:
for ROIs and for image planes).
is_a: NWBDataInterface
attributes:
- name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: RoiResponseSeries
name:
name: name
ifabsent: string(Fluorescence)
identifier: true
range: string
required: true
value:
name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: RoiResponseSeries
tree_root: true
ImageSegmentation:
name: ImageSegmentation
@ -142,12 +202,19 @@ classes:
is required and ROI names should remain consistent between them.
is_a: NWBDataInterface
attributes:
- name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: DynamicTable
name:
name: name
ifabsent: string(ImageSegmentation)
identifier: true
range: string
required: true
value:
name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: DynamicTable
tree_root: true
ImagingPlane:
name: ImagingPlane
@ -156,32 +223,28 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
description:
name: description
description: Description of the imaging plane.
range: text
required: false
multivalued: false
excitation_lambda:
name: excitation_lambda
description: Excitation wavelength, in nm.
range: float32
required: true
multivalued: false
imaging_rate:
name: imaging_rate
description: Rate that images are acquired, in Hz.
range: float32
required: true
multivalued: false
indicator:
name: indicator
description: Calcium indicator.
range: text
required: true
multivalued: false
location:
name: location
description: Location of the imaging plane. Specify the area, layer, comments
@ -189,31 +252,27 @@ classes:
standard atlas names for anatomical regions when possible.
range: text
required: true
multivalued: false
manifold:
name: manifold
description: DEPRECATED Physical position of each pixel. 'xyz' represents
the position of the pixel relative to the defined coordinate space. Deprecated
in favor of origin_coords and grid_spacing.
range: ImagingPlane__manifold
required: false
multivalued: false
inlined: true
origin_coords:
name: origin_coords
description: Physical location of the first element of the imaging plane (0,
0) for 2-D data or (0, 0, 0) for 3-D data. See also reference_frame for
what the physical location is relative to (e.g., bregma).
range: ImagingPlane__origin_coords
required: false
multivalued: false
inlined: true
grid_spacing:
name: grid_spacing
description: Space between pixels in (x, y) or voxels in (x, y, z) directions,
in the specified unit. Assumes imaging plane is a regular grid. See also
reference_frame to interpret the grid.
range: ImagingPlane__grid_spacing
required: false
multivalued: false
inlined: true
reference_frame:
name: reference_frame
description: Describes reference frame of origin_coords and grid_spacing.
@ -234,14 +293,13 @@ classes:
axis (larger index = more rightward). Third dimension corresponds to dorsal-ventral
axis (larger index = more ventral)."
range: text
required: false
multivalued: false
optical_channel:
name: optical_channel
description: An optical channel used to record from an imaging plane.
range: OpticalChannel
required: true
multivalued: false
inlined: true
inlined_as_list: true
device:
name: device
annotations:
@ -249,7 +307,7 @@ classes:
tag: source_type
value: link
required: true
multivalued: false
inlined: true
any_of:
- range: Device
- range: string
@ -263,6 +321,7 @@ classes:
name:
name: name
ifabsent: string(manifold)
identifier: true
range: string
required: true
equals_string: manifold
@ -312,6 +371,7 @@ classes:
name:
name: name
ifabsent: string(origin_coords)
identifier: true
range: string
required: true
equals_string: origin_coords
@ -339,6 +399,7 @@ classes:
name:
name: name
ifabsent: string(grid_spacing)
identifier: true
range: string
required: true
equals_string: grid_spacing
@ -364,6 +425,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
description:
@ -371,13 +433,11 @@ classes:
description: Description or other notes about the channel.
range: text
required: true
multivalued: false
emission_lambda:
name: emission_lambda
description: Emission wavelength for channel, in nm.
range: float32
required: true
multivalued: false
MotionCorrection:
name: MotionCorrection
description: 'An image stack where all frames are shifted (registered) to a common
@ -385,10 +445,17 @@ classes:
frame at each point in time is assumed to be 2-D (has only x & y dimensions).'
is_a: NWBDataInterface
attributes:
- name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: NWBDataInterface
name:
name: name
ifabsent: string(MotionCorrection)
identifier: true
range: string
required: true
value:
name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: NWBDataInterface
tree_root: true

21
nwb_models/src/nwb_models/schema/linkml/core/v2_2_0/core.nwb.retinotopy.yaml generated
View file

@ -21,6 +21,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
dimension:
@ -52,6 +53,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
unit:
@ -75,6 +77,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
bits_per_pixel:
@ -115,6 +118,7 @@ classes:
name:
name: name
ifabsent: string(ImagingRetinotopy)
identifier: true
range: string
required: true
axis_1_phase_map:
@ -129,7 +133,7 @@ classes:
description: Phase response to stimulus on the first measured axis.
range: AxisMap
required: true
multivalued: false
inlined: true
axis_1_power_map:
name: axis_1_power_map
annotations:
@ -142,8 +146,7 @@ classes:
description: Power response on the first measured axis. Response is scaled
so 0.0 is no power in the response and 1.0 is maximum relative power.
range: AxisMap
required: false
multivalued: false
inlined: true
axis_2_phase_map:
name: axis_2_phase_map
annotations:
@ -156,7 +159,7 @@ classes:
description: Phase response to stimulus on the second measured axis.
range: AxisMap
required: true
multivalued: false
inlined: true
axis_2_power_map:
name: axis_2_power_map
annotations:
@ -168,8 +171,7 @@ classes:
value: neurodata_type_inc
description: Power response to stimulus on the second measured axis.
range: AxisMap
required: false
multivalued: false
inlined: true
sign_map:
name: sign_map
annotations:
@ -183,7 +185,7 @@ classes:
and axis_2.
range: RetinotopyMap
required: true
multivalued: false
inlined: true
axis_descriptions:
name: axis_descriptions
description: Two-element array describing the contents of the two response
@ -202,7 +204,7 @@ classes:
focal depth, wavelength) as data collection. Array format: [rows][columns].'
range: ImagingRetinotopy__focal_depth_image
required: true
multivalued: false
inlined: true
vasculature_image:
name: vasculature_image
annotations:
@ -216,7 +218,7 @@ classes:
[rows][columns]'
range: RetinotopyImage
required: true
multivalued: false
inlined: true
tree_root: true
ImagingRetinotopy__focal_depth_image:
name: ImagingRetinotopy__focal_depth_image
@ -227,6 +229,7 @@ classes:
name:
name: name
ifabsent: string(focal_depth_image)
identifier: true
range: string
required: true
equals_string: focal_depth_image

43
nwb_models/src/nwb_models/schema/linkml/core/v2_2_1/core.nwb.base.yaml generated
View file

@ -20,6 +20,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
tree_root: true
@ -32,6 +33,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
resolution:
@ -73,6 +75,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
tree_root: true
@ -84,6 +87,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
tree_root: true
@ -94,6 +98,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
description:
@ -118,15 +123,14 @@ classes:
external file.
range: TimeSeries__data
required: true
multivalued: false
inlined: true
starting_time:
name: starting_time
description: Timestamp of the first sample in seconds. When timestamps are
uniformly spaced, the timestamp of the first sample can be specified and
all subsequent ones calculated from the sampling rate attribute.
range: TimeSeries__starting_time
required: false
multivalued: false
inlined: true
timestamps:
name: timestamps
description: Timestamps for samples stored in data, in seconds, relative to
@ -168,8 +172,8 @@ classes:
external to the NWB file, in files storing raw data. Once timestamp data
is calculated, the contents of 'sync' are mostly for archival purposes.
range: TimeSeries__sync
required: false
multivalued: false
inlined: true
inlined_as_list: true
tree_root: true
TimeSeries__data:
name: TimeSeries__data
@ -180,6 +184,7 @@ classes:
name:
name: name
ifabsent: string(data)
identifier: true
range: string
required: true
equals_string: data
@ -244,6 +249,7 @@ classes:
name:
name: name
ifabsent: string(starting_time)
identifier: true
range: string
required: true
equals_string: starting_time
@ -275,6 +281,7 @@ classes:
name:
name: name
ifabsent: string(sync)
identifier: true
range: string
required: true
equals_string: sync
@ -283,13 +290,24 @@ classes:
description: A collection of processed data.
is_a: NWBContainer
attributes:
- name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: NWBDataInterface
- range: DynamicTable
name:
name: name
identifier: true
range: string
required: true
description:
name: description
description: Description of this collection of processed data.
range: text
required: true
value:
name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: NWBDataInterface
- range: DynamicTable
tree_root: true
Images:
name: Images
@ -299,6 +317,7 @@ classes:
name:
name: name
ifabsent: string(Images)
identifier: true
range: string
required: true
description:

162
nwb_models/src/nwb_models/schema/linkml/core/v2_2_1/core.nwb.behavior.yaml generated
View file

@ -29,6 +29,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
data:
@ -37,13 +38,11 @@ classes:
reference frame.
range: SpatialSeries__data
required: true
multivalued: false
inlined: true
reference_frame:
name: reference_frame
description: Description defining what exactly 'straight-ahead' means.
range: text
required: false
multivalued: false
tree_root: true
SpatialSeries__data:
name: SpatialSeries__data
@ -53,9 +52,33 @@ classes:
name:
name: name
ifabsent: string(data)
identifier: true
range: string
required: true
equals_string: data
conversion:
name: conversion
description: Scalar to multiply each element in data to convert it to the
specified 'unit'. If the data are stored in acquisition system units or
other units that require a conversion to be interpretable, multiply the
data by 'conversion' to convert the data to the specified 'unit'. e.g. if
the data acquisition system stores values in this object as signed 16-bit
integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V
to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion'
multiplier to get from raw data acquisition values to recorded volts is
2.5/32768/8000 = 9.5367e-9.
ifabsent: float(1.0)
range: float32
required: false
resolution:
name: resolution
description: Smallest meaningful difference between values in data, stored
in the specified by unit, e.g., the change in value of the least significant
bit, or a larger number if signal noise is known to be present. If unknown,
use -1.0.
ifabsent: float(-1.0)
range: float32
required: false
unit:
name: unit
description: Base unit of measurement for working with the data. The default
@ -91,12 +114,19 @@ classes:
events. BehavioralTimeSeries is for continuous data.
is_a: NWBDataInterface
attributes:
- name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: IntervalSeries
name:
name: name
ifabsent: string(BehavioralEpochs)
identifier: true
range: string
required: true
value:
name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: IntervalSeries
tree_root: true
BehavioralEvents:
name: BehavioralEvents
@ -104,12 +134,19 @@ classes:
for more details.
is_a: NWBDataInterface
attributes:
- name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: TimeSeries
name:
name: name
ifabsent: string(BehavioralEvents)
identifier: true
range: string
required: true
value:
name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: TimeSeries
tree_root: true
BehavioralTimeSeries:
name: BehavioralTimeSeries
@ -117,36 +154,57 @@ classes:
of <a href="#BehavioralEpochs">BehavioralEpochs</a> for more details.
is_a: NWBDataInterface
attributes:
- name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: TimeSeries
name:
name: name
ifabsent: string(BehavioralTimeSeries)
identifier: true
range: string
required: true
value:
name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: TimeSeries
tree_root: true
PupilTracking:
name: PupilTracking
description: Eye-tracking data, representing pupil size.
is_a: NWBDataInterface
attributes:
- name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: TimeSeries
name:
name: name
ifabsent: string(PupilTracking)
identifier: true
range: string
required: true
value:
name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: TimeSeries
tree_root: true
EyeTracking:
name: EyeTracking
description: Eye-tracking data, representing direction of gaze.
is_a: NWBDataInterface
attributes:
- name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: SpatialSeries
name:
name: name
ifabsent: string(EyeTracking)
identifier: true
range: string
required: true
value:
name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: SpatialSeries
tree_root: true
CompassDirection:
name: CompassDirection
@ -157,22 +215,36 @@ classes:
be radians or degrees.
is_a: NWBDataInterface
attributes:
- name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: SpatialSeries
name:
name: name
ifabsent: string(CompassDirection)
identifier: true
range: string
required: true
value:
name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: SpatialSeries
tree_root: true
Position:
name: Position
description: Position data, whether along the x, x/y or x/y/z axis.
is_a: NWBDataInterface
attributes:
- name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: SpatialSeries
name:
name: name
ifabsent: string(Position)
identifier: true
range: string
required: true
value:
name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: SpatialSeries
tree_root: true

1
nwb_models/src/nwb_models/schema/linkml/core/v2_2_1/core.nwb.device.yaml generated
View file

@ -21,6 +21,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
description:

266
nwb_models/src/nwb_models/schema/linkml/core/v2_2_1/core.nwb.ecephys.yaml generated
View file

@ -25,41 +25,9 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
data:
name: data
description: Recorded voltage data.
range: numeric
required: true
multivalued: false
any_of:
- array:
dimensions:
- alias: num_times
- array:
dimensions:
- alias: num_times
- alias: num_channels
- array:
dimensions:
- alias: num_times
- alias: num_channels
- alias: num_samples
electrodes:
name: electrodes
annotations:
named:
tag: named
value: true
source_type:
tag: source_type
value: neurodata_type_inc
description: DynamicTableRegion pointer to the electrodes that this time series
was generated from.
range: DynamicTableRegion
required: true
multivalued: false
channel_conversion:
name: channel_conversion
description: Channel-specific conversion factor. Multiply the data in the
@ -77,7 +45,87 @@ classes:
range: float32
required: false
multivalued: false
data:
name: data
description: Recorded voltage data.
range: ElectricalSeries__data
required: true
inlined: true
electrodes:
name: electrodes
annotations:
named:
tag: named
value: true
source_type:
tag: source_type
value: neurodata_type_inc
description: DynamicTableRegion pointer to the electrodes that this time series
was generated from.
range: DynamicTableRegion
required: true
inlined: true
tree_root: true
ElectricalSeries__data:
name: ElectricalSeries__data
description: Recorded voltage data.
attributes:
name:
name: name
ifabsent: string(data)
identifier: true
range: string
required: true
equals_string: data
conversion:
name: conversion
description: Scalar to multiply each element in data to convert it to the
specified 'unit'. If the data are stored in acquisition system units or
other units that require a conversion to be interpretable, multiply the
data by 'conversion' to convert the data to the specified 'unit'. e.g. if
the data acquisition system stores values in this object as signed 16-bit
integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V
to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion'
multiplier to get from raw data acquisition values to recorded volts is
2.5/32768/8000 = 9.5367e-9.
ifabsent: float(1.0)
range: float32
required: false
resolution:
name: resolution
description: Smallest meaningful difference between values in data, stored
in the specified by unit, e.g., the change in value of the least significant
bit, or a larger number if signal noise is known to be present. If unknown,
use -1.0.
ifabsent: float(-1.0)
range: float32
required: false
unit:
name: unit
description: Base unit of measurement for working with the data. This value
is fixed to 'volts'. Actual stored values are not necessarily stored in
these units. To access the data in these units, multiply 'data' by 'conversion'
and 'channel_conversion' (if present).
ifabsent: string(volts)
range: text
required: true
equals_string: volts
value:
name: value
range: numeric
any_of:
- array:
dimensions:
- alias: num_times
- array:
dimensions:
- alias: num_times
- alias: num_channels
- array:
dimensions:
- alias: num_times
- alias: num_channels
- alias: num_samples
SpikeEventSeries:
name: SpikeEventSeries
description: 'Stores snapshots/snippets of recorded spike events (i.e., threshold
@ -92,24 +140,15 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
data:
name: data
description: Spike waveforms.
range: numeric
range: SpikeEventSeries__data
required: true
multivalued: false
any_of:
- array:
dimensions:
- alias: num_events
- alias: num_samples
- array:
dimensions:
- alias: num_events
- alias: num_channels
- alias: num_samples
inlined: true
timestamps:
name: timestamps
description: Timestamps for samples stored in data, in seconds, relative to
@ -123,6 +162,60 @@ classes:
required: true
multivalued: false
tree_root: true
SpikeEventSeries__data:
name: SpikeEventSeries__data
description: Spike waveforms.
attributes:
name:
name: name
ifabsent: string(data)
identifier: true
range: string
required: true
equals_string: data
conversion:
name: conversion
description: Scalar to multiply each element in data to convert it to the
specified 'unit'. If the data are stored in acquisition system units or
other units that require a conversion to be interpretable, multiply the
data by 'conversion' to convert the data to the specified 'unit'. e.g. if
the data acquisition system stores values in this object as signed 16-bit
integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V
to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion'
multiplier to get from raw data acquisition values to recorded volts is
2.5/32768/8000 = 9.5367e-9.
ifabsent: float(1.0)
range: float32
required: false
resolution:
name: resolution
description: Smallest meaningful difference between values in data, stored
in the specified by unit, e.g., the change in value of the least significant
bit, or a larger number if signal noise is known to be present. If unknown,
use -1.0.
ifabsent: float(-1.0)
range: float32
required: false
unit:
name: unit
description: Unit of measurement for waveforms, which is fixed to 'volts'.
ifabsent: string(volts)
range: text
required: true
equals_string: volts
value:
name: value
range: numeric
any_of:
- array:
dimensions:
- alias: num_events
- alias: num_samples
- array:
dimensions:
- alias: num_events
- alias: num_channels
- alias: num_samples
FeatureExtraction:
name: FeatureExtraction
description: Features, such as PC1 and PC2, that are extracted from signals stored
@ -132,6 +225,7 @@ classes:
name:
name: name
ifabsent: string(FeatureExtraction)
identifier: true
range: string
required: true
description:
@ -177,7 +271,7 @@ classes:
was generated from.
range: DynamicTableRegion
required: true
multivalued: false
inlined: true
tree_root: true
EventDetection:
name: EventDetection
@ -187,6 +281,7 @@ classes:
name:
name: name
ifabsent: string(EventDetection)
identifier: true
range: string
required: true
detection_method:
@ -195,7 +290,6 @@ classes:
or dV/dT threshold, as well as relevant values.
range: text
required: true
multivalued: false
source_idx:
name: source_idx
description: Indices (zero-based) into source ElectricalSeries::data array
@ -224,7 +318,7 @@ classes:
tag: source_type
value: link
required: true
multivalued: false
inlined: true
any_of:
- range: ElectricalSeries
- range: string
@ -236,12 +330,19 @@ classes:
during experiment acquisition.
is_a: NWBDataInterface
attributes:
- name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: SpikeEventSeries
name:
name: name
ifabsent: string(EventWaveform)
identifier: true
range: string
required: true
value:
name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: SpikeEventSeries
tree_root: true
FilteredEphys:
name: FilteredEphys
@ -258,12 +359,19 @@ classes:
the ElectricalSeries.
is_a: NWBDataInterface
attributes:
- name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: ElectricalSeries
name:
name: name
ifabsent: string(FilteredEphys)
identifier: true
range: string
required: true
value:
name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: ElectricalSeries
tree_root: true
LFP:
name: LFP
@ -272,12 +380,19 @@ classes:
properties should be noted in the ElectricalSeries description or comments field.
is_a: NWBDataInterface
attributes:
- name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: ElectricalSeries
name:
name: name
ifabsent: string(LFP)
identifier: true
range: string
required: true
value:
name: value
multivalued: true
inlined: true
inlined_as_list: false
any_of:
- range: ElectricalSeries
tree_root: true
ElectrodeGroup:
name: ElectrodeGroup
@ -286,6 +401,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
description:
@ -304,8 +420,7 @@ classes:
name: position
description: stereotaxic or common framework coordinates
range: ElectrodeGroup__position
required: false
multivalued: false
inlined: true
device:
name: device
annotations:
@ -313,7 +428,7 @@ classes:
tag: source_type
value: link
required: true
multivalued: false
inlined: true
any_of:
- range: Device
- range: string
@ -325,6 +440,7 @@ classes:
name:
name: name
ifabsent: string(position)
identifier: true
range: string
required: true
equals_string: position
@ -334,24 +450,18 @@ classes:
array:
exact_number_dimensions: 1
range: float32
required: false
multivalued: false
y:
name: y
description: y coordinate
array:
exact_number_dimensions: 1
range: float32
required: false
multivalued: false
z:
name: z
description: z coordinate
array:
exact_number_dimensions: 1
range: float32
required: false
multivalued: false
ClusterWaveforms:
name: ClusterWaveforms
description: DEPRECATED The mean waveform shape, including standard deviation,
@ -365,6 +475,7 @@ classes:
name:
name: name
ifabsent: string(ClusterWaveforms)
identifier: true
range: string
required: true
waveform_filtering:
@ -372,7 +483,6 @@ classes:
description: Filtering applied to data before generating mean/sd
range: text
required: true
multivalued: false
waveform_mean:
name: waveform_mean
description: The mean waveform for each cluster, using the same indices for
@ -404,7 +514,7 @@ classes:
tag: source_type
value: link
required: true
multivalued: false
inlined: true
any_of:
- range: Clustering
- range: string
@ -418,6 +528,7 @@ classes:
name:
name: name
ifabsent: string(Clustering)
identifier: true
range: string
required: true
description:
@ -426,7 +537,6 @@ classes:
clusters curated using Klusters, etc)
range: text
required: true
multivalued: false
num:
name: num
description: Cluster number of each event

18
nwb_models/src/nwb_models/schema/linkml/core/v2_2_1/core.nwb.epoch.yaml generated
View file

@ -22,6 +22,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
start_time:
@ -62,14 +63,12 @@ classes:
value: neurodata_type_inc
description: Index for tags.
range: VectorIndex
required: false
multivalued: false
inlined: true
timeseries:
name: timeseries
description: An index into a TimeSeries object.
range: TimeIntervals__timeseries
required: false
multivalued: false
inlined: true
timeseries_index:
name: timeseries_index
annotations:
@ -81,8 +80,7 @@ classes:
value: neurodata_type_inc
description: Index for timeseries.
range: VectorIndex
required: false
multivalued: false
inlined: true
tree_root: true
TimeIntervals__timeseries:
name: TimeIntervals__timeseries
@ -92,6 +90,7 @@ classes:
name:
name: name
ifabsent: string(timeseries)
identifier: true
range: string
required: true
equals_string: timeseries
@ -103,8 +102,6 @@ classes:
array:
exact_number_dimensions: 1
range: int32
required: false
multivalued: false
count:
name: count
description: Number of data samples available in this time series, during
@ -112,13 +109,10 @@ classes:
array:
exact_number_dimensions: 1
range: int32
required: false
multivalued: false
timeseries:
name: timeseries
description: the TimeSeries that this index applies to.
array:
exact_number_dimensions: 1
range: TimeSeries
required: false
multivalued: false
inlined: true

150
nwb_models/src/nwb_models/schema/linkml/core/v2_2_1/core.nwb.file.yaml generated
View file

@ -30,6 +30,7 @@ classes:
name:
name: name
ifabsent: string(root)
identifier: true
range: string
required: true
equals_string: root
@ -64,13 +65,11 @@ classes:
other files.
range: text
required: true
multivalued: false
session_description:
name: session_description
description: A description of the experimental session and data in the file.
range: text
required: true
multivalued: false
session_start_time:
name: session_start_time
description: 'Date and time of the experiment/session start. The date is stored
@ -79,7 +78,6 @@ classes:
offset. Date accuracy is up to milliseconds.'
range: isodatetime
required: true
multivalued: false
timestamps_reference_time:
name: timestamps_reference_time
description: 'Date and time corresponding to time zero of all timestamps.
@ -89,7 +87,6 @@ classes:
times stored in the file use this time as reference (i.e., time zero).'
range: isodatetime
required: true
multivalued: false
acquisition:
name: acquisition
description: Data streams recorded from the system, including ephys, ophys,
@ -168,7 +165,8 @@ classes:
can exist in the present file or can be linked to a remote library file.
range: NWBFile__stimulus
required: true
multivalued: false
inlined: true
inlined_as_list: true
general:
name: general
description: Experimental metadata, including protocol, notes and description
@ -188,7 +186,8 @@ classes:
should not be created unless there is data to store within them.
range: NWBFile__general
required: true
multivalued: false
inlined: true
inlined_as_list: true
intervals:
name: intervals
description: Experimental intervals, whether that be logically distinct sub-experiments
@ -196,14 +195,18 @@ classes:
an experiment, or epochs (see epochs subgroup) deriving from analysis of
data.
range: NWBFile__intervals
required: false
multivalued: false
inlined: true
inlined_as_list: true
units:
name: units
description: Data about sorted spike units.
range: Units
required: false
multivalued: false
inlined: true
inlined_as_list: false
specifications:
name: specifications
description: Nested dictionary of schema specifications
range: dict
tree_root: true
NWBFile__stimulus:
name: NWBFile__stimulus
@ -223,6 +226,7 @@ classes:
name:
name: name
ifabsent: string(stimulus)
identifier: true
range: string
required: true
equals_string: stimulus
@ -265,6 +269,7 @@ classes:
name:
name: name
ifabsent: string(general)
identifier: true
range: string
required: true
equals_string: general
@ -272,14 +277,10 @@ classes:
name: data_collection
description: Notes about data collection and analysis.
range: text
required: false
multivalued: false
experiment_description:
name: experiment_description
description: General description of the experiment.
range: text
required: false
multivalued: false
experimenter:
name: experimenter
description: Name of person(s) who performed the experiment. Can also specify
@ -294,8 +295,6 @@ classes:
name: institution
description: Institution(s) where experiment was performed.
range: text
required: false
multivalued: false
keywords:
name: keywords
description: Terms to search over.
@ -309,28 +308,20 @@ classes:
name: lab
description: Laboratory where experiment was performed.
range: text
required: false
multivalued: false
notes:
name: notes
description: Notes about the experiment.
range: text
required: false
multivalued: false
pharmacology:
name: pharmacology
description: Description of drugs used, including how and when they were administered.
Anesthesia(s), painkiller(s), etc., plus dosage, concentration, etc.
range: text
required: false
multivalued: false
protocol:
name: protocol
description: Experimental protocol, if applicable. e.g., include IACUC protocol
number.
range: text
required: false
multivalued: false
related_publications:
name: related_publications
description: Publication information. PMID, DOI, URL, etc.
@ -344,49 +335,39 @@ classes:
name: session_id
description: Lab-specific ID for the session.
range: text
required: false
multivalued: false
slices:
name: slices
description: Description of slices, including information about preparation
thickness, orientation, temperature, and bath solution.
range: text
required: false
multivalued: false
source_script:
name: source_script
description: Script file or link to public source code used to create this
NWB file.
range: general__source_script
required: false
multivalued: false
inlined: true
stimulus:
name: stimulus
description: Notes about stimuli, such as how and where they were presented.
range: text
required: false
multivalued: false
surgery:
name: surgery
description: Narrative description about surgery/surgeries, including date(s)
and who performed surgery.
range: text
required: false
multivalued: false
virus:
name: virus
description: Information about virus(es) used in experiments, including virus
ID, source, date made, injection location, volume, etc.
range: text
required: false
multivalued: false
nwb_container:
name: nwb_container
description: Place-holder than can be extended so that lab-specific meta-data
can be placed in /general.
range: NWBContainer
required: false
multivalued: true
inlined: true
inlined_as_list: false
devices:
name: devices
description: Description of hardware devices used during experiment, e.g.,
@ -401,20 +382,20 @@ classes:
description: Information about the animal or person from which the data was
measured.
range: Subject
required: false
multivalued: false
inlined: true
inlined_as_list: true
extracellular_ephys:
name: extracellular_ephys
description: Metadata related to extracellular electrophysiology.
range: general__extracellular_ephys
required: false
multivalued: false
inlined: true
inlined_as_list: true
intracellular_ephys:
name: intracellular_ephys
description: Metadata related to intracellular electrophysiology.
range: general__intracellular_ephys
required: false
multivalued: false
inlined: true
inlined_as_list: true
optogenetics:
name: optogenetics
description: Metadata describing optogenetic stimuluation.
@ -439,6 +420,7 @@ classes:
name:
name: name
ifabsent: string(source_script)
identifier: true
range: string
required: true
equals_string: source_script
@ -459,6 +441,7 @@ classes:
name:
name: name
ifabsent: string(subject)
identifier: true
range: string
required: true
equals_string: subject
@ -466,52 +449,36 @@ classes:
name: age
description: Age of subject. Can be supplied instead of 'date_of_birth'.
range: text
required: false
multivalued: false
date_of_birth:
name: date_of_birth
description: Date of birth of subject. Can be supplied instead of 'age'.
range: isodatetime
required: false
multivalued: false
description:
name: description
description: Description of subject and where subject came from (e.g., breeder,
if animal).
range: text
required: false
multivalued: false
genotype:
name: genotype
description: Genetic strain. If absent, assume Wild Type (WT).
range: text
required: false
multivalued: false
sex:
name: sex
description: Gender of subject.
range: text
required: false
multivalued: false
species:
name: species
description: Species of subject.
range: text
required: false
multivalued: false
subject_id:
name: subject_id
description: ID of animal/person used/participating in experiment (lab convention).
range: text
required: false
multivalued: false
weight:
name: weight
description: Weight at time of experiment, at time of surgery and at other
important times.
range: text
required: false
multivalued: false
general__extracellular_ephys:
name: general__extracellular_ephys
description: Metadata related to extracellular electrophysiology.
@ -519,21 +486,23 @@ classes:
name:
name: name
ifabsent: string(extracellular_ephys)
identifier: true
range: string
required: true
equals_string: extracellular_ephys
electrode_group:
name: electrode_group
description: Physical group of electrodes.
range: ElectrodeGroup
required: false
multivalued: true
electrodes:
name: electrodes
description: A table of all electrodes (i.e. channels) used for recording.
range: extracellular_ephys__electrodes
required: false
multivalued: false
inlined: true
inlined_as_list: true
value:
name: value
description: Physical group of electrodes.
range: ElectrodeGroup
multivalued: true
inlined: true
inlined_as_list: false
extracellular_ephys__electrodes:
name: extracellular_ephys__electrodes
description: A table of all electrodes (i.e. channels) used for recording.
@ -542,6 +511,7 @@ classes:
name:
name: name
ifabsent: string(electrodes)
identifier: true
range: string
required: true
equals_string: electrodes
@ -604,9 +574,13 @@ classes:
group:
name: group
description: Reference to the ElectrodeGroup this electrode is a part of.
array:
minimum_number_dimensions: 1
maximum_number_dimensions: false
range: ElectrodeGroup
required: true
multivalued: true
multivalued: false
inlined: true
group_name:
name: group_name
description: Name of the ElectrodeGroup this electrode is a part of.
@ -659,6 +633,7 @@ classes:
name:
name: name
ifabsent: string(intracellular_ephys)
identifier: true
range: string
required: true
equals_string: intracellular_ephys
@ -668,20 +643,19 @@ classes:
frequency fall-off, etc. If this changes between TimeSeries, filter description
should be stored as a text attribute for each TimeSeries.
range: text
required: false
multivalued: false
intracellular_electrode:
name: intracellular_electrode
description: An intracellular electrode.
range: IntracellularElectrode
required: false
multivalued: true
sweep_table:
name: sweep_table
description: The table which groups different PatchClampSeries together.
range: SweepTable
required: false
multivalued: false
inlined: true
inlined_as_list: false
value:
name: value
description: An intracellular electrode.
range: IntracellularElectrode
multivalued: true
inlined: true
inlined_as_list: false
NWBFile__intervals:
name: NWBFile__intervals
description: Experimental intervals, whether that be logically distinct sub-experiments
@ -691,6 +665,7 @@ classes:
name:
name: name
ifabsent: string(intervals)
identifier: true
range: string
required: true
equals_string: intervals
@ -699,24 +674,25 @@ classes:
description: Divisions in time marking experimental stages or sub-divisions
of a single recording session.
range: TimeIntervals
required: false
multivalued: false
inlined: true
inlined_as_list: false
trials:
name: trials
description: Repeated experimental events that have a logical grouping.
range: TimeIntervals
required: false
multivalued: false
inlined: true
inlined_as_list: false
invalid_times:
name: invalid_times
description: Time intervals that should be removed from analysis.
range: TimeIntervals
required: false
multivalued: false
time_intervals:
name: time_intervals
inlined: true
inlined_as_list: false
value:
name: value
description: Optional additional table(s) for describing other experimental
time intervals.
range: TimeIntervals
required: false
multivalued: true
inlined: true
inlined_as_list: false

324
nwb_models/src/nwb_models/schema/linkml/core/v2_2_1/core.nwb.icephys.yaml generated
View file

@ -23,6 +23,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
stimulus_description:
@ -40,14 +41,12 @@ classes:
description: Recorded voltage or current.
range: PatchClampSeries__data
required: true
multivalued: false
inlined: true
gain:
name: gain
description: Gain of the recording, in units Volt/Amp (v-clamp) or Volt/Volt
(c-clamp).
range: float32
required: false
multivalued: false
electrode:
name: electrode
annotations:
@ -55,7 +54,7 @@ classes:
tag: source_type
value: link
required: true
multivalued: false
inlined: true
any_of:
- range: IntracellularElectrode
- range: string
@ -67,9 +66,33 @@ classes:
name:
name: name
ifabsent: string(data)
identifier: true
range: string
required: true
equals_string: data
conversion:
name: conversion
description: Scalar to multiply each element in data to convert it to the
specified 'unit'. If the data are stored in acquisition system units or
other units that require a conversion to be interpretable, multiply the
data by 'conversion' to convert the data to the specified 'unit'. e.g. if
the data acquisition system stores values in this object as signed 16-bit
integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V
to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion'
multiplier to get from raw data acquisition values to recorded volts is
2.5/32768/8000 = 9.5367e-9.
ifabsent: float(1.0)
range: float32
required: false
resolution:
name: resolution
description: Smallest meaningful difference between values in data, stored
in the specified by unit, e.g., the change in value of the least significant
bit, or a larger number if signal noise is known to be present. If unknown,
use -1.0.
ifabsent: float(-1.0)
range: float32
required: false
unit:
name: unit
description: Base unit of measurement for working with the data. Actual stored
@ -92,32 +115,27 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
bias_current:
name: bias_current
description: Bias current, in amps.
range: float32
bridge_balance:
name: bridge_balance
description: Bridge balance, in ohms.
range: float32
capacitance_compensation:
name: capacitance_compensation
description: Capacitance compensation, in farads.
range: float32
data:
name: data
description: Recorded voltage.
range: CurrentClampSeries__data
required: true
multivalued: false
bias_current:
name: bias_current
description: Bias current, in amps.
range: float32
required: false
multivalued: false
bridge_balance:
name: bridge_balance
description: Bridge balance, in ohms.
range: float32
required: false
multivalued: false
capacitance_compensation:
name: capacitance_compensation
description: Capacitance compensation, in farads.
range: float32
required: false
multivalued: false
inlined: true
tree_root: true
CurrentClampSeries__data:
name: CurrentClampSeries__data
@ -126,9 +144,33 @@ classes:
name:
name: name
ifabsent: string(data)
identifier: true
range: string
required: true
equals_string: data
conversion:
name: conversion
description: Scalar to multiply each element in data to convert it to the
specified 'unit'. If the data are stored in acquisition system units or
other units that require a conversion to be interpretable, multiply the
data by 'conversion' to convert the data to the specified 'unit'. e.g. if
the data acquisition system stores values in this object as signed 16-bit
integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V
to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion'
multiplier to get from raw data acquisition values to recorded volts is
2.5/32768/8000 = 9.5367e-9.
ifabsent: float(1.0)
range: float32
required: false
resolution:
name: resolution
description: Smallest meaningful difference between values in data, stored
in the specified by unit, e.g., the change in value of the least significant
bit, or a larger number if signal noise is known to be present. If unknown,
use -1.0.
ifabsent: float(-1.0)
range: float32
required: false
unit:
name: unit
description: Base unit of measurement for working with the data. which is
@ -140,8 +182,10 @@ classes:
equals_string: volts
value:
name: value
range: AnyType
required: true
array:
dimensions:
- alias: num_times
range: numeric
IZeroClampSeries:
name: IZeroClampSeries
description: Voltage data from an intracellular recording when all current and
@ -152,6 +196,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
bias_current:
@ -159,19 +204,16 @@ classes:
description: Bias current, in amps, fixed to 0.0.
range: float32
required: true
multivalued: false
bridge_balance:
name: bridge_balance
description: Bridge balance, in ohms, fixed to 0.0.
range: float32
required: true
multivalued: false
capacitance_compensation:
name: capacitance_compensation
description: Capacitance compensation, in farads, fixed to 0.0.
range: float32
required: true
multivalued: false
tree_root: true
CurrentClampStimulusSeries:
name: CurrentClampStimulusSeries
@ -180,6 +222,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
data:
@ -187,7 +230,7 @@ classes:
description: Stimulus current applied.
range: CurrentClampStimulusSeries__data
required: true
multivalued: false
inlined: true
tree_root: true
CurrentClampStimulusSeries__data:
name: CurrentClampStimulusSeries__data
@ -196,9 +239,33 @@ classes:
name:
name: name
ifabsent: string(data)
identifier: true
range: string
required: true
equals_string: data
conversion:
name: conversion
description: Scalar to multiply each element in data to convert it to the
specified 'unit'. If the data are stored in acquisition system units or
other units that require a conversion to be interpretable, multiply the
data by 'conversion' to convert the data to the specified 'unit'. e.g. if
the data acquisition system stores values in this object as signed 16-bit
integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V
to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion'
multiplier to get from raw data acquisition values to recorded volts is
2.5/32768/8000 = 9.5367e-9.
ifabsent: float(1.0)
range: float32
required: false
resolution:
name: resolution
description: Smallest meaningful difference between values in data, stored
in the specified by unit, e.g., the change in value of the least significant
bit, or a larger number if signal noise is known to be present. If unknown,
use -1.0.
ifabsent: float(-1.0)
range: float32
required: false
unit:
name: unit
description: Base unit of measurement for working with the data. which is
@ -210,8 +277,10 @@ classes:
equals_string: amperes
value:
name: value
range: AnyType
required: true
array:
dimensions:
- alias: num_times
range: numeric
VoltageClampSeries:
name: VoltageClampSeries
description: Current data from an intracellular voltage-clamp recording. A corresponding
@ -221,80 +290,51 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
capacitance_fast:
name: capacitance_fast
description: Fast capacitance, in farads.
range: VoltageClampSeries__capacitance_fast
inlined: true
capacitance_slow:
name: capacitance_slow
description: Slow capacitance, in farads.
range: VoltageClampSeries__capacitance_slow
inlined: true
data:
name: data
description: Recorded current.
range: VoltageClampSeries__data
required: true
multivalued: false
capacitance_fast:
name: capacitance_fast
description: Fast capacitance, in farads.
range: VoltageClampSeries__capacitance_fast
required: false
multivalued: false
capacitance_slow:
name: capacitance_slow
description: Slow capacitance, in farads.
range: VoltageClampSeries__capacitance_slow
required: false
multivalued: false
inlined: true
resistance_comp_bandwidth:
name: resistance_comp_bandwidth
description: Resistance compensation bandwidth, in hertz.
range: VoltageClampSeries__resistance_comp_bandwidth
required: false
multivalued: false
inlined: true
resistance_comp_correction:
name: resistance_comp_correction
description: Resistance compensation correction, in percent.
range: VoltageClampSeries__resistance_comp_correction
required: false
multivalued: false
inlined: true
resistance_comp_prediction:
name: resistance_comp_prediction
description: Resistance compensation prediction, in percent.
range: VoltageClampSeries__resistance_comp_prediction
required: false
multivalued: false
inlined: true
whole_cell_capacitance_comp:
name: whole_cell_capacitance_comp
description: Whole cell capacitance compensation, in farads.
range: VoltageClampSeries__whole_cell_capacitance_comp
required: false
multivalued: false
inlined: true
whole_cell_series_resistance_comp:
name: whole_cell_series_resistance_comp
description: Whole cell series resistance compensation, in ohms.
range: VoltageClampSeries__whole_cell_series_resistance_comp
required: false
multivalued: false
inlined: true
tree_root: true
VoltageClampSeries__data:
name: VoltageClampSeries__data
description: Recorded current.
attributes:
name:
name: name
ifabsent: string(data)
range: string
required: true
equals_string: data
unit:
name: unit
description: Base unit of measurement for working with the data. which is
fixed to 'amperes'. Actual stored values are not necessarily stored in these
units. To access the data in these units, multiply 'data' by 'conversion'.
ifabsent: string(amperes)
range: text
required: true
equals_string: amperes
value:
name: value
range: AnyType
required: true
VoltageClampSeries__capacitance_fast:
name: VoltageClampSeries__capacitance_fast
description: Fast capacitance, in farads.
@ -302,6 +342,7 @@ classes:
name:
name: name
ifabsent: string(capacitance_fast)
identifier: true
range: string
required: true
equals_string: capacitance_fast
@ -323,6 +364,7 @@ classes:
name:
name: name
ifabsent: string(capacitance_slow)
identifier: true
range: string
required: true
equals_string: capacitance_slow
@ -337,6 +379,55 @@ classes:
name: value
range: float32
required: true
VoltageClampSeries__data:
name: VoltageClampSeries__data
description: Recorded current.
attributes:
name:
name: name
ifabsent: string(data)
identifier: true
range: string
required: true
equals_string: data
conversion:
name: conversion
description: Scalar to multiply each element in data to convert it to the
specified 'unit'. If the data are stored in acquisition system units or
other units that require a conversion to be interpretable, multiply the
data by 'conversion' to convert the data to the specified 'unit'. e.g. if
the data acquisition system stores values in this object as signed 16-bit
integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V
to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion'
multiplier to get from raw data acquisition values to recorded volts is
2.5/32768/8000 = 9.5367e-9.
ifabsent: float(1.0)
range: float32
required: false
resolution:
name: resolution
description: Smallest meaningful difference between values in data, stored
in the specified by unit, e.g., the change in value of the least significant
bit, or a larger number if signal noise is known to be present. If unknown,
use -1.0.
ifabsent: float(-1.0)
range: float32
required: false
unit:
name: unit
description: Base unit of measurement for working with the data. which is
fixed to 'amperes'. Actual stored values are not necessarily stored in these
units. To access the data in these units, multiply 'data' by 'conversion'.
ifabsent: string(amperes)
range: text
required: true
equals_string: amperes
value:
name: value
array:
dimensions:
- alias: num_times
range: numeric
VoltageClampSeries__resistance_comp_bandwidth:
name: VoltageClampSeries__resistance_comp_bandwidth
description: Resistance compensation bandwidth, in hertz.
@ -344,6 +435,7 @@ classes:
name:
name: name
ifabsent: string(resistance_comp_bandwidth)
identifier: true
range: string
required: true
equals_string: resistance_comp_bandwidth
@ -366,6 +458,7 @@ classes:
name:
name: name
ifabsent: string(resistance_comp_correction)
identifier: true
range: string
required: true
equals_string: resistance_comp_correction
@ -388,6 +481,7 @@ classes:
name:
name: name
ifabsent: string(resistance_comp_prediction)
identifier: true
range: string
required: true
equals_string: resistance_comp_prediction
@ -410,6 +504,7 @@ classes:
name:
name: name
ifabsent: string(whole_cell_capacitance_comp)
identifier: true
range: string
required: true
equals_string: whole_cell_capacitance_comp
@ -432,6 +527,7 @@ classes:
name:
name: name
ifabsent: string(whole_cell_series_resistance_comp)
identifier: true
range: string
required: true
equals_string: whole_cell_series_resistance_comp
@ -454,6 +550,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
data:
@ -461,7 +558,7 @@ classes:
description: Stimulus voltage applied.
range: VoltageClampStimulusSeries__data
required: true
multivalued: false
inlined: true
tree_root: true
VoltageClampStimulusSeries__data:
name: VoltageClampStimulusSeries__data
@ -470,9 +567,33 @@ classes:
name:
name: name
ifabsent: string(data)
identifier: true
range: string
required: true
equals_string: data
conversion:
name: conversion
description: Scalar to multiply each element in data to convert it to the
specified 'unit'. If the data are stored in acquisition system units or
other units that require a conversion to be interpretable, multiply the
data by 'conversion' to convert the data to the specified 'unit'. e.g. if
the data acquisition system stores values in this object as signed 16-bit
integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V
to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion'
multiplier to get from raw data acquisition values to recorded volts is
2.5/32768/8000 = 9.5367e-9.
ifabsent: float(1.0)
range: float32
required: false
resolution:
name: resolution
description: Smallest meaningful difference between values in data, stored
in the specified by unit, e.g., the change in value of the least significant
bit, or a larger number if signal noise is known to be present. If unknown,
use -1.0.
ifabsent: float(-1.0)
range: float32
required: false
unit:
name: unit
description: Base unit of measurement for working with the data. which is
@ -484,8 +605,10 @@ classes:
equals_string: volts
value:
name: value
range: AnyType
required: true
array:
dimensions:
- alias: num_times
range: numeric
IntracellularElectrode:
name: IntracellularElectrode
description: An intracellular electrode and its metadata.
@ -493,6 +616,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
description:
@ -500,45 +624,32 @@ classes:
description: Description of electrode (e.g., whole-cell, sharp, etc.).
range: text
required: true
multivalued: false
filtering:
name: filtering
description: Electrode specific filtering.
range: text
required: false
multivalued: false
initial_access_resistance:
name: initial_access_resistance
description: Initial access resistance.
range: text
required: false
multivalued: false
location:
name: location
description: Location of the electrode. Specify the area, layer, comments
on estimation of area/layer, stereotaxic coordinates if in vivo, etc. Use
standard atlas names for anatomical regions when possible.
range: text
required: false
multivalued: false
resistance:
name: resistance
description: Electrode resistance, in ohms.
range: text
required: false
multivalued: false
seal:
name: seal
description: Information about seal used for recording.
range: text
required: false
multivalued: false
slice:
name: slice
description: Information about slice used for recording.
range: text
required: false
multivalued: false
device:
name: device
annotations:
@ -546,7 +657,7 @@ classes:
tag: source_type
value: link
required: true
multivalued: false
inlined: true
any_of:
- range: Device
- range: string
@ -558,23 +669,19 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
sweep_number:
name: sweep_number
description: Sweep number of the PatchClampSeries in that row.
array:
minimum_number_dimensions: 1
maximum_number_dimensions: false
range: uint32
required: true
multivalued: false
series:
name: series
description: The PatchClampSeries with the sweep number in that row.
array:
minimum_number_dimensions: 1
maximum_number_dimensions: false
range: PatchClampSeries
required: true
multivalued: true
multivalued: false
inlined: true
series_index:
name: series_index
annotations:
@ -587,5 +694,14 @@ classes:
description: Index for series.
range: VectorIndex
required: true
inlined: true
sweep_number:
name: sweep_number
description: Sweep number of the PatchClampSeries in that row.
array:
minimum_number_dimensions: 1
maximum_number_dimensions: false
range: uint32
required: true
multivalued: false
tree_root: true

149
nwb_models/src/nwb_models/schema/linkml/core/v2_2_1/core.nwb.image.yaml generated
View file

@ -20,6 +20,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
value:
@ -37,6 +38,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
value:
@ -56,6 +58,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
value:
@ -80,26 +83,14 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
data:
name: data
description: Binary data representing images across frames.
range: numeric
required: false
multivalued: false
any_of:
- array:
dimensions:
- alias: frame
- alias: x
- alias: y
- array:
dimensions:
- alias: frame
- alias: x
- alias: y
- alias: z
range: ImageSeries__data
inlined: true
dimension:
name: dimension
description: Number of pixels on x, y, (and z) axes.
@ -117,18 +108,72 @@ classes:
used if the image is stored in another NWB file and that file is linked
to this file.
range: ImageSeries__external_file
required: false
multivalued: false
inlined: true
format:
name: format
description: Format of image. If this is 'external', then the attribute 'external_file'
contains the path information to the image files. If this is 'raw', then
the raw (single-channel) binary data is stored in the 'data' dataset. If
this attribute is not present, then the default format='raw' case is assumed.
ifabsent: string(raw)
range: text
required: false
multivalued: false
tree_root: true
ImageSeries__data:
name: ImageSeries__data
description: Binary data representing images across frames.
attributes:
name:
name: name
ifabsent: string(data)
identifier: true
range: string
required: true
equals_string: data
conversion:
name: conversion
description: Scalar to multiply each element in data to convert it to the
specified 'unit'. If the data are stored in acquisition system units or
other units that require a conversion to be interpretable, multiply the
data by 'conversion' to convert the data to the specified 'unit'. e.g. if
the data acquisition system stores values in this object as signed 16-bit
integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V
to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion'
multiplier to get from raw data acquisition values to recorded volts is
2.5/32768/8000 = 9.5367e-9.
ifabsent: float(1.0)
range: float32
required: false
resolution:
name: resolution
description: Smallest meaningful difference between values in data, stored
in the specified by unit, e.g., the change in value of the least significant
bit, or a larger number if signal noise is known to be present. If unknown,
use -1.0.
ifabsent: float(-1.0)
range: float32
required: false
unit:
name: unit
description: Base unit of measurement for working with the data. Actual stored
values are not necessarily stored in these units. To access the data in
these units, multiply 'data' by 'conversion'.
range: text
required: true
value:
name: value
range: numeric
any_of:
- array:
dimensions:
- alias: frame
- alias: x
- alias: y
- array:
dimensions:
- alias: frame
- alias: x
- alias: y
- alias: z
ImageSeries__external_file:
name: ImageSeries__external_file
description: Paths to one or more external file(s). The field is only present
@ -139,6 +184,7 @@ classes:
name:
name: name
ifabsent: string(external_file)
identifier: true
range: string
required: true
equals_string: external_file
@ -176,6 +222,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
masked_imageseries:
@ -185,7 +232,7 @@ classes:
tag: source_type
value: link
required: true
multivalued: false
inlined: true
any_of:
- range: ImageSeries
- range: string
@ -201,14 +248,13 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
distance:
name: distance
description: Distance from camera/monitor to target/eye.
range: float32
required: false
multivalued: false
field_of_view:
name: field_of_view
description: Width, height and depth of image, or imaged area, in meters.
@ -229,8 +275,6 @@ classes:
description: Description of image relative to some reference frame (e.g.,
which way is up). Must also specify frame of reference.
range: text
required: false
multivalued: false
tree_root: true
IndexSeries:
name: IndexSeries
@ -244,17 +288,15 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
data:
name: data
description: Index of the frame in the referenced ImageSeries.
array:
dimensions:
- alias: num_times
range: int32
range: IndexSeries__data
required: true
multivalued: false
inlined: true
indexed_timeseries:
name: indexed_timeseries
annotations:
@ -262,8 +304,55 @@ classes:
tag: source_type
value: link
required: true
multivalued: false
inlined: true
any_of:
- range: ImageSeries
- range: string
tree_root: true
IndexSeries__data:
name: IndexSeries__data
description: Index of the frame in the referenced ImageSeries.
attributes:
name:
name: name
ifabsent: string(data)
identifier: true
range: string
required: true
equals_string: data
conversion:
name: conversion
description: Scalar to multiply each element in data to convert it to the
specified 'unit'. If the data are stored in acquisition system units or
other units that require a conversion to be interpretable, multiply the
data by 'conversion' to convert the data to the specified 'unit'. e.g. if
the data acquisition system stores values in this object as signed 16-bit
integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V
to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion'
multiplier to get from raw data acquisition values to recorded volts is
2.5/32768/8000 = 9.5367e-9.
ifabsent: float(1.0)
range: float32
required: false
resolution:
name: resolution
description: Smallest meaningful difference between values in data, stored
in the specified by unit, e.g., the change in value of the least significant
bit, or a larger number if signal noise is known to be present. If unknown,
use -1.0.
ifabsent: float(-1.0)
range: float32
required: false
unit:
name: unit
description: Base unit of measurement for working with the data. Actual stored
values are not necessarily stored in these units. To access the data in
these units, multiply 'data' by 'conversion'.
range: text
required: true
value:
name: value
array:
dimensions:
- alias: num_times
range: int32

4
nwb_models/src/nwb_models/schema/linkml/core/v2_2_1/core.nwb.language.yaml generated
View file

@ -66,6 +66,7 @@ types:
numeric:
name: numeric
typeof: float
repr: float | int
text:
name: text
typeof: string
@ -87,6 +88,9 @@ types:
isodatetime:
name: isodatetime
typeof: datetime
dict:
name: dict
repr: dict
classes:
AnyType:
name: AnyType

335
nwb_models/src/nwb_models/schema/linkml/core/v2_2_1/core.nwb.misc.yaml generated
View file

@ -30,6 +30,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
data:
@ -37,7 +38,7 @@ classes:
description: Values of each feature at each time.
range: AbstractFeatureSeries__data
required: true
multivalued: false
inlined: true
feature_units:
name: feature_units
description: Units of each feature.
@ -64,9 +65,33 @@ classes:
name:
name: name
ifabsent: string(data)
identifier: true
range: string
required: true
equals_string: data
conversion:
name: conversion
description: Scalar to multiply each element in data to convert it to the
specified 'unit'. If the data are stored in acquisition system units or
other units that require a conversion to be interpretable, multiply the
data by 'conversion' to convert the data to the specified 'unit'. e.g. if
the data acquisition system stores values in this object as signed 16-bit
integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V
to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion'
multiplier to get from raw data acquisition values to recorded volts is
2.5/32768/8000 = 9.5367e-9.
ifabsent: float(1.0)
range: float32
required: false
resolution:
name: resolution
description: Smallest meaningful difference between values in data, stored
in the specified by unit, e.g., the change in value of the least significant
bit, or a larger number if signal noise is known to be present. If unknown,
use -1.0.
ifabsent: float(-1.0)
range: float32
required: false
unit:
name: unit
description: Since there can be different units for different features, store
@ -96,18 +121,63 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
data:
name: data
description: Annotations made during an experiment.
range: AnnotationSeries__data
required: true
inlined: true
tree_root: true
AnnotationSeries__data:
name: AnnotationSeries__data
description: Annotations made during an experiment.
attributes:
name:
name: name
ifabsent: string(data)
identifier: true
range: string
required: true
equals_string: data
conversion:
name: conversion
description: Scalar to multiply each element in data to convert it to the
specified 'unit'. If the data are stored in acquisition system units or
other units that require a conversion to be interpretable, multiply the
data by 'conversion' to convert the data to the specified 'unit'. e.g. if
the data acquisition system stores values in this object as signed 16-bit
integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V
to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion'
multiplier to get from raw data acquisition values to recorded volts is
2.5/32768/8000 = 9.5367e-9.
ifabsent: float(1.0)
range: float32
required: false
resolution:
name: resolution
description: Smallest meaningful difference between values in data. Annotations
have no units, so the value is fixed to -1.0.
ifabsent: float(-1.0)
range: float32
required: true
equals_number: -1
unit:
name: unit
description: Base unit of measurement for working with the data. Annotations
have no units, so the value is fixed to 'n/a'.
ifabsent: string(n/a)
range: text
required: true
equals_string: n/a
value:
name: value
array:
dimensions:
- alias: num_times
range: text
required: true
multivalued: false
tree_root: true
IntervalSeries:
name: IntervalSeries
description: Stores intervals of data. The timestamps field stores the beginning
@ -121,18 +191,63 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
data:
name: data
description: Use values >0 if interval started, <0 if interval ended.
range: IntervalSeries__data
required: true
inlined: true
tree_root: true
IntervalSeries__data:
name: IntervalSeries__data
description: Use values >0 if interval started, <0 if interval ended.
attributes:
name:
name: name
ifabsent: string(data)
identifier: true
range: string
required: true
equals_string: data
conversion:
name: conversion
description: Scalar to multiply each element in data to convert it to the
specified 'unit'. If the data are stored in acquisition system units or
other units that require a conversion to be interpretable, multiply the
data by 'conversion' to convert the data to the specified 'unit'. e.g. if
the data acquisition system stores values in this object as signed 16-bit
integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V
to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion'
multiplier to get from raw data acquisition values to recorded volts is
2.5/32768/8000 = 9.5367e-9.
ifabsent: float(1.0)
range: float32
required: false
resolution:
name: resolution
description: Smallest meaningful difference between values in data. Annotations
have no units, so the value is fixed to -1.0.
ifabsent: float(-1.0)
range: float32
required: true
equals_number: -1
unit:
name: unit
description: Base unit of measurement for working with the data. Annotations
have no units, so the value is fixed to 'n/a'.
ifabsent: string(n/a)
range: text
required: true
equals_string: n/a
value:
name: value
array:
dimensions:
- alias: num_times
range: int8
required: true
multivalued: false
tree_root: true
DecompositionSeries:
name: DecompositionSeries
description: Spectral analysis of a time series, e.g. of an LFP or a speech signal.
@ -140,6 +255,7 @@ classes:
attributes:
name:
name: name
identifier: true
range: string
required: true
data:
@ -147,28 +263,27 @@ classes:
description: Data decomposed into frequency bands.
range: DecompositionSeries__data
required: true
multivalued: false
inlined: true
metric:
name: metric
description: The metric used, e.g. phase, amplitude, power.
range: text
required: true
multivalued: false
bands:
name: bands
description: Table for describing the bands that this series was generated
from. There should be one row in this table for each band.
range: DecompositionSeries__bands
required: true
multivalued: false
inlined: true
inlined_as_list: true
source_timeseries:
name: source_timeseries
annotations:
source_type:
tag: source_type
value: link
required: false
multivalued: false
inlined: true
any_of:
- range: TimeSeries
- range: string
@ -180,9 +295,33 @@ classes:
name:
name: name
ifabsent: string(data)
identifier: true
range: string
required: true
equals_string: data
conversion:
name: conversion
description: Scalar to multiply each element in data to convert it to the
specified 'unit'. If the data are stored in acquisition system units or
other units that require a conversion to be interpretable, multiply the
data by 'conversion' to convert the data to the specified 'unit'. e.g. if
the data acquisition system stores values in this object as signed 16-bit
integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V
to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion'
multiplier to get from raw data acquisition values to recorded volts is
2.5/32768/8000 = 9.5367e-9.
ifabsent: float(1.0)
range: float32
required: false
resolution:
name: resolution
description: Smallest meaningful difference between values in data, stored
in the specified by unit, e.g., the change in value of the least significant
bit, or a larger number if signal noise is known to be present. If unknown,
use -1.0.
ifabsent: float(-1.0)
range: float32
required: false
unit:
name: unit
description: Base unit of measurement for working with the data. Actual stored
@ -208,6 +347,7 @@ classes:
name:
name: name
ifabsent: string(bands)
identifier: true
range: string
required: true
equals_string: bands
@ -259,10 +399,21 @@ classes:
name:
name: name
ifabsent: string(Units)
identifier: true
range: string
required: true
spike_times_index:
name: spike_times_index
electrode_group:
name: electrode_group
description: Electrode group that each spike unit came from.
array:
minimum_number_dimensions: 1
maximum_number_dimensions: false
range: ElectrodeGroup
required: false
multivalued: false
inlined: true
electrodes:
name: electrodes
annotations:
named:
tag: named
@ -270,14 +421,30 @@ classes:
source_type:
tag: source_type
value: neurodata_type_inc
description: Index into the spike_times dataset.
description: Electrode that each spike unit came from, specified using a DynamicTableRegion.
range: DynamicTableRegion
inlined: true
electrodes_index:
name: electrodes_index
annotations:
named:
tag: named
value: true
source_type:
tag: source_type
value: neurodata_type_inc
description: Index into electrodes.
range: VectorIndex
required: false
multivalued: false
spike_times:
name: spike_times
description: Spike times for each unit.
range: Units__spike_times
inlined: true
obs_intervals:
name: obs_intervals
description: Observation intervals for each unit.
array:
dimensions:
- alias: num_intervals
- alias: start_end
exact_cardinality: 2
range: float64
required: false
multivalued: false
obs_intervals_index:
@ -291,21 +458,14 @@ classes:
value: neurodata_type_inc
description: Index into the obs_intervals dataset.
range: VectorIndex
required: false
multivalued: false
obs_intervals:
name: obs_intervals
description: Observation intervals for each unit.
array:
dimensions:
- alias: num_intervals
- alias: start_end
exact_cardinality: 2
range: float64
required: false
multivalued: false
electrodes_index:
name: electrodes_index
inlined: true
spike_times:
name: spike_times
description: Spike times for each unit.
range: Units__spike_times
inlined: true
spike_times_index:
name: spike_times_index
annotations:
named:
tag: named
@ -313,61 +473,19 @@ classes:
source_type:
tag: source_type
value: neurodata_type_inc
description: Index into electrodes.
description: Index into the spike_times dataset.
range: VectorIndex
required: false
multivalued: false
electrodes:
name: electrodes
annotations:
named:
tag: named
value: true
source_type:
tag: source_type
value: neurodata_type_inc
description: Electrode that each spike unit came from, specified using a DynamicTableRegion.
range: DynamicTableRegion
required: false
multivalued: false
electrode_group:
name: electrode_group
description: Electrode group that each spike unit came from.
range: ElectrodeGroup
required: false
multivalued: true
inlined: true
waveform_mean:
name: waveform_mean
description: Spike waveform mean for each spike unit.
range: float32
required: false
multivalued: false
any_of:
- array:
dimensions:
- alias: num_units
- alias: num_samples
- array:
dimensions:
- alias: num_units
- alias: num_samples
- alias: num_electrodes
range: Units__waveform_mean
inlined: true
waveform_sd:
name: waveform_sd
description: Spike waveform standard deviation for each spike unit.
range: float32
required: false
multivalued: false
any_of:
- array:
dimensions:
- alias: num_units
- alias: num_samples
- array:
dimensions:
- alias: num_units
- alias: num_samples
- alias: num_electrodes
range: Units__waveform_sd
inlined: true
tree_root: true
Units__spike_times:
name: Units__spike_times
@ -377,6 +495,7 @@ classes:
name:
name: name
ifabsent: string(spike_times)
identifier: true
range: string
required: true
equals_string: spike_times
@ -389,3 +508,51 @@ classes:
for the spike time to be between samples.
range: float64
required: false
Units__waveform_mean:
name: Units__waveform_mean
description: Spike waveform mean for each spike unit.
is_a: VectorData
attributes:
name:
name: name
ifabsent: string(waveform_mean)
identifier: true
range: string
required: true
equals_string: waveform_mean
sampling_rate:
name: sampling_rate
description: Sampling rate, in hertz.
range: float32
required: false
unit:
name: unit
description: Unit of measurement. This value is fixed to 'volts'.
ifabsent: string(volts)
range: text
required: false
equals_string: volts
Units__waveform_sd:
name: Units__waveform_sd
description: Spike waveform standard deviation for each spike unit.
is_a: VectorData
attributes:
name:
name: name
ifabsent: string(waveform_sd)
identifier: true
range: string
required: true
equals_string: waveform_sd
sampling_rate:
name: sampling_rate
description: Sampling rate, in hertz.
range: float32
required: false
unit:
name: unit
description: Unit of measurement. This value is fixed to 'volts'.
ifabsent: string(volts)
range: text
required: false
equals_string: volts

Some files were not shown because too many files have changed in this diff Show more