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functional docs build
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176 changed files with 541 additions and 6091 deletions
25
docs/conf.py
25
docs/conf.py
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@ -27,7 +27,7 @@ extensions = [
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'sphinx.ext.graphviz',
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'sphinx.ext.napoleon',
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'sphinx.ext.autodoc',
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'sphinxcontrib.autodoc_pydantic',
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#'sphinxcontrib.autodoc_pydantic',
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'sphinx.ext.intersphinx',
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'sphinx.ext.doctest',
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"sphinx_design",
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@ -93,7 +93,8 @@ graphviz_output_format = "svg"
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# autodoc
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autodoc_pydantic_model_show_json_error_strategy = 'coerce'
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autodoc_pydantic_model_show_json = False
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autodoc_mock_imports = []
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#autodoc_mock_imports = ['pydantic', 'curies', 'pydantic_settings']
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autodoc_mock_imports = ['curies']
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autoclass_content = "both"
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autodoc_member_order='bysource'
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add_module_names = False
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@ -109,16 +110,16 @@ nb_append_css = False
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# --------------------------------------------------
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# doctest
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doctest_global_setup = """
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from linkml_runtime.linkml_model import ClassDefinition, SlotDefinition, SchemaDefinition
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from nwb_schema_language import Namespaces, Namespace, Dataset, Group, Schema
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from linkml_runtime.dumpers import yaml_dumper
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import yaml
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from pydantic import BaseModel, Field
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import numpy as np
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from nwb_linkml.adapters import BuildResult
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"""
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# doctest_global_setup = """
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# from linkml_runtime.linkml_model import ClassDefinition, SlotDefinition, SchemaDefinition
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# from nwb_schema_language import Namespaces, Namespace, Dataset, Group, Schema
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# from linkml_runtime.dumpers import yaml_dumper
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# import yaml
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# from pydantic import BaseModel, Field
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# import numpy as np
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#
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# from nwb_linkml.adapters import BuildResult
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# """
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# --------------------------------------------------
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# Etc one-off settings
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@ -118,7 +118,7 @@ class Adapter(BaseModel):
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Generate the corresponding linkML element for this adapter
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"""
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def walk(self, input: BaseModel | list | dict) -> Generator[BaseModel | Any | None, None, None]:
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def walk(self, input: Union[BaseModel, dict, list]) -> Generator[Union[BaseModel, Any, None], None, None]:
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"""
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Iterate through all items in the given model.
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@ -158,7 +158,7 @@ class Adapter(BaseModel):
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pass
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def walk_fields(
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self, input: BaseModel | list | dict, field: str | Tuple[str, ...]
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self, input: Union[BaseModel, dict, list], field: str | Tuple[str, ...]
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) -> Generator[Any, None, None]:
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"""
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Recursively walk input for fields that match ``field``
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@ -178,7 +178,7 @@ class Adapter(BaseModel):
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yield item[1]
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def walk_field_values(
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self, input: BaseModel | list | dict, field: str, value: Optional[Any] = None
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self, input: Union[BaseModel, dict, list], field: str, value: Optional[Any] = None
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) -> Generator[BaseModel, None, None]:
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"""
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Recursively walk input for **models** that contain a ``field`` as a direct child
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@ -202,7 +202,7 @@ class Adapter(BaseModel):
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yield item
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def walk_types(
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self, input: BaseModel | list | dict, get_type: Type[T] | Tuple[Type[T], Type[Unpack[Ts]]]
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self, input: Union[BaseModel, dict, list], get_type: Type[T] | Tuple[Type[T], Type[Unpack[Ts]]]
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) -> Generator[T | Ts, None, None]:
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"""
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Walk a model, yielding items that are the same type as the given type
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@ -67,10 +67,10 @@ from nwb_linkml.maps import flat_to_nptyping
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from nwb_linkml.maps.naming import module_case, version_module_case
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class LinkML_Meta(BaseModel):
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"""Extra LinkML Metadata stored as a class attribute"""
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tree_root: bool = False
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# class LinkML_Meta(BaseModel):
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# """Extra LinkML Metadata stored as a class attribute"""
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#
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# tree_root: bool = False
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def default_template(
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@ -112,32 +112,9 @@ from {{ import_module }} import (
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metamodel_version = "{{metamodel_version}}"
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version = "{{version if version else None}}"
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"""
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### BASE MODEL ###
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if pydantic_ver == "1": # pragma: no cover
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template += """
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List = BaseList
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class WeakRefShimBaseModel(BaseModel):
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__slots__ = '__weakref__'
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class ConfiguredBaseModel(WeakRefShimBaseModel,
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validate_assignment = False,
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validate_all = True,
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underscore_attrs_are_private = True,
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extra = {% if allow_extra %}'allow'{% else %}'forbid'{% endif %},
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arbitrary_types_allowed = True,
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use_enum_values = True):
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"""
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else:
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template += """
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class ConfiguredBaseModel(BaseModel):
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model_config = ConfigDict(
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validate_assignment = True,
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validate_default = True,
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extra = {% if allow_extra %}'allow'{% else %}'forbid'{% endif %},
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arbitrary_types_allowed = True,
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use_enum_values = True
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)
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"""
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### Injected Fields
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template += """
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@ -233,23 +210,6 @@ class {{ c.name }}
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None
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{% endfor %}
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{% endfor %}
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"""
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### FWD REFS / REBUILD MODEL ###
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if pydantic_ver == "1": # pragma: no cover
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template += """
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# Update forward refs
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# see https://pydantic-docs.helpmanual.io/usage/postponed_annotations/
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{% for c in schema.classes.values() -%}
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{{ c.name }}.update_forward_refs()
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{% endfor %}
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"""
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else:
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template += """
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# Model rebuild
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# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
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{% for c in schema.classes.values() -%}
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{{ c.name }}.model_rebuild()
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{% endfor %}
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"""
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return template
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@ -455,18 +415,18 @@ class NWBPydanticGenerator(PydanticGenerator):
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if not base_range_subsumes_any_of:
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raise ValueError("Slot cannot have both range and any_of defined")
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def _get_linkml_classvar(self, cls: ClassDefinition) -> SlotDefinition:
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"""A class variable that holds additional linkml attrs"""
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slot = SlotDefinition(name="linkml_meta")
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slot.annotations["python_range"] = Annotation("python_range", "ClassVar[LinkML_Meta]")
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meta_fields = {k: getattr(cls, k, None) for k in LinkML_Meta.model_fields.keys()}
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meta_field_strings = [f"{k}={v}" for k, v in meta_fields.items() if v is not None]
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meta_field_string = ", ".join(meta_field_strings)
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slot.annotations["fixed_field"] = Annotation(
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"fixed_field", f"Field(LinkML_Meta({meta_field_string}), frozen=True)"
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)
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return slot
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# def _get_linkml_classvar(self, cls: ClassDefinition) -> SlotDefinition:
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# """A class variable that holds additional linkml attrs"""
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# slot = SlotDefinition(name="linkml_meta")
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# slot.annotations["python_range"] = Annotation("python_range", "ClassVar[LinkML_Meta]")
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# meta_fields = {k: getattr(cls, k, None) for k in LinkML_Meta.model_fields.keys()}
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# meta_field_strings = [f"{k}={v}" for k, v in meta_fields.items() if v is not None]
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# meta_field_string = ", ".join(meta_field_strings)
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# slot.annotations["fixed_field"] = Annotation(
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# "fixed_field", f"Field(LinkML_Meta({meta_field_string}), frozen=True)"
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# )
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#
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# return slot
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def sort_classes(
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self, clist: List[ClassDefinition], imports: Dict[str, List[str]]
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@ -673,7 +633,7 @@ class NWBPydanticGenerator(PydanticGenerator):
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template_obj = Template(template_file.read())
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else:
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template_obj = Template(
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default_template(self.pydantic_version, extra_classes=[LinkML_Meta])
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default_template(self.pydantic_version, extra_classes=[])
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)
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sv: SchemaView
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@ -718,7 +678,7 @@ class NWBPydanticGenerator(PydanticGenerator):
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del class_def.attributes[attribute]
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# make class attr that stores extra linkml attrs
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class_def.attributes["linkml_meta"] = self._get_linkml_classvar(class_def)
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# class_def.attributes["linkml_meta"] = self._get_linkml_classvar(class_def)
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class_name = class_original.name
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predefined_slot_values[camelcase(class_name)] = {}
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@ -27,7 +27,7 @@ if TYPE_CHECKING:
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import numpy as np
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from ...hdmf_common.v1_1_0.hdmf_common_table import Container, Data, DynamicTable
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from ...hdmf_common.v1_1_0.hdmf_common_table import Data, Container, DynamicTable
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metamodel_version = "None"
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@ -35,13 +35,6 @@ version = "2.2.0"
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class ConfiguredBaseModel(BaseModel):
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model_config = ConfigDict(
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validate_assignment=True,
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validate_default=True,
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extra="allow",
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arbitrary_types_allowed=True,
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use_enum_values=True,
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)
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hdf5_path: Optional[str] = Field(
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None, description="The absolute path that this object is stored in an NWB file"
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)
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@ -61,18 +54,11 @@ class ConfiguredBaseModel(BaseModel):
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super().__setitem__(i, value)
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class LinkML_Meta(BaseModel):
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"""Extra LinkML Metadata stored as a class attribute"""
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tree_root: bool = False
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class NWBData(Data):
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"""
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An abstract data type for a dataset.
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"""
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linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
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name: str = Field(...)
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@ -81,7 +67,6 @@ class Image(NWBData):
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An abstract data type for an image. Shape can be 2-D (x, y), or 3-D where the third dimension can have three or four elements, e.g. (x, y, (r, g, b)) or (x, y, (r, g, b, a)).
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"""
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linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
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name: str = Field(...)
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resolution: Optional[float] = Field(
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None, description="""Pixel resolution of the image, in pixels per centimeter."""
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@ -103,7 +88,6 @@ class NWBContainer(Container):
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An abstract data type for a generic container storing collections of data and metadata. Base type for all data and metadata containers.
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"""
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linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
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name: str = Field(...)
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@ -112,7 +96,6 @@ class NWBDataInterface(NWBContainer):
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An abstract data type for a generic container storing collections of data, as opposed to metadata.
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"""
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linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
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name: str = Field(...)
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@ -121,7 +104,6 @@ class TimeSeries(NWBDataInterface):
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General purpose time series.
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"""
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linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
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name: str = Field(...)
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description: Optional[str] = Field(
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None, description="""Description of the time series."""
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@ -161,7 +143,6 @@ class TimeSeriesData(ConfiguredBaseModel):
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Data values. Data can be in 1-D, 2-D, 3-D, or 4-D. The first dimension should always represent time. This can also be used to store binary data (e.g., image frames). This can also be a link to data stored in an external file.
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"""
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linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
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name: Literal["data"] = Field("data")
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conversion: Optional[float] = Field(
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None,
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@ -190,7 +171,6 @@ class TimeSeriesStartingTime(ConfiguredBaseModel):
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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.
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"""
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linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
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name: Literal["starting_time"] = Field("starting_time")
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rate: Optional[float] = Field(None, description="""Sampling rate, in Hz.""")
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unit: Optional[str] = Field(
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@ -205,7 +185,6 @@ class TimeSeriesSync(ConfiguredBaseModel):
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Lab-specific time and sync information as provided directly from hardware devices and that is necessary for aligning all acquired time information to a common timebase. The timestamp array stores time in the common timebase. This group will usually only be populated in TimeSeries that are stored external to the NWB file, in files storing raw data. Once timestamp data is calculated, the contents of 'sync' are mostly for archival purposes.
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"""
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linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
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name: Literal["sync"] = Field("sync")
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@ -214,7 +193,6 @@ class ProcessingModule(NWBContainer):
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A collection of processed data.
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"""
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linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
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children: Optional[
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List[Union[BaseModel, DynamicTable, NWBDataInterface]]
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| Union[BaseModel, DynamicTable, NWBDataInterface]
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@ -227,7 +205,6 @@ class Images(NWBDataInterface):
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A collection of images.
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"""
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linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
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name: str = Field("Images")
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description: Optional[str] = Field(
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None, description="""Description of this collection of images."""
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@ -235,17 +212,3 @@ class Images(NWBDataInterface):
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image: List[str] | str = Field(
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default_factory=list, description="""Images stored in this collection."""
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)
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# Model rebuild
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# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
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NWBData.model_rebuild()
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Image.model_rebuild()
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NWBContainer.model_rebuild()
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NWBDataInterface.model_rebuild()
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TimeSeries.model_rebuild()
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TimeSeriesData.model_rebuild()
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TimeSeriesStartingTime.model_rebuild()
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TimeSeriesSync.model_rebuild()
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ProcessingModule.model_rebuild()
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Images.model_rebuild()
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|
|
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@ -28,9 +28,9 @@ if TYPE_CHECKING:
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from .core_nwb_base import (
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NWBDataInterface,
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TimeSeries,
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TimeSeriesStartingTime,
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NWBDataInterface,
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TimeSeriesSync,
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)
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@ -42,13 +42,6 @@ version = "2.2.0"
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class ConfiguredBaseModel(BaseModel):
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model_config = ConfigDict(
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validate_assignment=True,
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validate_default=True,
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extra="allow",
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arbitrary_types_allowed=True,
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||||
use_enum_values=True,
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)
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hdf5_path: Optional[str] = Field(
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None, description="The absolute path that this object is stored in an NWB file"
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)
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|
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@ -68,18 +61,11 @@ class ConfiguredBaseModel(BaseModel):
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super().__setitem__(i, value)
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class LinkML_Meta(BaseModel):
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"""Extra LinkML Metadata stored as a class attribute"""
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tree_root: bool = False
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class SpatialSeries(TimeSeries):
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"""
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Direction, e.g., of gaze or travel, or position. The TimeSeries::data field is a 2D array storing position or direction relative to some reference frame. Array structure: [num measurements] [num dimensions]. Each SpatialSeries has a text dataset reference_frame that indicates the zero-position, or the zero-axes for direction. For example, if representing gaze direction, 'straight-ahead' might be a specific pixel on the monitor, or some other point in space. For position data, the 0,0 point might be the top-left corner of an enclosure, as viewed from the tracking camera. The unit of data will indicate how to interpret SpatialSeries values.
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"""
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linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
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name: str = Field(...)
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data: str = Field(
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||||
...,
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||||
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@ -123,7 +109,6 @@ class SpatialSeriesData(ConfiguredBaseModel):
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1-D or 2-D array storing position or direction relative to some reference frame.
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"""
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linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
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name: Literal["data"] = Field("data")
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unit: Optional[str] = Field(
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None,
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@ -142,7 +127,6 @@ class BehavioralEpochs(NWBDataInterface):
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TimeSeries for storing behavioral epochs. The objective of this and the other two Behavioral interfaces (e.g. BehavioralEvents and BehavioralTimeSeries) is to provide generic hooks for software tools/scripts. This allows a tool/script to take the output one specific interface (e.g., UnitTimes) and plot that data relative to another data modality (e.g., behavioral events) without having to define all possible modalities in advance. Declaring one of these interfaces means that one or more TimeSeries of the specified type is published. These TimeSeries should reside in a group having the same name as the interface. For example, if a BehavioralTimeSeries interface is declared, the module will have one or more TimeSeries defined in the module sub-group 'BehavioralTimeSeries'. BehavioralEpochs should use IntervalSeries. BehavioralEvents is used for irregular events. BehavioralTimeSeries is for continuous data.
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"""
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||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
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children: Optional[List[IntervalSeries] | IntervalSeries] = Field(
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default_factory=dict
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)
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@ -154,7 +138,6 @@ class BehavioralEvents(NWBDataInterface):
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TimeSeries for storing behavioral events. See description of <a href=\"#BehavioralEpochs\">BehavioralEpochs</a> for more details.
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"""
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||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[TimeSeries] | TimeSeries] = Field(default_factory=dict)
|
||||
name: str = Field(...)
|
||||
|
||||
|
|
@ -164,7 +147,6 @@ class BehavioralTimeSeries(NWBDataInterface):
|
|||
TimeSeries for storing Behavoioral time series data. See description of <a href=\"#BehavioralEpochs\">BehavioralEpochs</a> for more details.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[TimeSeries] | TimeSeries] = Field(default_factory=dict)
|
||||
name: str = Field(...)
|
||||
|
||||
|
|
@ -174,7 +156,6 @@ class PupilTracking(NWBDataInterface):
|
|||
Eye-tracking data, representing pupil size.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[TimeSeries] | TimeSeries] = Field(default_factory=dict)
|
||||
name: str = Field(...)
|
||||
|
||||
|
|
@ -184,7 +165,6 @@ class EyeTracking(NWBDataInterface):
|
|||
Eye-tracking data, representing direction of gaze.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[SpatialSeries] | SpatialSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -196,7 +176,6 @@ class CompassDirection(NWBDataInterface):
|
|||
With a CompassDirection interface, a module publishes a SpatialSeries object representing a floating point value for theta. The SpatialSeries::reference_frame field should indicate what direction corresponds to 0 and which is the direction of rotation (this should be clockwise). The si_unit for the SpatialSeries should be radians or degrees.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[SpatialSeries] | SpatialSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -208,21 +187,7 @@ class Position(NWBDataInterface):
|
|||
Position data, whether along the x, x/y or x/y/z axis.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[SpatialSeries] | SpatialSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
name: str = Field(...)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
SpatialSeries.model_rebuild()
|
||||
SpatialSeriesData.model_rebuild()
|
||||
BehavioralEpochs.model_rebuild()
|
||||
BehavioralEvents.model_rebuild()
|
||||
BehavioralTimeSeries.model_rebuild()
|
||||
PupilTracking.model_rebuild()
|
||||
EyeTracking.model_rebuild()
|
||||
CompassDirection.model_rebuild()
|
||||
Position.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -35,13 +35,6 @@ version = "2.2.0"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -61,18 +54,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class Device(NWBContainer):
|
||||
"""
|
||||
Metadata about a data acquisition device, e.g., recording system, electrode, microscope.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -81,8 +67,3 @@ class Device(NWBContainer):
|
|||
manufacturer: Optional[str] = Field(
|
||||
None, description="""The name of the manufacturer of the device."""
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
Device.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -27,29 +27,22 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from ...hdmf_common.v1_1_0.hdmf_common_table import DynamicTableRegion, DynamicTable
|
||||
|
||||
from .core_nwb_base import (
|
||||
NWBContainer,
|
||||
TimeSeriesStartingTime,
|
||||
NWBDataInterface,
|
||||
TimeSeries,
|
||||
TimeSeriesStartingTime,
|
||||
NWBContainer,
|
||||
TimeSeriesSync,
|
||||
)
|
||||
|
||||
from ...hdmf_common.v1_1_0.hdmf_common_table import DynamicTableRegion, DynamicTable
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
version = "2.2.0"
|
||||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -69,18 +62,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class ElectricalSeries(TimeSeries):
|
||||
"""
|
||||
A time series of acquired voltage data from extracellular recordings. The data field is an int or float array storing data in volts. The first dimension should always represent time. The second dimension, if present, should represent channels.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: Union[
|
||||
NDArray[Shape["* num_times"], float],
|
||||
|
|
@ -129,7 +115,6 @@ class ElectricalSeriesElectrodes(DynamicTableRegion):
|
|||
DynamicTableRegion pointer to the electrodes that this time series was generated from.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["electrodes"] = Field("electrodes")
|
||||
table: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -145,7 +130,6 @@ 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).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: Union[
|
||||
NDArray[Shape["* num_events, * num_channels, * num_samples"], float],
|
||||
|
|
@ -193,7 +177,6 @@ class FeatureExtraction(NWBDataInterface):
|
|||
Features, such as PC1 and PC2, that are extracted from signals stored in a SpikeEventSeries or other source.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("FeatureExtraction")
|
||||
description: NDArray[Shape["* num_features"], str] = Field(
|
||||
...,
|
||||
|
|
@ -220,7 +203,6 @@ class FeatureExtractionElectrodes(DynamicTableRegion):
|
|||
DynamicTableRegion pointer to the electrodes that this time series was generated from.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["electrodes"] = Field("electrodes")
|
||||
table: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -236,7 +218,6 @@ class EventDetection(NWBDataInterface):
|
|||
Detected spike events from voltage trace(s).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("EventDetection")
|
||||
detection_method: str = Field(
|
||||
...,
|
||||
|
|
@ -256,7 +237,6 @@ class EventWaveform(NWBDataInterface):
|
|||
Represents either the waveforms of detected events, as extracted from a raw data trace in /acquisition, or the event waveforms that were stored during experiment acquisition.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[SpikeEventSeries] | SpikeEventSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -268,7 +248,6 @@ class FilteredEphys(NWBDataInterface):
|
|||
Electrophysiology data from one or more channels that has been subjected to filtering. Examples of filtered data include Theta and Gamma (LFP has its own interface). FilteredEphys modules publish an ElectricalSeries for each filtered channel or set of channels. The name of each ElectricalSeries is arbitrary but should be informative. The source of the filtered data, whether this is from analysis of another time series or as acquired by hardware, should be noted in each's TimeSeries::description field. There is no assumed 1::1 correspondence between filtered ephys signals and electrodes, as a single signal can apply to many nearby electrodes, and one electrode may have different filtered (e.g., theta and/or gamma) signals represented. Filter properties should be noted in the ElectricalSeries.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[ElectricalSeries] | ElectricalSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -280,7 +259,6 @@ class LFP(NWBDataInterface):
|
|||
LFP data from one or more channels. The electrode map in each published ElectricalSeries will identify which channels are providing LFP data. Filter properties should be noted in the ElectricalSeries description or comments field.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[ElectricalSeries] | ElectricalSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -292,7 +270,6 @@ class ElectrodeGroup(NWBContainer):
|
|||
A physical grouping of electrodes, e.g. a shank of an array.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: Optional[str] = Field(
|
||||
None, description="""Description of this electrode group."""
|
||||
|
|
@ -311,7 +288,6 @@ class ElectrodeGroupPosition(ConfiguredBaseModel):
|
|||
stereotaxic or common framework coordinates
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["position"] = Field("position")
|
||||
x: Optional[float] = Field(None, description="""x coordinate""")
|
||||
y: Optional[float] = Field(None, description="""y coordinate""")
|
||||
|
|
@ -323,7 +299,6 @@ class ClusterWaveforms(NWBDataInterface):
|
|||
DEPRECATED The mean waveform shape, including standard deviation, of the different clusters. Ideally, the waveform analysis should be performed on data that is only high-pass filtered. This is a separate module because it is expected to require updating. For example, IMEC probes may require different storage requirements to store/display mean waveforms, requiring a new interface or an extension of this one.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("ClusterWaveforms")
|
||||
waveform_filtering: str = Field(
|
||||
..., description="""Filtering applied to data before generating mean/sd"""
|
||||
|
|
@ -343,7 +318,6 @@ class Clustering(NWBDataInterface):
|
|||
DEPRECATED Clustered spike data, whether from automatic clustering tools (e.g., klustakwik) or as a result of manual sorting.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("Clustering")
|
||||
description: str = Field(
|
||||
...,
|
||||
|
|
@ -360,20 +334,3 @@ class Clustering(NWBDataInterface):
|
|||
...,
|
||||
description="""Times of clustered events, in seconds. This may be a link to times field in associated FeatureExtraction module.""",
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
ElectricalSeries.model_rebuild()
|
||||
ElectricalSeriesElectrodes.model_rebuild()
|
||||
SpikeEventSeries.model_rebuild()
|
||||
FeatureExtraction.model_rebuild()
|
||||
FeatureExtractionElectrodes.model_rebuild()
|
||||
EventDetection.model_rebuild()
|
||||
EventWaveform.model_rebuild()
|
||||
FilteredEphys.model_rebuild()
|
||||
LFP.model_rebuild()
|
||||
ElectrodeGroup.model_rebuild()
|
||||
ElectrodeGroupPosition.model_rebuild()
|
||||
ClusterWaveforms.model_rebuild()
|
||||
Clustering.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -27,27 +27,20 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from .core_nwb_base import TimeSeries
|
||||
|
||||
from ...hdmf_common.v1_1_0.hdmf_common_table import (
|
||||
DynamicTable,
|
||||
VectorIndex,
|
||||
DynamicTable,
|
||||
VectorData,
|
||||
)
|
||||
|
||||
from .core_nwb_base import TimeSeries
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
version = "2.2.0"
|
||||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -67,18 +60,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class TimeIntervals(DynamicTable):
|
||||
"""
|
||||
A container for aggregating epoch data and the TimeSeries that each epoch applies to.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
start_time: Optional[List[float] | float] = Field(
|
||||
default_factory=list, description="""Start time of epoch, in seconds."""
|
||||
|
|
@ -122,7 +108,6 @@ class TimeIntervalsTagsIndex(VectorIndex):
|
|||
Index for tags.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["tags_index"] = Field("tags_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -135,7 +120,6 @@ class TimeIntervalsTimeseries(VectorData):
|
|||
An index into a TimeSeries object.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["timeseries"] = Field("timeseries")
|
||||
idx_start: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -158,17 +142,8 @@ class TimeIntervalsTimeseriesIndex(VectorIndex):
|
|||
Index for timeseries.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["timeseries_index"] = Field("timeseries_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
description="""Reference to the target dataset that this index applies to.""",
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
TimeIntervals.model_rebuild()
|
||||
TimeIntervalsTagsIndex.model_rebuild()
|
||||
TimeIntervalsTimeseries.model_rebuild()
|
||||
TimeIntervalsTimeseriesIndex.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -27,27 +27,27 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from .core_nwb_ophys import ImagingPlane
|
||||
|
||||
from .core_nwb_base import TimeSeries, NWBDataInterface, ProcessingModule, NWBContainer
|
||||
from .core_nwb_icephys import SweepTable, IntracellularElectrode
|
||||
|
||||
from .core_nwb_epoch import TimeIntervals
|
||||
|
||||
from ...hdmf_common.v1_1_0.hdmf_common_table import (
|
||||
DynamicTable,
|
||||
VectorData,
|
||||
VectorIndex,
|
||||
)
|
||||
from .core_nwb_ecephys import ElectrodeGroup
|
||||
|
||||
from .core_nwb_icephys import IntracellularElectrode, SweepTable
|
||||
from .core_nwb_base import NWBContainer, ProcessingModule, NWBDataInterface, TimeSeries
|
||||
|
||||
from .core_nwb_ogen import OptogeneticStimulusSite
|
||||
from .core_nwb_device import Device
|
||||
|
||||
from .core_nwb_misc import Units
|
||||
|
||||
from .core_nwb_ecephys import ElectrodeGroup
|
||||
from .core_nwb_ogen import OptogeneticStimulusSite
|
||||
|
||||
from .core_nwb_device import Device
|
||||
from ...hdmf_common.v1_1_0.hdmf_common_table import (
|
||||
VectorIndex,
|
||||
DynamicTable,
|
||||
VectorData,
|
||||
)
|
||||
|
||||
from .core_nwb_ophys import ImagingPlane
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
|
|
@ -55,13 +55,6 @@ version = "2.2.0"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -81,18 +74,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class NWBFile(NWBContainer):
|
||||
"""
|
||||
An NWB:N file storing cellular-based neurophysiology data from a single experimental session.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: Literal["root"] = Field("root")
|
||||
nwb_version: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -163,7 +149,6 @@ class NWBFileStimulus(ConfiguredBaseModel):
|
|||
Data pushed into the system (eg, video stimulus, sound, voltage, etc) and secondary representations of that data (eg, measurements of something used as a stimulus). This group should be made read-only after experiment complete and timestamps are corrected to common timebase. Stores both presented stimuli and stimulus templates, the latter in case the same stimulus is presented multiple times, or is pulled from an external stimulus library. Stimuli are here defined as any signal that is pushed into the system as part of the experiment (eg, sound, video, voltage, etc). Many different experiments can use the same stimuli, and stimuli can be re-used during an experiment. The stimulus group is organized so that one version of template stimuli can be stored and these be used multiple times. These templates can exist in the present file or can be linked to a remote library file.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["stimulus"] = Field("stimulus")
|
||||
presentation: Optional[List[TimeSeries] | TimeSeries] = Field(
|
||||
default_factory=dict, description="""Stimuli presented during the experiment."""
|
||||
|
|
@ -179,7 +164,6 @@ class NWBFileGeneral(ConfiguredBaseModel):
|
|||
Experimental metadata, including protocol, notes and description of hardware device(s). The metadata stored in this section should be used to describe the experiment. Metadata necessary for interpreting the data is stored with the data. General experimental metadata, including animal strain, experimental protocols, experimenter, devices, etc, are stored under 'general'. Core metadata (e.g., that required to interpret data fields) is stored with the data itself, and implicitly defined by the file specification (e.g., time is in seconds). The strategy used here for storing non-core metadata is to use free-form text fields, such as would appear in sentences or paragraphs from a Methods section. Metadata fields are text to enable them to be more general, for example to represent ranges instead of numerical values. Machine-readable metadata is stored as attributes to these free-form datasets. All entries in the below table are to be included when data is present. Unused groups (e.g., intracellular_ephys in an optophysiology experiment) should not be created unless there is data to store within them.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["general"] = Field("general")
|
||||
data_collection: Optional[str] = Field(
|
||||
None, description="""Notes about data collection and analysis."""
|
||||
|
|
@ -269,7 +253,6 @@ class NWBFileGeneralSourceScript(ConfiguredBaseModel):
|
|||
Script file or link to public source code used to create this NWB file.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["source_script"] = Field("source_script")
|
||||
file_name: Optional[str] = Field(None, description="""Name of script file.""")
|
||||
value: str = Field(...)
|
||||
|
|
@ -280,7 +263,6 @@ class Subject(NWBContainer):
|
|||
Information about the animal or person from which the data was measured.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["subject"] = Field("subject")
|
||||
age: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -314,7 +296,6 @@ class NWBFileGeneralExtracellularEphys(ConfiguredBaseModel):
|
|||
Metadata related to extracellular electrophysiology.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["extracellular_ephys"] = Field("extracellular_ephys")
|
||||
electrode_group: Optional[List[str] | str] = Field(
|
||||
default_factory=list, description="""Physical group of electrodes."""
|
||||
|
|
@ -330,7 +311,6 @@ class NWBFileGeneralExtracellularEphysElectrodes(DynamicTable):
|
|||
A table of all electrodes (i.e. channels) used for recording.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["electrodes"] = Field("electrodes")
|
||||
x: Optional[List[float] | float] = Field(
|
||||
default_factory=list,
|
||||
|
|
@ -400,7 +380,6 @@ class NWBFileGeneralIntracellularEphys(ConfiguredBaseModel):
|
|||
Metadata related to intracellular electrophysiology.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["intracellular_ephys"] = Field("intracellular_ephys")
|
||||
filtering: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -413,15 +392,3 @@ class NWBFileGeneralIntracellularEphys(ConfiguredBaseModel):
|
|||
None,
|
||||
description="""The table which groups different PatchClampSeries together.""",
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
NWBFile.model_rebuild()
|
||||
NWBFileStimulus.model_rebuild()
|
||||
NWBFileGeneral.model_rebuild()
|
||||
NWBFileGeneralSourceScript.model_rebuild()
|
||||
Subject.model_rebuild()
|
||||
NWBFileGeneralExtracellularEphys.model_rebuild()
|
||||
NWBFileGeneralExtracellularEphysElectrodes.model_rebuild()
|
||||
NWBFileGeneralIntracellularEphys.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -28,16 +28,16 @@ if TYPE_CHECKING:
|
|||
|
||||
|
||||
from .core_nwb_base import (
|
||||
TimeSeries,
|
||||
NWBContainer,
|
||||
TimeSeriesSync,
|
||||
TimeSeriesStartingTime,
|
||||
NWBContainer,
|
||||
TimeSeries,
|
||||
)
|
||||
|
||||
from ...hdmf_common.v1_1_0.hdmf_common_table import (
|
||||
VectorIndex,
|
||||
DynamicTable,
|
||||
VectorData,
|
||||
VectorIndex,
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -46,13 +46,6 @@ version = "2.2.0"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -72,18 +65,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class PatchClampSeries(TimeSeries):
|
||||
"""
|
||||
An abstract base class for patch-clamp data - stimulus or response, current or voltage.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
stimulus_description: Optional[str] = Field(
|
||||
None, description="""Protocol/stimulus name for this patch-clamp dataset."""
|
||||
|
|
@ -131,7 +117,6 @@ class PatchClampSeriesData(ConfiguredBaseModel):
|
|||
Recorded voltage or current.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -145,7 +130,6 @@ class CurrentClampSeries(PatchClampSeries):
|
|||
Voltage data from an intracellular current-clamp recording. A corresponding CurrentClampStimulusSeries (stored separately as a stimulus) is used to store the current injected.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Recorded voltage.""")
|
||||
bias_current: Optional[float] = Field(
|
||||
|
|
@ -202,7 +186,6 @@ class CurrentClampSeriesData(ConfiguredBaseModel):
|
|||
Recorded voltage.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -216,7 +199,6 @@ class IZeroClampSeries(CurrentClampSeries):
|
|||
Voltage data from an intracellular recording when all current and amplifier settings are off (i.e., CurrentClampSeries fields will be zero). There is no CurrentClampStimulusSeries associated with an IZero series because the amplifier is disconnected and no stimulus can reach the cell.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
bias_current: float = Field(
|
||||
..., description="""Bias current, in amps, fixed to 0.0."""
|
||||
|
|
@ -273,7 +255,6 @@ class CurrentClampStimulusSeries(PatchClampSeries):
|
|||
Stimulus current applied during current clamp recording.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Stimulus current applied.""")
|
||||
stimulus_description: Optional[str] = Field(
|
||||
|
|
@ -321,7 +302,6 @@ class CurrentClampStimulusSeriesData(ConfiguredBaseModel):
|
|||
Stimulus current applied.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -335,7 +315,6 @@ class VoltageClampSeries(PatchClampSeries):
|
|||
Current data from an intracellular voltage-clamp recording. A corresponding VoltageClampStimulusSeries (stored separately as a stimulus) is used to store the voltage injected.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Recorded current.""")
|
||||
capacitance_fast: Optional[str] = Field(
|
||||
|
|
@ -404,7 +383,6 @@ class VoltageClampSeriesData(ConfiguredBaseModel):
|
|||
Recorded current.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -418,7 +396,6 @@ class VoltageClampSeriesCapacitanceFast(ConfiguredBaseModel):
|
|||
Fast capacitance, in farads.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["capacitance_fast"] = Field("capacitance_fast")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -432,7 +409,6 @@ class VoltageClampSeriesCapacitanceSlow(ConfiguredBaseModel):
|
|||
Slow capacitance, in farads.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["capacitance_slow"] = Field("capacitance_slow")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -446,7 +422,6 @@ class VoltageClampSeriesResistanceCompBandwidth(ConfiguredBaseModel):
|
|||
Resistance compensation bandwidth, in hertz.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["resistance_comp_bandwidth"] = Field("resistance_comp_bandwidth")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -460,7 +435,6 @@ class VoltageClampSeriesResistanceCompCorrection(ConfiguredBaseModel):
|
|||
Resistance compensation correction, in percent.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["resistance_comp_correction"] = Field("resistance_comp_correction")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -474,7 +448,6 @@ class VoltageClampSeriesResistanceCompPrediction(ConfiguredBaseModel):
|
|||
Resistance compensation prediction, in percent.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["resistance_comp_prediction"] = Field("resistance_comp_prediction")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -488,7 +461,6 @@ class VoltageClampSeriesWholeCellCapacitanceComp(ConfiguredBaseModel):
|
|||
Whole cell capacitance compensation, in farads.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["whole_cell_capacitance_comp"] = Field("whole_cell_capacitance_comp")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -502,7 +474,6 @@ class VoltageClampSeriesWholeCellSeriesResistanceComp(ConfiguredBaseModel):
|
|||
Whole cell series resistance compensation, in ohms.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["whole_cell_series_resistance_comp"] = Field(
|
||||
"whole_cell_series_resistance_comp"
|
||||
)
|
||||
|
|
@ -518,7 +489,6 @@ class VoltageClampStimulusSeries(PatchClampSeries):
|
|||
Stimulus voltage applied during a voltage clamp recording.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Stimulus voltage applied.""")
|
||||
stimulus_description: Optional[str] = Field(
|
||||
|
|
@ -566,7 +536,6 @@ class VoltageClampStimulusSeriesData(ConfiguredBaseModel):
|
|||
Stimulus voltage applied.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -580,7 +549,6 @@ class IntracellularElectrode(NWBContainer):
|
|||
An intracellular electrode and its metadata.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: str = Field(
|
||||
...,
|
||||
|
|
@ -612,7 +580,6 @@ class SweepTable(DynamicTable):
|
|||
The table which groups different PatchClampSeries together.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
sweep_number: Optional[List[int] | int] = Field(
|
||||
default_factory=list,
|
||||
|
|
@ -648,34 +615,8 @@ class SweepTableSeriesIndex(VectorIndex):
|
|||
Index for series.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["series_index"] = Field("series_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
description="""Reference to the target dataset that this index applies to.""",
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
PatchClampSeries.model_rebuild()
|
||||
PatchClampSeriesData.model_rebuild()
|
||||
CurrentClampSeries.model_rebuild()
|
||||
CurrentClampSeriesData.model_rebuild()
|
||||
IZeroClampSeries.model_rebuild()
|
||||
CurrentClampStimulusSeries.model_rebuild()
|
||||
CurrentClampStimulusSeriesData.model_rebuild()
|
||||
VoltageClampSeries.model_rebuild()
|
||||
VoltageClampSeriesData.model_rebuild()
|
||||
VoltageClampSeriesCapacitanceFast.model_rebuild()
|
||||
VoltageClampSeriesCapacitanceSlow.model_rebuild()
|
||||
VoltageClampSeriesResistanceCompBandwidth.model_rebuild()
|
||||
VoltageClampSeriesResistanceCompCorrection.model_rebuild()
|
||||
VoltageClampSeriesResistanceCompPrediction.model_rebuild()
|
||||
VoltageClampSeriesWholeCellCapacitanceComp.model_rebuild()
|
||||
VoltageClampSeriesWholeCellSeriesResistanceComp.model_rebuild()
|
||||
VoltageClampStimulusSeries.model_rebuild()
|
||||
VoltageClampStimulusSeriesData.model_rebuild()
|
||||
IntracellularElectrode.model_rebuild()
|
||||
SweepTable.model_rebuild()
|
||||
SweepTableSeriesIndex.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from .core_nwb_base import Image, TimeSeries, TimeSeriesStartingTime, TimeSeriesSync
|
||||
from .core_nwb_base import Image, TimeSeriesStartingTime, TimeSeries, TimeSeriesSync
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
|
|
@ -35,13 +35,6 @@ version = "2.2.0"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -61,18 +54,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class GrayscaleImage(Image):
|
||||
"""
|
||||
A grayscale image.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
resolution: Optional[float] = Field(
|
||||
None, description="""Pixel resolution of the image, in pixels per centimeter."""
|
||||
|
|
@ -94,7 +80,6 @@ class RGBImage(Image):
|
|||
A color image.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
resolution: Optional[float] = Field(
|
||||
None, description="""Pixel resolution of the image, in pixels per centimeter."""
|
||||
|
|
@ -116,7 +101,6 @@ class RGBAImage(Image):
|
|||
A color image with transparency.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
resolution: Optional[float] = Field(
|
||||
None, description="""Pixel resolution of the image, in pixels per centimeter."""
|
||||
|
|
@ -138,7 +122,6 @@ class ImageSeries(TimeSeries):
|
|||
General image data that is common between acquisition and stimulus time series. Sometimes the image data is stored in the file in a raw format while other times it will be stored as a series of external image files in the host file system. The data field will either be binary data, if the data is stored in the NWB file, or empty, if the data is stored in an external image stack. [frame][x][y] or [frame][x][y][z].
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: Optional[
|
||||
Union[
|
||||
|
|
@ -191,7 +174,6 @@ 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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["external_file"] = Field("external_file")
|
||||
starting_frame: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -205,7 +187,6 @@ class ImageMaskSeries(ImageSeries):
|
|||
An alpha mask that is applied to a presented visual stimulus. The 'data' array contains an array of mask values that are applied to the displayed image. Mask values are stored as RGBA. Mask can vary with time. The timestamps array indicates the starting time of a mask, and that mask pattern continues until it's explicitly changed.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: Optional[
|
||||
Union[
|
||||
|
|
@ -258,7 +239,6 @@ class OpticalSeries(ImageSeries):
|
|||
Image data that is presented or recorded. A stimulus template movie will be stored only as an image. When the image is presented as stimulus, additional data is required, such as field of view (e.g., how much of the visual field the image covers, or how what is the area of the target being imaged). If the OpticalSeries represents acquired imaging data, orientation is also important.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
distance: Optional[float] = Field(
|
||||
None, description="""Distance from camera/monitor to target/eye."""
|
||||
|
|
@ -327,7 +307,6 @@ 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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: NDArray[Shape["* num_times"], int] = Field(
|
||||
..., description="""Index of the frame in the referenced ImageSeries."""
|
||||
|
|
@ -359,15 +338,3 @@ class IndexSeries(TimeSeries):
|
|||
None,
|
||||
description="""Lab-specific time and sync information as provided directly from hardware devices and that is necessary for aligning all acquired time information to a common timebase. The timestamp array stores time in the common timebase. This group will usually only be populated in TimeSeries that are stored external to the NWB file, in files storing raw data. Once timestamp data is calculated, the contents of 'sync' are mostly for archival purposes.""",
|
||||
)
|
||||
|
||||
|
||||
# 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()
|
||||
ImageSeriesExternalFile.model_rebuild()
|
||||
ImageMaskSeries.model_rebuild()
|
||||
OpticalSeries.model_rebuild()
|
||||
IndexSeries.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -32,13 +32,6 @@ version = "None"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -56,13 +49,3 @@ class ConfiguredBaseModel(BaseModel):
|
|||
self.array[i] = value
|
||||
else:
|
||||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
|
|
|
|||
|
|
@ -27,15 +27,15 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from .core_nwb_base import TimeSeries, TimeSeriesStartingTime, TimeSeriesSync
|
||||
|
||||
from ...hdmf_common.v1_1_0.hdmf_common_table import (
|
||||
DynamicTableRegion,
|
||||
DynamicTable,
|
||||
VectorIndex,
|
||||
DynamicTable,
|
||||
VectorData,
|
||||
)
|
||||
|
||||
from .core_nwb_base import TimeSeriesStartingTime, TimeSeries, TimeSeriesSync
|
||||
|
||||
from .core_nwb_ecephys import ElectrodeGroup
|
||||
|
||||
|
||||
|
|
@ -44,13 +44,6 @@ version = "2.2.0"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -70,18 +63,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class AbstractFeatureSeries(TimeSeries):
|
||||
"""
|
||||
Abstract features, such as quantitative descriptions of sensory stimuli. The TimeSeries::data field is a 2D array, storing those features (e.g., for visual grating stimulus this might be orientation, spatial frequency and contrast). Null stimuli (eg, uniform gray) can be marked as being an independent feature (eg, 1.0 for gray, 0.0 for actual stimulus) or by storing NaNs for feature values, or through use of the TimeSeries::control fields. A set of features is considered to persist until the next set of features is defined. The final set of features stored should be the null set. This is useful when storing the raw stimulus is impractical.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Values of each feature at each time.""")
|
||||
feature_units: Optional[NDArray[Shape["* num_features"], str]] = Field(
|
||||
|
|
@ -125,7 +111,6 @@ class AbstractFeatureSeriesData(ConfiguredBaseModel):
|
|||
Values of each feature at each time.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -144,7 +129,6 @@ class AnnotationSeries(TimeSeries):
|
|||
Stores user annotations made during an experiment. The data[] field stores a text array, and timestamps are stored for each annotation (ie, interval=1). This is largely an alias to a standard TimeSeries storing a text array but that is identifiable as storing annotations in a machine-readable way.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: NDArray[Shape["* num_times"], str] = Field(
|
||||
..., description="""Annotations made during an experiment."""
|
||||
|
|
@ -183,7 +167,6 @@ 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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: NDArray[Shape["* num_times"], int] = Field(
|
||||
..., description="""Use values >0 if interval started, <0 if interval ended."""
|
||||
|
|
@ -222,7 +205,6 @@ class DecompositionSeries(TimeSeries):
|
|||
Spectral analysis of a time series, e.g. of an LFP or a speech signal.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Data decomposed into frequency bands.""")
|
||||
metric: str = Field(
|
||||
|
|
@ -266,7 +248,6 @@ class DecompositionSeriesData(ConfiguredBaseModel):
|
|||
Data decomposed into frequency bands.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -282,7 +263,6 @@ class DecompositionSeriesBands(DynamicTable):
|
|||
Table for describing the bands that this series was generated from. There should be one row in this table for each band.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["bands"] = Field("bands")
|
||||
band_name: Optional[List[str] | str] = Field(
|
||||
default_factory=list, description="""Name of the band, e.g. theta."""
|
||||
|
|
@ -322,7 +302,6 @@ class Units(DynamicTable):
|
|||
Data about spiking units. Event times of observed units (e.g. cell, synapse, etc.) should be concatenated and stored in spike_times.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("Units")
|
||||
spike_times_index: Optional[str] = Field(
|
||||
None, description="""Index into the spike_times dataset."""
|
||||
|
|
@ -386,7 +365,6 @@ class UnitsSpikeTimesIndex(VectorIndex):
|
|||
Index into the spike_times dataset.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["spike_times_index"] = Field("spike_times_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -399,7 +377,6 @@ class UnitsSpikeTimes(VectorData):
|
|||
Spike times for each unit.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["spike_times"] = Field("spike_times")
|
||||
resolution: Optional[float] = Field(
|
||||
None,
|
||||
|
|
@ -415,7 +392,6 @@ class UnitsObsIntervalsIndex(VectorIndex):
|
|||
Index into the obs_intervals dataset.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["obs_intervals_index"] = Field("obs_intervals_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -428,7 +404,6 @@ class UnitsElectrodesIndex(VectorIndex):
|
|||
Index into electrodes.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["electrodes_index"] = Field("electrodes_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -441,7 +416,6 @@ class UnitsElectrodes(DynamicTableRegion):
|
|||
Electrode that each spike unit came from, specified using a DynamicTableRegion.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["electrodes"] = Field("electrodes")
|
||||
table: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -450,20 +424,3 @@ class UnitsElectrodes(DynamicTableRegion):
|
|||
description: Optional[str] = Field(
|
||||
None, description="""Description of what this table region points to."""
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
AbstractFeatureSeries.model_rebuild()
|
||||
AbstractFeatureSeriesData.model_rebuild()
|
||||
AnnotationSeries.model_rebuild()
|
||||
IntervalSeries.model_rebuild()
|
||||
DecompositionSeries.model_rebuild()
|
||||
DecompositionSeriesData.model_rebuild()
|
||||
DecompositionSeriesBands.model_rebuild()
|
||||
Units.model_rebuild()
|
||||
UnitsSpikeTimesIndex.model_rebuild()
|
||||
UnitsSpikeTimes.model_rebuild()
|
||||
UnitsObsIntervalsIndex.model_rebuild()
|
||||
UnitsElectrodesIndex.model_rebuild()
|
||||
UnitsElectrodes.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -29,9 +29,9 @@ if TYPE_CHECKING:
|
|||
|
||||
from .core_nwb_base import (
|
||||
TimeSeriesSync,
|
||||
TimeSeries,
|
||||
NWBContainer,
|
||||
TimeSeriesStartingTime,
|
||||
TimeSeries,
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -40,13 +40,6 @@ version = "2.2.0"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -66,18 +59,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class OptogeneticSeries(TimeSeries):
|
||||
"""
|
||||
An optogenetic stimulus.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: NDArray[Shape["* num_times"], float] = Field(
|
||||
..., description="""Applied power for optogenetic stimulus, in watts."""
|
||||
|
|
@ -116,7 +102,6 @@ class OptogeneticStimulusSite(NWBContainer):
|
|||
A site of optogenetic stimulation.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: str = Field(..., description="""Description of stimulation site.""")
|
||||
excitation_lambda: float = Field(
|
||||
|
|
@ -126,9 +111,3 @@ class OptogeneticStimulusSite(NWBContainer):
|
|||
...,
|
||||
description="""Location of the stimulation site. 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.""",
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
OptogeneticSeries.model_rebuild()
|
||||
OptogeneticStimulusSite.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -27,31 +27,24 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from ...hdmf_common.v1_1_0.hdmf_common_table import DynamicTable, DynamicTableRegion
|
||||
from ...hdmf_common.v1_1_0.hdmf_common_table import DynamicTableRegion, DynamicTable
|
||||
|
||||
from .core_nwb_image import ImageSeriesExternalFile, ImageSeries
|
||||
|
||||
from .core_nwb_base import (
|
||||
NWBDataInterface,
|
||||
TimeSeriesStartingTime,
|
||||
TimeSeries,
|
||||
NWBContainer,
|
||||
TimeSeriesStartingTime,
|
||||
NWBDataInterface,
|
||||
TimeSeries,
|
||||
TimeSeriesSync,
|
||||
)
|
||||
|
||||
from .core_nwb_image import ImageSeries, ImageSeriesExternalFile
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
version = "2.2.0"
|
||||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -71,18 +64,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class TwoPhotonSeries(ImageSeries):
|
||||
"""
|
||||
Image stack recorded over time from 2-photon microscope.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
pmt_gain: Optional[float] = Field(None, description="""Photomultiplier gain.""")
|
||||
scan_line_rate: Optional[float] = Field(
|
||||
|
|
@ -149,7 +135,6 @@ class RoiResponseSeries(TimeSeries):
|
|||
ROI responses over an imaging plane. The first dimension represents time. The second dimension, if present, represents ROIs.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: Union[
|
||||
NDArray[Shape["* num_times"], float],
|
||||
|
|
@ -193,7 +178,6 @@ class RoiResponseSeriesRois(DynamicTableRegion):
|
|||
DynamicTableRegion referencing into an ROITable containing information on the ROIs stored in this timeseries.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["rois"] = Field("rois")
|
||||
table: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -209,7 +193,6 @@ 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).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[RoiResponseSeries] | RoiResponseSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -221,7 +204,6 @@ class Fluorescence(NWBDataInterface):
|
|||
Fluorescence information about a region of interest (ROI). Storage hierarchy of fluorescence should be the same as for segmentation (ie, same names for ROIs and for image planes).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[RoiResponseSeries] | RoiResponseSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -233,7 +215,6 @@ class ImageSegmentation(NWBDataInterface):
|
|||
Stores pixels in an image that represent different regions of interest (ROIs) or masks. All segmentation for a given imaging plane is stored together, with storage for multiple imaging planes (masks) supported. Each ROI is stored in its own subgroup, with the ROI group containing both a 2D mask and a list of pixels that make up this mask. Segments can also be used for masking neuropil. If segmentation is allowed to change with time, a new imaging plane (or module) is required and ROI names should remain consistent between them.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[
|
||||
List[Union[BaseModel, DynamicTable]] | Union[BaseModel, DynamicTable]
|
||||
] = Field(default_factory=dict)
|
||||
|
|
@ -245,7 +226,6 @@ class ImagingPlane(NWBContainer):
|
|||
An imaging plane and its metadata.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: Optional[str] = Field(
|
||||
None, description="""Description of the imaging plane."""
|
||||
|
|
@ -287,7 +267,6 @@ class ImagingPlaneManifold(ConfiguredBaseModel):
|
|||
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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["manifold"] = Field("manifold")
|
||||
conversion: Optional[float] = Field(
|
||||
None,
|
||||
|
|
@ -310,7 +289,6 @@ class ImagingPlaneOriginCoords(ConfiguredBaseModel):
|
|||
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).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["origin_coords"] = Field("origin_coords")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -324,7 +302,6 @@ class ImagingPlaneGridSpacing(ConfiguredBaseModel):
|
|||
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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["grid_spacing"] = Field("grid_spacing")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -338,7 +315,6 @@ class OpticalChannel(NWBContainer):
|
|||
An optical channel used to record from an imaging plane.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: str = Field(
|
||||
..., description="""Description or other notes about the channel."""
|
||||
|
|
@ -353,24 +329,7 @@ class MotionCorrection(NWBDataInterface):
|
|||
An image stack where all frames are shifted (registered) to a common coordinate system, to account for movement and drift between frames. Note: each frame at each point in time is assumed to be 2-D (has only x & y dimensions).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[NWBDataInterface] | NWBDataInterface] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
name: str = Field(...)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
TwoPhotonSeries.model_rebuild()
|
||||
RoiResponseSeries.model_rebuild()
|
||||
RoiResponseSeriesRois.model_rebuild()
|
||||
DfOverF.model_rebuild()
|
||||
Fluorescence.model_rebuild()
|
||||
ImageSegmentation.model_rebuild()
|
||||
ImagingPlane.model_rebuild()
|
||||
ImagingPlaneManifold.model_rebuild()
|
||||
ImagingPlaneOriginCoords.model_rebuild()
|
||||
ImagingPlaneGridSpacing.model_rebuild()
|
||||
OpticalChannel.model_rebuild()
|
||||
MotionCorrection.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -27,23 +27,16 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from .core_nwb_base import NWBDataInterface, NWBData
|
||||
|
||||
from .core_nwb_image import GrayscaleImage
|
||||
|
||||
from .core_nwb_base import NWBData, NWBDataInterface
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
version = "2.2.0"
|
||||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -63,18 +56,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class RetinotopyMap(NWBData):
|
||||
"""
|
||||
Abstract two-dimensional map of responses. Array structure: [num_rows][num_columns]
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
dimension: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -91,7 +77,6 @@ class AxisMap(RetinotopyMap):
|
|||
Abstract two-dimensional map of responses to stimuli along a single response axis (e.g. eccentricity)
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
unit: Optional[str] = Field(
|
||||
None, description="""Unit that axis data is stored in (e.g., degrees)."""
|
||||
|
|
@ -111,7 +96,6 @@ class RetinotopyImage(GrayscaleImage):
|
|||
Gray-scale image related to retinotopic mapping. Array structure: [num_rows][num_columns]
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
bits_per_pixel: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -147,7 +131,6 @@ class ImagingRetinotopy(NWBDataInterface):
|
|||
Intrinsic signal optical imaging or widefield imaging for measuring retinotopy. Stores orthogonal maps (e.g., altitude/azimuth; radius/theta) of responses to specific stimuli and a combined polarity map from which to identify visual areas. NOTE: for data consistency, all images and arrays are stored in the format [row][column] and [row, col], which equates to [y][x]. Field of view and dimension arrays may appear backward (i.e., y before x).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("ImagingRetinotopy")
|
||||
axis_1_phase_map: str = Field(
|
||||
..., description="""Phase response to stimulus on the first measured axis."""
|
||||
|
|
@ -185,7 +168,6 @@ class ImagingRetinotopyAxis1PhaseMap(AxisMap):
|
|||
Phase response to stimulus on the first measured axis.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["axis_1_phase_map"] = Field("axis_1_phase_map")
|
||||
unit: Optional[str] = Field(
|
||||
None, description="""Unit that axis data is stored in (e.g., degrees)."""
|
||||
|
|
@ -205,7 +187,6 @@ class ImagingRetinotopyAxis1PowerMap(AxisMap):
|
|||
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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["axis_1_power_map"] = Field("axis_1_power_map")
|
||||
unit: Optional[str] = Field(
|
||||
None, description="""Unit that axis data is stored in (e.g., degrees)."""
|
||||
|
|
@ -225,7 +206,6 @@ class ImagingRetinotopyAxis2PhaseMap(AxisMap):
|
|||
Phase response to stimulus on the second measured axis.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["axis_2_phase_map"] = Field("axis_2_phase_map")
|
||||
unit: Optional[str] = Field(
|
||||
None, description="""Unit that axis data is stored in (e.g., degrees)."""
|
||||
|
|
@ -245,7 +225,6 @@ class ImagingRetinotopyAxis2PowerMap(AxisMap):
|
|||
Power response to stimulus on the second measured axis.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["axis_2_power_map"] = Field("axis_2_power_map")
|
||||
unit: Optional[str] = Field(
|
||||
None, description="""Unit that axis data is stored in (e.g., degrees)."""
|
||||
|
|
@ -265,7 +244,6 @@ class ImagingRetinotopySignMap(RetinotopyMap):
|
|||
Sine of the angle between the direction of the gradient in axis_1 and axis_2.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["sign_map"] = Field("sign_map")
|
||||
dimension: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -282,7 +260,6 @@ class ImagingRetinotopyFocalDepthImage(RetinotopyImage):
|
|||
Gray-scale image taken with same settings/parameters (e.g., focal depth, wavelength) as data collection. Array format: [rows][columns].
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["focal_depth_image"] = Field("focal_depth_image")
|
||||
focal_depth: Optional[float] = Field(
|
||||
None, description="""Focal depth offset, in meters."""
|
||||
|
|
@ -321,7 +298,6 @@ class ImagingRetinotopyVasculatureImage(RetinotopyImage):
|
|||
Gray-scale anatomical image of cortical surface. Array structure: [rows][columns]
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["vasculature_image"] = Field("vasculature_image")
|
||||
bits_per_pixel: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -350,18 +326,3 @@ class ImagingRetinotopyVasculatureImage(RetinotopyImage):
|
|||
NDArray[Shape["* x, * y, 4 r_g_b_a"], float],
|
||||
]
|
||||
] = Field(None)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
RetinotopyMap.model_rebuild()
|
||||
AxisMap.model_rebuild()
|
||||
RetinotopyImage.model_rebuild()
|
||||
ImagingRetinotopy.model_rebuild()
|
||||
ImagingRetinotopyAxis1PhaseMap.model_rebuild()
|
||||
ImagingRetinotopyAxis1PowerMap.model_rebuild()
|
||||
ImagingRetinotopyAxis2PhaseMap.model_rebuild()
|
||||
ImagingRetinotopyAxis2PowerMap.model_rebuild()
|
||||
ImagingRetinotopySignMap.model_rebuild()
|
||||
ImagingRetinotopyFocalDepthImage.model_rebuild()
|
||||
ImagingRetinotopyVasculatureImage.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -194,13 +194,6 @@ version = "2.2.0"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -218,13 +211,3 @@ class ConfiguredBaseModel(BaseModel):
|
|||
self.array[i] = value
|
||||
else:
|
||||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from ...hdmf_common.v1_1_2.hdmf_common_table import Container, Data, DynamicTable
|
||||
from ...hdmf_common.v1_1_2.hdmf_common_table import Data, Container, DynamicTable
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
|
|
@ -35,13 +35,6 @@ version = "2.2.1"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -61,18 +54,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class NWBData(Data):
|
||||
"""
|
||||
An abstract data type for a dataset.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
|
||||
|
||||
|
|
@ -81,7 +67,6 @@ class Image(NWBData):
|
|||
An abstract data type for an image. Shape can be 2-D (x, y), or 3-D where the third dimension can have three or four elements, e.g. (x, y, (r, g, b)) or (x, y, (r, g, b, a)).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
resolution: Optional[float] = Field(
|
||||
None, description="""Pixel resolution of the image, in pixels per centimeter."""
|
||||
|
|
@ -103,7 +88,6 @@ class NWBContainer(Container):
|
|||
An abstract data type for a generic container storing collections of data and metadata. Base type for all data and metadata containers.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
|
||||
|
||||
|
|
@ -112,7 +96,6 @@ class NWBDataInterface(NWBContainer):
|
|||
An abstract data type for a generic container storing collections of data, as opposed to metadata.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
|
||||
|
||||
|
|
@ -121,7 +104,6 @@ class TimeSeries(NWBDataInterface):
|
|||
General purpose time series.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: Optional[str] = Field(
|
||||
None, description="""Description of the time series."""
|
||||
|
|
@ -161,7 +143,6 @@ class TimeSeriesData(ConfiguredBaseModel):
|
|||
Data values. Data can be in 1-D, 2-D, 3-D, or 4-D. The first dimension should always represent time. This can also be used to store binary data (e.g., image frames). This can also be a link to data stored in an external file.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
conversion: Optional[float] = Field(
|
||||
None,
|
||||
|
|
@ -190,7 +171,6 @@ class TimeSeriesStartingTime(ConfiguredBaseModel):
|
|||
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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["starting_time"] = Field("starting_time")
|
||||
rate: Optional[float] = Field(None, description="""Sampling rate, in Hz.""")
|
||||
unit: Optional[str] = Field(
|
||||
|
|
@ -205,7 +185,6 @@ class TimeSeriesSync(ConfiguredBaseModel):
|
|||
Lab-specific time and sync information as provided directly from hardware devices and that is necessary for aligning all acquired time information to a common timebase. The timestamp array stores time in the common timebase. This group will usually only be populated in TimeSeries that are stored external to the NWB file, in files storing raw data. Once timestamp data is calculated, the contents of 'sync' are mostly for archival purposes.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["sync"] = Field("sync")
|
||||
|
||||
|
||||
|
|
@ -214,7 +193,6 @@ class ProcessingModule(NWBContainer):
|
|||
A collection of processed data.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[
|
||||
List[Union[BaseModel, DynamicTable, NWBDataInterface]]
|
||||
| Union[BaseModel, DynamicTable, NWBDataInterface]
|
||||
|
|
@ -227,7 +205,6 @@ class Images(NWBDataInterface):
|
|||
A collection of images.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("Images")
|
||||
description: Optional[str] = Field(
|
||||
None, description="""Description of this collection of images."""
|
||||
|
|
@ -235,17 +212,3 @@ class Images(NWBDataInterface):
|
|||
image: List[str] | str = Field(
|
||||
default_factory=list, description="""Images stored in this collection."""
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
NWBData.model_rebuild()
|
||||
Image.model_rebuild()
|
||||
NWBContainer.model_rebuild()
|
||||
NWBDataInterface.model_rebuild()
|
||||
TimeSeries.model_rebuild()
|
||||
TimeSeriesData.model_rebuild()
|
||||
TimeSeriesStartingTime.model_rebuild()
|
||||
TimeSeriesSync.model_rebuild()
|
||||
ProcessingModule.model_rebuild()
|
||||
Images.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -28,9 +28,9 @@ if TYPE_CHECKING:
|
|||
|
||||
|
||||
from .core_nwb_base import (
|
||||
NWBDataInterface,
|
||||
TimeSeries,
|
||||
TimeSeriesStartingTime,
|
||||
NWBDataInterface,
|
||||
TimeSeriesSync,
|
||||
)
|
||||
|
||||
|
|
@ -42,13 +42,6 @@ version = "2.2.1"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -68,18 +61,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class SpatialSeries(TimeSeries):
|
||||
"""
|
||||
Direction, e.g., of gaze or travel, or position. The TimeSeries::data field is a 2D array storing position or direction relative to some reference frame. Array structure: [num measurements] [num dimensions]. Each SpatialSeries has a text dataset reference_frame that indicates the zero-position, or the zero-axes for direction. For example, if representing gaze direction, 'straight-ahead' might be a specific pixel on the monitor, or some other point in space. For position data, the 0,0 point might be the top-left corner of an enclosure, as viewed from the tracking camera. The unit of data will indicate how to interpret SpatialSeries values.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(
|
||||
...,
|
||||
|
|
@ -123,7 +109,6 @@ class SpatialSeriesData(ConfiguredBaseModel):
|
|||
1-D or 2-D array storing position or direction relative to some reference frame.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -142,7 +127,6 @@ class BehavioralEpochs(NWBDataInterface):
|
|||
TimeSeries for storing behavioral epochs. The objective of this and the other two Behavioral interfaces (e.g. BehavioralEvents and BehavioralTimeSeries) is to provide generic hooks for software tools/scripts. This allows a tool/script to take the output one specific interface (e.g., UnitTimes) and plot that data relative to another data modality (e.g., behavioral events) without having to define all possible modalities in advance. Declaring one of these interfaces means that one or more TimeSeries of the specified type is published. These TimeSeries should reside in a group having the same name as the interface. For example, if a BehavioralTimeSeries interface is declared, the module will have one or more TimeSeries defined in the module sub-group 'BehavioralTimeSeries'. BehavioralEpochs should use IntervalSeries. BehavioralEvents is used for irregular events. BehavioralTimeSeries is for continuous data.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[IntervalSeries] | IntervalSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -154,7 +138,6 @@ class BehavioralEvents(NWBDataInterface):
|
|||
TimeSeries for storing behavioral events. See description of <a href=\"#BehavioralEpochs\">BehavioralEpochs</a> for more details.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[TimeSeries] | TimeSeries] = Field(default_factory=dict)
|
||||
name: str = Field(...)
|
||||
|
||||
|
|
@ -164,7 +147,6 @@ class BehavioralTimeSeries(NWBDataInterface):
|
|||
TimeSeries for storing Behavoioral time series data. See description of <a href=\"#BehavioralEpochs\">BehavioralEpochs</a> for more details.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[TimeSeries] | TimeSeries] = Field(default_factory=dict)
|
||||
name: str = Field(...)
|
||||
|
||||
|
|
@ -174,7 +156,6 @@ class PupilTracking(NWBDataInterface):
|
|||
Eye-tracking data, representing pupil size.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[TimeSeries] | TimeSeries] = Field(default_factory=dict)
|
||||
name: str = Field(...)
|
||||
|
||||
|
|
@ -184,7 +165,6 @@ class EyeTracking(NWBDataInterface):
|
|||
Eye-tracking data, representing direction of gaze.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[SpatialSeries] | SpatialSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -196,7 +176,6 @@ class CompassDirection(NWBDataInterface):
|
|||
With a CompassDirection interface, a module publishes a SpatialSeries object representing a floating point value for theta. The SpatialSeries::reference_frame field should indicate what direction corresponds to 0 and which is the direction of rotation (this should be clockwise). The si_unit for the SpatialSeries should be radians or degrees.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[SpatialSeries] | SpatialSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -208,21 +187,7 @@ class Position(NWBDataInterface):
|
|||
Position data, whether along the x, x/y or x/y/z axis.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[SpatialSeries] | SpatialSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
name: str = Field(...)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
SpatialSeries.model_rebuild()
|
||||
SpatialSeriesData.model_rebuild()
|
||||
BehavioralEpochs.model_rebuild()
|
||||
BehavioralEvents.model_rebuild()
|
||||
BehavioralTimeSeries.model_rebuild()
|
||||
PupilTracking.model_rebuild()
|
||||
EyeTracking.model_rebuild()
|
||||
CompassDirection.model_rebuild()
|
||||
Position.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -35,13 +35,6 @@ version = "2.2.1"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -61,18 +54,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class Device(NWBContainer):
|
||||
"""
|
||||
Metadata about a data acquisition device, e.g., recording system, electrode, microscope.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -81,8 +67,3 @@ class Device(NWBContainer):
|
|||
manufacturer: Optional[str] = Field(
|
||||
None, description="""The name of the manufacturer of the device."""
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
Device.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -27,29 +27,22 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from ...hdmf_common.v1_1_2.hdmf_common_table import DynamicTableRegion, DynamicTable
|
||||
|
||||
from .core_nwb_base import (
|
||||
NWBContainer,
|
||||
TimeSeriesStartingTime,
|
||||
NWBDataInterface,
|
||||
TimeSeries,
|
||||
TimeSeriesStartingTime,
|
||||
NWBContainer,
|
||||
TimeSeriesSync,
|
||||
)
|
||||
|
||||
from ...hdmf_common.v1_1_2.hdmf_common_table import DynamicTableRegion, DynamicTable
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
version = "2.2.1"
|
||||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -69,18 +62,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class ElectricalSeries(TimeSeries):
|
||||
"""
|
||||
A time series of acquired voltage data from extracellular recordings. The data field is an int or float array storing data in volts. The first dimension should always represent time. The second dimension, if present, should represent channels.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: Union[
|
||||
NDArray[Shape["* num_times"], float],
|
||||
|
|
@ -129,7 +115,6 @@ class ElectricalSeriesElectrodes(DynamicTableRegion):
|
|||
DynamicTableRegion pointer to the electrodes that this time series was generated from.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["electrodes"] = Field("electrodes")
|
||||
table: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -145,7 +130,6 @@ 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).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: Union[
|
||||
NDArray[Shape["* num_events, * num_channels, * num_samples"], float],
|
||||
|
|
@ -193,7 +177,6 @@ class FeatureExtraction(NWBDataInterface):
|
|||
Features, such as PC1 and PC2, that are extracted from signals stored in a SpikeEventSeries or other source.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("FeatureExtraction")
|
||||
description: NDArray[Shape["* num_features"], str] = Field(
|
||||
...,
|
||||
|
|
@ -220,7 +203,6 @@ class FeatureExtractionElectrodes(DynamicTableRegion):
|
|||
DynamicTableRegion pointer to the electrodes that this time series was generated from.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["electrodes"] = Field("electrodes")
|
||||
table: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -236,7 +218,6 @@ class EventDetection(NWBDataInterface):
|
|||
Detected spike events from voltage trace(s).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("EventDetection")
|
||||
detection_method: str = Field(
|
||||
...,
|
||||
|
|
@ -256,7 +237,6 @@ class EventWaveform(NWBDataInterface):
|
|||
Represents either the waveforms of detected events, as extracted from a raw data trace in /acquisition, or the event waveforms that were stored during experiment acquisition.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[SpikeEventSeries] | SpikeEventSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -268,7 +248,6 @@ class FilteredEphys(NWBDataInterface):
|
|||
Electrophysiology data from one or more channels that has been subjected to filtering. Examples of filtered data include Theta and Gamma (LFP has its own interface). FilteredEphys modules publish an ElectricalSeries for each filtered channel or set of channels. The name of each ElectricalSeries is arbitrary but should be informative. The source of the filtered data, whether this is from analysis of another time series or as acquired by hardware, should be noted in each's TimeSeries::description field. There is no assumed 1::1 correspondence between filtered ephys signals and electrodes, as a single signal can apply to many nearby electrodes, and one electrode may have different filtered (e.g., theta and/or gamma) signals represented. Filter properties should be noted in the ElectricalSeries.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[ElectricalSeries] | ElectricalSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -280,7 +259,6 @@ class LFP(NWBDataInterface):
|
|||
LFP data from one or more channels. The electrode map in each published ElectricalSeries will identify which channels are providing LFP data. Filter properties should be noted in the ElectricalSeries description or comments field.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[ElectricalSeries] | ElectricalSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -292,7 +270,6 @@ class ElectrodeGroup(NWBContainer):
|
|||
A physical grouping of electrodes, e.g. a shank of an array.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: Optional[str] = Field(
|
||||
None, description="""Description of this electrode group."""
|
||||
|
|
@ -311,7 +288,6 @@ class ElectrodeGroupPosition(ConfiguredBaseModel):
|
|||
stereotaxic or common framework coordinates
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["position"] = Field("position")
|
||||
x: Optional[float] = Field(None, description="""x coordinate""")
|
||||
y: Optional[float] = Field(None, description="""y coordinate""")
|
||||
|
|
@ -323,7 +299,6 @@ class ClusterWaveforms(NWBDataInterface):
|
|||
DEPRECATED The mean waveform shape, including standard deviation, of the different clusters. Ideally, the waveform analysis should be performed on data that is only high-pass filtered. This is a separate module because it is expected to require updating. For example, IMEC probes may require different storage requirements to store/display mean waveforms, requiring a new interface or an extension of this one.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("ClusterWaveforms")
|
||||
waveform_filtering: str = Field(
|
||||
..., description="""Filtering applied to data before generating mean/sd"""
|
||||
|
|
@ -343,7 +318,6 @@ class Clustering(NWBDataInterface):
|
|||
DEPRECATED Clustered spike data, whether from automatic clustering tools (e.g., klustakwik) or as a result of manual sorting.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("Clustering")
|
||||
description: str = Field(
|
||||
...,
|
||||
|
|
@ -360,20 +334,3 @@ class Clustering(NWBDataInterface):
|
|||
...,
|
||||
description="""Times of clustered events, in seconds. This may be a link to times field in associated FeatureExtraction module.""",
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
ElectricalSeries.model_rebuild()
|
||||
ElectricalSeriesElectrodes.model_rebuild()
|
||||
SpikeEventSeries.model_rebuild()
|
||||
FeatureExtraction.model_rebuild()
|
||||
FeatureExtractionElectrodes.model_rebuild()
|
||||
EventDetection.model_rebuild()
|
||||
EventWaveform.model_rebuild()
|
||||
FilteredEphys.model_rebuild()
|
||||
LFP.model_rebuild()
|
||||
ElectrodeGroup.model_rebuild()
|
||||
ElectrodeGroupPosition.model_rebuild()
|
||||
ClusterWaveforms.model_rebuild()
|
||||
Clustering.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -27,27 +27,20 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from .core_nwb_base import TimeSeries
|
||||
|
||||
from ...hdmf_common.v1_1_2.hdmf_common_table import (
|
||||
DynamicTable,
|
||||
VectorIndex,
|
||||
DynamicTable,
|
||||
VectorData,
|
||||
)
|
||||
|
||||
from .core_nwb_base import TimeSeries
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
version = "2.2.1"
|
||||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -67,18 +60,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class TimeIntervals(DynamicTable):
|
||||
"""
|
||||
A container for aggregating epoch data and the TimeSeries that each epoch applies to.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
start_time: Optional[List[float] | float] = Field(
|
||||
default_factory=list, description="""Start time of epoch, in seconds."""
|
||||
|
|
@ -122,7 +108,6 @@ class TimeIntervalsTagsIndex(VectorIndex):
|
|||
Index for tags.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["tags_index"] = Field("tags_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -135,7 +120,6 @@ class TimeIntervalsTimeseries(VectorData):
|
|||
An index into a TimeSeries object.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["timeseries"] = Field("timeseries")
|
||||
idx_start: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -158,17 +142,8 @@ class TimeIntervalsTimeseriesIndex(VectorIndex):
|
|||
Index for timeseries.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["timeseries_index"] = Field("timeseries_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
description="""Reference to the target dataset that this index applies to.""",
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
TimeIntervals.model_rebuild()
|
||||
TimeIntervalsTagsIndex.model_rebuild()
|
||||
TimeIntervalsTimeseries.model_rebuild()
|
||||
TimeIntervalsTimeseriesIndex.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -27,27 +27,27 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from .core_nwb_ophys import ImagingPlane
|
||||
|
||||
from .core_nwb_base import TimeSeries, NWBDataInterface, ProcessingModule, NWBContainer
|
||||
from .core_nwb_icephys import SweepTable, IntracellularElectrode
|
||||
|
||||
from .core_nwb_epoch import TimeIntervals
|
||||
|
||||
from ...hdmf_common.v1_1_2.hdmf_common_table import (
|
||||
DynamicTable,
|
||||
VectorData,
|
||||
VectorIndex,
|
||||
)
|
||||
from .core_nwb_ecephys import ElectrodeGroup
|
||||
|
||||
from .core_nwb_icephys import IntracellularElectrode, SweepTable
|
||||
from .core_nwb_base import NWBContainer, ProcessingModule, NWBDataInterface, TimeSeries
|
||||
|
||||
from .core_nwb_ogen import OptogeneticStimulusSite
|
||||
from .core_nwb_device import Device
|
||||
|
||||
from .core_nwb_misc import Units
|
||||
|
||||
from .core_nwb_ecephys import ElectrodeGroup
|
||||
from .core_nwb_ogen import OptogeneticStimulusSite
|
||||
|
||||
from .core_nwb_device import Device
|
||||
from ...hdmf_common.v1_1_2.hdmf_common_table import (
|
||||
VectorIndex,
|
||||
DynamicTable,
|
||||
VectorData,
|
||||
)
|
||||
|
||||
from .core_nwb_ophys import ImagingPlane
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
|
|
@ -55,13 +55,6 @@ version = "2.2.1"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -81,18 +74,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class NWBFile(NWBContainer):
|
||||
"""
|
||||
An NWB:N file storing cellular-based neurophysiology data from a single experimental session.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: Literal["root"] = Field("root")
|
||||
nwb_version: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -163,7 +149,6 @@ class NWBFileStimulus(ConfiguredBaseModel):
|
|||
Data pushed into the system (eg, video stimulus, sound, voltage, etc) and secondary representations of that data (eg, measurements of something used as a stimulus). This group should be made read-only after experiment complete and timestamps are corrected to common timebase. Stores both presented stimuli and stimulus templates, the latter in case the same stimulus is presented multiple times, or is pulled from an external stimulus library. Stimuli are here defined as any signal that is pushed into the system as part of the experiment (eg, sound, video, voltage, etc). Many different experiments can use the same stimuli, and stimuli can be re-used during an experiment. The stimulus group is organized so that one version of template stimuli can be stored and these be used multiple times. These templates can exist in the present file or can be linked to a remote library file.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["stimulus"] = Field("stimulus")
|
||||
presentation: Optional[List[TimeSeries] | TimeSeries] = Field(
|
||||
default_factory=dict, description="""Stimuli presented during the experiment."""
|
||||
|
|
@ -179,7 +164,6 @@ class NWBFileGeneral(ConfiguredBaseModel):
|
|||
Experimental metadata, including protocol, notes and description of hardware device(s). The metadata stored in this section should be used to describe the experiment. Metadata necessary for interpreting the data is stored with the data. General experimental metadata, including animal strain, experimental protocols, experimenter, devices, etc, are stored under 'general'. Core metadata (e.g., that required to interpret data fields) is stored with the data itself, and implicitly defined by the file specification (e.g., time is in seconds). The strategy used here for storing non-core metadata is to use free-form text fields, such as would appear in sentences or paragraphs from a Methods section. Metadata fields are text to enable them to be more general, for example to represent ranges instead of numerical values. Machine-readable metadata is stored as attributes to these free-form datasets. All entries in the below table are to be included when data is present. Unused groups (e.g., intracellular_ephys in an optophysiology experiment) should not be created unless there is data to store within them.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["general"] = Field("general")
|
||||
data_collection: Optional[str] = Field(
|
||||
None, description="""Notes about data collection and analysis."""
|
||||
|
|
@ -269,7 +253,6 @@ class NWBFileGeneralSourceScript(ConfiguredBaseModel):
|
|||
Script file or link to public source code used to create this NWB file.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["source_script"] = Field("source_script")
|
||||
file_name: Optional[str] = Field(None, description="""Name of script file.""")
|
||||
value: str = Field(...)
|
||||
|
|
@ -280,7 +263,6 @@ class Subject(NWBContainer):
|
|||
Information about the animal or person from which the data was measured.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["subject"] = Field("subject")
|
||||
age: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -314,7 +296,6 @@ class NWBFileGeneralExtracellularEphys(ConfiguredBaseModel):
|
|||
Metadata related to extracellular electrophysiology.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["extracellular_ephys"] = Field("extracellular_ephys")
|
||||
electrode_group: Optional[List[str] | str] = Field(
|
||||
default_factory=list, description="""Physical group of electrodes."""
|
||||
|
|
@ -330,7 +311,6 @@ class NWBFileGeneralExtracellularEphysElectrodes(DynamicTable):
|
|||
A table of all electrodes (i.e. channels) used for recording.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["electrodes"] = Field("electrodes")
|
||||
x: Optional[List[float] | float] = Field(
|
||||
default_factory=list,
|
||||
|
|
@ -400,7 +380,6 @@ class NWBFileGeneralIntracellularEphys(ConfiguredBaseModel):
|
|||
Metadata related to intracellular electrophysiology.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["intracellular_ephys"] = Field("intracellular_ephys")
|
||||
filtering: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -413,15 +392,3 @@ class NWBFileGeneralIntracellularEphys(ConfiguredBaseModel):
|
|||
None,
|
||||
description="""The table which groups different PatchClampSeries together.""",
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
NWBFile.model_rebuild()
|
||||
NWBFileStimulus.model_rebuild()
|
||||
NWBFileGeneral.model_rebuild()
|
||||
NWBFileGeneralSourceScript.model_rebuild()
|
||||
Subject.model_rebuild()
|
||||
NWBFileGeneralExtracellularEphys.model_rebuild()
|
||||
NWBFileGeneralExtracellularEphysElectrodes.model_rebuild()
|
||||
NWBFileGeneralIntracellularEphys.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -28,16 +28,16 @@ if TYPE_CHECKING:
|
|||
|
||||
|
||||
from .core_nwb_base import (
|
||||
TimeSeries,
|
||||
NWBContainer,
|
||||
TimeSeriesSync,
|
||||
TimeSeriesStartingTime,
|
||||
NWBContainer,
|
||||
TimeSeries,
|
||||
)
|
||||
|
||||
from ...hdmf_common.v1_1_2.hdmf_common_table import (
|
||||
VectorIndex,
|
||||
DynamicTable,
|
||||
VectorData,
|
||||
VectorIndex,
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -46,13 +46,6 @@ version = "2.2.1"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -72,18 +65,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class PatchClampSeries(TimeSeries):
|
||||
"""
|
||||
An abstract base class for patch-clamp data - stimulus or response, current or voltage.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
stimulus_description: Optional[str] = Field(
|
||||
None, description="""Protocol/stimulus name for this patch-clamp dataset."""
|
||||
|
|
@ -131,7 +117,6 @@ class PatchClampSeriesData(ConfiguredBaseModel):
|
|||
Recorded voltage or current.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -145,7 +130,6 @@ class CurrentClampSeries(PatchClampSeries):
|
|||
Voltage data from an intracellular current-clamp recording. A corresponding CurrentClampStimulusSeries (stored separately as a stimulus) is used to store the current injected.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Recorded voltage.""")
|
||||
bias_current: Optional[float] = Field(
|
||||
|
|
@ -202,7 +186,6 @@ class CurrentClampSeriesData(ConfiguredBaseModel):
|
|||
Recorded voltage.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -216,7 +199,6 @@ class IZeroClampSeries(CurrentClampSeries):
|
|||
Voltage data from an intracellular recording when all current and amplifier settings are off (i.e., CurrentClampSeries fields will be zero). There is no CurrentClampStimulusSeries associated with an IZero series because the amplifier is disconnected and no stimulus can reach the cell.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
bias_current: float = Field(
|
||||
..., description="""Bias current, in amps, fixed to 0.0."""
|
||||
|
|
@ -273,7 +255,6 @@ class CurrentClampStimulusSeries(PatchClampSeries):
|
|||
Stimulus current applied during current clamp recording.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Stimulus current applied.""")
|
||||
stimulus_description: Optional[str] = Field(
|
||||
|
|
@ -321,7 +302,6 @@ class CurrentClampStimulusSeriesData(ConfiguredBaseModel):
|
|||
Stimulus current applied.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -335,7 +315,6 @@ class VoltageClampSeries(PatchClampSeries):
|
|||
Current data from an intracellular voltage-clamp recording. A corresponding VoltageClampStimulusSeries (stored separately as a stimulus) is used to store the voltage injected.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Recorded current.""")
|
||||
capacitance_fast: Optional[str] = Field(
|
||||
|
|
@ -404,7 +383,6 @@ class VoltageClampSeriesData(ConfiguredBaseModel):
|
|||
Recorded current.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -418,7 +396,6 @@ class VoltageClampSeriesCapacitanceFast(ConfiguredBaseModel):
|
|||
Fast capacitance, in farads.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["capacitance_fast"] = Field("capacitance_fast")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -432,7 +409,6 @@ class VoltageClampSeriesCapacitanceSlow(ConfiguredBaseModel):
|
|||
Slow capacitance, in farads.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["capacitance_slow"] = Field("capacitance_slow")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -446,7 +422,6 @@ class VoltageClampSeriesResistanceCompBandwidth(ConfiguredBaseModel):
|
|||
Resistance compensation bandwidth, in hertz.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["resistance_comp_bandwidth"] = Field("resistance_comp_bandwidth")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -460,7 +435,6 @@ class VoltageClampSeriesResistanceCompCorrection(ConfiguredBaseModel):
|
|||
Resistance compensation correction, in percent.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["resistance_comp_correction"] = Field("resistance_comp_correction")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -474,7 +448,6 @@ class VoltageClampSeriesResistanceCompPrediction(ConfiguredBaseModel):
|
|||
Resistance compensation prediction, in percent.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["resistance_comp_prediction"] = Field("resistance_comp_prediction")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -488,7 +461,6 @@ class VoltageClampSeriesWholeCellCapacitanceComp(ConfiguredBaseModel):
|
|||
Whole cell capacitance compensation, in farads.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["whole_cell_capacitance_comp"] = Field("whole_cell_capacitance_comp")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -502,7 +474,6 @@ class VoltageClampSeriesWholeCellSeriesResistanceComp(ConfiguredBaseModel):
|
|||
Whole cell series resistance compensation, in ohms.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["whole_cell_series_resistance_comp"] = Field(
|
||||
"whole_cell_series_resistance_comp"
|
||||
)
|
||||
|
|
@ -518,7 +489,6 @@ class VoltageClampStimulusSeries(PatchClampSeries):
|
|||
Stimulus voltage applied during a voltage clamp recording.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Stimulus voltage applied.""")
|
||||
stimulus_description: Optional[str] = Field(
|
||||
|
|
@ -566,7 +536,6 @@ class VoltageClampStimulusSeriesData(ConfiguredBaseModel):
|
|||
Stimulus voltage applied.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -580,7 +549,6 @@ class IntracellularElectrode(NWBContainer):
|
|||
An intracellular electrode and its metadata.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: str = Field(
|
||||
...,
|
||||
|
|
@ -612,7 +580,6 @@ class SweepTable(DynamicTable):
|
|||
The table which groups different PatchClampSeries together.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
sweep_number: Optional[List[int] | int] = Field(
|
||||
default_factory=list,
|
||||
|
|
@ -648,34 +615,8 @@ class SweepTableSeriesIndex(VectorIndex):
|
|||
Index for series.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["series_index"] = Field("series_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
description="""Reference to the target dataset that this index applies to.""",
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
PatchClampSeries.model_rebuild()
|
||||
PatchClampSeriesData.model_rebuild()
|
||||
CurrentClampSeries.model_rebuild()
|
||||
CurrentClampSeriesData.model_rebuild()
|
||||
IZeroClampSeries.model_rebuild()
|
||||
CurrentClampStimulusSeries.model_rebuild()
|
||||
CurrentClampStimulusSeriesData.model_rebuild()
|
||||
VoltageClampSeries.model_rebuild()
|
||||
VoltageClampSeriesData.model_rebuild()
|
||||
VoltageClampSeriesCapacitanceFast.model_rebuild()
|
||||
VoltageClampSeriesCapacitanceSlow.model_rebuild()
|
||||
VoltageClampSeriesResistanceCompBandwidth.model_rebuild()
|
||||
VoltageClampSeriesResistanceCompCorrection.model_rebuild()
|
||||
VoltageClampSeriesResistanceCompPrediction.model_rebuild()
|
||||
VoltageClampSeriesWholeCellCapacitanceComp.model_rebuild()
|
||||
VoltageClampSeriesWholeCellSeriesResistanceComp.model_rebuild()
|
||||
VoltageClampStimulusSeries.model_rebuild()
|
||||
VoltageClampStimulusSeriesData.model_rebuild()
|
||||
IntracellularElectrode.model_rebuild()
|
||||
SweepTable.model_rebuild()
|
||||
SweepTableSeriesIndex.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from .core_nwb_base import Image, TimeSeries, TimeSeriesStartingTime, TimeSeriesSync
|
||||
from .core_nwb_base import Image, TimeSeriesStartingTime, TimeSeries, TimeSeriesSync
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
|
|
@ -35,13 +35,6 @@ version = "2.2.1"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -61,18 +54,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class GrayscaleImage(Image):
|
||||
"""
|
||||
A grayscale image.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
resolution: Optional[float] = Field(
|
||||
None, description="""Pixel resolution of the image, in pixels per centimeter."""
|
||||
|
|
@ -94,7 +80,6 @@ class RGBImage(Image):
|
|||
A color image.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
resolution: Optional[float] = Field(
|
||||
None, description="""Pixel resolution of the image, in pixels per centimeter."""
|
||||
|
|
@ -116,7 +101,6 @@ class RGBAImage(Image):
|
|||
A color image with transparency.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
resolution: Optional[float] = Field(
|
||||
None, description="""Pixel resolution of the image, in pixels per centimeter."""
|
||||
|
|
@ -138,7 +122,6 @@ class ImageSeries(TimeSeries):
|
|||
General image data that is common between acquisition and stimulus time series. Sometimes the image data is stored in the file in a raw format while other times it will be stored as a series of external image files in the host file system. The data field will either be binary data, if the data is stored in the NWB file, or empty, if the data is stored in an external image stack. [frame][x][y] or [frame][x][y][z].
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: Optional[
|
||||
Union[
|
||||
|
|
@ -191,7 +174,6 @@ 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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["external_file"] = Field("external_file")
|
||||
starting_frame: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -205,7 +187,6 @@ class ImageMaskSeries(ImageSeries):
|
|||
An alpha mask that is applied to a presented visual stimulus. The 'data' array contains an array of mask values that are applied to the displayed image. Mask values are stored as RGBA. Mask can vary with time. The timestamps array indicates the starting time of a mask, and that mask pattern continues until it's explicitly changed.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: Optional[
|
||||
Union[
|
||||
|
|
@ -258,7 +239,6 @@ class OpticalSeries(ImageSeries):
|
|||
Image data that is presented or recorded. A stimulus template movie will be stored only as an image. When the image is presented as stimulus, additional data is required, such as field of view (e.g., how much of the visual field the image covers, or how what is the area of the target being imaged). If the OpticalSeries represents acquired imaging data, orientation is also important.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
distance: Optional[float] = Field(
|
||||
None, description="""Distance from camera/monitor to target/eye."""
|
||||
|
|
@ -327,7 +307,6 @@ 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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: NDArray[Shape["* num_times"], int] = Field(
|
||||
..., description="""Index of the frame in the referenced ImageSeries."""
|
||||
|
|
@ -359,15 +338,3 @@ class IndexSeries(TimeSeries):
|
|||
None,
|
||||
description="""Lab-specific time and sync information as provided directly from hardware devices and that is necessary for aligning all acquired time information to a common timebase. The timestamp array stores time in the common timebase. This group will usually only be populated in TimeSeries that are stored external to the NWB file, in files storing raw data. Once timestamp data is calculated, the contents of 'sync' are mostly for archival purposes.""",
|
||||
)
|
||||
|
||||
|
||||
# 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()
|
||||
ImageSeriesExternalFile.model_rebuild()
|
||||
ImageMaskSeries.model_rebuild()
|
||||
OpticalSeries.model_rebuild()
|
||||
IndexSeries.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -32,13 +32,6 @@ version = "None"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -56,13 +49,3 @@ class ConfiguredBaseModel(BaseModel):
|
|||
self.array[i] = value
|
||||
else:
|
||||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
|
|
|
|||
|
|
@ -27,15 +27,15 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from .core_nwb_base import TimeSeries, TimeSeriesStartingTime, TimeSeriesSync
|
||||
|
||||
from ...hdmf_common.v1_1_2.hdmf_common_table import (
|
||||
DynamicTableRegion,
|
||||
DynamicTable,
|
||||
VectorIndex,
|
||||
DynamicTable,
|
||||
VectorData,
|
||||
)
|
||||
|
||||
from .core_nwb_base import TimeSeriesStartingTime, TimeSeries, TimeSeriesSync
|
||||
|
||||
from .core_nwb_ecephys import ElectrodeGroup
|
||||
|
||||
|
||||
|
|
@ -44,13 +44,6 @@ version = "2.2.1"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -70,18 +63,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class AbstractFeatureSeries(TimeSeries):
|
||||
"""
|
||||
Abstract features, such as quantitative descriptions of sensory stimuli. The TimeSeries::data field is a 2D array, storing those features (e.g., for visual grating stimulus this might be orientation, spatial frequency and contrast). Null stimuli (eg, uniform gray) can be marked as being an independent feature (eg, 1.0 for gray, 0.0 for actual stimulus) or by storing NaNs for feature values, or through use of the TimeSeries::control fields. A set of features is considered to persist until the next set of features is defined. The final set of features stored should be the null set. This is useful when storing the raw stimulus is impractical.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Values of each feature at each time.""")
|
||||
feature_units: Optional[NDArray[Shape["* num_features"], str]] = Field(
|
||||
|
|
@ -125,7 +111,6 @@ class AbstractFeatureSeriesData(ConfiguredBaseModel):
|
|||
Values of each feature at each time.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -144,7 +129,6 @@ class AnnotationSeries(TimeSeries):
|
|||
Stores user annotations made during an experiment. The data[] field stores a text array, and timestamps are stored for each annotation (ie, interval=1). This is largely an alias to a standard TimeSeries storing a text array but that is identifiable as storing annotations in a machine-readable way.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: NDArray[Shape["* num_times"], str] = Field(
|
||||
..., description="""Annotations made during an experiment."""
|
||||
|
|
@ -183,7 +167,6 @@ 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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: NDArray[Shape["* num_times"], int] = Field(
|
||||
..., description="""Use values >0 if interval started, <0 if interval ended."""
|
||||
|
|
@ -222,7 +205,6 @@ class DecompositionSeries(TimeSeries):
|
|||
Spectral analysis of a time series, e.g. of an LFP or a speech signal.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Data decomposed into frequency bands.""")
|
||||
metric: str = Field(
|
||||
|
|
@ -266,7 +248,6 @@ class DecompositionSeriesData(ConfiguredBaseModel):
|
|||
Data decomposed into frequency bands.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -282,7 +263,6 @@ class DecompositionSeriesBands(DynamicTable):
|
|||
Table for describing the bands that this series was generated from. There should be one row in this table for each band.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["bands"] = Field("bands")
|
||||
band_name: Optional[List[str] | str] = Field(
|
||||
default_factory=list, description="""Name of the band, e.g. theta."""
|
||||
|
|
@ -322,7 +302,6 @@ class Units(DynamicTable):
|
|||
Data about spiking units. Event times of observed units (e.g. cell, synapse, etc.) should be concatenated and stored in spike_times.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("Units")
|
||||
spike_times_index: Optional[str] = Field(
|
||||
None, description="""Index into the spike_times dataset."""
|
||||
|
|
@ -386,7 +365,6 @@ class UnitsSpikeTimesIndex(VectorIndex):
|
|||
Index into the spike_times dataset.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["spike_times_index"] = Field("spike_times_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -399,7 +377,6 @@ class UnitsSpikeTimes(VectorData):
|
|||
Spike times for each unit.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["spike_times"] = Field("spike_times")
|
||||
resolution: Optional[float] = Field(
|
||||
None,
|
||||
|
|
@ -415,7 +392,6 @@ class UnitsObsIntervalsIndex(VectorIndex):
|
|||
Index into the obs_intervals dataset.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["obs_intervals_index"] = Field("obs_intervals_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -428,7 +404,6 @@ class UnitsElectrodesIndex(VectorIndex):
|
|||
Index into electrodes.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["electrodes_index"] = Field("electrodes_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -441,7 +416,6 @@ class UnitsElectrodes(DynamicTableRegion):
|
|||
Electrode that each spike unit came from, specified using a DynamicTableRegion.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["electrodes"] = Field("electrodes")
|
||||
table: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -450,20 +424,3 @@ class UnitsElectrodes(DynamicTableRegion):
|
|||
description: Optional[str] = Field(
|
||||
None, description="""Description of what this table region points to."""
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
AbstractFeatureSeries.model_rebuild()
|
||||
AbstractFeatureSeriesData.model_rebuild()
|
||||
AnnotationSeries.model_rebuild()
|
||||
IntervalSeries.model_rebuild()
|
||||
DecompositionSeries.model_rebuild()
|
||||
DecompositionSeriesData.model_rebuild()
|
||||
DecompositionSeriesBands.model_rebuild()
|
||||
Units.model_rebuild()
|
||||
UnitsSpikeTimesIndex.model_rebuild()
|
||||
UnitsSpikeTimes.model_rebuild()
|
||||
UnitsObsIntervalsIndex.model_rebuild()
|
||||
UnitsElectrodesIndex.model_rebuild()
|
||||
UnitsElectrodes.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -29,9 +29,9 @@ if TYPE_CHECKING:
|
|||
|
||||
from .core_nwb_base import (
|
||||
TimeSeriesSync,
|
||||
TimeSeries,
|
||||
NWBContainer,
|
||||
TimeSeriesStartingTime,
|
||||
TimeSeries,
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -40,13 +40,6 @@ version = "2.2.1"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -66,18 +59,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class OptogeneticSeries(TimeSeries):
|
||||
"""
|
||||
An optogenetic stimulus.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: NDArray[Shape["* num_times"], float] = Field(
|
||||
..., description="""Applied power for optogenetic stimulus, in watts."""
|
||||
|
|
@ -116,7 +102,6 @@ class OptogeneticStimulusSite(NWBContainer):
|
|||
A site of optogenetic stimulation.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: str = Field(..., description="""Description of stimulation site.""")
|
||||
excitation_lambda: float = Field(
|
||||
|
|
@ -126,9 +111,3 @@ class OptogeneticStimulusSite(NWBContainer):
|
|||
...,
|
||||
description="""Location of the stimulation site. 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.""",
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
OptogeneticSeries.model_rebuild()
|
||||
OptogeneticStimulusSite.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -27,31 +27,24 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from ...hdmf_common.v1_1_2.hdmf_common_table import DynamicTable, DynamicTableRegion
|
||||
from ...hdmf_common.v1_1_2.hdmf_common_table import DynamicTableRegion, DynamicTable
|
||||
|
||||
from .core_nwb_image import ImageSeriesExternalFile, ImageSeries
|
||||
|
||||
from .core_nwb_base import (
|
||||
NWBDataInterface,
|
||||
TimeSeriesStartingTime,
|
||||
TimeSeries,
|
||||
NWBContainer,
|
||||
TimeSeriesStartingTime,
|
||||
NWBDataInterface,
|
||||
TimeSeries,
|
||||
TimeSeriesSync,
|
||||
)
|
||||
|
||||
from .core_nwb_image import ImageSeries, ImageSeriesExternalFile
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
version = "2.2.1"
|
||||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -71,18 +64,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class TwoPhotonSeries(ImageSeries):
|
||||
"""
|
||||
Image stack recorded over time from 2-photon microscope.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
pmt_gain: Optional[float] = Field(None, description="""Photomultiplier gain.""")
|
||||
scan_line_rate: Optional[float] = Field(
|
||||
|
|
@ -149,7 +135,6 @@ class RoiResponseSeries(TimeSeries):
|
|||
ROI responses over an imaging plane. The first dimension represents time. The second dimension, if present, represents ROIs.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: Union[
|
||||
NDArray[Shape["* num_times"], float],
|
||||
|
|
@ -193,7 +178,6 @@ class RoiResponseSeriesRois(DynamicTableRegion):
|
|||
DynamicTableRegion referencing into an ROITable containing information on the ROIs stored in this timeseries.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["rois"] = Field("rois")
|
||||
table: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -209,7 +193,6 @@ 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).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[RoiResponseSeries] | RoiResponseSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -221,7 +204,6 @@ class Fluorescence(NWBDataInterface):
|
|||
Fluorescence information about a region of interest (ROI). Storage hierarchy of fluorescence should be the same as for segmentation (ie, same names for ROIs and for image planes).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[RoiResponseSeries] | RoiResponseSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -233,7 +215,6 @@ class ImageSegmentation(NWBDataInterface):
|
|||
Stores pixels in an image that represent different regions of interest (ROIs) or masks. All segmentation for a given imaging plane is stored together, with storage for multiple imaging planes (masks) supported. Each ROI is stored in its own subgroup, with the ROI group containing both a 2D mask and a list of pixels that make up this mask. Segments can also be used for masking neuropil. If segmentation is allowed to change with time, a new imaging plane (or module) is required and ROI names should remain consistent between them.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[
|
||||
List[Union[BaseModel, DynamicTable]] | Union[BaseModel, DynamicTable]
|
||||
] = Field(default_factory=dict)
|
||||
|
|
@ -245,7 +226,6 @@ class ImagingPlane(NWBContainer):
|
|||
An imaging plane and its metadata.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: Optional[str] = Field(
|
||||
None, description="""Description of the imaging plane."""
|
||||
|
|
@ -287,7 +267,6 @@ class ImagingPlaneManifold(ConfiguredBaseModel):
|
|||
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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["manifold"] = Field("manifold")
|
||||
conversion: Optional[float] = Field(
|
||||
None,
|
||||
|
|
@ -310,7 +289,6 @@ class ImagingPlaneOriginCoords(ConfiguredBaseModel):
|
|||
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).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["origin_coords"] = Field("origin_coords")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -324,7 +302,6 @@ class ImagingPlaneGridSpacing(ConfiguredBaseModel):
|
|||
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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["grid_spacing"] = Field("grid_spacing")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -338,7 +315,6 @@ class OpticalChannel(NWBContainer):
|
|||
An optical channel used to record from an imaging plane.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: str = Field(
|
||||
..., description="""Description or other notes about the channel."""
|
||||
|
|
@ -353,24 +329,7 @@ class MotionCorrection(NWBDataInterface):
|
|||
An image stack where all frames are shifted (registered) to a common coordinate system, to account for movement and drift between frames. Note: each frame at each point in time is assumed to be 2-D (has only x & y dimensions).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[NWBDataInterface] | NWBDataInterface] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
name: str = Field(...)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
TwoPhotonSeries.model_rebuild()
|
||||
RoiResponseSeries.model_rebuild()
|
||||
RoiResponseSeriesRois.model_rebuild()
|
||||
DfOverF.model_rebuild()
|
||||
Fluorescence.model_rebuild()
|
||||
ImageSegmentation.model_rebuild()
|
||||
ImagingPlane.model_rebuild()
|
||||
ImagingPlaneManifold.model_rebuild()
|
||||
ImagingPlaneOriginCoords.model_rebuild()
|
||||
ImagingPlaneGridSpacing.model_rebuild()
|
||||
OpticalChannel.model_rebuild()
|
||||
MotionCorrection.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -27,23 +27,16 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from .core_nwb_base import NWBDataInterface, NWBData
|
||||
|
||||
from .core_nwb_image import GrayscaleImage
|
||||
|
||||
from .core_nwb_base import NWBData, NWBDataInterface
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
version = "2.2.1"
|
||||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -63,18 +56,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class RetinotopyMap(NWBData):
|
||||
"""
|
||||
Abstract two-dimensional map of responses. Array structure: [num_rows][num_columns]
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
dimension: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -91,7 +77,6 @@ class AxisMap(RetinotopyMap):
|
|||
Abstract two-dimensional map of responses to stimuli along a single response axis (e.g. eccentricity)
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
unit: Optional[str] = Field(
|
||||
None, description="""Unit that axis data is stored in (e.g., degrees)."""
|
||||
|
|
@ -111,7 +96,6 @@ class RetinotopyImage(GrayscaleImage):
|
|||
Gray-scale image related to retinotopic mapping. Array structure: [num_rows][num_columns]
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
bits_per_pixel: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -147,7 +131,6 @@ class ImagingRetinotopy(NWBDataInterface):
|
|||
Intrinsic signal optical imaging or widefield imaging for measuring retinotopy. Stores orthogonal maps (e.g., altitude/azimuth; radius/theta) of responses to specific stimuli and a combined polarity map from which to identify visual areas. NOTE: for data consistency, all images and arrays are stored in the format [row][column] and [row, col], which equates to [y][x]. Field of view and dimension arrays may appear backward (i.e., y before x).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("ImagingRetinotopy")
|
||||
axis_1_phase_map: str = Field(
|
||||
..., description="""Phase response to stimulus on the first measured axis."""
|
||||
|
|
@ -185,7 +168,6 @@ class ImagingRetinotopyAxis1PhaseMap(AxisMap):
|
|||
Phase response to stimulus on the first measured axis.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["axis_1_phase_map"] = Field("axis_1_phase_map")
|
||||
unit: Optional[str] = Field(
|
||||
None, description="""Unit that axis data is stored in (e.g., degrees)."""
|
||||
|
|
@ -205,7 +187,6 @@ class ImagingRetinotopyAxis1PowerMap(AxisMap):
|
|||
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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["axis_1_power_map"] = Field("axis_1_power_map")
|
||||
unit: Optional[str] = Field(
|
||||
None, description="""Unit that axis data is stored in (e.g., degrees)."""
|
||||
|
|
@ -225,7 +206,6 @@ class ImagingRetinotopyAxis2PhaseMap(AxisMap):
|
|||
Phase response to stimulus on the second measured axis.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["axis_2_phase_map"] = Field("axis_2_phase_map")
|
||||
unit: Optional[str] = Field(
|
||||
None, description="""Unit that axis data is stored in (e.g., degrees)."""
|
||||
|
|
@ -245,7 +225,6 @@ class ImagingRetinotopyAxis2PowerMap(AxisMap):
|
|||
Power response to stimulus on the second measured axis.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["axis_2_power_map"] = Field("axis_2_power_map")
|
||||
unit: Optional[str] = Field(
|
||||
None, description="""Unit that axis data is stored in (e.g., degrees)."""
|
||||
|
|
@ -265,7 +244,6 @@ class ImagingRetinotopySignMap(RetinotopyMap):
|
|||
Sine of the angle between the direction of the gradient in axis_1 and axis_2.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["sign_map"] = Field("sign_map")
|
||||
dimension: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -282,7 +260,6 @@ class ImagingRetinotopyFocalDepthImage(RetinotopyImage):
|
|||
Gray-scale image taken with same settings/parameters (e.g., focal depth, wavelength) as data collection. Array format: [rows][columns].
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["focal_depth_image"] = Field("focal_depth_image")
|
||||
focal_depth: Optional[float] = Field(
|
||||
None, description="""Focal depth offset, in meters."""
|
||||
|
|
@ -321,7 +298,6 @@ class ImagingRetinotopyVasculatureImage(RetinotopyImage):
|
|||
Gray-scale anatomical image of cortical surface. Array structure: [rows][columns]
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["vasculature_image"] = Field("vasculature_image")
|
||||
bits_per_pixel: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -350,18 +326,3 @@ class ImagingRetinotopyVasculatureImage(RetinotopyImage):
|
|||
NDArray[Shape["* x, * y, 4 r_g_b_a"], float],
|
||||
]
|
||||
] = Field(None)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
RetinotopyMap.model_rebuild()
|
||||
AxisMap.model_rebuild()
|
||||
RetinotopyImage.model_rebuild()
|
||||
ImagingRetinotopy.model_rebuild()
|
||||
ImagingRetinotopyAxis1PhaseMap.model_rebuild()
|
||||
ImagingRetinotopyAxis1PowerMap.model_rebuild()
|
||||
ImagingRetinotopyAxis2PhaseMap.model_rebuild()
|
||||
ImagingRetinotopyAxis2PowerMap.model_rebuild()
|
||||
ImagingRetinotopySignMap.model_rebuild()
|
||||
ImagingRetinotopyFocalDepthImage.model_rebuild()
|
||||
ImagingRetinotopyVasculatureImage.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -194,13 +194,6 @@ version = "2.2.1"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -218,13 +211,3 @@ class ConfiguredBaseModel(BaseModel):
|
|||
self.array[i] = value
|
||||
else:
|
||||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from ...hdmf_common.v1_1_3.hdmf_common_table import Container, Data, DynamicTable
|
||||
from ...hdmf_common.v1_1_3.hdmf_common_table import Data, Container, DynamicTable
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
|
|
@ -35,13 +35,6 @@ version = "2.2.2"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -61,18 +54,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class NWBData(Data):
|
||||
"""
|
||||
An abstract data type for a dataset.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
|
||||
|
||||
|
|
@ -81,7 +67,6 @@ class Image(NWBData):
|
|||
An abstract data type for an image. Shape can be 2-D (x, y), or 3-D where the third dimension can have three or four elements, e.g. (x, y, (r, g, b)) or (x, y, (r, g, b, a)).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
resolution: Optional[float] = Field(
|
||||
None, description="""Pixel resolution of the image, in pixels per centimeter."""
|
||||
|
|
@ -103,7 +88,6 @@ class NWBContainer(Container):
|
|||
An abstract data type for a generic container storing collections of data and metadata. Base type for all data and metadata containers.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
|
||||
|
||||
|
|
@ -112,7 +96,6 @@ class NWBDataInterface(NWBContainer):
|
|||
An abstract data type for a generic container storing collections of data, as opposed to metadata.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
|
||||
|
||||
|
|
@ -121,7 +104,6 @@ class TimeSeries(NWBDataInterface):
|
|||
General purpose time series.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: Optional[str] = Field(
|
||||
None, description="""Description of the time series."""
|
||||
|
|
@ -161,7 +143,6 @@ class TimeSeriesData(ConfiguredBaseModel):
|
|||
Data values. Data can be in 1-D, 2-D, 3-D, or 4-D. The first dimension should always represent time. This can also be used to store binary data (e.g., image frames). This can also be a link to data stored in an external file.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
conversion: Optional[float] = Field(
|
||||
None,
|
||||
|
|
@ -190,7 +171,6 @@ class TimeSeriesStartingTime(ConfiguredBaseModel):
|
|||
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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["starting_time"] = Field("starting_time")
|
||||
rate: Optional[float] = Field(None, description="""Sampling rate, in Hz.""")
|
||||
unit: Optional[str] = Field(
|
||||
|
|
@ -205,7 +185,6 @@ class TimeSeriesSync(ConfiguredBaseModel):
|
|||
Lab-specific time and sync information as provided directly from hardware devices and that is necessary for aligning all acquired time information to a common timebase. The timestamp array stores time in the common timebase. This group will usually only be populated in TimeSeries that are stored external to the NWB file, in files storing raw data. Once timestamp data is calculated, the contents of 'sync' are mostly for archival purposes.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["sync"] = Field("sync")
|
||||
|
||||
|
||||
|
|
@ -214,7 +193,6 @@ class ProcessingModule(NWBContainer):
|
|||
A collection of processed data.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[
|
||||
List[Union[BaseModel, DynamicTable, NWBDataInterface]]
|
||||
| Union[BaseModel, DynamicTable, NWBDataInterface]
|
||||
|
|
@ -227,7 +205,6 @@ class Images(NWBDataInterface):
|
|||
A collection of images.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("Images")
|
||||
description: Optional[str] = Field(
|
||||
None, description="""Description of this collection of images."""
|
||||
|
|
@ -235,17 +212,3 @@ class Images(NWBDataInterface):
|
|||
image: List[str] | str = Field(
|
||||
default_factory=list, description="""Images stored in this collection."""
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
NWBData.model_rebuild()
|
||||
Image.model_rebuild()
|
||||
NWBContainer.model_rebuild()
|
||||
NWBDataInterface.model_rebuild()
|
||||
TimeSeries.model_rebuild()
|
||||
TimeSeriesData.model_rebuild()
|
||||
TimeSeriesStartingTime.model_rebuild()
|
||||
TimeSeriesSync.model_rebuild()
|
||||
ProcessingModule.model_rebuild()
|
||||
Images.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -28,9 +28,9 @@ if TYPE_CHECKING:
|
|||
|
||||
|
||||
from .core_nwb_base import (
|
||||
NWBDataInterface,
|
||||
TimeSeries,
|
||||
TimeSeriesStartingTime,
|
||||
NWBDataInterface,
|
||||
TimeSeriesSync,
|
||||
)
|
||||
|
||||
|
|
@ -42,13 +42,6 @@ version = "2.2.2"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -68,18 +61,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class SpatialSeries(TimeSeries):
|
||||
"""
|
||||
Direction, e.g., of gaze or travel, or position. The TimeSeries::data field is a 2D array storing position or direction relative to some reference frame. Array structure: [num measurements] [num dimensions]. Each SpatialSeries has a text dataset reference_frame that indicates the zero-position, or the zero-axes for direction. For example, if representing gaze direction, 'straight-ahead' might be a specific pixel on the monitor, or some other point in space. For position data, the 0,0 point might be the top-left corner of an enclosure, as viewed from the tracking camera. The unit of data will indicate how to interpret SpatialSeries values.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(
|
||||
...,
|
||||
|
|
@ -123,7 +109,6 @@ class SpatialSeriesData(ConfiguredBaseModel):
|
|||
1-D or 2-D array storing position or direction relative to some reference frame.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -142,7 +127,6 @@ class BehavioralEpochs(NWBDataInterface):
|
|||
TimeSeries for storing behavioral epochs. The objective of this and the other two Behavioral interfaces (e.g. BehavioralEvents and BehavioralTimeSeries) is to provide generic hooks for software tools/scripts. This allows a tool/script to take the output one specific interface (e.g., UnitTimes) and plot that data relative to another data modality (e.g., behavioral events) without having to define all possible modalities in advance. Declaring one of these interfaces means that one or more TimeSeries of the specified type is published. These TimeSeries should reside in a group having the same name as the interface. For example, if a BehavioralTimeSeries interface is declared, the module will have one or more TimeSeries defined in the module sub-group 'BehavioralTimeSeries'. BehavioralEpochs should use IntervalSeries. BehavioralEvents is used for irregular events. BehavioralTimeSeries is for continuous data.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[IntervalSeries] | IntervalSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -154,7 +138,6 @@ class BehavioralEvents(NWBDataInterface):
|
|||
TimeSeries for storing behavioral events. See description of <a href=\"#BehavioralEpochs\">BehavioralEpochs</a> for more details.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[TimeSeries] | TimeSeries] = Field(default_factory=dict)
|
||||
name: str = Field(...)
|
||||
|
||||
|
|
@ -164,7 +147,6 @@ class BehavioralTimeSeries(NWBDataInterface):
|
|||
TimeSeries for storing Behavoioral time series data. See description of <a href=\"#BehavioralEpochs\">BehavioralEpochs</a> for more details.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[TimeSeries] | TimeSeries] = Field(default_factory=dict)
|
||||
name: str = Field(...)
|
||||
|
||||
|
|
@ -174,7 +156,6 @@ class PupilTracking(NWBDataInterface):
|
|||
Eye-tracking data, representing pupil size.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[TimeSeries] | TimeSeries] = Field(default_factory=dict)
|
||||
name: str = Field(...)
|
||||
|
||||
|
|
@ -184,7 +165,6 @@ class EyeTracking(NWBDataInterface):
|
|||
Eye-tracking data, representing direction of gaze.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[SpatialSeries] | SpatialSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -196,7 +176,6 @@ class CompassDirection(NWBDataInterface):
|
|||
With a CompassDirection interface, a module publishes a SpatialSeries object representing a floating point value for theta. The SpatialSeries::reference_frame field should indicate what direction corresponds to 0 and which is the direction of rotation (this should be clockwise). The si_unit for the SpatialSeries should be radians or degrees.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[SpatialSeries] | SpatialSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -208,21 +187,7 @@ class Position(NWBDataInterface):
|
|||
Position data, whether along the x, x/y or x/y/z axis.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[SpatialSeries] | SpatialSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
name: str = Field(...)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
SpatialSeries.model_rebuild()
|
||||
SpatialSeriesData.model_rebuild()
|
||||
BehavioralEpochs.model_rebuild()
|
||||
BehavioralEvents.model_rebuild()
|
||||
BehavioralTimeSeries.model_rebuild()
|
||||
PupilTracking.model_rebuild()
|
||||
EyeTracking.model_rebuild()
|
||||
CompassDirection.model_rebuild()
|
||||
Position.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -35,13 +35,6 @@ version = "2.2.2"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -61,18 +54,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class Device(NWBContainer):
|
||||
"""
|
||||
Metadata about a data acquisition device, e.g., recording system, electrode, microscope.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -81,8 +67,3 @@ class Device(NWBContainer):
|
|||
manufacturer: Optional[str] = Field(
|
||||
None, description="""The name of the manufacturer of the device."""
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
Device.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -27,29 +27,22 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from ...hdmf_common.v1_1_3.hdmf_common_table import DynamicTableRegion, DynamicTable
|
||||
|
||||
from .core_nwb_base import (
|
||||
NWBContainer,
|
||||
TimeSeriesStartingTime,
|
||||
NWBDataInterface,
|
||||
TimeSeries,
|
||||
TimeSeriesStartingTime,
|
||||
NWBContainer,
|
||||
TimeSeriesSync,
|
||||
)
|
||||
|
||||
from ...hdmf_common.v1_1_3.hdmf_common_table import DynamicTableRegion, DynamicTable
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
version = "2.2.2"
|
||||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -69,18 +62,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class ElectricalSeries(TimeSeries):
|
||||
"""
|
||||
A time series of acquired voltage data from extracellular recordings. The data field is an int or float array storing data in volts. The first dimension should always represent time. The second dimension, if present, should represent channels.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: Union[
|
||||
NDArray[Shape["* num_times"], float],
|
||||
|
|
@ -129,7 +115,6 @@ class ElectricalSeriesElectrodes(DynamicTableRegion):
|
|||
DynamicTableRegion pointer to the electrodes that this time series was generated from.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["electrodes"] = Field("electrodes")
|
||||
table: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -153,7 +138,6 @@ 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).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: Union[
|
||||
NDArray[Shape["* num_events, * num_channels, * num_samples"], float],
|
||||
|
|
@ -201,7 +185,6 @@ class FeatureExtraction(NWBDataInterface):
|
|||
Features, such as PC1 and PC2, that are extracted from signals stored in a SpikeEventSeries or other source.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("FeatureExtraction")
|
||||
description: NDArray[Shape["* num_features"], str] = Field(
|
||||
...,
|
||||
|
|
@ -228,7 +211,6 @@ class FeatureExtractionElectrodes(DynamicTableRegion):
|
|||
DynamicTableRegion pointer to the electrodes that this time series was generated from.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["electrodes"] = Field("electrodes")
|
||||
table: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -252,7 +234,6 @@ class EventDetection(NWBDataInterface):
|
|||
Detected spike events from voltage trace(s).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("EventDetection")
|
||||
detection_method: str = Field(
|
||||
...,
|
||||
|
|
@ -272,7 +253,6 @@ class EventWaveform(NWBDataInterface):
|
|||
Represents either the waveforms of detected events, as extracted from a raw data trace in /acquisition, or the event waveforms that were stored during experiment acquisition.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[SpikeEventSeries] | SpikeEventSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -284,7 +264,6 @@ class FilteredEphys(NWBDataInterface):
|
|||
Electrophysiology data from one or more channels that has been subjected to filtering. Examples of filtered data include Theta and Gamma (LFP has its own interface). FilteredEphys modules publish an ElectricalSeries for each filtered channel or set of channels. The name of each ElectricalSeries is arbitrary but should be informative. The source of the filtered data, whether this is from analysis of another time series or as acquired by hardware, should be noted in each's TimeSeries::description field. There is no assumed 1::1 correspondence between filtered ephys signals and electrodes, as a single signal can apply to many nearby electrodes, and one electrode may have different filtered (e.g., theta and/or gamma) signals represented. Filter properties should be noted in the ElectricalSeries.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[ElectricalSeries] | ElectricalSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -296,7 +275,6 @@ class LFP(NWBDataInterface):
|
|||
LFP data from one or more channels. The electrode map in each published ElectricalSeries will identify which channels are providing LFP data. Filter properties should be noted in the ElectricalSeries description or comments field.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[ElectricalSeries] | ElectricalSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -308,7 +286,6 @@ class ElectrodeGroup(NWBContainer):
|
|||
A physical grouping of electrodes, e.g. a shank of an array.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: Optional[str] = Field(
|
||||
None, description="""Description of this electrode group."""
|
||||
|
|
@ -327,7 +304,6 @@ class ElectrodeGroupPosition(ConfiguredBaseModel):
|
|||
stereotaxic or common framework coordinates
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["position"] = Field("position")
|
||||
x: Optional[float] = Field(None, description="""x coordinate""")
|
||||
y: Optional[float] = Field(None, description="""y coordinate""")
|
||||
|
|
@ -339,7 +315,6 @@ class ClusterWaveforms(NWBDataInterface):
|
|||
DEPRECATED The mean waveform shape, including standard deviation, of the different clusters. Ideally, the waveform analysis should be performed on data that is only high-pass filtered. This is a separate module because it is expected to require updating. For example, IMEC probes may require different storage requirements to store/display mean waveforms, requiring a new interface or an extension of this one.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("ClusterWaveforms")
|
||||
waveform_filtering: str = Field(
|
||||
..., description="""Filtering applied to data before generating mean/sd"""
|
||||
|
|
@ -359,7 +334,6 @@ class Clustering(NWBDataInterface):
|
|||
DEPRECATED Clustered spike data, whether from automatic clustering tools (e.g., klustakwik) or as a result of manual sorting.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("Clustering")
|
||||
description: str = Field(
|
||||
...,
|
||||
|
|
@ -376,20 +350,3 @@ class Clustering(NWBDataInterface):
|
|||
...,
|
||||
description="""Times of clustered events, in seconds. This may be a link to times field in associated FeatureExtraction module.""",
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
ElectricalSeries.model_rebuild()
|
||||
ElectricalSeriesElectrodes.model_rebuild()
|
||||
SpikeEventSeries.model_rebuild()
|
||||
FeatureExtraction.model_rebuild()
|
||||
FeatureExtractionElectrodes.model_rebuild()
|
||||
EventDetection.model_rebuild()
|
||||
EventWaveform.model_rebuild()
|
||||
FilteredEphys.model_rebuild()
|
||||
LFP.model_rebuild()
|
||||
ElectrodeGroup.model_rebuild()
|
||||
ElectrodeGroupPosition.model_rebuild()
|
||||
ClusterWaveforms.model_rebuild()
|
||||
Clustering.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -27,27 +27,20 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from .core_nwb_base import TimeSeries
|
||||
|
||||
from ...hdmf_common.v1_1_3.hdmf_common_table import (
|
||||
DynamicTable,
|
||||
VectorIndex,
|
||||
DynamicTable,
|
||||
VectorData,
|
||||
)
|
||||
|
||||
from .core_nwb_base import TimeSeries
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
version = "2.2.2"
|
||||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -67,18 +60,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class TimeIntervals(DynamicTable):
|
||||
"""
|
||||
A container for aggregating epoch data and the TimeSeries that each epoch applies to.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
start_time: Optional[List[float] | float] = Field(
|
||||
default_factory=list, description="""Start time of epoch, in seconds."""
|
||||
|
|
@ -122,7 +108,6 @@ class TimeIntervalsTagsIndex(VectorIndex):
|
|||
Index for tags.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["tags_index"] = Field("tags_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -136,7 +121,6 @@ class TimeIntervalsTimeseries(VectorData):
|
|||
An index into a TimeSeries object.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["timeseries"] = Field("timeseries")
|
||||
idx_start: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -167,18 +151,9 @@ class TimeIntervalsTimeseriesIndex(VectorIndex):
|
|||
Index for timeseries.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["timeseries_index"] = Field("timeseries_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
description="""Reference to the target dataset that this index applies to.""",
|
||||
)
|
||||
array: Optional[NDArray[Shape["* num_rows"], Any]] = Field(None)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
TimeIntervals.model_rebuild()
|
||||
TimeIntervalsTagsIndex.model_rebuild()
|
||||
TimeIntervalsTimeseries.model_rebuild()
|
||||
TimeIntervalsTimeseriesIndex.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -27,27 +27,27 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from .core_nwb_ophys import ImagingPlane
|
||||
|
||||
from .core_nwb_base import TimeSeries, NWBDataInterface, ProcessingModule, NWBContainer
|
||||
from .core_nwb_icephys import SweepTable, IntracellularElectrode
|
||||
|
||||
from .core_nwb_epoch import TimeIntervals
|
||||
|
||||
from ...hdmf_common.v1_1_3.hdmf_common_table import (
|
||||
DynamicTable,
|
||||
VectorData,
|
||||
VectorIndex,
|
||||
)
|
||||
from .core_nwb_ecephys import ElectrodeGroup
|
||||
|
||||
from .core_nwb_icephys import IntracellularElectrode, SweepTable
|
||||
from .core_nwb_base import NWBContainer, ProcessingModule, NWBDataInterface, TimeSeries
|
||||
|
||||
from .core_nwb_ogen import OptogeneticStimulusSite
|
||||
from .core_nwb_device import Device
|
||||
|
||||
from .core_nwb_misc import Units
|
||||
|
||||
from .core_nwb_ecephys import ElectrodeGroup
|
||||
from .core_nwb_ogen import OptogeneticStimulusSite
|
||||
|
||||
from .core_nwb_device import Device
|
||||
from ...hdmf_common.v1_1_3.hdmf_common_table import (
|
||||
VectorIndex,
|
||||
DynamicTable,
|
||||
VectorData,
|
||||
)
|
||||
|
||||
from .core_nwb_ophys import ImagingPlane
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
|
|
@ -55,13 +55,6 @@ version = "2.2.2"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -81,18 +74,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class NWBFile(NWBContainer):
|
||||
"""
|
||||
An NWB:N file storing cellular-based neurophysiology data from a single experimental session.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: Literal["root"] = Field("root")
|
||||
nwb_version: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -163,7 +149,6 @@ class NWBFileStimulus(ConfiguredBaseModel):
|
|||
Data pushed into the system (eg, video stimulus, sound, voltage, etc) and secondary representations of that data (eg, measurements of something used as a stimulus). This group should be made read-only after experiment complete and timestamps are corrected to common timebase. Stores both presented stimuli and stimulus templates, the latter in case the same stimulus is presented multiple times, or is pulled from an external stimulus library. Stimuli are here defined as any signal that is pushed into the system as part of the experiment (eg, sound, video, voltage, etc). Many different experiments can use the same stimuli, and stimuli can be re-used during an experiment. The stimulus group is organized so that one version of template stimuli can be stored and these be used multiple times. These templates can exist in the present file or can be linked to a remote library file.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["stimulus"] = Field("stimulus")
|
||||
presentation: Optional[List[TimeSeries] | TimeSeries] = Field(
|
||||
default_factory=dict, description="""Stimuli presented during the experiment."""
|
||||
|
|
@ -179,7 +164,6 @@ class NWBFileGeneral(ConfiguredBaseModel):
|
|||
Experimental metadata, including protocol, notes and description of hardware device(s). The metadata stored in this section should be used to describe the experiment. Metadata necessary for interpreting the data is stored with the data. General experimental metadata, including animal strain, experimental protocols, experimenter, devices, etc, are stored under 'general'. Core metadata (e.g., that required to interpret data fields) is stored with the data itself, and implicitly defined by the file specification (e.g., time is in seconds). The strategy used here for storing non-core metadata is to use free-form text fields, such as would appear in sentences or paragraphs from a Methods section. Metadata fields are text to enable them to be more general, for example to represent ranges instead of numerical values. Machine-readable metadata is stored as attributes to these free-form datasets. All entries in the below table are to be included when data is present. Unused groups (e.g., intracellular_ephys in an optophysiology experiment) should not be created unless there is data to store within them.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["general"] = Field("general")
|
||||
data_collection: Optional[str] = Field(
|
||||
None, description="""Notes about data collection and analysis."""
|
||||
|
|
@ -269,7 +253,6 @@ class NWBFileGeneralSourceScript(ConfiguredBaseModel):
|
|||
Script file or link to public source code used to create this NWB file.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["source_script"] = Field("source_script")
|
||||
file_name: Optional[str] = Field(None, description="""Name of script file.""")
|
||||
value: str = Field(...)
|
||||
|
|
@ -280,7 +263,6 @@ class Subject(NWBContainer):
|
|||
Information about the animal or person from which the data was measured.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["subject"] = Field("subject")
|
||||
age: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -314,7 +296,6 @@ class NWBFileGeneralExtracellularEphys(ConfiguredBaseModel):
|
|||
Metadata related to extracellular electrophysiology.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["extracellular_ephys"] = Field("extracellular_ephys")
|
||||
electrode_group: Optional[List[str] | str] = Field(
|
||||
default_factory=list, description="""Physical group of electrodes."""
|
||||
|
|
@ -330,7 +311,6 @@ class NWBFileGeneralExtracellularEphysElectrodes(DynamicTable):
|
|||
A table of all electrodes (i.e. channels) used for recording.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["electrodes"] = Field("electrodes")
|
||||
x: Optional[List[float] | float] = Field(
|
||||
default_factory=list,
|
||||
|
|
@ -400,7 +380,6 @@ class NWBFileGeneralIntracellularEphys(ConfiguredBaseModel):
|
|||
Metadata related to intracellular electrophysiology.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["intracellular_ephys"] = Field("intracellular_ephys")
|
||||
filtering: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -413,15 +392,3 @@ class NWBFileGeneralIntracellularEphys(ConfiguredBaseModel):
|
|||
None,
|
||||
description="""The table which groups different PatchClampSeries together.""",
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
NWBFile.model_rebuild()
|
||||
NWBFileStimulus.model_rebuild()
|
||||
NWBFileGeneral.model_rebuild()
|
||||
NWBFileGeneralSourceScript.model_rebuild()
|
||||
Subject.model_rebuild()
|
||||
NWBFileGeneralExtracellularEphys.model_rebuild()
|
||||
NWBFileGeneralExtracellularEphysElectrodes.model_rebuild()
|
||||
NWBFileGeneralIntracellularEphys.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -28,16 +28,16 @@ if TYPE_CHECKING:
|
|||
|
||||
|
||||
from .core_nwb_base import (
|
||||
TimeSeries,
|
||||
NWBContainer,
|
||||
TimeSeriesSync,
|
||||
TimeSeriesStartingTime,
|
||||
NWBContainer,
|
||||
TimeSeries,
|
||||
)
|
||||
|
||||
from ...hdmf_common.v1_1_3.hdmf_common_table import (
|
||||
VectorIndex,
|
||||
DynamicTable,
|
||||
VectorData,
|
||||
VectorIndex,
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -46,13 +46,6 @@ version = "2.2.2"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -72,18 +65,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class PatchClampSeries(TimeSeries):
|
||||
"""
|
||||
An abstract base class for patch-clamp data - stimulus or response, current or voltage.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
stimulus_description: Optional[str] = Field(
|
||||
None, description="""Protocol/stimulus name for this patch-clamp dataset."""
|
||||
|
|
@ -131,7 +117,6 @@ class PatchClampSeriesData(ConfiguredBaseModel):
|
|||
Recorded voltage or current.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -145,7 +130,6 @@ class CurrentClampSeries(PatchClampSeries):
|
|||
Voltage data from an intracellular current-clamp recording. A corresponding CurrentClampStimulusSeries (stored separately as a stimulus) is used to store the current injected.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Recorded voltage.""")
|
||||
bias_current: Optional[float] = Field(
|
||||
|
|
@ -202,7 +186,6 @@ class CurrentClampSeriesData(ConfiguredBaseModel):
|
|||
Recorded voltage.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -216,7 +199,6 @@ class IZeroClampSeries(CurrentClampSeries):
|
|||
Voltage data from an intracellular recording when all current and amplifier settings are off (i.e., CurrentClampSeries fields will be zero). There is no CurrentClampStimulusSeries associated with an IZero series because the amplifier is disconnected and no stimulus can reach the cell.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
bias_current: float = Field(
|
||||
..., description="""Bias current, in amps, fixed to 0.0."""
|
||||
|
|
@ -273,7 +255,6 @@ class CurrentClampStimulusSeries(PatchClampSeries):
|
|||
Stimulus current applied during current clamp recording.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Stimulus current applied.""")
|
||||
stimulus_description: Optional[str] = Field(
|
||||
|
|
@ -321,7 +302,6 @@ class CurrentClampStimulusSeriesData(ConfiguredBaseModel):
|
|||
Stimulus current applied.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -335,7 +315,6 @@ class VoltageClampSeries(PatchClampSeries):
|
|||
Current data from an intracellular voltage-clamp recording. A corresponding VoltageClampStimulusSeries (stored separately as a stimulus) is used to store the voltage injected.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Recorded current.""")
|
||||
capacitance_fast: Optional[str] = Field(
|
||||
|
|
@ -404,7 +383,6 @@ class VoltageClampSeriesData(ConfiguredBaseModel):
|
|||
Recorded current.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -418,7 +396,6 @@ class VoltageClampSeriesCapacitanceFast(ConfiguredBaseModel):
|
|||
Fast capacitance, in farads.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["capacitance_fast"] = Field("capacitance_fast")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -432,7 +409,6 @@ class VoltageClampSeriesCapacitanceSlow(ConfiguredBaseModel):
|
|||
Slow capacitance, in farads.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["capacitance_slow"] = Field("capacitance_slow")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -446,7 +422,6 @@ class VoltageClampSeriesResistanceCompBandwidth(ConfiguredBaseModel):
|
|||
Resistance compensation bandwidth, in hertz.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["resistance_comp_bandwidth"] = Field("resistance_comp_bandwidth")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -460,7 +435,6 @@ class VoltageClampSeriesResistanceCompCorrection(ConfiguredBaseModel):
|
|||
Resistance compensation correction, in percent.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["resistance_comp_correction"] = Field("resistance_comp_correction")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -474,7 +448,6 @@ class VoltageClampSeriesResistanceCompPrediction(ConfiguredBaseModel):
|
|||
Resistance compensation prediction, in percent.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["resistance_comp_prediction"] = Field("resistance_comp_prediction")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -488,7 +461,6 @@ class VoltageClampSeriesWholeCellCapacitanceComp(ConfiguredBaseModel):
|
|||
Whole cell capacitance compensation, in farads.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["whole_cell_capacitance_comp"] = Field("whole_cell_capacitance_comp")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -502,7 +474,6 @@ class VoltageClampSeriesWholeCellSeriesResistanceComp(ConfiguredBaseModel):
|
|||
Whole cell series resistance compensation, in ohms.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["whole_cell_series_resistance_comp"] = Field(
|
||||
"whole_cell_series_resistance_comp"
|
||||
)
|
||||
|
|
@ -518,7 +489,6 @@ class VoltageClampStimulusSeries(PatchClampSeries):
|
|||
Stimulus voltage applied during a voltage clamp recording.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Stimulus voltage applied.""")
|
||||
stimulus_description: Optional[str] = Field(
|
||||
|
|
@ -566,7 +536,6 @@ class VoltageClampStimulusSeriesData(ConfiguredBaseModel):
|
|||
Stimulus voltage applied.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -580,7 +549,6 @@ class IntracellularElectrode(NWBContainer):
|
|||
An intracellular electrode and its metadata.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: str = Field(
|
||||
...,
|
||||
|
|
@ -612,7 +580,6 @@ class SweepTable(DynamicTable):
|
|||
The table which groups different PatchClampSeries together.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
sweep_number: Optional[List[int] | int] = Field(
|
||||
default_factory=list,
|
||||
|
|
@ -648,35 +615,9 @@ class SweepTableSeriesIndex(VectorIndex):
|
|||
Index for series.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["series_index"] = Field("series_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
description="""Reference to the target dataset that this index applies to.""",
|
||||
)
|
||||
array: Optional[NDArray[Shape["* num_rows"], Any]] = Field(None)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
PatchClampSeries.model_rebuild()
|
||||
PatchClampSeriesData.model_rebuild()
|
||||
CurrentClampSeries.model_rebuild()
|
||||
CurrentClampSeriesData.model_rebuild()
|
||||
IZeroClampSeries.model_rebuild()
|
||||
CurrentClampStimulusSeries.model_rebuild()
|
||||
CurrentClampStimulusSeriesData.model_rebuild()
|
||||
VoltageClampSeries.model_rebuild()
|
||||
VoltageClampSeriesData.model_rebuild()
|
||||
VoltageClampSeriesCapacitanceFast.model_rebuild()
|
||||
VoltageClampSeriesCapacitanceSlow.model_rebuild()
|
||||
VoltageClampSeriesResistanceCompBandwidth.model_rebuild()
|
||||
VoltageClampSeriesResistanceCompCorrection.model_rebuild()
|
||||
VoltageClampSeriesResistanceCompPrediction.model_rebuild()
|
||||
VoltageClampSeriesWholeCellCapacitanceComp.model_rebuild()
|
||||
VoltageClampSeriesWholeCellSeriesResistanceComp.model_rebuild()
|
||||
VoltageClampStimulusSeries.model_rebuild()
|
||||
VoltageClampStimulusSeriesData.model_rebuild()
|
||||
IntracellularElectrode.model_rebuild()
|
||||
SweepTable.model_rebuild()
|
||||
SweepTableSeriesIndex.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from .core_nwb_base import Image, TimeSeries, TimeSeriesStartingTime, TimeSeriesSync
|
||||
from .core_nwb_base import Image, TimeSeriesStartingTime, TimeSeries, TimeSeriesSync
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
|
|
@ -35,13 +35,6 @@ version = "2.2.2"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -61,18 +54,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class GrayscaleImage(Image):
|
||||
"""
|
||||
A grayscale image.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
resolution: Optional[float] = Field(
|
||||
None, description="""Pixel resolution of the image, in pixels per centimeter."""
|
||||
|
|
@ -94,7 +80,6 @@ class RGBImage(Image):
|
|||
A color image.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
resolution: Optional[float] = Field(
|
||||
None, description="""Pixel resolution of the image, in pixels per centimeter."""
|
||||
|
|
@ -116,7 +101,6 @@ class RGBAImage(Image):
|
|||
A color image with transparency.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
resolution: Optional[float] = Field(
|
||||
None, description="""Pixel resolution of the image, in pixels per centimeter."""
|
||||
|
|
@ -138,7 +122,6 @@ class ImageSeries(TimeSeries):
|
|||
General image data that is common between acquisition and stimulus time series. Sometimes the image data is stored in the file in a raw format while other times it will be stored as a series of external image files in the host file system. The data field will either be binary data, if the data is stored in the NWB file, or empty, if the data is stored in an external image stack. [frame][x][y] or [frame][x][y][z].
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: Optional[
|
||||
Union[
|
||||
|
|
@ -191,7 +174,6 @@ 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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["external_file"] = Field("external_file")
|
||||
starting_frame: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -205,7 +187,6 @@ class ImageMaskSeries(ImageSeries):
|
|||
An alpha mask that is applied to a presented visual stimulus. The 'data' array contains an array of mask values that are applied to the displayed image. Mask values are stored as RGBA. Mask can vary with time. The timestamps array indicates the starting time of a mask, and that mask pattern continues until it's explicitly changed.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: Optional[
|
||||
Union[
|
||||
|
|
@ -258,7 +239,6 @@ class OpticalSeries(ImageSeries):
|
|||
Image data that is presented or recorded. A stimulus template movie will be stored only as an image. When the image is presented as stimulus, additional data is required, such as field of view (e.g., how much of the visual field the image covers, or how what is the area of the target being imaged). If the OpticalSeries represents acquired imaging data, orientation is also important.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
distance: Optional[float] = Field(
|
||||
None, description="""Distance from camera/monitor to target/eye."""
|
||||
|
|
@ -327,7 +307,6 @@ 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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: NDArray[Shape["* num_times"], int] = Field(
|
||||
..., description="""Index of the frame in the referenced ImageSeries."""
|
||||
|
|
@ -359,15 +338,3 @@ class IndexSeries(TimeSeries):
|
|||
None,
|
||||
description="""Lab-specific time and sync information as provided directly from hardware devices and that is necessary for aligning all acquired time information to a common timebase. The timestamp array stores time in the common timebase. This group will usually only be populated in TimeSeries that are stored external to the NWB file, in files storing raw data. Once timestamp data is calculated, the contents of 'sync' are mostly for archival purposes.""",
|
||||
)
|
||||
|
||||
|
||||
# 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()
|
||||
ImageSeriesExternalFile.model_rebuild()
|
||||
ImageMaskSeries.model_rebuild()
|
||||
OpticalSeries.model_rebuild()
|
||||
IndexSeries.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -32,13 +32,6 @@ version = "None"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -56,13 +49,3 @@ class ConfiguredBaseModel(BaseModel):
|
|||
self.array[i] = value
|
||||
else:
|
||||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
|
|
|
|||
|
|
@ -27,15 +27,15 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from .core_nwb_base import TimeSeries, TimeSeriesStartingTime, TimeSeriesSync
|
||||
|
||||
from ...hdmf_common.v1_1_3.hdmf_common_table import (
|
||||
VectorIndex,
|
||||
DynamicTableRegion,
|
||||
DynamicTable,
|
||||
VectorIndex,
|
||||
VectorData,
|
||||
)
|
||||
|
||||
from .core_nwb_base import TimeSeriesStartingTime, TimeSeries, TimeSeriesSync
|
||||
|
||||
from .core_nwb_ecephys import ElectrodeGroup
|
||||
|
||||
|
||||
|
|
@ -44,13 +44,6 @@ version = "2.2.2"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -70,18 +63,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class AbstractFeatureSeries(TimeSeries):
|
||||
"""
|
||||
Abstract features, such as quantitative descriptions of sensory stimuli. The TimeSeries::data field is a 2D array, storing those features (e.g., for visual grating stimulus this might be orientation, spatial frequency and contrast). Null stimuli (eg, uniform gray) can be marked as being an independent feature (eg, 1.0 for gray, 0.0 for actual stimulus) or by storing NaNs for feature values, or through use of the TimeSeries::control fields. A set of features is considered to persist until the next set of features is defined. The final set of features stored should be the null set. This is useful when storing the raw stimulus is impractical.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Values of each feature at each time.""")
|
||||
feature_units: Optional[NDArray[Shape["* num_features"], str]] = Field(
|
||||
|
|
@ -125,7 +111,6 @@ class AbstractFeatureSeriesData(ConfiguredBaseModel):
|
|||
Values of each feature at each time.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -144,7 +129,6 @@ class AnnotationSeries(TimeSeries):
|
|||
Stores user annotations made during an experiment. The data[] field stores a text array, and timestamps are stored for each annotation (ie, interval=1). This is largely an alias to a standard TimeSeries storing a text array but that is identifiable as storing annotations in a machine-readable way.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: NDArray[Shape["* num_times"], str] = Field(
|
||||
..., description="""Annotations made during an experiment."""
|
||||
|
|
@ -183,7 +167,6 @@ 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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: NDArray[Shape["* num_times"], int] = Field(
|
||||
..., description="""Use values >0 if interval started, <0 if interval ended."""
|
||||
|
|
@ -222,7 +205,6 @@ class DecompositionSeries(TimeSeries):
|
|||
Spectral analysis of a time series, e.g. of an LFP or a speech signal.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Data decomposed into frequency bands.""")
|
||||
metric: str = Field(
|
||||
|
|
@ -266,7 +248,6 @@ class DecompositionSeriesData(ConfiguredBaseModel):
|
|||
Data decomposed into frequency bands.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -282,7 +263,6 @@ class DecompositionSeriesBands(DynamicTable):
|
|||
Table for describing the bands that this series was generated from. There should be one row in this table for each band.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["bands"] = Field("bands")
|
||||
band_name: Optional[List[str] | str] = Field(
|
||||
default_factory=list, description="""Name of the band, e.g. theta."""
|
||||
|
|
@ -322,7 +302,6 @@ class Units(DynamicTable):
|
|||
Data about spiking units. Event times of observed units (e.g. cell, synapse, etc.) should be concatenated and stored in spike_times.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("Units")
|
||||
spike_times_index: Optional[str] = Field(
|
||||
None, description="""Index into the spike_times dataset."""
|
||||
|
|
@ -386,7 +365,6 @@ class UnitsSpikeTimesIndex(VectorIndex):
|
|||
Index into the spike_times dataset.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["spike_times_index"] = Field("spike_times_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -400,7 +378,6 @@ class UnitsSpikeTimes(VectorData):
|
|||
Spike times for each unit.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["spike_times"] = Field("spike_times")
|
||||
resolution: Optional[float] = Field(
|
||||
None,
|
||||
|
|
@ -424,7 +401,6 @@ class UnitsObsIntervalsIndex(VectorIndex):
|
|||
Index into the obs_intervals dataset.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["obs_intervals_index"] = Field("obs_intervals_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -438,7 +414,6 @@ class UnitsElectrodesIndex(VectorIndex):
|
|||
Index into electrodes.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["electrodes_index"] = Field("electrodes_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -452,7 +427,6 @@ class UnitsElectrodes(DynamicTableRegion):
|
|||
Electrode that each spike unit came from, specified using a DynamicTableRegion.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["electrodes"] = Field("electrodes")
|
||||
table: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -469,20 +443,3 @@ class UnitsElectrodes(DynamicTableRegion):
|
|||
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()
|
||||
IntervalSeries.model_rebuild()
|
||||
DecompositionSeries.model_rebuild()
|
||||
DecompositionSeriesData.model_rebuild()
|
||||
DecompositionSeriesBands.model_rebuild()
|
||||
Units.model_rebuild()
|
||||
UnitsSpikeTimesIndex.model_rebuild()
|
||||
UnitsSpikeTimes.model_rebuild()
|
||||
UnitsObsIntervalsIndex.model_rebuild()
|
||||
UnitsElectrodesIndex.model_rebuild()
|
||||
UnitsElectrodes.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -29,9 +29,9 @@ if TYPE_CHECKING:
|
|||
|
||||
from .core_nwb_base import (
|
||||
TimeSeriesSync,
|
||||
TimeSeries,
|
||||
NWBContainer,
|
||||
TimeSeriesStartingTime,
|
||||
TimeSeries,
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -40,13 +40,6 @@ version = "2.2.2"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -66,18 +59,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class OptogeneticSeries(TimeSeries):
|
||||
"""
|
||||
An optogenetic stimulus.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: NDArray[Shape["* num_times"], float] = Field(
|
||||
..., description="""Applied power for optogenetic stimulus, in watts."""
|
||||
|
|
@ -116,7 +102,6 @@ class OptogeneticStimulusSite(NWBContainer):
|
|||
A site of optogenetic stimulation.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: str = Field(..., description="""Description of stimulation site.""")
|
||||
excitation_lambda: float = Field(
|
||||
|
|
@ -126,9 +111,3 @@ class OptogeneticStimulusSite(NWBContainer):
|
|||
...,
|
||||
description="""Location of the stimulation site. 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.""",
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
OptogeneticSeries.model_rebuild()
|
||||
OptogeneticStimulusSite.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -27,31 +27,24 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from ...hdmf_common.v1_1_3.hdmf_common_table import DynamicTable, DynamicTableRegion
|
||||
from ...hdmf_common.v1_1_3.hdmf_common_table import DynamicTableRegion, DynamicTable
|
||||
|
||||
from .core_nwb_image import ImageSeriesExternalFile, ImageSeries
|
||||
|
||||
from .core_nwb_base import (
|
||||
NWBDataInterface,
|
||||
TimeSeriesStartingTime,
|
||||
TimeSeries,
|
||||
NWBContainer,
|
||||
TimeSeriesStartingTime,
|
||||
NWBDataInterface,
|
||||
TimeSeries,
|
||||
TimeSeriesSync,
|
||||
)
|
||||
|
||||
from .core_nwb_image import ImageSeries, ImageSeriesExternalFile
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
version = "2.2.2"
|
||||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -71,18 +64,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class TwoPhotonSeries(ImageSeries):
|
||||
"""
|
||||
Image stack recorded over time from 2-photon microscope.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
pmt_gain: Optional[float] = Field(None, description="""Photomultiplier gain.""")
|
||||
scan_line_rate: Optional[float] = Field(
|
||||
|
|
@ -149,7 +135,6 @@ class RoiResponseSeries(TimeSeries):
|
|||
ROI responses over an imaging plane. The first dimension represents time. The second dimension, if present, represents ROIs.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: Union[
|
||||
NDArray[Shape["* num_times"], float],
|
||||
|
|
@ -193,7 +178,6 @@ class RoiResponseSeriesRois(DynamicTableRegion):
|
|||
DynamicTableRegion referencing into an ROITable containing information on the ROIs stored in this timeseries.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["rois"] = Field("rois")
|
||||
table: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -217,7 +201,6 @@ 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).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[RoiResponseSeries] | RoiResponseSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -229,7 +212,6 @@ class Fluorescence(NWBDataInterface):
|
|||
Fluorescence information about a region of interest (ROI). Storage hierarchy of fluorescence should be the same as for segmentation (ie, same names for ROIs and for image planes).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[RoiResponseSeries] | RoiResponseSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -241,7 +223,6 @@ class ImageSegmentation(NWBDataInterface):
|
|||
Stores pixels in an image that represent different regions of interest (ROIs) or masks. All segmentation for a given imaging plane is stored together, with storage for multiple imaging planes (masks) supported. Each ROI is stored in its own subgroup, with the ROI group containing both a 2D mask and a list of pixels that make up this mask. Segments can also be used for masking neuropil. If segmentation is allowed to change with time, a new imaging plane (or module) is required and ROI names should remain consistent between them.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[
|
||||
List[Union[BaseModel, DynamicTable]] | Union[BaseModel, DynamicTable]
|
||||
] = Field(default_factory=dict)
|
||||
|
|
@ -253,7 +234,6 @@ class ImagingPlane(NWBContainer):
|
|||
An imaging plane and its metadata.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[NWBContainer] | NWBContainer] = Field(default_factory=dict)
|
||||
name: str = Field(...)
|
||||
|
||||
|
|
@ -263,20 +243,7 @@ class MotionCorrection(NWBDataInterface):
|
|||
An image stack where all frames are shifted (registered) to a common coordinate system, to account for movement and drift between frames. Note: each frame at each point in time is assumed to be 2-D (has only x & y dimensions).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[NWBDataInterface] | NWBDataInterface] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
name: str = Field(...)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
TwoPhotonSeries.model_rebuild()
|
||||
RoiResponseSeries.model_rebuild()
|
||||
RoiResponseSeriesRois.model_rebuild()
|
||||
DfOverF.model_rebuild()
|
||||
Fluorescence.model_rebuild()
|
||||
ImageSegmentation.model_rebuild()
|
||||
ImagingPlane.model_rebuild()
|
||||
MotionCorrection.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -35,13 +35,6 @@ version = "2.2.2"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -61,18 +54,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class ImagingRetinotopy(NWBDataInterface):
|
||||
"""
|
||||
Intrinsic signal optical imaging or widefield imaging for measuring retinotopy. Stores orthogonal maps (e.g., altitude/azimuth; radius/theta) of responses to specific stimuli and a combined polarity map from which to identify visual areas. This group does not store the raw responses imaged during retinotopic mapping or the stimuli presented, but rather the resulting phase and power maps after applying a Fourier transform on the averaged responses. Note: for data consistency, all images and arrays are stored in the format [row][column] and [row, col], which equates to [y][x]. Field of view and dimension arrays may appear backward (i.e., y before x).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("ImagingRetinotopy")
|
||||
axis_1_phase_map: str = Field(
|
||||
..., description="""Phase response to stimulus on the first measured axis."""
|
||||
|
|
@ -111,7 +97,6 @@ class ImagingRetinotopyAxis1PhaseMap(ConfiguredBaseModel):
|
|||
Phase response to stimulus on the first measured axis.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["axis_1_phase_map"] = Field("axis_1_phase_map")
|
||||
dimension: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -131,7 +116,6 @@ class ImagingRetinotopyAxis1PowerMap(ConfiguredBaseModel):
|
|||
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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["axis_1_power_map"] = Field("axis_1_power_map")
|
||||
dimension: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -151,7 +135,6 @@ class ImagingRetinotopyAxis2PhaseMap(ConfiguredBaseModel):
|
|||
Phase response to stimulus on the second measured axis.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["axis_2_phase_map"] = Field("axis_2_phase_map")
|
||||
dimension: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -171,7 +154,6 @@ class ImagingRetinotopyAxis2PowerMap(ConfiguredBaseModel):
|
|||
Power response on the second measured axis. Response is scaled so 0.0 is no power in the response and 1.0 is maximum relative power.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["axis_2_power_map"] = Field("axis_2_power_map")
|
||||
dimension: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -191,7 +173,6 @@ class ImagingRetinotopyFocalDepthImage(ConfiguredBaseModel):
|
|||
Gray-scale image taken with same settings/parameters (e.g., focal depth, wavelength) as data collection. Array format: [rows][columns].
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["focal_depth_image"] = Field("focal_depth_image")
|
||||
bits_per_pixel: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -218,7 +199,6 @@ class ImagingRetinotopySignMap(ConfiguredBaseModel):
|
|||
Sine of the angle between the direction of the gradient in axis_1 and axis_2.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["sign_map"] = Field("sign_map")
|
||||
dimension: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -235,7 +215,6 @@ class ImagingRetinotopyVasculatureImage(ConfiguredBaseModel):
|
|||
Gray-scale anatomical image of cortical surface. Array structure: [rows][columns]
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["vasculature_image"] = Field("vasculature_image")
|
||||
bits_per_pixel: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -252,15 +231,3 @@ class ImagingRetinotopyVasculatureImage(ConfiguredBaseModel):
|
|||
None, description="""Format of image. Right now only 'raw' is supported."""
|
||||
)
|
||||
array: Optional[NDArray[Shape["* num_rows, * num_cols"], int]] = Field(None)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
ImagingRetinotopy.model_rebuild()
|
||||
ImagingRetinotopyAxis1PhaseMap.model_rebuild()
|
||||
ImagingRetinotopyAxis1PowerMap.model_rebuild()
|
||||
ImagingRetinotopyAxis2PhaseMap.model_rebuild()
|
||||
ImagingRetinotopyAxis2PowerMap.model_rebuild()
|
||||
ImagingRetinotopyFocalDepthImage.model_rebuild()
|
||||
ImagingRetinotopySignMap.model_rebuild()
|
||||
ImagingRetinotopyVasculatureImage.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -187,13 +187,6 @@ version = "2.2.2"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -211,13 +204,3 @@ class ConfiguredBaseModel(BaseModel):
|
|||
self.array[i] = value
|
||||
else:
|
||||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from ...hdmf_common.v1_1_3.hdmf_common_table import Container, Data, DynamicTable
|
||||
from ...hdmf_common.v1_1_3.hdmf_common_table import Data, Container, DynamicTable
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
|
|
@ -35,13 +35,6 @@ version = "2.2.4"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -61,18 +54,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class NWBData(Data):
|
||||
"""
|
||||
An abstract data type for a dataset.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
|
||||
|
||||
|
|
@ -81,7 +67,6 @@ class Image(NWBData):
|
|||
An abstract data type for an image. Shape can be 2-D (x, y), or 3-D where the third dimension can have three or four elements, e.g. (x, y, (r, g, b)) or (x, y, (r, g, b, a)).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
resolution: Optional[float] = Field(
|
||||
None, description="""Pixel resolution of the image, in pixels per centimeter."""
|
||||
|
|
@ -103,7 +88,6 @@ class NWBContainer(Container):
|
|||
An abstract data type for a generic container storing collections of data and metadata. Base type for all data and metadata containers.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
|
||||
|
||||
|
|
@ -112,7 +96,6 @@ class NWBDataInterface(NWBContainer):
|
|||
An abstract data type for a generic container storing collections of data, as opposed to metadata.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
|
||||
|
||||
|
|
@ -121,7 +104,6 @@ class TimeSeries(NWBDataInterface):
|
|||
General purpose time series.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: Optional[str] = Field(
|
||||
None, description="""Description of the time series."""
|
||||
|
|
@ -161,7 +143,6 @@ class TimeSeriesData(ConfiguredBaseModel):
|
|||
Data values. Data can be in 1-D, 2-D, 3-D, or 4-D. The first dimension should always represent time. This can also be used to store binary data (e.g., image frames). This can also be a link to data stored in an external file.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
conversion: Optional[float] = Field(
|
||||
None,
|
||||
|
|
@ -190,7 +171,6 @@ class TimeSeriesStartingTime(ConfiguredBaseModel):
|
|||
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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["starting_time"] = Field("starting_time")
|
||||
rate: Optional[float] = Field(None, description="""Sampling rate, in Hz.""")
|
||||
unit: Optional[str] = Field(
|
||||
|
|
@ -205,7 +185,6 @@ class TimeSeriesSync(ConfiguredBaseModel):
|
|||
Lab-specific time and sync information as provided directly from hardware devices and that is necessary for aligning all acquired time information to a common timebase. The timestamp array stores time in the common timebase. This group will usually only be populated in TimeSeries that are stored external to the NWB file, in files storing raw data. Once timestamp data is calculated, the contents of 'sync' are mostly for archival purposes.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["sync"] = Field("sync")
|
||||
|
||||
|
||||
|
|
@ -214,7 +193,6 @@ class ProcessingModule(NWBContainer):
|
|||
A collection of processed data.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[
|
||||
List[Union[BaseModel, DynamicTable, NWBDataInterface]]
|
||||
| Union[BaseModel, DynamicTable, NWBDataInterface]
|
||||
|
|
@ -227,7 +205,6 @@ class Images(NWBDataInterface):
|
|||
A collection of images.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("Images")
|
||||
description: Optional[str] = Field(
|
||||
None, description="""Description of this collection of images."""
|
||||
|
|
@ -235,17 +212,3 @@ class Images(NWBDataInterface):
|
|||
image: List[str] | str = Field(
|
||||
default_factory=list, description="""Images stored in this collection."""
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
NWBData.model_rebuild()
|
||||
Image.model_rebuild()
|
||||
NWBContainer.model_rebuild()
|
||||
NWBDataInterface.model_rebuild()
|
||||
TimeSeries.model_rebuild()
|
||||
TimeSeriesData.model_rebuild()
|
||||
TimeSeriesStartingTime.model_rebuild()
|
||||
TimeSeriesSync.model_rebuild()
|
||||
ProcessingModule.model_rebuild()
|
||||
Images.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -28,9 +28,9 @@ if TYPE_CHECKING:
|
|||
|
||||
|
||||
from .core_nwb_base import (
|
||||
NWBDataInterface,
|
||||
TimeSeries,
|
||||
TimeSeriesStartingTime,
|
||||
NWBDataInterface,
|
||||
TimeSeriesSync,
|
||||
)
|
||||
|
||||
|
|
@ -42,13 +42,6 @@ version = "2.2.4"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -68,18 +61,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class SpatialSeries(TimeSeries):
|
||||
"""
|
||||
Direction, e.g., of gaze or travel, or position. The TimeSeries::data field is a 2D array storing position or direction relative to some reference frame. Array structure: [num measurements] [num dimensions]. Each SpatialSeries has a text dataset reference_frame that indicates the zero-position, or the zero-axes for direction. For example, if representing gaze direction, 'straight-ahead' might be a specific pixel on the monitor, or some other point in space. For position data, the 0,0 point might be the top-left corner of an enclosure, as viewed from the tracking camera. The unit of data will indicate how to interpret SpatialSeries values.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(
|
||||
...,
|
||||
|
|
@ -123,7 +109,6 @@ class SpatialSeriesData(ConfiguredBaseModel):
|
|||
1-D or 2-D array storing position or direction relative to some reference frame.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -142,7 +127,6 @@ class BehavioralEpochs(NWBDataInterface):
|
|||
TimeSeries for storing behavioral epochs. The objective of this and the other two Behavioral interfaces (e.g. BehavioralEvents and BehavioralTimeSeries) is to provide generic hooks for software tools/scripts. This allows a tool/script to take the output one specific interface (e.g., UnitTimes) and plot that data relative to another data modality (e.g., behavioral events) without having to define all possible modalities in advance. Declaring one of these interfaces means that one or more TimeSeries of the specified type is published. These TimeSeries should reside in a group having the same name as the interface. For example, if a BehavioralTimeSeries interface is declared, the module will have one or more TimeSeries defined in the module sub-group 'BehavioralTimeSeries'. BehavioralEpochs should use IntervalSeries. BehavioralEvents is used for irregular events. BehavioralTimeSeries is for continuous data.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[IntervalSeries] | IntervalSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -154,7 +138,6 @@ class BehavioralEvents(NWBDataInterface):
|
|||
TimeSeries for storing behavioral events. See description of <a href=\"#BehavioralEpochs\">BehavioralEpochs</a> for more details.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[TimeSeries] | TimeSeries] = Field(default_factory=dict)
|
||||
name: str = Field(...)
|
||||
|
||||
|
|
@ -164,7 +147,6 @@ class BehavioralTimeSeries(NWBDataInterface):
|
|||
TimeSeries for storing Behavoioral time series data. See description of <a href=\"#BehavioralEpochs\">BehavioralEpochs</a> for more details.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[TimeSeries] | TimeSeries] = Field(default_factory=dict)
|
||||
name: str = Field(...)
|
||||
|
||||
|
|
@ -174,7 +156,6 @@ class PupilTracking(NWBDataInterface):
|
|||
Eye-tracking data, representing pupil size.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[TimeSeries] | TimeSeries] = Field(default_factory=dict)
|
||||
name: str = Field(...)
|
||||
|
||||
|
|
@ -184,7 +165,6 @@ class EyeTracking(NWBDataInterface):
|
|||
Eye-tracking data, representing direction of gaze.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[SpatialSeries] | SpatialSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -196,7 +176,6 @@ class CompassDirection(NWBDataInterface):
|
|||
With a CompassDirection interface, a module publishes a SpatialSeries object representing a floating point value for theta. The SpatialSeries::reference_frame field should indicate what direction corresponds to 0 and which is the direction of rotation (this should be clockwise). The si_unit for the SpatialSeries should be radians or degrees.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[SpatialSeries] | SpatialSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -208,21 +187,7 @@ class Position(NWBDataInterface):
|
|||
Position data, whether along the x, x/y or x/y/z axis.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[SpatialSeries] | SpatialSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
name: str = Field(...)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
SpatialSeries.model_rebuild()
|
||||
SpatialSeriesData.model_rebuild()
|
||||
BehavioralEpochs.model_rebuild()
|
||||
BehavioralEvents.model_rebuild()
|
||||
BehavioralTimeSeries.model_rebuild()
|
||||
PupilTracking.model_rebuild()
|
||||
EyeTracking.model_rebuild()
|
||||
CompassDirection.model_rebuild()
|
||||
Position.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -35,13 +35,6 @@ version = "2.2.4"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -61,18 +54,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class Device(NWBContainer):
|
||||
"""
|
||||
Metadata about a data acquisition device, e.g., recording system, electrode, microscope.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -81,8 +67,3 @@ class Device(NWBContainer):
|
|||
manufacturer: Optional[str] = Field(
|
||||
None, description="""The name of the manufacturer of the device."""
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
Device.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -27,29 +27,22 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from ...hdmf_common.v1_1_3.hdmf_common_table import DynamicTableRegion, DynamicTable
|
||||
|
||||
from .core_nwb_base import (
|
||||
NWBContainer,
|
||||
TimeSeriesStartingTime,
|
||||
NWBDataInterface,
|
||||
TimeSeries,
|
||||
TimeSeriesStartingTime,
|
||||
NWBContainer,
|
||||
TimeSeriesSync,
|
||||
)
|
||||
|
||||
from ...hdmf_common.v1_1_3.hdmf_common_table import DynamicTableRegion, DynamicTable
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
version = "2.2.4"
|
||||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -69,18 +62,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class ElectricalSeries(TimeSeries):
|
||||
"""
|
||||
A time series of acquired voltage data from extracellular recordings. The data field is an int or float array storing data in volts. The first dimension should always represent time. The second dimension, if present, should represent channels.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: Union[
|
||||
NDArray[Shape["* num_times"], float],
|
||||
|
|
@ -129,7 +115,6 @@ class ElectricalSeriesElectrodes(DynamicTableRegion):
|
|||
DynamicTableRegion pointer to the electrodes that this time series was generated from.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["electrodes"] = Field("electrodes")
|
||||
table: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -153,7 +138,6 @@ 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).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: Union[
|
||||
NDArray[Shape["* num_events, * num_channels, * num_samples"], float],
|
||||
|
|
@ -201,7 +185,6 @@ class FeatureExtraction(NWBDataInterface):
|
|||
Features, such as PC1 and PC2, that are extracted from signals stored in a SpikeEventSeries or other source.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("FeatureExtraction")
|
||||
description: NDArray[Shape["* num_features"], str] = Field(
|
||||
...,
|
||||
|
|
@ -228,7 +211,6 @@ class FeatureExtractionElectrodes(DynamicTableRegion):
|
|||
DynamicTableRegion pointer to the electrodes that this time series was generated from.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["electrodes"] = Field("electrodes")
|
||||
table: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -252,7 +234,6 @@ class EventDetection(NWBDataInterface):
|
|||
Detected spike events from voltage trace(s).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("EventDetection")
|
||||
detection_method: str = Field(
|
||||
...,
|
||||
|
|
@ -272,7 +253,6 @@ class EventWaveform(NWBDataInterface):
|
|||
Represents either the waveforms of detected events, as extracted from a raw data trace in /acquisition, or the event waveforms that were stored during experiment acquisition.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[SpikeEventSeries] | SpikeEventSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -284,7 +264,6 @@ class FilteredEphys(NWBDataInterface):
|
|||
Electrophysiology data from one or more channels that has been subjected to filtering. Examples of filtered data include Theta and Gamma (LFP has its own interface). FilteredEphys modules publish an ElectricalSeries for each filtered channel or set of channels. The name of each ElectricalSeries is arbitrary but should be informative. The source of the filtered data, whether this is from analysis of another time series or as acquired by hardware, should be noted in each's TimeSeries::description field. There is no assumed 1::1 correspondence between filtered ephys signals and electrodes, as a single signal can apply to many nearby electrodes, and one electrode may have different filtered (e.g., theta and/or gamma) signals represented. Filter properties should be noted in the ElectricalSeries.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[ElectricalSeries] | ElectricalSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -296,7 +275,6 @@ class LFP(NWBDataInterface):
|
|||
LFP data from one or more channels. The electrode map in each published ElectricalSeries will identify which channels are providing LFP data. Filter properties should be noted in the ElectricalSeries description or comments field.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[ElectricalSeries] | ElectricalSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -308,7 +286,6 @@ class ElectrodeGroup(NWBContainer):
|
|||
A physical grouping of electrodes, e.g. a shank of an array.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: Optional[str] = Field(
|
||||
None, description="""Description of this electrode group."""
|
||||
|
|
@ -327,7 +304,6 @@ class ElectrodeGroupPosition(ConfiguredBaseModel):
|
|||
stereotaxic or common framework coordinates
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["position"] = Field("position")
|
||||
x: Optional[float] = Field(None, description="""x coordinate""")
|
||||
y: Optional[float] = Field(None, description="""y coordinate""")
|
||||
|
|
@ -339,7 +315,6 @@ class ClusterWaveforms(NWBDataInterface):
|
|||
DEPRECATED The mean waveform shape, including standard deviation, of the different clusters. Ideally, the waveform analysis should be performed on data that is only high-pass filtered. This is a separate module because it is expected to require updating. For example, IMEC probes may require different storage requirements to store/display mean waveforms, requiring a new interface or an extension of this one.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("ClusterWaveforms")
|
||||
waveform_filtering: str = Field(
|
||||
..., description="""Filtering applied to data before generating mean/sd"""
|
||||
|
|
@ -359,7 +334,6 @@ class Clustering(NWBDataInterface):
|
|||
DEPRECATED Clustered spike data, whether from automatic clustering tools (e.g., klustakwik) or as a result of manual sorting.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("Clustering")
|
||||
description: str = Field(
|
||||
...,
|
||||
|
|
@ -376,20 +350,3 @@ class Clustering(NWBDataInterface):
|
|||
...,
|
||||
description="""Times of clustered events, in seconds. This may be a link to times field in associated FeatureExtraction module.""",
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
ElectricalSeries.model_rebuild()
|
||||
ElectricalSeriesElectrodes.model_rebuild()
|
||||
SpikeEventSeries.model_rebuild()
|
||||
FeatureExtraction.model_rebuild()
|
||||
FeatureExtractionElectrodes.model_rebuild()
|
||||
EventDetection.model_rebuild()
|
||||
EventWaveform.model_rebuild()
|
||||
FilteredEphys.model_rebuild()
|
||||
LFP.model_rebuild()
|
||||
ElectrodeGroup.model_rebuild()
|
||||
ElectrodeGroupPosition.model_rebuild()
|
||||
ClusterWaveforms.model_rebuild()
|
||||
Clustering.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -27,27 +27,20 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from .core_nwb_base import TimeSeries
|
||||
|
||||
from ...hdmf_common.v1_1_3.hdmf_common_table import (
|
||||
DynamicTable,
|
||||
VectorIndex,
|
||||
DynamicTable,
|
||||
VectorData,
|
||||
)
|
||||
|
||||
from .core_nwb_base import TimeSeries
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
version = "2.2.4"
|
||||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -67,18 +60,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class TimeIntervals(DynamicTable):
|
||||
"""
|
||||
A container for aggregating epoch data and the TimeSeries that each epoch applies to.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
start_time: Optional[List[float] | float] = Field(
|
||||
default_factory=list, description="""Start time of epoch, in seconds."""
|
||||
|
|
@ -122,7 +108,6 @@ class TimeIntervalsTagsIndex(VectorIndex):
|
|||
Index for tags.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["tags_index"] = Field("tags_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -136,7 +121,6 @@ class TimeIntervalsTimeseries(VectorData):
|
|||
An index into a TimeSeries object.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["timeseries"] = Field("timeseries")
|
||||
idx_start: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -167,18 +151,9 @@ class TimeIntervalsTimeseriesIndex(VectorIndex):
|
|||
Index for timeseries.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["timeseries_index"] = Field("timeseries_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
description="""Reference to the target dataset that this index applies to.""",
|
||||
)
|
||||
array: Optional[NDArray[Shape["* num_rows"], Any]] = Field(None)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
TimeIntervals.model_rebuild()
|
||||
TimeIntervalsTagsIndex.model_rebuild()
|
||||
TimeIntervalsTimeseries.model_rebuild()
|
||||
TimeIntervalsTimeseriesIndex.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -27,33 +27,33 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from .core_nwb_ophys import ImagingPlane
|
||||
|
||||
from .core_nwb_base import (
|
||||
TimeSeries,
|
||||
NWBDataInterface,
|
||||
ProcessingModule,
|
||||
NWBContainer,
|
||||
NWBData,
|
||||
)
|
||||
from .core_nwb_icephys import SweepTable, IntracellularElectrode
|
||||
|
||||
from .core_nwb_epoch import TimeIntervals
|
||||
|
||||
from ...hdmf_common.v1_1_3.hdmf_common_table import (
|
||||
DynamicTable,
|
||||
VectorData,
|
||||
VectorIndex,
|
||||
from .core_nwb_ecephys import ElectrodeGroup
|
||||
|
||||
from .core_nwb_base import (
|
||||
NWBContainer,
|
||||
ProcessingModule,
|
||||
NWBData,
|
||||
NWBDataInterface,
|
||||
TimeSeries,
|
||||
)
|
||||
|
||||
from .core_nwb_icephys import IntracellularElectrode, SweepTable
|
||||
|
||||
from .core_nwb_ogen import OptogeneticStimulusSite
|
||||
from .core_nwb_device import Device
|
||||
|
||||
from .core_nwb_misc import Units
|
||||
|
||||
from .core_nwb_ecephys import ElectrodeGroup
|
||||
from .core_nwb_ogen import OptogeneticStimulusSite
|
||||
|
||||
from .core_nwb_device import Device
|
||||
from ...hdmf_common.v1_1_3.hdmf_common_table import (
|
||||
VectorIndex,
|
||||
DynamicTable,
|
||||
VectorData,
|
||||
)
|
||||
|
||||
from .core_nwb_ophys import ImagingPlane
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
|
|
@ -61,13 +61,6 @@ version = "2.2.4"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -87,18 +80,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class ScratchData(NWBData):
|
||||
"""
|
||||
Any one-off datasets
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
notes: Optional[str] = Field(
|
||||
None, description="""Any notes the user has about the dataset being stored"""
|
||||
|
|
@ -110,7 +96,6 @@ class NWBFile(NWBContainer):
|
|||
An NWB:N file storing cellular-based neurophysiology data from a single experimental session.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: Literal["root"] = Field("root")
|
||||
nwb_version: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -181,7 +166,6 @@ class NWBFileStimulus(ConfiguredBaseModel):
|
|||
Data pushed into the system (eg, video stimulus, sound, voltage, etc) and secondary representations of that data (eg, measurements of something used as a stimulus). This group should be made read-only after experiment complete and timestamps are corrected to common timebase. Stores both presented stimuli and stimulus templates, the latter in case the same stimulus is presented multiple times, or is pulled from an external stimulus library. Stimuli are here defined as any signal that is pushed into the system as part of the experiment (eg, sound, video, voltage, etc). Many different experiments can use the same stimuli, and stimuli can be re-used during an experiment. The stimulus group is organized so that one version of template stimuli can be stored and these be used multiple times. These templates can exist in the present file or can be linked to a remote library file.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["stimulus"] = Field("stimulus")
|
||||
presentation: Optional[List[TimeSeries] | TimeSeries] = Field(
|
||||
default_factory=dict, description="""Stimuli presented during the experiment."""
|
||||
|
|
@ -197,7 +181,6 @@ class NWBFileGeneral(ConfiguredBaseModel):
|
|||
Experimental metadata, including protocol, notes and description of hardware device(s). The metadata stored in this section should be used to describe the experiment. Metadata necessary for interpreting the data is stored with the data. General experimental metadata, including animal strain, experimental protocols, experimenter, devices, etc, are stored under 'general'. Core metadata (e.g., that required to interpret data fields) is stored with the data itself, and implicitly defined by the file specification (e.g., time is in seconds). The strategy used here for storing non-core metadata is to use free-form text fields, such as would appear in sentences or paragraphs from a Methods section. Metadata fields are text to enable them to be more general, for example to represent ranges instead of numerical values. Machine-readable metadata is stored as attributes to these free-form datasets. All entries in the below table are to be included when data is present. Unused groups (e.g., intracellular_ephys in an optophysiology experiment) should not be created unless there is data to store within them.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["general"] = Field("general")
|
||||
data_collection: Optional[str] = Field(
|
||||
None, description="""Notes about data collection and analysis."""
|
||||
|
|
@ -287,7 +270,6 @@ class NWBFileGeneralSourceScript(ConfiguredBaseModel):
|
|||
Script file or link to public source code used to create this NWB file.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["source_script"] = Field("source_script")
|
||||
file_name: Optional[str] = Field(None, description="""Name of script file.""")
|
||||
value: str = Field(...)
|
||||
|
|
@ -298,7 +280,6 @@ class NWBFileGeneralExtracellularEphys(ConfiguredBaseModel):
|
|||
Metadata related to extracellular electrophysiology.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["extracellular_ephys"] = Field("extracellular_ephys")
|
||||
electrode_group: Optional[List[str] | str] = Field(
|
||||
default_factory=list, description="""Physical group of electrodes."""
|
||||
|
|
@ -314,7 +295,6 @@ class NWBFileGeneralExtracellularEphysElectrodes(DynamicTable):
|
|||
A table of all electrodes (i.e. channels) used for recording.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["electrodes"] = Field("electrodes")
|
||||
x: Optional[List[float] | float] = Field(
|
||||
default_factory=list,
|
||||
|
|
@ -384,7 +364,6 @@ class NWBFileGeneralIntracellularEphys(ConfiguredBaseModel):
|
|||
Metadata related to intracellular electrophysiology.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["intracellular_ephys"] = Field("intracellular_ephys")
|
||||
filtering: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -404,7 +383,6 @@ class LabMetaData(NWBContainer):
|
|||
Lab-specific meta-data.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
|
||||
|
||||
|
|
@ -413,7 +391,6 @@ class Subject(NWBContainer):
|
|||
Information about the animal or person from which the data was measured.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
age: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -440,17 +417,3 @@ class Subject(NWBContainer):
|
|||
None,
|
||||
description="""Weight at time of experiment, at time of surgery and at other important times.""",
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
ScratchData.model_rebuild()
|
||||
NWBFile.model_rebuild()
|
||||
NWBFileStimulus.model_rebuild()
|
||||
NWBFileGeneral.model_rebuild()
|
||||
NWBFileGeneralSourceScript.model_rebuild()
|
||||
NWBFileGeneralExtracellularEphys.model_rebuild()
|
||||
NWBFileGeneralExtracellularEphysElectrodes.model_rebuild()
|
||||
NWBFileGeneralIntracellularEphys.model_rebuild()
|
||||
LabMetaData.model_rebuild()
|
||||
Subject.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -28,16 +28,16 @@ if TYPE_CHECKING:
|
|||
|
||||
|
||||
from .core_nwb_base import (
|
||||
TimeSeries,
|
||||
NWBContainer,
|
||||
TimeSeriesSync,
|
||||
TimeSeriesStartingTime,
|
||||
NWBContainer,
|
||||
TimeSeries,
|
||||
)
|
||||
|
||||
from ...hdmf_common.v1_1_3.hdmf_common_table import (
|
||||
VectorIndex,
|
||||
DynamicTable,
|
||||
VectorData,
|
||||
VectorIndex,
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -46,13 +46,6 @@ version = "2.2.4"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -72,18 +65,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class PatchClampSeries(TimeSeries):
|
||||
"""
|
||||
An abstract base class for patch-clamp data - stimulus or response, current or voltage.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
stimulus_description: Optional[str] = Field(
|
||||
None, description="""Protocol/stimulus name for this patch-clamp dataset."""
|
||||
|
|
@ -131,7 +117,6 @@ class PatchClampSeriesData(ConfiguredBaseModel):
|
|||
Recorded voltage or current.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -145,7 +130,6 @@ class CurrentClampSeries(PatchClampSeries):
|
|||
Voltage data from an intracellular current-clamp recording. A corresponding CurrentClampStimulusSeries (stored separately as a stimulus) is used to store the current injected.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Recorded voltage.""")
|
||||
bias_current: Optional[float] = Field(
|
||||
|
|
@ -202,7 +186,6 @@ class CurrentClampSeriesData(ConfiguredBaseModel):
|
|||
Recorded voltage.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -216,7 +199,6 @@ class IZeroClampSeries(CurrentClampSeries):
|
|||
Voltage data from an intracellular recording when all current and amplifier settings are off (i.e., CurrentClampSeries fields will be zero). There is no CurrentClampStimulusSeries associated with an IZero series because the amplifier is disconnected and no stimulus can reach the cell.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
bias_current: float = Field(
|
||||
..., description="""Bias current, in amps, fixed to 0.0."""
|
||||
|
|
@ -273,7 +255,6 @@ class CurrentClampStimulusSeries(PatchClampSeries):
|
|||
Stimulus current applied during current clamp recording.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Stimulus current applied.""")
|
||||
stimulus_description: Optional[str] = Field(
|
||||
|
|
@ -321,7 +302,6 @@ class CurrentClampStimulusSeriesData(ConfiguredBaseModel):
|
|||
Stimulus current applied.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -335,7 +315,6 @@ class VoltageClampSeries(PatchClampSeries):
|
|||
Current data from an intracellular voltage-clamp recording. A corresponding VoltageClampStimulusSeries (stored separately as a stimulus) is used to store the voltage injected.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Recorded current.""")
|
||||
capacitance_fast: Optional[str] = Field(
|
||||
|
|
@ -404,7 +383,6 @@ class VoltageClampSeriesData(ConfiguredBaseModel):
|
|||
Recorded current.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -418,7 +396,6 @@ class VoltageClampSeriesCapacitanceFast(ConfiguredBaseModel):
|
|||
Fast capacitance, in farads.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["capacitance_fast"] = Field("capacitance_fast")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -432,7 +409,6 @@ class VoltageClampSeriesCapacitanceSlow(ConfiguredBaseModel):
|
|||
Slow capacitance, in farads.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["capacitance_slow"] = Field("capacitance_slow")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -446,7 +422,6 @@ class VoltageClampSeriesResistanceCompBandwidth(ConfiguredBaseModel):
|
|||
Resistance compensation bandwidth, in hertz.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["resistance_comp_bandwidth"] = Field("resistance_comp_bandwidth")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -460,7 +435,6 @@ class VoltageClampSeriesResistanceCompCorrection(ConfiguredBaseModel):
|
|||
Resistance compensation correction, in percent.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["resistance_comp_correction"] = Field("resistance_comp_correction")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -474,7 +448,6 @@ class VoltageClampSeriesResistanceCompPrediction(ConfiguredBaseModel):
|
|||
Resistance compensation prediction, in percent.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["resistance_comp_prediction"] = Field("resistance_comp_prediction")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -488,7 +461,6 @@ class VoltageClampSeriesWholeCellCapacitanceComp(ConfiguredBaseModel):
|
|||
Whole cell capacitance compensation, in farads.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["whole_cell_capacitance_comp"] = Field("whole_cell_capacitance_comp")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -502,7 +474,6 @@ class VoltageClampSeriesWholeCellSeriesResistanceComp(ConfiguredBaseModel):
|
|||
Whole cell series resistance compensation, in ohms.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["whole_cell_series_resistance_comp"] = Field(
|
||||
"whole_cell_series_resistance_comp"
|
||||
)
|
||||
|
|
@ -518,7 +489,6 @@ class VoltageClampStimulusSeries(PatchClampSeries):
|
|||
Stimulus voltage applied during a voltage clamp recording.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Stimulus voltage applied.""")
|
||||
stimulus_description: Optional[str] = Field(
|
||||
|
|
@ -566,7 +536,6 @@ class VoltageClampStimulusSeriesData(ConfiguredBaseModel):
|
|||
Stimulus voltage applied.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -580,7 +549,6 @@ class IntracellularElectrode(NWBContainer):
|
|||
An intracellular electrode and its metadata.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: str = Field(
|
||||
...,
|
||||
|
|
@ -612,7 +580,6 @@ class SweepTable(DynamicTable):
|
|||
The table which groups different PatchClampSeries together.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
sweep_number: Optional[List[int] | int] = Field(
|
||||
default_factory=list,
|
||||
|
|
@ -648,35 +615,9 @@ class SweepTableSeriesIndex(VectorIndex):
|
|||
Index for series.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["series_index"] = Field("series_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
description="""Reference to the target dataset that this index applies to.""",
|
||||
)
|
||||
array: Optional[NDArray[Shape["* num_rows"], Any]] = Field(None)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
PatchClampSeries.model_rebuild()
|
||||
PatchClampSeriesData.model_rebuild()
|
||||
CurrentClampSeries.model_rebuild()
|
||||
CurrentClampSeriesData.model_rebuild()
|
||||
IZeroClampSeries.model_rebuild()
|
||||
CurrentClampStimulusSeries.model_rebuild()
|
||||
CurrentClampStimulusSeriesData.model_rebuild()
|
||||
VoltageClampSeries.model_rebuild()
|
||||
VoltageClampSeriesData.model_rebuild()
|
||||
VoltageClampSeriesCapacitanceFast.model_rebuild()
|
||||
VoltageClampSeriesCapacitanceSlow.model_rebuild()
|
||||
VoltageClampSeriesResistanceCompBandwidth.model_rebuild()
|
||||
VoltageClampSeriesResistanceCompCorrection.model_rebuild()
|
||||
VoltageClampSeriesResistanceCompPrediction.model_rebuild()
|
||||
VoltageClampSeriesWholeCellCapacitanceComp.model_rebuild()
|
||||
VoltageClampSeriesWholeCellSeriesResistanceComp.model_rebuild()
|
||||
VoltageClampStimulusSeries.model_rebuild()
|
||||
VoltageClampStimulusSeriesData.model_rebuild()
|
||||
IntracellularElectrode.model_rebuild()
|
||||
SweepTable.model_rebuild()
|
||||
SweepTableSeriesIndex.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from .core_nwb_base import Image, TimeSeries, TimeSeriesStartingTime, TimeSeriesSync
|
||||
from .core_nwb_base import Image, TimeSeriesStartingTime, TimeSeries, TimeSeriesSync
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
|
|
@ -35,13 +35,6 @@ version = "2.2.4"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -61,18 +54,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class GrayscaleImage(Image):
|
||||
"""
|
||||
A grayscale image.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
resolution: Optional[float] = Field(
|
||||
None, description="""Pixel resolution of the image, in pixels per centimeter."""
|
||||
|
|
@ -94,7 +80,6 @@ class RGBImage(Image):
|
|||
A color image.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
resolution: Optional[float] = Field(
|
||||
None, description="""Pixel resolution of the image, in pixels per centimeter."""
|
||||
|
|
@ -116,7 +101,6 @@ class RGBAImage(Image):
|
|||
A color image with transparency.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
resolution: Optional[float] = Field(
|
||||
None, description="""Pixel resolution of the image, in pixels per centimeter."""
|
||||
|
|
@ -138,7 +122,6 @@ class ImageSeries(TimeSeries):
|
|||
General image data that is common between acquisition and stimulus time series. Sometimes the image data is stored in the file in a raw format while other times it will be stored as a series of external image files in the host file system. The data field will either be binary data, if the data is stored in the NWB file, or empty, if the data is stored in an external image stack. [frame][x][y] or [frame][x][y][z].
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: Optional[
|
||||
Union[
|
||||
|
|
@ -191,7 +174,6 @@ 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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["external_file"] = Field("external_file")
|
||||
starting_frame: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -205,7 +187,6 @@ class ImageMaskSeries(ImageSeries):
|
|||
An alpha mask that is applied to a presented visual stimulus. The 'data' array contains an array of mask values that are applied to the displayed image. Mask values are stored as RGBA. Mask can vary with time. The timestamps array indicates the starting time of a mask, and that mask pattern continues until it's explicitly changed.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: Optional[
|
||||
Union[
|
||||
|
|
@ -258,7 +239,6 @@ class OpticalSeries(ImageSeries):
|
|||
Image data that is presented or recorded. A stimulus template movie will be stored only as an image. When the image is presented as stimulus, additional data is required, such as field of view (e.g., how much of the visual field the image covers, or how what is the area of the target being imaged). If the OpticalSeries represents acquired imaging data, orientation is also important.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
distance: Optional[float] = Field(
|
||||
None, description="""Distance from camera/monitor to target/eye."""
|
||||
|
|
@ -327,7 +307,6 @@ 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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: NDArray[Shape["* num_times"], int] = Field(
|
||||
..., description="""Index of the frame in the referenced ImageSeries."""
|
||||
|
|
@ -359,15 +338,3 @@ class IndexSeries(TimeSeries):
|
|||
None,
|
||||
description="""Lab-specific time and sync information as provided directly from hardware devices and that is necessary for aligning all acquired time information to a common timebase. The timestamp array stores time in the common timebase. This group will usually only be populated in TimeSeries that are stored external to the NWB file, in files storing raw data. Once timestamp data is calculated, the contents of 'sync' are mostly for archival purposes.""",
|
||||
)
|
||||
|
||||
|
||||
# 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()
|
||||
ImageSeriesExternalFile.model_rebuild()
|
||||
ImageMaskSeries.model_rebuild()
|
||||
OpticalSeries.model_rebuild()
|
||||
IndexSeries.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -32,13 +32,6 @@ version = "None"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -56,13 +49,3 @@ class ConfiguredBaseModel(BaseModel):
|
|||
self.array[i] = value
|
||||
else:
|
||||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
|
|
|
|||
|
|
@ -27,15 +27,15 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from .core_nwb_base import TimeSeries, TimeSeriesStartingTime, TimeSeriesSync
|
||||
|
||||
from ...hdmf_common.v1_1_3.hdmf_common_table import (
|
||||
VectorIndex,
|
||||
DynamicTableRegion,
|
||||
DynamicTable,
|
||||
VectorIndex,
|
||||
VectorData,
|
||||
)
|
||||
|
||||
from .core_nwb_base import TimeSeriesStartingTime, TimeSeries, TimeSeriesSync
|
||||
|
||||
from .core_nwb_ecephys import ElectrodeGroup
|
||||
|
||||
|
||||
|
|
@ -44,13 +44,6 @@ version = "2.2.4"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -70,18 +63,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class AbstractFeatureSeries(TimeSeries):
|
||||
"""
|
||||
Abstract features, such as quantitative descriptions of sensory stimuli. The TimeSeries::data field is a 2D array, storing those features (e.g., for visual grating stimulus this might be orientation, spatial frequency and contrast). Null stimuli (eg, uniform gray) can be marked as being an independent feature (eg, 1.0 for gray, 0.0 for actual stimulus) or by storing NaNs for feature values, or through use of the TimeSeries::control fields. A set of features is considered to persist until the next set of features is defined. The final set of features stored should be the null set. This is useful when storing the raw stimulus is impractical.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Values of each feature at each time.""")
|
||||
feature_units: Optional[NDArray[Shape["* num_features"], str]] = Field(
|
||||
|
|
@ -125,7 +111,6 @@ class AbstractFeatureSeriesData(ConfiguredBaseModel):
|
|||
Values of each feature at each time.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -144,7 +129,6 @@ class AnnotationSeries(TimeSeries):
|
|||
Stores user annotations made during an experiment. The data[] field stores a text array, and timestamps are stored for each annotation (ie, interval=1). This is largely an alias to a standard TimeSeries storing a text array but that is identifiable as storing annotations in a machine-readable way.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: NDArray[Shape["* num_times"], str] = Field(
|
||||
..., description="""Annotations made during an experiment."""
|
||||
|
|
@ -183,7 +167,6 @@ 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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: NDArray[Shape["* num_times"], int] = Field(
|
||||
..., description="""Use values >0 if interval started, <0 if interval ended."""
|
||||
|
|
@ -222,7 +205,6 @@ class DecompositionSeries(TimeSeries):
|
|||
Spectral analysis of a time series, e.g. of an LFP or a speech signal.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Data decomposed into frequency bands.""")
|
||||
metric: str = Field(
|
||||
|
|
@ -266,7 +248,6 @@ class DecompositionSeriesData(ConfiguredBaseModel):
|
|||
Data decomposed into frequency bands.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -282,7 +263,6 @@ class DecompositionSeriesBands(DynamicTable):
|
|||
Table for describing the bands that this series was generated from. There should be one row in this table for each band.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["bands"] = Field("bands")
|
||||
band_name: Optional[List[str] | str] = Field(
|
||||
default_factory=list, description="""Name of the band, e.g. theta."""
|
||||
|
|
@ -322,7 +302,6 @@ class Units(DynamicTable):
|
|||
Data about spiking units. Event times of observed units (e.g. cell, synapse, etc.) should be concatenated and stored in spike_times.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("Units")
|
||||
spike_times_index: Optional[str] = Field(
|
||||
None, description="""Index into the spike_times dataset."""
|
||||
|
|
@ -386,7 +365,6 @@ class UnitsSpikeTimesIndex(VectorIndex):
|
|||
Index into the spike_times dataset.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["spike_times_index"] = Field("spike_times_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -400,7 +378,6 @@ class UnitsSpikeTimes(VectorData):
|
|||
Spike times for each unit.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["spike_times"] = Field("spike_times")
|
||||
resolution: Optional[float] = Field(
|
||||
None,
|
||||
|
|
@ -424,7 +401,6 @@ class UnitsObsIntervalsIndex(VectorIndex):
|
|||
Index into the obs_intervals dataset.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["obs_intervals_index"] = Field("obs_intervals_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -438,7 +414,6 @@ class UnitsElectrodesIndex(VectorIndex):
|
|||
Index into electrodes.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["electrodes_index"] = Field("electrodes_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -452,7 +427,6 @@ class UnitsElectrodes(DynamicTableRegion):
|
|||
Electrode that each spike unit came from, specified using a DynamicTableRegion.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["electrodes"] = Field("electrodes")
|
||||
table: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -469,20 +443,3 @@ class UnitsElectrodes(DynamicTableRegion):
|
|||
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()
|
||||
IntervalSeries.model_rebuild()
|
||||
DecompositionSeries.model_rebuild()
|
||||
DecompositionSeriesData.model_rebuild()
|
||||
DecompositionSeriesBands.model_rebuild()
|
||||
Units.model_rebuild()
|
||||
UnitsSpikeTimesIndex.model_rebuild()
|
||||
UnitsSpikeTimes.model_rebuild()
|
||||
UnitsObsIntervalsIndex.model_rebuild()
|
||||
UnitsElectrodesIndex.model_rebuild()
|
||||
UnitsElectrodes.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -29,9 +29,9 @@ if TYPE_CHECKING:
|
|||
|
||||
from .core_nwb_base import (
|
||||
TimeSeriesSync,
|
||||
TimeSeries,
|
||||
NWBContainer,
|
||||
TimeSeriesStartingTime,
|
||||
TimeSeries,
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -40,13 +40,6 @@ version = "2.2.4"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -66,18 +59,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class OptogeneticSeries(TimeSeries):
|
||||
"""
|
||||
An optogenetic stimulus.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: NDArray[Shape["* num_times"], float] = Field(
|
||||
..., description="""Applied power for optogenetic stimulus, in watts."""
|
||||
|
|
@ -116,7 +102,6 @@ class OptogeneticStimulusSite(NWBContainer):
|
|||
A site of optogenetic stimulation.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: str = Field(..., description="""Description of stimulation site.""")
|
||||
excitation_lambda: float = Field(
|
||||
|
|
@ -126,9 +111,3 @@ class OptogeneticStimulusSite(NWBContainer):
|
|||
...,
|
||||
description="""Location of the stimulation site. 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.""",
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
OptogeneticSeries.model_rebuild()
|
||||
OptogeneticStimulusSite.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -28,18 +28,18 @@ if TYPE_CHECKING:
|
|||
|
||||
|
||||
from .core_nwb_base import (
|
||||
TimeSeries,
|
||||
TimeSeriesSync,
|
||||
NWBDataInterface,
|
||||
TimeSeriesStartingTime,
|
||||
NWBContainer,
|
||||
TimeSeriesSync,
|
||||
TimeSeriesStartingTime,
|
||||
NWBDataInterface,
|
||||
TimeSeries,
|
||||
)
|
||||
|
||||
from ...hdmf_common.v1_1_3.hdmf_common_table import (
|
||||
DynamicTableRegion,
|
||||
VectorIndex,
|
||||
DynamicTable,
|
||||
VectorData,
|
||||
VectorIndex,
|
||||
)
|
||||
|
||||
from .core_nwb_image import ImageSeries, ImageSeriesExternalFile
|
||||
|
|
@ -50,13 +50,6 @@ version = "2.2.4"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -76,18 +69,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class TwoPhotonSeries(ImageSeries):
|
||||
"""
|
||||
Image stack recorded over time from 2-photon microscope.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
pmt_gain: Optional[float] = Field(None, description="""Photomultiplier gain.""")
|
||||
scan_line_rate: Optional[float] = Field(
|
||||
|
|
@ -154,7 +140,6 @@ class RoiResponseSeries(TimeSeries):
|
|||
ROI responses over an imaging plane. The first dimension represents time. The second dimension, if present, represents ROIs.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: Union[
|
||||
NDArray[Shape["* num_times"], float],
|
||||
|
|
@ -198,7 +183,6 @@ class RoiResponseSeriesRois(DynamicTableRegion):
|
|||
DynamicTableRegion referencing into an ROITable containing information on the ROIs stored in this timeseries.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["rois"] = Field("rois")
|
||||
table: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -222,7 +206,6 @@ 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).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[RoiResponseSeries] | RoiResponseSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -234,7 +217,6 @@ class Fluorescence(NWBDataInterface):
|
|||
Fluorescence information about a region of interest (ROI). Storage hierarchy of fluorescence should be the same as for segmentation (ie, same names for ROIs and for image planes).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[RoiResponseSeries] | RoiResponseSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -246,7 +228,6 @@ class ImageSegmentation(NWBDataInterface):
|
|||
Stores pixels in an image that represent different regions of interest (ROIs) or masks. All segmentation for a given imaging plane is stored together, with storage for multiple imaging planes (masks) supported. Each ROI is stored in its own subgroup, with the ROI group containing both a 2D mask and a list of pixels that make up this mask. Segments can also be used for masking neuropil. If segmentation is allowed to change with time, a new imaging plane (or module) is required and ROI names should remain consistent between them.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[PlaneSegmentation] | PlaneSegmentation] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -258,7 +239,6 @@ class PlaneSegmentation(DynamicTable):
|
|||
Results from image segmentation of a specific imaging plane.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
image_mask: Optional[
|
||||
Union[
|
||||
|
|
@ -312,7 +292,6 @@ class PlaneSegmentationPixelMaskIndex(VectorIndex):
|
|||
Index into pixel_mask.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["pixel_mask_index"] = Field("pixel_mask_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -326,7 +305,6 @@ class PlaneSegmentationPixelMask(VectorData):
|
|||
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
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["pixel_mask"] = Field("pixel_mask")
|
||||
x: Optional[int] = Field(None, description="""Pixel x-coordinate.""")
|
||||
y: Optional[int] = Field(None, description="""Pixel y-coordinate.""")
|
||||
|
|
@ -349,7 +327,6 @@ class PlaneSegmentationVoxelMaskIndex(VectorIndex):
|
|||
Index into voxel_mask.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["voxel_mask_index"] = Field("voxel_mask_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -363,7 +340,6 @@ class PlaneSegmentationVoxelMask(VectorData):
|
|||
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
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["voxel_mask"] = Field("voxel_mask")
|
||||
x: Optional[int] = Field(None, description="""Voxel x-coordinate.""")
|
||||
y: Optional[int] = Field(None, description="""Voxel y-coordinate.""")
|
||||
|
|
@ -387,7 +363,6 @@ class ImagingPlane(NWBContainer):
|
|||
An imaging plane and its metadata.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[OpticalChannel] | OpticalChannel] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -399,7 +374,6 @@ class OpticalChannel(NWBContainer):
|
|||
An optical channel used to record from an imaging plane.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: str = Field(
|
||||
..., description="""Description or other notes about the channel."""
|
||||
|
|
@ -414,7 +388,6 @@ class MotionCorrection(NWBDataInterface):
|
|||
An image stack where all frames are shifted (registered) to a common coordinate system, to account for movement and drift between frames. Note: each frame at each point in time is assumed to be 2-D (has only x & y dimensions).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[CorrectedImageStack] | CorrectedImageStack] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -426,7 +399,6 @@ class CorrectedImageStack(NWBDataInterface):
|
|||
Reuslts from motion correction of an image stack.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
corrected: str = Field(
|
||||
...,
|
||||
|
|
@ -436,22 +408,3 @@ class CorrectedImageStack(NWBDataInterface):
|
|||
...,
|
||||
description="""Stores the x,y delta necessary to align each frame to the common coordinates, for example, to align each frame to a reference image.""",
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
TwoPhotonSeries.model_rebuild()
|
||||
RoiResponseSeries.model_rebuild()
|
||||
RoiResponseSeriesRois.model_rebuild()
|
||||
DfOverF.model_rebuild()
|
||||
Fluorescence.model_rebuild()
|
||||
ImageSegmentation.model_rebuild()
|
||||
PlaneSegmentation.model_rebuild()
|
||||
PlaneSegmentationPixelMaskIndex.model_rebuild()
|
||||
PlaneSegmentationPixelMask.model_rebuild()
|
||||
PlaneSegmentationVoxelMaskIndex.model_rebuild()
|
||||
PlaneSegmentationVoxelMask.model_rebuild()
|
||||
ImagingPlane.model_rebuild()
|
||||
OpticalChannel.model_rebuild()
|
||||
MotionCorrection.model_rebuild()
|
||||
CorrectedImageStack.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -35,13 +35,6 @@ version = "2.2.4"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -61,18 +54,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class ImagingRetinotopy(NWBDataInterface):
|
||||
"""
|
||||
Intrinsic signal optical imaging or widefield imaging for measuring retinotopy. Stores orthogonal maps (e.g., altitude/azimuth; radius/theta) of responses to specific stimuli and a combined polarity map from which to identify visual areas. This group does not store the raw responses imaged during retinotopic mapping or the stimuli presented, but rather the resulting phase and power maps after applying a Fourier transform on the averaged responses. Note: for data consistency, all images and arrays are stored in the format [row][column] and [row, col], which equates to [y][x]. Field of view and dimension arrays may appear backward (i.e., y before x).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("ImagingRetinotopy")
|
||||
axis_1_phase_map: str = Field(
|
||||
..., description="""Phase response to stimulus on the first measured axis."""
|
||||
|
|
@ -111,7 +97,6 @@ class ImagingRetinotopyAxis1PhaseMap(ConfiguredBaseModel):
|
|||
Phase response to stimulus on the first measured axis.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["axis_1_phase_map"] = Field("axis_1_phase_map")
|
||||
dimension: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -131,7 +116,6 @@ class ImagingRetinotopyAxis1PowerMap(ConfiguredBaseModel):
|
|||
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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["axis_1_power_map"] = Field("axis_1_power_map")
|
||||
dimension: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -151,7 +135,6 @@ class ImagingRetinotopyAxis2PhaseMap(ConfiguredBaseModel):
|
|||
Phase response to stimulus on the second measured axis.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["axis_2_phase_map"] = Field("axis_2_phase_map")
|
||||
dimension: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -171,7 +154,6 @@ class ImagingRetinotopyAxis2PowerMap(ConfiguredBaseModel):
|
|||
Power response on the second measured axis. Response is scaled so 0.0 is no power in the response and 1.0 is maximum relative power.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["axis_2_power_map"] = Field("axis_2_power_map")
|
||||
dimension: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -191,7 +173,6 @@ class ImagingRetinotopyFocalDepthImage(ConfiguredBaseModel):
|
|||
Gray-scale image taken with same settings/parameters (e.g., focal depth, wavelength) as data collection. Array format: [rows][columns].
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["focal_depth_image"] = Field("focal_depth_image")
|
||||
bits_per_pixel: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -218,7 +199,6 @@ class ImagingRetinotopySignMap(ConfiguredBaseModel):
|
|||
Sine of the angle between the direction of the gradient in axis_1 and axis_2.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["sign_map"] = Field("sign_map")
|
||||
dimension: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -235,7 +215,6 @@ class ImagingRetinotopyVasculatureImage(ConfiguredBaseModel):
|
|||
Gray-scale anatomical image of cortical surface. Array structure: [rows][columns]
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["vasculature_image"] = Field("vasculature_image")
|
||||
bits_per_pixel: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -252,15 +231,3 @@ class ImagingRetinotopyVasculatureImage(ConfiguredBaseModel):
|
|||
None, description="""Format of image. Right now only 'raw' is supported."""
|
||||
)
|
||||
array: Optional[NDArray[Shape["* num_rows, * num_cols"], int]] = Field(None)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
ImagingRetinotopy.model_rebuild()
|
||||
ImagingRetinotopyAxis1PhaseMap.model_rebuild()
|
||||
ImagingRetinotopyAxis1PowerMap.model_rebuild()
|
||||
ImagingRetinotopyAxis2PhaseMap.model_rebuild()
|
||||
ImagingRetinotopyAxis2PowerMap.model_rebuild()
|
||||
ImagingRetinotopyFocalDepthImage.model_rebuild()
|
||||
ImagingRetinotopySignMap.model_rebuild()
|
||||
ImagingRetinotopyVasculatureImage.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -196,13 +196,6 @@ version = "2.2.4"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -220,13 +213,3 @@ class ConfiguredBaseModel(BaseModel):
|
|||
self.array[i] = value
|
||||
else:
|
||||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from ...hdmf_common.v1_1_3.hdmf_common_table import Container, Data, DynamicTable
|
||||
from ...hdmf_common.v1_1_3.hdmf_common_table import Data, Container, DynamicTable
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
|
|
@ -35,13 +35,6 @@ version = "2.2.5"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -61,18 +54,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class NWBData(Data):
|
||||
"""
|
||||
An abstract data type for a dataset.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
|
||||
|
||||
|
|
@ -81,7 +67,6 @@ class Image(NWBData):
|
|||
An abstract data type for an image. Shape can be 2-D (x, y), or 3-D where the third dimension can have three or four elements, e.g. (x, y, (r, g, b)) or (x, y, (r, g, b, a)).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
resolution: Optional[float] = Field(
|
||||
None, description="""Pixel resolution of the image, in pixels per centimeter."""
|
||||
|
|
@ -103,7 +88,6 @@ class NWBContainer(Container):
|
|||
An abstract data type for a generic container storing collections of data and metadata. Base type for all data and metadata containers.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
|
||||
|
||||
|
|
@ -112,7 +96,6 @@ class NWBDataInterface(NWBContainer):
|
|||
An abstract data type for a generic container storing collections of data, as opposed to metadata.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
|
||||
|
||||
|
|
@ -121,7 +104,6 @@ class TimeSeries(NWBDataInterface):
|
|||
General purpose time series.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: Optional[str] = Field(
|
||||
None, description="""Description of the time series."""
|
||||
|
|
@ -161,7 +143,6 @@ class TimeSeriesData(ConfiguredBaseModel):
|
|||
Data values. Data can be in 1-D, 2-D, 3-D, or 4-D. The first dimension should always represent time. This can also be used to store binary data (e.g., image frames). This can also be a link to data stored in an external file.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
conversion: Optional[float] = Field(
|
||||
None,
|
||||
|
|
@ -190,7 +171,6 @@ class TimeSeriesStartingTime(ConfiguredBaseModel):
|
|||
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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["starting_time"] = Field("starting_time")
|
||||
rate: Optional[float] = Field(None, description="""Sampling rate, in Hz.""")
|
||||
unit: Optional[str] = Field(
|
||||
|
|
@ -205,7 +185,6 @@ class TimeSeriesSync(ConfiguredBaseModel):
|
|||
Lab-specific time and sync information as provided directly from hardware devices and that is necessary for aligning all acquired time information to a common timebase. The timestamp array stores time in the common timebase. This group will usually only be populated in TimeSeries that are stored external to the NWB file, in files storing raw data. Once timestamp data is calculated, the contents of 'sync' are mostly for archival purposes.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["sync"] = Field("sync")
|
||||
|
||||
|
||||
|
|
@ -214,7 +193,6 @@ class ProcessingModule(NWBContainer):
|
|||
A collection of processed data.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[
|
||||
List[Union[BaseModel, DynamicTable, NWBDataInterface]]
|
||||
| Union[BaseModel, DynamicTable, NWBDataInterface]
|
||||
|
|
@ -227,7 +205,6 @@ class Images(NWBDataInterface):
|
|||
A collection of images.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("Images")
|
||||
description: Optional[str] = Field(
|
||||
None, description="""Description of this collection of images."""
|
||||
|
|
@ -235,17 +212,3 @@ class Images(NWBDataInterface):
|
|||
image: List[str] | str = Field(
|
||||
default_factory=list, description="""Images stored in this collection."""
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
NWBData.model_rebuild()
|
||||
Image.model_rebuild()
|
||||
NWBContainer.model_rebuild()
|
||||
NWBDataInterface.model_rebuild()
|
||||
TimeSeries.model_rebuild()
|
||||
TimeSeriesData.model_rebuild()
|
||||
TimeSeriesStartingTime.model_rebuild()
|
||||
TimeSeriesSync.model_rebuild()
|
||||
ProcessingModule.model_rebuild()
|
||||
Images.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -28,9 +28,9 @@ if TYPE_CHECKING:
|
|||
|
||||
|
||||
from .core_nwb_base import (
|
||||
NWBDataInterface,
|
||||
TimeSeries,
|
||||
TimeSeriesStartingTime,
|
||||
NWBDataInterface,
|
||||
TimeSeriesSync,
|
||||
)
|
||||
|
||||
|
|
@ -42,13 +42,6 @@ version = "2.2.5"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -68,18 +61,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class SpatialSeries(TimeSeries):
|
||||
"""
|
||||
Direction, e.g., of gaze or travel, or position. The TimeSeries::data field is a 2D array storing position or direction relative to some reference frame. Array structure: [num measurements] [num dimensions]. Each SpatialSeries has a text dataset reference_frame that indicates the zero-position, or the zero-axes for direction. For example, if representing gaze direction, 'straight-ahead' might be a specific pixel on the monitor, or some other point in space. For position data, the 0,0 point might be the top-left corner of an enclosure, as viewed from the tracking camera. The unit of data will indicate how to interpret SpatialSeries values.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(
|
||||
...,
|
||||
|
|
@ -123,7 +109,6 @@ class SpatialSeriesData(ConfiguredBaseModel):
|
|||
1-D or 2-D array storing position or direction relative to some reference frame.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -142,7 +127,6 @@ class BehavioralEpochs(NWBDataInterface):
|
|||
TimeSeries for storing behavioral epochs. The objective of this and the other two Behavioral interfaces (e.g. BehavioralEvents and BehavioralTimeSeries) is to provide generic hooks for software tools/scripts. This allows a tool/script to take the output one specific interface (e.g., UnitTimes) and plot that data relative to another data modality (e.g., behavioral events) without having to define all possible modalities in advance. Declaring one of these interfaces means that one or more TimeSeries of the specified type is published. These TimeSeries should reside in a group having the same name as the interface. For example, if a BehavioralTimeSeries interface is declared, the module will have one or more TimeSeries defined in the module sub-group 'BehavioralTimeSeries'. BehavioralEpochs should use IntervalSeries. BehavioralEvents is used for irregular events. BehavioralTimeSeries is for continuous data.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[IntervalSeries] | IntervalSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -154,7 +138,6 @@ class BehavioralEvents(NWBDataInterface):
|
|||
TimeSeries for storing behavioral events. See description of <a href=\"#BehavioralEpochs\">BehavioralEpochs</a> for more details.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[TimeSeries] | TimeSeries] = Field(default_factory=dict)
|
||||
name: str = Field(...)
|
||||
|
||||
|
|
@ -164,7 +147,6 @@ class BehavioralTimeSeries(NWBDataInterface):
|
|||
TimeSeries for storing Behavoioral time series data. See description of <a href=\"#BehavioralEpochs\">BehavioralEpochs</a> for more details.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[TimeSeries] | TimeSeries] = Field(default_factory=dict)
|
||||
name: str = Field(...)
|
||||
|
||||
|
|
@ -174,7 +156,6 @@ class PupilTracking(NWBDataInterface):
|
|||
Eye-tracking data, representing pupil size.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[TimeSeries] | TimeSeries] = Field(default_factory=dict)
|
||||
name: str = Field(...)
|
||||
|
||||
|
|
@ -184,7 +165,6 @@ class EyeTracking(NWBDataInterface):
|
|||
Eye-tracking data, representing direction of gaze.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[SpatialSeries] | SpatialSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -196,7 +176,6 @@ class CompassDirection(NWBDataInterface):
|
|||
With a CompassDirection interface, a module publishes a SpatialSeries object representing a floating point value for theta. The SpatialSeries::reference_frame field should indicate what direction corresponds to 0 and which is the direction of rotation (this should be clockwise). The si_unit for the SpatialSeries should be radians or degrees.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[SpatialSeries] | SpatialSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -208,21 +187,7 @@ class Position(NWBDataInterface):
|
|||
Position data, whether along the x, x/y or x/y/z axis.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[SpatialSeries] | SpatialSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
name: str = Field(...)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
SpatialSeries.model_rebuild()
|
||||
SpatialSeriesData.model_rebuild()
|
||||
BehavioralEpochs.model_rebuild()
|
||||
BehavioralEvents.model_rebuild()
|
||||
BehavioralTimeSeries.model_rebuild()
|
||||
PupilTracking.model_rebuild()
|
||||
EyeTracking.model_rebuild()
|
||||
CompassDirection.model_rebuild()
|
||||
Position.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -35,13 +35,6 @@ version = "2.2.5"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -61,18 +54,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class Device(NWBContainer):
|
||||
"""
|
||||
Metadata about a data acquisition device, e.g., recording system, electrode, microscope.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -81,8 +67,3 @@ class Device(NWBContainer):
|
|||
manufacturer: Optional[str] = Field(
|
||||
None, description="""The name of the manufacturer of the device."""
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
Device.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -27,29 +27,22 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from ...hdmf_common.v1_1_3.hdmf_common_table import DynamicTableRegion, DynamicTable
|
||||
|
||||
from .core_nwb_base import (
|
||||
NWBContainer,
|
||||
TimeSeriesStartingTime,
|
||||
NWBDataInterface,
|
||||
TimeSeries,
|
||||
TimeSeriesStartingTime,
|
||||
NWBContainer,
|
||||
TimeSeriesSync,
|
||||
)
|
||||
|
||||
from ...hdmf_common.v1_1_3.hdmf_common_table import DynamicTableRegion, DynamicTable
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
version = "2.2.5"
|
||||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -69,18 +62,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class ElectricalSeries(TimeSeries):
|
||||
"""
|
||||
A time series of acquired voltage data from extracellular recordings. The data field is an int or float array storing data in volts. The first dimension should always represent time. The second dimension, if present, should represent channels.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: Union[
|
||||
NDArray[Shape["* num_times"], float],
|
||||
|
|
@ -129,7 +115,6 @@ class ElectricalSeriesElectrodes(DynamicTableRegion):
|
|||
DynamicTableRegion pointer to the electrodes that this time series was generated from.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["electrodes"] = Field("electrodes")
|
||||
table: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -153,7 +138,6 @@ 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).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: Union[
|
||||
NDArray[Shape["* num_events, * num_channels, * num_samples"], float],
|
||||
|
|
@ -201,7 +185,6 @@ class FeatureExtraction(NWBDataInterface):
|
|||
Features, such as PC1 and PC2, that are extracted from signals stored in a SpikeEventSeries or other source.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("FeatureExtraction")
|
||||
description: NDArray[Shape["* num_features"], str] = Field(
|
||||
...,
|
||||
|
|
@ -228,7 +211,6 @@ class FeatureExtractionElectrodes(DynamicTableRegion):
|
|||
DynamicTableRegion pointer to the electrodes that this time series was generated from.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["electrodes"] = Field("electrodes")
|
||||
table: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -252,7 +234,6 @@ class EventDetection(NWBDataInterface):
|
|||
Detected spike events from voltage trace(s).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("EventDetection")
|
||||
detection_method: str = Field(
|
||||
...,
|
||||
|
|
@ -272,7 +253,6 @@ class EventWaveform(NWBDataInterface):
|
|||
Represents either the waveforms of detected events, as extracted from a raw data trace in /acquisition, or the event waveforms that were stored during experiment acquisition.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[SpikeEventSeries] | SpikeEventSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -284,7 +264,6 @@ class FilteredEphys(NWBDataInterface):
|
|||
Electrophysiology data from one or more channels that has been subjected to filtering. Examples of filtered data include Theta and Gamma (LFP has its own interface). FilteredEphys modules publish an ElectricalSeries for each filtered channel or set of channels. The name of each ElectricalSeries is arbitrary but should be informative. The source of the filtered data, whether this is from analysis of another time series or as acquired by hardware, should be noted in each's TimeSeries::description field. There is no assumed 1::1 correspondence between filtered ephys signals and electrodes, as a single signal can apply to many nearby electrodes, and one electrode may have different filtered (e.g., theta and/or gamma) signals represented. Filter properties should be noted in the ElectricalSeries.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[ElectricalSeries] | ElectricalSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -296,7 +275,6 @@ class LFP(NWBDataInterface):
|
|||
LFP data from one or more channels. The electrode map in each published ElectricalSeries will identify which channels are providing LFP data. Filter properties should be noted in the ElectricalSeries description or comments field.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[ElectricalSeries] | ElectricalSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -308,7 +286,6 @@ class ElectrodeGroup(NWBContainer):
|
|||
A physical grouping of electrodes, e.g. a shank of an array.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: Optional[str] = Field(
|
||||
None, description="""Description of this electrode group."""
|
||||
|
|
@ -327,7 +304,6 @@ class ElectrodeGroupPosition(ConfiguredBaseModel):
|
|||
stereotaxic or common framework coordinates
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["position"] = Field("position")
|
||||
x: Optional[float] = Field(None, description="""x coordinate""")
|
||||
y: Optional[float] = Field(None, description="""y coordinate""")
|
||||
|
|
@ -339,7 +315,6 @@ class ClusterWaveforms(NWBDataInterface):
|
|||
DEPRECATED The mean waveform shape, including standard deviation, of the different clusters. Ideally, the waveform analysis should be performed on data that is only high-pass filtered. This is a separate module because it is expected to require updating. For example, IMEC probes may require different storage requirements to store/display mean waveforms, requiring a new interface or an extension of this one.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("ClusterWaveforms")
|
||||
waveform_filtering: str = Field(
|
||||
..., description="""Filtering applied to data before generating mean/sd"""
|
||||
|
|
@ -359,7 +334,6 @@ class Clustering(NWBDataInterface):
|
|||
DEPRECATED Clustered spike data, whether from automatic clustering tools (e.g., klustakwik) or as a result of manual sorting.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("Clustering")
|
||||
description: str = Field(
|
||||
...,
|
||||
|
|
@ -376,20 +350,3 @@ class Clustering(NWBDataInterface):
|
|||
...,
|
||||
description="""Times of clustered events, in seconds. This may be a link to times field in associated FeatureExtraction module.""",
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
ElectricalSeries.model_rebuild()
|
||||
ElectricalSeriesElectrodes.model_rebuild()
|
||||
SpikeEventSeries.model_rebuild()
|
||||
FeatureExtraction.model_rebuild()
|
||||
FeatureExtractionElectrodes.model_rebuild()
|
||||
EventDetection.model_rebuild()
|
||||
EventWaveform.model_rebuild()
|
||||
FilteredEphys.model_rebuild()
|
||||
LFP.model_rebuild()
|
||||
ElectrodeGroup.model_rebuild()
|
||||
ElectrodeGroupPosition.model_rebuild()
|
||||
ClusterWaveforms.model_rebuild()
|
||||
Clustering.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -27,27 +27,20 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from .core_nwb_base import TimeSeries
|
||||
|
||||
from ...hdmf_common.v1_1_3.hdmf_common_table import (
|
||||
DynamicTable,
|
||||
VectorIndex,
|
||||
DynamicTable,
|
||||
VectorData,
|
||||
)
|
||||
|
||||
from .core_nwb_base import TimeSeries
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
version = "2.2.5"
|
||||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -67,18 +60,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class TimeIntervals(DynamicTable):
|
||||
"""
|
||||
A container for aggregating epoch data and the TimeSeries that each epoch applies to.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
start_time: Optional[List[float] | float] = Field(
|
||||
default_factory=list, description="""Start time of epoch, in seconds."""
|
||||
|
|
@ -122,7 +108,6 @@ class TimeIntervalsTagsIndex(VectorIndex):
|
|||
Index for tags.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["tags_index"] = Field("tags_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -136,7 +121,6 @@ class TimeIntervalsTimeseries(VectorData):
|
|||
An index into a TimeSeries object.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["timeseries"] = Field("timeseries")
|
||||
idx_start: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -167,18 +151,9 @@ class TimeIntervalsTimeseriesIndex(VectorIndex):
|
|||
Index for timeseries.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["timeseries_index"] = Field("timeseries_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
description="""Reference to the target dataset that this index applies to.""",
|
||||
)
|
||||
array: Optional[NDArray[Shape["* num_rows"], Any]] = Field(None)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
TimeIntervals.model_rebuild()
|
||||
TimeIntervalsTagsIndex.model_rebuild()
|
||||
TimeIntervalsTimeseries.model_rebuild()
|
||||
TimeIntervalsTimeseriesIndex.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -27,33 +27,33 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from .core_nwb_ophys import ImagingPlane
|
||||
|
||||
from .core_nwb_base import (
|
||||
TimeSeries,
|
||||
NWBDataInterface,
|
||||
ProcessingModule,
|
||||
NWBContainer,
|
||||
NWBData,
|
||||
)
|
||||
from .core_nwb_icephys import SweepTable, IntracellularElectrode
|
||||
|
||||
from .core_nwb_epoch import TimeIntervals
|
||||
|
||||
from ...hdmf_common.v1_1_3.hdmf_common_table import (
|
||||
DynamicTable,
|
||||
VectorData,
|
||||
VectorIndex,
|
||||
from .core_nwb_ecephys import ElectrodeGroup
|
||||
|
||||
from .core_nwb_base import (
|
||||
NWBContainer,
|
||||
ProcessingModule,
|
||||
NWBData,
|
||||
NWBDataInterface,
|
||||
TimeSeries,
|
||||
)
|
||||
|
||||
from .core_nwb_icephys import IntracellularElectrode, SweepTable
|
||||
|
||||
from .core_nwb_ogen import OptogeneticStimulusSite
|
||||
from .core_nwb_device import Device
|
||||
|
||||
from .core_nwb_misc import Units
|
||||
|
||||
from .core_nwb_ecephys import ElectrodeGroup
|
||||
from .core_nwb_ogen import OptogeneticStimulusSite
|
||||
|
||||
from .core_nwb_device import Device
|
||||
from ...hdmf_common.v1_1_3.hdmf_common_table import (
|
||||
VectorIndex,
|
||||
DynamicTable,
|
||||
VectorData,
|
||||
)
|
||||
|
||||
from .core_nwb_ophys import ImagingPlane
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
|
|
@ -61,13 +61,6 @@ version = "2.2.5"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -87,18 +80,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class ScratchData(NWBData):
|
||||
"""
|
||||
Any one-off datasets
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
notes: Optional[str] = Field(
|
||||
None, description="""Any notes the user has about the dataset being stored"""
|
||||
|
|
@ -110,7 +96,6 @@ class NWBFile(NWBContainer):
|
|||
An NWB:N file storing cellular-based neurophysiology data from a single experimental session.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: Literal["root"] = Field("root")
|
||||
nwb_version: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -181,7 +166,6 @@ class NWBFileStimulus(ConfiguredBaseModel):
|
|||
Data pushed into the system (eg, video stimulus, sound, voltage, etc) and secondary representations of that data (eg, measurements of something used as a stimulus). This group should be made read-only after experiment complete and timestamps are corrected to common timebase. Stores both presented stimuli and stimulus templates, the latter in case the same stimulus is presented multiple times, or is pulled from an external stimulus library. Stimuli are here defined as any signal that is pushed into the system as part of the experiment (eg, sound, video, voltage, etc). Many different experiments can use the same stimuli, and stimuli can be re-used during an experiment. The stimulus group is organized so that one version of template stimuli can be stored and these be used multiple times. These templates can exist in the present file or can be linked to a remote library file.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["stimulus"] = Field("stimulus")
|
||||
presentation: Optional[List[TimeSeries] | TimeSeries] = Field(
|
||||
default_factory=dict, description="""Stimuli presented during the experiment."""
|
||||
|
|
@ -197,7 +181,6 @@ class NWBFileGeneral(ConfiguredBaseModel):
|
|||
Experimental metadata, including protocol, notes and description of hardware device(s). The metadata stored in this section should be used to describe the experiment. Metadata necessary for interpreting the data is stored with the data. General experimental metadata, including animal strain, experimental protocols, experimenter, devices, etc, are stored under 'general'. Core metadata (e.g., that required to interpret data fields) is stored with the data itself, and implicitly defined by the file specification (e.g., time is in seconds). The strategy used here for storing non-core metadata is to use free-form text fields, such as would appear in sentences or paragraphs from a Methods section. Metadata fields are text to enable them to be more general, for example to represent ranges instead of numerical values. Machine-readable metadata is stored as attributes to these free-form datasets. All entries in the below table are to be included when data is present. Unused groups (e.g., intracellular_ephys in an optophysiology experiment) should not be created unless there is data to store within them.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["general"] = Field("general")
|
||||
data_collection: Optional[str] = Field(
|
||||
None, description="""Notes about data collection and analysis."""
|
||||
|
|
@ -287,7 +270,6 @@ class NWBFileGeneralSourceScript(ConfiguredBaseModel):
|
|||
Script file or link to public source code used to create this NWB file.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["source_script"] = Field("source_script")
|
||||
file_name: Optional[str] = Field(None, description="""Name of script file.""")
|
||||
value: str = Field(...)
|
||||
|
|
@ -298,7 +280,6 @@ class NWBFileGeneralExtracellularEphys(ConfiguredBaseModel):
|
|||
Metadata related to extracellular electrophysiology.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["extracellular_ephys"] = Field("extracellular_ephys")
|
||||
electrode_group: Optional[List[str] | str] = Field(
|
||||
default_factory=list, description="""Physical group of electrodes."""
|
||||
|
|
@ -314,7 +295,6 @@ class NWBFileGeneralExtracellularEphysElectrodes(DynamicTable):
|
|||
A table of all electrodes (i.e. channels) used for recording.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["electrodes"] = Field("electrodes")
|
||||
x: Optional[List[float] | float] = Field(
|
||||
default_factory=list,
|
||||
|
|
@ -384,7 +364,6 @@ class NWBFileGeneralIntracellularEphys(ConfiguredBaseModel):
|
|||
Metadata related to intracellular electrophysiology.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["intracellular_ephys"] = Field("intracellular_ephys")
|
||||
filtering: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -404,7 +383,6 @@ class LabMetaData(NWBContainer):
|
|||
Lab-specific meta-data.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
|
||||
|
||||
|
|
@ -413,7 +391,6 @@ class Subject(NWBContainer):
|
|||
Information about the animal or person from which the data was measured.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
age: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -440,17 +417,3 @@ class Subject(NWBContainer):
|
|||
None,
|
||||
description="""Weight at time of experiment, at time of surgery and at other important times.""",
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
ScratchData.model_rebuild()
|
||||
NWBFile.model_rebuild()
|
||||
NWBFileStimulus.model_rebuild()
|
||||
NWBFileGeneral.model_rebuild()
|
||||
NWBFileGeneralSourceScript.model_rebuild()
|
||||
NWBFileGeneralExtracellularEphys.model_rebuild()
|
||||
NWBFileGeneralExtracellularEphysElectrodes.model_rebuild()
|
||||
NWBFileGeneralIntracellularEphys.model_rebuild()
|
||||
LabMetaData.model_rebuild()
|
||||
Subject.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -28,16 +28,16 @@ if TYPE_CHECKING:
|
|||
|
||||
|
||||
from .core_nwb_base import (
|
||||
TimeSeries,
|
||||
NWBContainer,
|
||||
TimeSeriesSync,
|
||||
TimeSeriesStartingTime,
|
||||
NWBContainer,
|
||||
TimeSeries,
|
||||
)
|
||||
|
||||
from ...hdmf_common.v1_1_3.hdmf_common_table import (
|
||||
VectorIndex,
|
||||
DynamicTable,
|
||||
VectorData,
|
||||
VectorIndex,
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -46,13 +46,6 @@ version = "2.2.5"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -72,18 +65,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class PatchClampSeries(TimeSeries):
|
||||
"""
|
||||
An abstract base class for patch-clamp data - stimulus or response, current or voltage.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
stimulus_description: Optional[str] = Field(
|
||||
None, description="""Protocol/stimulus name for this patch-clamp dataset."""
|
||||
|
|
@ -131,7 +117,6 @@ class PatchClampSeriesData(ConfiguredBaseModel):
|
|||
Recorded voltage or current.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -145,7 +130,6 @@ class CurrentClampSeries(PatchClampSeries):
|
|||
Voltage data from an intracellular current-clamp recording. A corresponding CurrentClampStimulusSeries (stored separately as a stimulus) is used to store the current injected.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Recorded voltage.""")
|
||||
bias_current: Optional[float] = Field(
|
||||
|
|
@ -202,7 +186,6 @@ class CurrentClampSeriesData(ConfiguredBaseModel):
|
|||
Recorded voltage.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -216,7 +199,6 @@ class IZeroClampSeries(CurrentClampSeries):
|
|||
Voltage data from an intracellular recording when all current and amplifier settings are off (i.e., CurrentClampSeries fields will be zero). There is no CurrentClampStimulusSeries associated with an IZero series because the amplifier is disconnected and no stimulus can reach the cell.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
bias_current: float = Field(
|
||||
..., description="""Bias current, in amps, fixed to 0.0."""
|
||||
|
|
@ -273,7 +255,6 @@ class CurrentClampStimulusSeries(PatchClampSeries):
|
|||
Stimulus current applied during current clamp recording.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Stimulus current applied.""")
|
||||
stimulus_description: Optional[str] = Field(
|
||||
|
|
@ -321,7 +302,6 @@ class CurrentClampStimulusSeriesData(ConfiguredBaseModel):
|
|||
Stimulus current applied.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -335,7 +315,6 @@ class VoltageClampSeries(PatchClampSeries):
|
|||
Current data from an intracellular voltage-clamp recording. A corresponding VoltageClampStimulusSeries (stored separately as a stimulus) is used to store the voltage injected.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Recorded current.""")
|
||||
capacitance_fast: Optional[str] = Field(
|
||||
|
|
@ -404,7 +383,6 @@ class VoltageClampSeriesData(ConfiguredBaseModel):
|
|||
Recorded current.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -418,7 +396,6 @@ class VoltageClampSeriesCapacitanceFast(ConfiguredBaseModel):
|
|||
Fast capacitance, in farads.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["capacitance_fast"] = Field("capacitance_fast")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -432,7 +409,6 @@ class VoltageClampSeriesCapacitanceSlow(ConfiguredBaseModel):
|
|||
Slow capacitance, in farads.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["capacitance_slow"] = Field("capacitance_slow")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -446,7 +422,6 @@ class VoltageClampSeriesResistanceCompBandwidth(ConfiguredBaseModel):
|
|||
Resistance compensation bandwidth, in hertz.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["resistance_comp_bandwidth"] = Field("resistance_comp_bandwidth")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -460,7 +435,6 @@ class VoltageClampSeriesResistanceCompCorrection(ConfiguredBaseModel):
|
|||
Resistance compensation correction, in percent.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["resistance_comp_correction"] = Field("resistance_comp_correction")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -474,7 +448,6 @@ class VoltageClampSeriesResistanceCompPrediction(ConfiguredBaseModel):
|
|||
Resistance compensation prediction, in percent.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["resistance_comp_prediction"] = Field("resistance_comp_prediction")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -488,7 +461,6 @@ class VoltageClampSeriesWholeCellCapacitanceComp(ConfiguredBaseModel):
|
|||
Whole cell capacitance compensation, in farads.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["whole_cell_capacitance_comp"] = Field("whole_cell_capacitance_comp")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -502,7 +474,6 @@ class VoltageClampSeriesWholeCellSeriesResistanceComp(ConfiguredBaseModel):
|
|||
Whole cell series resistance compensation, in ohms.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["whole_cell_series_resistance_comp"] = Field(
|
||||
"whole_cell_series_resistance_comp"
|
||||
)
|
||||
|
|
@ -518,7 +489,6 @@ class VoltageClampStimulusSeries(PatchClampSeries):
|
|||
Stimulus voltage applied during a voltage clamp recording.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Stimulus voltage applied.""")
|
||||
stimulus_description: Optional[str] = Field(
|
||||
|
|
@ -566,7 +536,6 @@ class VoltageClampStimulusSeriesData(ConfiguredBaseModel):
|
|||
Stimulus voltage applied.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -580,7 +549,6 @@ class IntracellularElectrode(NWBContainer):
|
|||
An intracellular electrode and its metadata.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: str = Field(
|
||||
...,
|
||||
|
|
@ -612,7 +580,6 @@ class SweepTable(DynamicTable):
|
|||
The table which groups different PatchClampSeries together.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
sweep_number: Optional[List[int] | int] = Field(
|
||||
default_factory=list,
|
||||
|
|
@ -648,35 +615,9 @@ class SweepTableSeriesIndex(VectorIndex):
|
|||
Index for series.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["series_index"] = Field("series_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
description="""Reference to the target dataset that this index applies to.""",
|
||||
)
|
||||
array: Optional[NDArray[Shape["* num_rows"], Any]] = Field(None)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
PatchClampSeries.model_rebuild()
|
||||
PatchClampSeriesData.model_rebuild()
|
||||
CurrentClampSeries.model_rebuild()
|
||||
CurrentClampSeriesData.model_rebuild()
|
||||
IZeroClampSeries.model_rebuild()
|
||||
CurrentClampStimulusSeries.model_rebuild()
|
||||
CurrentClampStimulusSeriesData.model_rebuild()
|
||||
VoltageClampSeries.model_rebuild()
|
||||
VoltageClampSeriesData.model_rebuild()
|
||||
VoltageClampSeriesCapacitanceFast.model_rebuild()
|
||||
VoltageClampSeriesCapacitanceSlow.model_rebuild()
|
||||
VoltageClampSeriesResistanceCompBandwidth.model_rebuild()
|
||||
VoltageClampSeriesResistanceCompCorrection.model_rebuild()
|
||||
VoltageClampSeriesResistanceCompPrediction.model_rebuild()
|
||||
VoltageClampSeriesWholeCellCapacitanceComp.model_rebuild()
|
||||
VoltageClampSeriesWholeCellSeriesResistanceComp.model_rebuild()
|
||||
VoltageClampStimulusSeries.model_rebuild()
|
||||
VoltageClampStimulusSeriesData.model_rebuild()
|
||||
IntracellularElectrode.model_rebuild()
|
||||
SweepTable.model_rebuild()
|
||||
SweepTableSeriesIndex.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from .core_nwb_base import Image, TimeSeries, TimeSeriesStartingTime, TimeSeriesSync
|
||||
from .core_nwb_base import Image, TimeSeriesStartingTime, TimeSeries, TimeSeriesSync
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
|
|
@ -35,13 +35,6 @@ version = "2.2.5"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -61,18 +54,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class GrayscaleImage(Image):
|
||||
"""
|
||||
A grayscale image.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
resolution: Optional[float] = Field(
|
||||
None, description="""Pixel resolution of the image, in pixels per centimeter."""
|
||||
|
|
@ -94,7 +80,6 @@ class RGBImage(Image):
|
|||
A color image.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
resolution: Optional[float] = Field(
|
||||
None, description="""Pixel resolution of the image, in pixels per centimeter."""
|
||||
|
|
@ -116,7 +101,6 @@ class RGBAImage(Image):
|
|||
A color image with transparency.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
resolution: Optional[float] = Field(
|
||||
None, description="""Pixel resolution of the image, in pixels per centimeter."""
|
||||
|
|
@ -138,7 +122,6 @@ class ImageSeries(TimeSeries):
|
|||
General image data that is common between acquisition and stimulus time series. Sometimes the image data is stored in the file in a raw format while other times it will be stored as a series of external image files in the host file system. The data field will either be binary data, if the data is stored in the NWB file, or empty, if the data is stored in an external image stack. [frame][x][y] or [frame][x][y][z].
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: Optional[
|
||||
Union[
|
||||
|
|
@ -191,7 +174,6 @@ 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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["external_file"] = Field("external_file")
|
||||
starting_frame: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -205,7 +187,6 @@ class ImageMaskSeries(ImageSeries):
|
|||
An alpha mask that is applied to a presented visual stimulus. The 'data' array contains an array of mask values that are applied to the displayed image. Mask values are stored as RGBA. Mask can vary with time. The timestamps array indicates the starting time of a mask, and that mask pattern continues until it's explicitly changed.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: Optional[
|
||||
Union[
|
||||
|
|
@ -258,7 +239,6 @@ class OpticalSeries(ImageSeries):
|
|||
Image data that is presented or recorded. A stimulus template movie will be stored only as an image. When the image is presented as stimulus, additional data is required, such as field of view (e.g., how much of the visual field the image covers, or how what is the area of the target being imaged). If the OpticalSeries represents acquired imaging data, orientation is also important.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
distance: Optional[float] = Field(
|
||||
None, description="""Distance from camera/monitor to target/eye."""
|
||||
|
|
@ -327,7 +307,6 @@ 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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: NDArray[Shape["* num_times"], int] = Field(
|
||||
..., description="""Index of the frame in the referenced ImageSeries."""
|
||||
|
|
@ -359,15 +338,3 @@ class IndexSeries(TimeSeries):
|
|||
None,
|
||||
description="""Lab-specific time and sync information as provided directly from hardware devices and that is necessary for aligning all acquired time information to a common timebase. The timestamp array stores time in the common timebase. This group will usually only be populated in TimeSeries that are stored external to the NWB file, in files storing raw data. Once timestamp data is calculated, the contents of 'sync' are mostly for archival purposes.""",
|
||||
)
|
||||
|
||||
|
||||
# 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()
|
||||
ImageSeriesExternalFile.model_rebuild()
|
||||
ImageMaskSeries.model_rebuild()
|
||||
OpticalSeries.model_rebuild()
|
||||
IndexSeries.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -32,13 +32,6 @@ version = "None"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -56,13 +49,3 @@ class ConfiguredBaseModel(BaseModel):
|
|||
self.array[i] = value
|
||||
else:
|
||||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
|
|
|
|||
|
|
@ -27,15 +27,15 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from .core_nwb_base import TimeSeries, TimeSeriesStartingTime, TimeSeriesSync
|
||||
|
||||
from ...hdmf_common.v1_1_3.hdmf_common_table import (
|
||||
VectorIndex,
|
||||
DynamicTableRegion,
|
||||
DynamicTable,
|
||||
VectorIndex,
|
||||
VectorData,
|
||||
)
|
||||
|
||||
from .core_nwb_base import TimeSeriesStartingTime, TimeSeries, TimeSeriesSync
|
||||
|
||||
from .core_nwb_ecephys import ElectrodeGroup
|
||||
|
||||
|
||||
|
|
@ -44,13 +44,6 @@ version = "2.2.5"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -70,18 +63,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class AbstractFeatureSeries(TimeSeries):
|
||||
"""
|
||||
Abstract features, such as quantitative descriptions of sensory stimuli. The TimeSeries::data field is a 2D array, storing those features (e.g., for visual grating stimulus this might be orientation, spatial frequency and contrast). Null stimuli (eg, uniform gray) can be marked as being an independent feature (eg, 1.0 for gray, 0.0 for actual stimulus) or by storing NaNs for feature values, or through use of the TimeSeries::control fields. A set of features is considered to persist until the next set of features is defined. The final set of features stored should be the null set. This is useful when storing the raw stimulus is impractical.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Values of each feature at each time.""")
|
||||
feature_units: Optional[NDArray[Shape["* num_features"], str]] = Field(
|
||||
|
|
@ -125,7 +111,6 @@ class AbstractFeatureSeriesData(ConfiguredBaseModel):
|
|||
Values of each feature at each time.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -144,7 +129,6 @@ class AnnotationSeries(TimeSeries):
|
|||
Stores user annotations made during an experiment. The data[] field stores a text array, and timestamps are stored for each annotation (ie, interval=1). This is largely an alias to a standard TimeSeries storing a text array but that is identifiable as storing annotations in a machine-readable way.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: NDArray[Shape["* num_times"], str] = Field(
|
||||
..., description="""Annotations made during an experiment."""
|
||||
|
|
@ -183,7 +167,6 @@ 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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: NDArray[Shape["* num_times"], int] = Field(
|
||||
..., description="""Use values >0 if interval started, <0 if interval ended."""
|
||||
|
|
@ -222,7 +205,6 @@ class DecompositionSeries(TimeSeries):
|
|||
Spectral analysis of a time series, e.g. of an LFP or a speech signal.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Data decomposed into frequency bands.""")
|
||||
metric: str = Field(
|
||||
|
|
@ -266,7 +248,6 @@ class DecompositionSeriesData(ConfiguredBaseModel):
|
|||
Data decomposed into frequency bands.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -282,7 +263,6 @@ class DecompositionSeriesBands(DynamicTable):
|
|||
Table for describing the bands that this series was generated from. There should be one row in this table for each band.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["bands"] = Field("bands")
|
||||
band_name: Optional[List[str] | str] = Field(
|
||||
default_factory=list, description="""Name of the band, e.g. theta."""
|
||||
|
|
@ -322,7 +302,6 @@ class Units(DynamicTable):
|
|||
Data about spiking units. Event times of observed units (e.g. cell, synapse, etc.) should be concatenated and stored in spike_times.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("Units")
|
||||
spike_times_index: Optional[str] = Field(
|
||||
None, description="""Index into the spike_times dataset."""
|
||||
|
|
@ -386,7 +365,6 @@ class UnitsSpikeTimesIndex(VectorIndex):
|
|||
Index into the spike_times dataset.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["spike_times_index"] = Field("spike_times_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -400,7 +378,6 @@ class UnitsSpikeTimes(VectorData):
|
|||
Spike times for each unit.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["spike_times"] = Field("spike_times")
|
||||
resolution: Optional[float] = Field(
|
||||
None,
|
||||
|
|
@ -424,7 +401,6 @@ class UnitsObsIntervalsIndex(VectorIndex):
|
|||
Index into the obs_intervals dataset.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["obs_intervals_index"] = Field("obs_intervals_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -438,7 +414,6 @@ class UnitsElectrodesIndex(VectorIndex):
|
|||
Index into electrodes.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["electrodes_index"] = Field("electrodes_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -452,7 +427,6 @@ class UnitsElectrodes(DynamicTableRegion):
|
|||
Electrode that each spike unit came from, specified using a DynamicTableRegion.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["electrodes"] = Field("electrodes")
|
||||
table: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -469,20 +443,3 @@ class UnitsElectrodes(DynamicTableRegion):
|
|||
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()
|
||||
IntervalSeries.model_rebuild()
|
||||
DecompositionSeries.model_rebuild()
|
||||
DecompositionSeriesData.model_rebuild()
|
||||
DecompositionSeriesBands.model_rebuild()
|
||||
Units.model_rebuild()
|
||||
UnitsSpikeTimesIndex.model_rebuild()
|
||||
UnitsSpikeTimes.model_rebuild()
|
||||
UnitsObsIntervalsIndex.model_rebuild()
|
||||
UnitsElectrodesIndex.model_rebuild()
|
||||
UnitsElectrodes.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -29,9 +29,9 @@ if TYPE_CHECKING:
|
|||
|
||||
from .core_nwb_base import (
|
||||
TimeSeriesSync,
|
||||
TimeSeries,
|
||||
NWBContainer,
|
||||
TimeSeriesStartingTime,
|
||||
TimeSeries,
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -40,13 +40,6 @@ version = "2.2.5"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -66,18 +59,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class OptogeneticSeries(TimeSeries):
|
||||
"""
|
||||
An optogenetic stimulus.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: NDArray[Shape["* num_times"], float] = Field(
|
||||
..., description="""Applied power for optogenetic stimulus, in watts."""
|
||||
|
|
@ -116,7 +102,6 @@ class OptogeneticStimulusSite(NWBContainer):
|
|||
A site of optogenetic stimulation.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: str = Field(..., description="""Description of stimulation site.""")
|
||||
excitation_lambda: float = Field(
|
||||
|
|
@ -126,9 +111,3 @@ class OptogeneticStimulusSite(NWBContainer):
|
|||
...,
|
||||
description="""Location of the stimulation site. 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.""",
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
OptogeneticSeries.model_rebuild()
|
||||
OptogeneticStimulusSite.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -28,18 +28,18 @@ if TYPE_CHECKING:
|
|||
|
||||
|
||||
from .core_nwb_base import (
|
||||
TimeSeries,
|
||||
TimeSeriesSync,
|
||||
NWBDataInterface,
|
||||
TimeSeriesStartingTime,
|
||||
NWBContainer,
|
||||
TimeSeriesSync,
|
||||
TimeSeriesStartingTime,
|
||||
NWBDataInterface,
|
||||
TimeSeries,
|
||||
)
|
||||
|
||||
from ...hdmf_common.v1_1_3.hdmf_common_table import (
|
||||
DynamicTableRegion,
|
||||
VectorIndex,
|
||||
DynamicTable,
|
||||
VectorData,
|
||||
VectorIndex,
|
||||
)
|
||||
|
||||
from .core_nwb_image import ImageSeries, ImageSeriesExternalFile
|
||||
|
|
@ -50,13 +50,6 @@ version = "2.2.5"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -76,18 +69,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class TwoPhotonSeries(ImageSeries):
|
||||
"""
|
||||
Image stack recorded over time from 2-photon microscope.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
pmt_gain: Optional[float] = Field(None, description="""Photomultiplier gain.""")
|
||||
scan_line_rate: Optional[float] = Field(
|
||||
|
|
@ -154,7 +140,6 @@ class RoiResponseSeries(TimeSeries):
|
|||
ROI responses over an imaging plane. The first dimension represents time. The second dimension, if present, represents ROIs.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: Union[
|
||||
NDArray[Shape["* num_times"], float],
|
||||
|
|
@ -198,7 +183,6 @@ class RoiResponseSeriesRois(DynamicTableRegion):
|
|||
DynamicTableRegion referencing into an ROITable containing information on the ROIs stored in this timeseries.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["rois"] = Field("rois")
|
||||
table: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -222,7 +206,6 @@ 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).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[RoiResponseSeries] | RoiResponseSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -234,7 +217,6 @@ class Fluorescence(NWBDataInterface):
|
|||
Fluorescence information about a region of interest (ROI). Storage hierarchy of fluorescence should be the same as for segmentation (ie, same names for ROIs and for image planes).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[RoiResponseSeries] | RoiResponseSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -246,7 +228,6 @@ class ImageSegmentation(NWBDataInterface):
|
|||
Stores pixels in an image that represent different regions of interest (ROIs) or masks. All segmentation for a given imaging plane is stored together, with storage for multiple imaging planes (masks) supported. Each ROI is stored in its own subgroup, with the ROI group containing both a 2D mask and a list of pixels that make up this mask. Segments can also be used for masking neuropil. If segmentation is allowed to change with time, a new imaging plane (or module) is required and ROI names should remain consistent between them.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[PlaneSegmentation] | PlaneSegmentation] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -258,7 +239,6 @@ class PlaneSegmentation(DynamicTable):
|
|||
Results from image segmentation of a specific imaging plane.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
image_mask: Optional[
|
||||
Union[
|
||||
|
|
@ -312,7 +292,6 @@ class PlaneSegmentationPixelMaskIndex(VectorIndex):
|
|||
Index into pixel_mask.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["pixel_mask_index"] = Field("pixel_mask_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -326,7 +305,6 @@ class PlaneSegmentationPixelMask(VectorData):
|
|||
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
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["pixel_mask"] = Field("pixel_mask")
|
||||
x: Optional[int] = Field(None, description="""Pixel x-coordinate.""")
|
||||
y: Optional[int] = Field(None, description="""Pixel y-coordinate.""")
|
||||
|
|
@ -349,7 +327,6 @@ class PlaneSegmentationVoxelMaskIndex(VectorIndex):
|
|||
Index into voxel_mask.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["voxel_mask_index"] = Field("voxel_mask_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -363,7 +340,6 @@ class PlaneSegmentationVoxelMask(VectorData):
|
|||
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
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["voxel_mask"] = Field("voxel_mask")
|
||||
x: Optional[int] = Field(None, description="""Voxel x-coordinate.""")
|
||||
y: Optional[int] = Field(None, description="""Voxel y-coordinate.""")
|
||||
|
|
@ -387,7 +363,6 @@ class ImagingPlane(NWBContainer):
|
|||
An imaging plane and its metadata.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[OpticalChannel] | OpticalChannel] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -399,7 +374,6 @@ class OpticalChannel(NWBContainer):
|
|||
An optical channel used to record from an imaging plane.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: str = Field(
|
||||
..., description="""Description or other notes about the channel."""
|
||||
|
|
@ -414,7 +388,6 @@ class MotionCorrection(NWBDataInterface):
|
|||
An image stack where all frames are shifted (registered) to a common coordinate system, to account for movement and drift between frames. Note: each frame at each point in time is assumed to be 2-D (has only x & y dimensions).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[CorrectedImageStack] | CorrectedImageStack] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -426,7 +399,6 @@ class CorrectedImageStack(NWBDataInterface):
|
|||
Reuslts from motion correction of an image stack.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
corrected: str = Field(
|
||||
...,
|
||||
|
|
@ -436,22 +408,3 @@ class CorrectedImageStack(NWBDataInterface):
|
|||
...,
|
||||
description="""Stores the x,y delta necessary to align each frame to the common coordinates, for example, to align each frame to a reference image.""",
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
TwoPhotonSeries.model_rebuild()
|
||||
RoiResponseSeries.model_rebuild()
|
||||
RoiResponseSeriesRois.model_rebuild()
|
||||
DfOverF.model_rebuild()
|
||||
Fluorescence.model_rebuild()
|
||||
ImageSegmentation.model_rebuild()
|
||||
PlaneSegmentation.model_rebuild()
|
||||
PlaneSegmentationPixelMaskIndex.model_rebuild()
|
||||
PlaneSegmentationPixelMask.model_rebuild()
|
||||
PlaneSegmentationVoxelMaskIndex.model_rebuild()
|
||||
PlaneSegmentationVoxelMask.model_rebuild()
|
||||
ImagingPlane.model_rebuild()
|
||||
OpticalChannel.model_rebuild()
|
||||
MotionCorrection.model_rebuild()
|
||||
CorrectedImageStack.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -35,13 +35,6 @@ version = "2.2.5"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -61,18 +54,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class ImagingRetinotopy(NWBDataInterface):
|
||||
"""
|
||||
Intrinsic signal optical imaging or widefield imaging for measuring retinotopy. Stores orthogonal maps (e.g., altitude/azimuth; radius/theta) of responses to specific stimuli and a combined polarity map from which to identify visual areas. This group does not store the raw responses imaged during retinotopic mapping or the stimuli presented, but rather the resulting phase and power maps after applying a Fourier transform on the averaged responses. Note: for data consistency, all images and arrays are stored in the format [row][column] and [row, col], which equates to [y][x]. Field of view and dimension arrays may appear backward (i.e., y before x).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("ImagingRetinotopy")
|
||||
axis_1_phase_map: str = Field(
|
||||
..., description="""Phase response to stimulus on the first measured axis."""
|
||||
|
|
@ -111,7 +97,6 @@ class ImagingRetinotopyAxis1PhaseMap(ConfiguredBaseModel):
|
|||
Phase response to stimulus on the first measured axis.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["axis_1_phase_map"] = Field("axis_1_phase_map")
|
||||
dimension: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -131,7 +116,6 @@ class ImagingRetinotopyAxis1PowerMap(ConfiguredBaseModel):
|
|||
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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["axis_1_power_map"] = Field("axis_1_power_map")
|
||||
dimension: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -151,7 +135,6 @@ class ImagingRetinotopyAxis2PhaseMap(ConfiguredBaseModel):
|
|||
Phase response to stimulus on the second measured axis.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["axis_2_phase_map"] = Field("axis_2_phase_map")
|
||||
dimension: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -171,7 +154,6 @@ class ImagingRetinotopyAxis2PowerMap(ConfiguredBaseModel):
|
|||
Power response on the second measured axis. Response is scaled so 0.0 is no power in the response and 1.0 is maximum relative power.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["axis_2_power_map"] = Field("axis_2_power_map")
|
||||
dimension: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -191,7 +173,6 @@ class ImagingRetinotopyFocalDepthImage(ConfiguredBaseModel):
|
|||
Gray-scale image taken with same settings/parameters (e.g., focal depth, wavelength) as data collection. Array format: [rows][columns].
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["focal_depth_image"] = Field("focal_depth_image")
|
||||
bits_per_pixel: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -218,7 +199,6 @@ class ImagingRetinotopySignMap(ConfiguredBaseModel):
|
|||
Sine of the angle between the direction of the gradient in axis_1 and axis_2.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["sign_map"] = Field("sign_map")
|
||||
dimension: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -235,7 +215,6 @@ class ImagingRetinotopyVasculatureImage(ConfiguredBaseModel):
|
|||
Gray-scale anatomical image of cortical surface. Array structure: [rows][columns]
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["vasculature_image"] = Field("vasculature_image")
|
||||
bits_per_pixel: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -252,15 +231,3 @@ class ImagingRetinotopyVasculatureImage(ConfiguredBaseModel):
|
|||
None, description="""Format of image. Right now only 'raw' is supported."""
|
||||
)
|
||||
array: Optional[NDArray[Shape["* num_rows, * num_cols"], int]] = Field(None)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
ImagingRetinotopy.model_rebuild()
|
||||
ImagingRetinotopyAxis1PhaseMap.model_rebuild()
|
||||
ImagingRetinotopyAxis1PowerMap.model_rebuild()
|
||||
ImagingRetinotopyAxis2PhaseMap.model_rebuild()
|
||||
ImagingRetinotopyAxis2PowerMap.model_rebuild()
|
||||
ImagingRetinotopyFocalDepthImage.model_rebuild()
|
||||
ImagingRetinotopySignMap.model_rebuild()
|
||||
ImagingRetinotopyVasculatureImage.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -196,13 +196,6 @@ version = "2.2.5"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -220,13 +213,3 @@ class ConfiguredBaseModel(BaseModel):
|
|||
self.array[i] = value
|
||||
else:
|
||||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from ...hdmf_common.v1_5_0.hdmf_common_base import Container, Data
|
||||
from ...hdmf_common.v1_5_0.hdmf_common_base import Data, Container
|
||||
|
||||
from ...hdmf_common.v1_5_0.hdmf_common_table import DynamicTable
|
||||
|
||||
|
|
@ -37,13 +37,6 @@ version = "2.3.0"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -63,18 +56,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class NWBData(Data):
|
||||
"""
|
||||
An abstract data type for a dataset.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
|
||||
|
||||
|
|
@ -83,7 +69,6 @@ class Image(NWBData):
|
|||
An abstract data type for an image. Shape can be 2-D (x, y), or 3-D where the third dimension can have three or four elements, e.g. (x, y, (r, g, b)) or (x, y, (r, g, b, a)).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
resolution: Optional[float] = Field(
|
||||
None, description="""Pixel resolution of the image, in pixels per centimeter."""
|
||||
|
|
@ -105,7 +90,6 @@ class NWBContainer(Container):
|
|||
An abstract data type for a generic container storing collections of data and metadata. Base type for all data and metadata containers.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
|
||||
|
||||
|
|
@ -114,7 +98,6 @@ class NWBDataInterface(NWBContainer):
|
|||
An abstract data type for a generic container storing collections of data, as opposed to metadata.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
|
||||
|
||||
|
|
@ -123,7 +106,6 @@ class TimeSeries(NWBDataInterface):
|
|||
General purpose time series.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: Optional[str] = Field(
|
||||
None, description="""Description of the time series."""
|
||||
|
|
@ -163,7 +145,6 @@ class TimeSeriesData(ConfiguredBaseModel):
|
|||
Data values. Data can be in 1-D, 2-D, 3-D, or 4-D. The first dimension should always represent time. This can also be used to store binary data (e.g., image frames). This can also be a link to data stored in an external file.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
conversion: Optional[float] = Field(
|
||||
None,
|
||||
|
|
@ -196,7 +177,6 @@ class TimeSeriesStartingTime(ConfiguredBaseModel):
|
|||
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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["starting_time"] = Field("starting_time")
|
||||
rate: Optional[float] = Field(None, description="""Sampling rate, in Hz.""")
|
||||
unit: Optional[str] = Field(
|
||||
|
|
@ -211,7 +191,6 @@ class TimeSeriesSync(ConfiguredBaseModel):
|
|||
Lab-specific time and sync information as provided directly from hardware devices and that is necessary for aligning all acquired time information to a common timebase. The timestamp array stores time in the common timebase. This group will usually only be populated in TimeSeries that are stored external to the NWB file, in files storing raw data. Once timestamp data is calculated, the contents of 'sync' are mostly for archival purposes.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["sync"] = Field("sync")
|
||||
|
||||
|
||||
|
|
@ -220,7 +199,6 @@ class ProcessingModule(NWBContainer):
|
|||
A collection of processed data.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[
|
||||
List[Union[BaseModel, DynamicTable, NWBDataInterface]]
|
||||
| Union[BaseModel, DynamicTable, NWBDataInterface]
|
||||
|
|
@ -233,7 +211,6 @@ class Images(NWBDataInterface):
|
|||
A collection of images.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("Images")
|
||||
description: Optional[str] = Field(
|
||||
None, description="""Description of this collection of images."""
|
||||
|
|
@ -241,17 +218,3 @@ class Images(NWBDataInterface):
|
|||
image: List[str] | str = Field(
|
||||
default_factory=list, description="""Images stored in this collection."""
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
NWBData.model_rebuild()
|
||||
Image.model_rebuild()
|
||||
NWBContainer.model_rebuild()
|
||||
NWBDataInterface.model_rebuild()
|
||||
TimeSeries.model_rebuild()
|
||||
TimeSeriesData.model_rebuild()
|
||||
TimeSeriesStartingTime.model_rebuild()
|
||||
TimeSeriesSync.model_rebuild()
|
||||
ProcessingModule.model_rebuild()
|
||||
Images.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -28,9 +28,9 @@ if TYPE_CHECKING:
|
|||
|
||||
|
||||
from .core_nwb_base import (
|
||||
NWBDataInterface,
|
||||
TimeSeries,
|
||||
TimeSeriesStartingTime,
|
||||
NWBDataInterface,
|
||||
TimeSeriesSync,
|
||||
)
|
||||
|
||||
|
|
@ -42,13 +42,6 @@ version = "2.3.0"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -68,18 +61,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class SpatialSeries(TimeSeries):
|
||||
"""
|
||||
Direction, e.g., of gaze or travel, or position. The TimeSeries::data field is a 2D array storing position or direction relative to some reference frame. Array structure: [num measurements] [num dimensions]. Each SpatialSeries has a text dataset reference_frame that indicates the zero-position, or the zero-axes for direction. For example, if representing gaze direction, 'straight-ahead' might be a specific pixel on the monitor, or some other point in space. For position data, the 0,0 point might be the top-left corner of an enclosure, as viewed from the tracking camera. The unit of data will indicate how to interpret SpatialSeries values.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(
|
||||
...,
|
||||
|
|
@ -123,7 +109,6 @@ class SpatialSeriesData(ConfiguredBaseModel):
|
|||
1-D or 2-D array storing position or direction relative to some reference frame.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -142,7 +127,6 @@ class BehavioralEpochs(NWBDataInterface):
|
|||
TimeSeries for storing behavioral epochs. The objective of this and the other two Behavioral interfaces (e.g. BehavioralEvents and BehavioralTimeSeries) is to provide generic hooks for software tools/scripts. This allows a tool/script to take the output one specific interface (e.g., UnitTimes) and plot that data relative to another data modality (e.g., behavioral events) without having to define all possible modalities in advance. Declaring one of these interfaces means that one or more TimeSeries of the specified type is published. These TimeSeries should reside in a group having the same name as the interface. For example, if a BehavioralTimeSeries interface is declared, the module will have one or more TimeSeries defined in the module sub-group 'BehavioralTimeSeries'. BehavioralEpochs should use IntervalSeries. BehavioralEvents is used for irregular events. BehavioralTimeSeries is for continuous data.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[IntervalSeries] | IntervalSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -154,7 +138,6 @@ class BehavioralEvents(NWBDataInterface):
|
|||
TimeSeries for storing behavioral events. See description of <a href=\"#BehavioralEpochs\">BehavioralEpochs</a> for more details.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[TimeSeries] | TimeSeries] = Field(default_factory=dict)
|
||||
name: str = Field(...)
|
||||
|
||||
|
|
@ -164,7 +147,6 @@ class BehavioralTimeSeries(NWBDataInterface):
|
|||
TimeSeries for storing Behavoioral time series data. See description of <a href=\"#BehavioralEpochs\">BehavioralEpochs</a> for more details.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[TimeSeries] | TimeSeries] = Field(default_factory=dict)
|
||||
name: str = Field(...)
|
||||
|
||||
|
|
@ -174,7 +156,6 @@ class PupilTracking(NWBDataInterface):
|
|||
Eye-tracking data, representing pupil size.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[TimeSeries] | TimeSeries] = Field(default_factory=dict)
|
||||
name: str = Field(...)
|
||||
|
||||
|
|
@ -184,7 +165,6 @@ class EyeTracking(NWBDataInterface):
|
|||
Eye-tracking data, representing direction of gaze.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[SpatialSeries] | SpatialSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -196,7 +176,6 @@ class CompassDirection(NWBDataInterface):
|
|||
With a CompassDirection interface, a module publishes a SpatialSeries object representing a floating point value for theta. The SpatialSeries::reference_frame field should indicate what direction corresponds to 0 and which is the direction of rotation (this should be clockwise). The si_unit for the SpatialSeries should be radians or degrees.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[SpatialSeries] | SpatialSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -208,21 +187,7 @@ class Position(NWBDataInterface):
|
|||
Position data, whether along the x, x/y or x/y/z axis.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[SpatialSeries] | SpatialSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
name: str = Field(...)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
SpatialSeries.model_rebuild()
|
||||
SpatialSeriesData.model_rebuild()
|
||||
BehavioralEpochs.model_rebuild()
|
||||
BehavioralEvents.model_rebuild()
|
||||
BehavioralTimeSeries.model_rebuild()
|
||||
PupilTracking.model_rebuild()
|
||||
EyeTracking.model_rebuild()
|
||||
CompassDirection.model_rebuild()
|
||||
Position.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -35,13 +35,6 @@ version = "2.3.0"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -61,18 +54,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class Device(NWBContainer):
|
||||
"""
|
||||
Metadata about a data acquisition device, e.g., recording system, electrode, microscope.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -81,8 +67,3 @@ class Device(NWBContainer):
|
|||
manufacturer: Optional[str] = Field(
|
||||
None, description="""The name of the manufacturer of the device."""
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
Device.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -27,29 +27,22 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from ...hdmf_common.v1_5_0.hdmf_common_table import DynamicTableRegion, DynamicTable
|
||||
|
||||
from .core_nwb_base import (
|
||||
NWBContainer,
|
||||
TimeSeriesStartingTime,
|
||||
NWBDataInterface,
|
||||
TimeSeries,
|
||||
TimeSeriesStartingTime,
|
||||
NWBContainer,
|
||||
TimeSeriesSync,
|
||||
)
|
||||
|
||||
from ...hdmf_common.v1_5_0.hdmf_common_table import DynamicTableRegion, DynamicTable
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
version = "2.3.0"
|
||||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -69,18 +62,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class ElectricalSeries(TimeSeries):
|
||||
"""
|
||||
A time series of acquired voltage data from extracellular recordings. The data field is an int or float array storing data in volts. The first dimension should always represent time. The second dimension, if present, should represent channels.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
filtering: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -133,7 +119,6 @@ class ElectricalSeriesElectrodes(DynamicTableRegion):
|
|||
DynamicTableRegion pointer to the electrodes that this time series was generated from.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["electrodes"] = Field("electrodes")
|
||||
table: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -157,7 +142,6 @@ 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).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: Union[
|
||||
NDArray[Shape["* num_events, * num_channels, * num_samples"], float],
|
||||
|
|
@ -209,7 +193,6 @@ class FeatureExtraction(NWBDataInterface):
|
|||
Features, such as PC1 and PC2, that are extracted from signals stored in a SpikeEventSeries or other source.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("FeatureExtraction")
|
||||
description: NDArray[Shape["* num_features"], str] = Field(
|
||||
...,
|
||||
|
|
@ -236,7 +219,6 @@ class FeatureExtractionElectrodes(DynamicTableRegion):
|
|||
DynamicTableRegion pointer to the electrodes that this time series was generated from.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["electrodes"] = Field("electrodes")
|
||||
table: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -260,7 +242,6 @@ class EventDetection(NWBDataInterface):
|
|||
Detected spike events from voltage trace(s).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("EventDetection")
|
||||
detection_method: str = Field(
|
||||
...,
|
||||
|
|
@ -280,7 +261,6 @@ class EventWaveform(NWBDataInterface):
|
|||
Represents either the waveforms of detected events, as extracted from a raw data trace in /acquisition, or the event waveforms that were stored during experiment acquisition.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[SpikeEventSeries] | SpikeEventSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -292,7 +272,6 @@ class FilteredEphys(NWBDataInterface):
|
|||
Electrophysiology data from one or more channels that has been subjected to filtering. Examples of filtered data include Theta and Gamma (LFP has its own interface). FilteredEphys modules publish an ElectricalSeries for each filtered channel or set of channels. The name of each ElectricalSeries is arbitrary but should be informative. The source of the filtered data, whether this is from analysis of another time series or as acquired by hardware, should be noted in each's TimeSeries::description field. There is no assumed 1::1 correspondence between filtered ephys signals and electrodes, as a single signal can apply to many nearby electrodes, and one electrode may have different filtered (e.g., theta and/or gamma) signals represented. Filter properties should be noted in the ElectricalSeries 'filtering' attribute.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[ElectricalSeries] | ElectricalSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -304,7 +283,6 @@ class LFP(NWBDataInterface):
|
|||
LFP data from one or more channels. The electrode map in each published ElectricalSeries will identify which channels are providing LFP data. Filter properties should be noted in the ElectricalSeries 'filtering' attribute.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[ElectricalSeries] | ElectricalSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -316,7 +294,6 @@ class ElectrodeGroup(NWBContainer):
|
|||
A physical grouping of electrodes, e.g. a shank of an array.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: Optional[str] = Field(
|
||||
None, description="""Description of this electrode group."""
|
||||
|
|
@ -335,7 +312,6 @@ class ElectrodeGroupPosition(ConfiguredBaseModel):
|
|||
stereotaxic or common framework coordinates
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["position"] = Field("position")
|
||||
x: Optional[float] = Field(None, description="""x coordinate""")
|
||||
y: Optional[float] = Field(None, description="""y coordinate""")
|
||||
|
|
@ -347,7 +323,6 @@ class ClusterWaveforms(NWBDataInterface):
|
|||
DEPRECATED The mean waveform shape, including standard deviation, of the different clusters. Ideally, the waveform analysis should be performed on data that is only high-pass filtered. This is a separate module because it is expected to require updating. For example, IMEC probes may require different storage requirements to store/display mean waveforms, requiring a new interface or an extension of this one.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("ClusterWaveforms")
|
||||
waveform_filtering: str = Field(
|
||||
..., description="""Filtering applied to data before generating mean/sd"""
|
||||
|
|
@ -367,7 +342,6 @@ class Clustering(NWBDataInterface):
|
|||
DEPRECATED Clustered spike data, whether from automatic clustering tools (e.g., klustakwik) or as a result of manual sorting.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("Clustering")
|
||||
description: str = Field(
|
||||
...,
|
||||
|
|
@ -384,20 +358,3 @@ class Clustering(NWBDataInterface):
|
|||
...,
|
||||
description="""Times of clustered events, in seconds. This may be a link to times field in associated FeatureExtraction module.""",
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
ElectricalSeries.model_rebuild()
|
||||
ElectricalSeriesElectrodes.model_rebuild()
|
||||
SpikeEventSeries.model_rebuild()
|
||||
FeatureExtraction.model_rebuild()
|
||||
FeatureExtractionElectrodes.model_rebuild()
|
||||
EventDetection.model_rebuild()
|
||||
EventWaveform.model_rebuild()
|
||||
FilteredEphys.model_rebuild()
|
||||
LFP.model_rebuild()
|
||||
ElectrodeGroup.model_rebuild()
|
||||
ElectrodeGroupPosition.model_rebuild()
|
||||
ClusterWaveforms.model_rebuild()
|
||||
Clustering.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -27,27 +27,20 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from .core_nwb_base import TimeSeries
|
||||
|
||||
from ...hdmf_common.v1_5_0.hdmf_common_table import (
|
||||
DynamicTable,
|
||||
VectorIndex,
|
||||
DynamicTable,
|
||||
VectorData,
|
||||
)
|
||||
|
||||
from .core_nwb_base import TimeSeries
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
version = "2.3.0"
|
||||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -67,18 +60,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class TimeIntervals(DynamicTable):
|
||||
"""
|
||||
A container for aggregating epoch data and the TimeSeries that each epoch applies to.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
start_time: Optional[List[float] | float] = Field(
|
||||
default_factory=list, description="""Start time of epoch, in seconds."""
|
||||
|
|
@ -119,7 +105,6 @@ class TimeIntervalsTagsIndex(VectorIndex):
|
|||
Index for tags.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["tags_index"] = Field("tags_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -143,7 +128,6 @@ class TimeIntervalsTimeseries(VectorData):
|
|||
An index into a TimeSeries object.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["timeseries"] = Field("timeseries")
|
||||
idx_start: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -174,7 +158,6 @@ class TimeIntervalsTimeseriesIndex(VectorIndex):
|
|||
Index for timeseries.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["timeseries_index"] = Field("timeseries_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -191,11 +174,3 @@ class TimeIntervalsTimeseriesIndex(VectorIndex):
|
|||
NDArray[Shape["* dim0, * dim1, * dim2, * dim3"], Any],
|
||||
]
|
||||
] = Field(None)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
TimeIntervals.model_rebuild()
|
||||
TimeIntervalsTagsIndex.model_rebuild()
|
||||
TimeIntervalsTimeseries.model_rebuild()
|
||||
TimeIntervalsTimeseriesIndex.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -27,29 +27,29 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from .core_nwb_ophys import ImagingPlane
|
||||
|
||||
from .core_nwb_base import (
|
||||
TimeSeries,
|
||||
NWBDataInterface,
|
||||
ProcessingModule,
|
||||
NWBContainer,
|
||||
NWBData,
|
||||
)
|
||||
from .core_nwb_icephys import SweepTable, IntracellularElectrode
|
||||
|
||||
from .core_nwb_epoch import TimeIntervals
|
||||
|
||||
from ...hdmf_common.v1_5_0.hdmf_common_table import DynamicTable, VectorData
|
||||
from .core_nwb_ecephys import ElectrodeGroup
|
||||
|
||||
from .core_nwb_icephys import IntracellularElectrode, SweepTable
|
||||
from .core_nwb_base import (
|
||||
NWBContainer,
|
||||
ProcessingModule,
|
||||
NWBData,
|
||||
NWBDataInterface,
|
||||
TimeSeries,
|
||||
)
|
||||
|
||||
from .core_nwb_ogen import OptogeneticStimulusSite
|
||||
from .core_nwb_device import Device
|
||||
|
||||
from .core_nwb_misc import Units
|
||||
|
||||
from .core_nwb_ecephys import ElectrodeGroup
|
||||
from .core_nwb_ogen import OptogeneticStimulusSite
|
||||
|
||||
from .core_nwb_device import Device
|
||||
from ...hdmf_common.v1_5_0.hdmf_common_table import DynamicTable, VectorData
|
||||
|
||||
from .core_nwb_ophys import ImagingPlane
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
|
|
@ -57,13 +57,6 @@ version = "2.3.0"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -83,18 +76,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class ScratchData(NWBData):
|
||||
"""
|
||||
Any one-off datasets
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
notes: Optional[str] = Field(
|
||||
None, description="""Any notes the user has about the dataset being stored"""
|
||||
|
|
@ -106,7 +92,6 @@ class NWBFile(NWBContainer):
|
|||
An NWB:N file storing cellular-based neurophysiology data from a single experimental session.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: Literal["root"] = Field("root")
|
||||
nwb_version: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -177,7 +162,6 @@ class NWBFileStimulus(ConfiguredBaseModel):
|
|||
Data pushed into the system (eg, video stimulus, sound, voltage, etc) and secondary representations of that data (eg, measurements of something used as a stimulus). This group should be made read-only after experiment complete and timestamps are corrected to common timebase. Stores both presented stimuli and stimulus templates, the latter in case the same stimulus is presented multiple times, or is pulled from an external stimulus library. Stimuli are here defined as any signal that is pushed into the system as part of the experiment (eg, sound, video, voltage, etc). Many different experiments can use the same stimuli, and stimuli can be re-used during an experiment. The stimulus group is organized so that one version of template stimuli can be stored and these be used multiple times. These templates can exist in the present file or can be linked to a remote library file.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["stimulus"] = Field("stimulus")
|
||||
presentation: Optional[List[TimeSeries] | TimeSeries] = Field(
|
||||
default_factory=dict, description="""Stimuli presented during the experiment."""
|
||||
|
|
@ -193,7 +177,6 @@ class NWBFileGeneral(ConfiguredBaseModel):
|
|||
Experimental metadata, including protocol, notes and description of hardware device(s). The metadata stored in this section should be used to describe the experiment. Metadata necessary for interpreting the data is stored with the data. General experimental metadata, including animal strain, experimental protocols, experimenter, devices, etc, are stored under 'general'. Core metadata (e.g., that required to interpret data fields) is stored with the data itself, and implicitly defined by the file specification (e.g., time is in seconds). The strategy used here for storing non-core metadata is to use free-form text fields, such as would appear in sentences or paragraphs from a Methods section. Metadata fields are text to enable them to be more general, for example to represent ranges instead of numerical values. Machine-readable metadata is stored as attributes to these free-form datasets. All entries in the below table are to be included when data is present. Unused groups (e.g., intracellular_ephys in an optophysiology experiment) should not be created unless there is data to store within them.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["general"] = Field("general")
|
||||
data_collection: Optional[str] = Field(
|
||||
None, description="""Notes about data collection and analysis."""
|
||||
|
|
@ -283,7 +266,6 @@ class NWBFileGeneralSourceScript(ConfiguredBaseModel):
|
|||
Script file or link to public source code used to create this NWB file.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["source_script"] = Field("source_script")
|
||||
file_name: Optional[str] = Field(None, description="""Name of script file.""")
|
||||
value: str = Field(...)
|
||||
|
|
@ -294,7 +276,6 @@ class NWBFileGeneralExtracellularEphys(ConfiguredBaseModel):
|
|||
Metadata related to extracellular electrophysiology.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["extracellular_ephys"] = Field("extracellular_ephys")
|
||||
electrode_group: Optional[List[str] | str] = Field(
|
||||
default_factory=list, description="""Physical group of electrodes."""
|
||||
|
|
@ -310,7 +291,6 @@ class NWBFileGeneralExtracellularEphysElectrodes(DynamicTable):
|
|||
A table of all electrodes (i.e. channels) used for recording.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["electrodes"] = Field("electrodes")
|
||||
x: Optional[List[float] | float] = Field(
|
||||
default_factory=list,
|
||||
|
|
@ -378,7 +358,6 @@ class NWBFileGeneralIntracellularEphys(ConfiguredBaseModel):
|
|||
Metadata related to intracellular electrophysiology.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["intracellular_ephys"] = Field("intracellular_ephys")
|
||||
filtering: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -398,7 +377,6 @@ class LabMetaData(NWBContainer):
|
|||
Lab-specific meta-data.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
|
||||
|
||||
|
|
@ -407,7 +385,6 @@ class Subject(NWBContainer):
|
|||
Information about the animal or person from which the data was measured.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
age: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -435,17 +412,3 @@ class Subject(NWBContainer):
|
|||
None,
|
||||
description="""Weight at time of experiment, at time of surgery and at other important times.""",
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
ScratchData.model_rebuild()
|
||||
NWBFile.model_rebuild()
|
||||
NWBFileStimulus.model_rebuild()
|
||||
NWBFileGeneral.model_rebuild()
|
||||
NWBFileGeneralSourceScript.model_rebuild()
|
||||
NWBFileGeneralExtracellularEphys.model_rebuild()
|
||||
NWBFileGeneralExtracellularEphysElectrodes.model_rebuild()
|
||||
NWBFileGeneralIntracellularEphys.model_rebuild()
|
||||
LabMetaData.model_rebuild()
|
||||
Subject.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -28,16 +28,16 @@ if TYPE_CHECKING:
|
|||
|
||||
|
||||
from .core_nwb_base import (
|
||||
TimeSeries,
|
||||
NWBContainer,
|
||||
TimeSeriesSync,
|
||||
TimeSeriesStartingTime,
|
||||
NWBContainer,
|
||||
TimeSeries,
|
||||
)
|
||||
|
||||
from ...hdmf_common.v1_5_0.hdmf_common_table import (
|
||||
VectorIndex,
|
||||
DynamicTable,
|
||||
VectorData,
|
||||
VectorIndex,
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -46,13 +46,6 @@ version = "2.3.0"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -72,18 +65,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class PatchClampSeries(TimeSeries):
|
||||
"""
|
||||
An abstract base class for patch-clamp data - stimulus or response, current or voltage.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
stimulus_description: Optional[str] = Field(
|
||||
None, description="""Protocol/stimulus name for this patch-clamp dataset."""
|
||||
|
|
@ -131,7 +117,6 @@ class PatchClampSeriesData(ConfiguredBaseModel):
|
|||
Recorded voltage or current.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -145,7 +130,6 @@ class CurrentClampSeries(PatchClampSeries):
|
|||
Voltage data from an intracellular current-clamp recording. A corresponding CurrentClampStimulusSeries (stored separately as a stimulus) is used to store the current injected.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Recorded voltage.""")
|
||||
bias_current: Optional[float] = Field(
|
||||
|
|
@ -202,7 +186,6 @@ class CurrentClampSeriesData(ConfiguredBaseModel):
|
|||
Recorded voltage.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -216,7 +199,6 @@ class IZeroClampSeries(CurrentClampSeries):
|
|||
Voltage data from an intracellular recording when all current and amplifier settings are off (i.e., CurrentClampSeries fields will be zero). There is no CurrentClampStimulusSeries associated with an IZero series because the amplifier is disconnected and no stimulus can reach the cell.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
stimulus_description: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -274,7 +256,6 @@ class CurrentClampStimulusSeries(PatchClampSeries):
|
|||
Stimulus current applied during current clamp recording.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Stimulus current applied.""")
|
||||
stimulus_description: Optional[str] = Field(
|
||||
|
|
@ -322,7 +303,6 @@ class CurrentClampStimulusSeriesData(ConfiguredBaseModel):
|
|||
Stimulus current applied.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -336,7 +316,6 @@ class VoltageClampSeries(PatchClampSeries):
|
|||
Current data from an intracellular voltage-clamp recording. A corresponding VoltageClampStimulusSeries (stored separately as a stimulus) is used to store the voltage injected.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Recorded current.""")
|
||||
capacitance_fast: Optional[str] = Field(
|
||||
|
|
@ -405,7 +384,6 @@ class VoltageClampSeriesData(ConfiguredBaseModel):
|
|||
Recorded current.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -419,7 +397,6 @@ class VoltageClampSeriesCapacitanceFast(ConfiguredBaseModel):
|
|||
Fast capacitance, in farads.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["capacitance_fast"] = Field("capacitance_fast")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -433,7 +410,6 @@ class VoltageClampSeriesCapacitanceSlow(ConfiguredBaseModel):
|
|||
Slow capacitance, in farads.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["capacitance_slow"] = Field("capacitance_slow")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -447,7 +423,6 @@ class VoltageClampSeriesResistanceCompBandwidth(ConfiguredBaseModel):
|
|||
Resistance compensation bandwidth, in hertz.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["resistance_comp_bandwidth"] = Field("resistance_comp_bandwidth")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -461,7 +436,6 @@ class VoltageClampSeriesResistanceCompCorrection(ConfiguredBaseModel):
|
|||
Resistance compensation correction, in percent.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["resistance_comp_correction"] = Field("resistance_comp_correction")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -475,7 +449,6 @@ class VoltageClampSeriesResistanceCompPrediction(ConfiguredBaseModel):
|
|||
Resistance compensation prediction, in percent.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["resistance_comp_prediction"] = Field("resistance_comp_prediction")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -489,7 +462,6 @@ class VoltageClampSeriesWholeCellCapacitanceComp(ConfiguredBaseModel):
|
|||
Whole cell capacitance compensation, in farads.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["whole_cell_capacitance_comp"] = Field("whole_cell_capacitance_comp")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -503,7 +475,6 @@ class VoltageClampSeriesWholeCellSeriesResistanceComp(ConfiguredBaseModel):
|
|||
Whole cell series resistance compensation, in ohms.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["whole_cell_series_resistance_comp"] = Field(
|
||||
"whole_cell_series_resistance_comp"
|
||||
)
|
||||
|
|
@ -519,7 +490,6 @@ class VoltageClampStimulusSeries(PatchClampSeries):
|
|||
Stimulus voltage applied during a voltage clamp recording.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Stimulus voltage applied.""")
|
||||
stimulus_description: Optional[str] = Field(
|
||||
|
|
@ -567,7 +537,6 @@ class VoltageClampStimulusSeriesData(ConfiguredBaseModel):
|
|||
Stimulus voltage applied.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -581,7 +550,6 @@ class IntracellularElectrode(NWBContainer):
|
|||
An intracellular electrode and its metadata.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: str = Field(
|
||||
...,
|
||||
|
|
@ -613,7 +581,6 @@ class SweepTable(DynamicTable):
|
|||
The table which groups different PatchClampSeries together.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
sweep_number: Optional[List[int] | int] = Field(
|
||||
default_factory=list,
|
||||
|
|
@ -646,7 +613,6 @@ class SweepTableSeriesIndex(VectorIndex):
|
|||
Index for series.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["series_index"] = Field("series_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -663,28 +629,3 @@ class SweepTableSeriesIndex(VectorIndex):
|
|||
NDArray[Shape["* dim0, * dim1, * dim2, * dim3"], Any],
|
||||
]
|
||||
] = Field(None)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
PatchClampSeries.model_rebuild()
|
||||
PatchClampSeriesData.model_rebuild()
|
||||
CurrentClampSeries.model_rebuild()
|
||||
CurrentClampSeriesData.model_rebuild()
|
||||
IZeroClampSeries.model_rebuild()
|
||||
CurrentClampStimulusSeries.model_rebuild()
|
||||
CurrentClampStimulusSeriesData.model_rebuild()
|
||||
VoltageClampSeries.model_rebuild()
|
||||
VoltageClampSeriesData.model_rebuild()
|
||||
VoltageClampSeriesCapacitanceFast.model_rebuild()
|
||||
VoltageClampSeriesCapacitanceSlow.model_rebuild()
|
||||
VoltageClampSeriesResistanceCompBandwidth.model_rebuild()
|
||||
VoltageClampSeriesResistanceCompCorrection.model_rebuild()
|
||||
VoltageClampSeriesResistanceCompPrediction.model_rebuild()
|
||||
VoltageClampSeriesWholeCellCapacitanceComp.model_rebuild()
|
||||
VoltageClampSeriesWholeCellSeriesResistanceComp.model_rebuild()
|
||||
VoltageClampStimulusSeries.model_rebuild()
|
||||
VoltageClampStimulusSeriesData.model_rebuild()
|
||||
IntracellularElectrode.model_rebuild()
|
||||
SweepTable.model_rebuild()
|
||||
SweepTableSeriesIndex.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from .core_nwb_base import Image, TimeSeries, TimeSeriesStartingTime, TimeSeriesSync
|
||||
from .core_nwb_base import Image, TimeSeriesStartingTime, TimeSeries, TimeSeriesSync
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
|
|
@ -35,13 +35,6 @@ version = "2.3.0"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -61,18 +54,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class GrayscaleImage(Image):
|
||||
"""
|
||||
A grayscale image.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
resolution: Optional[float] = Field(
|
||||
None, description="""Pixel resolution of the image, in pixels per centimeter."""
|
||||
|
|
@ -94,7 +80,6 @@ class RGBImage(Image):
|
|||
A color image.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
resolution: Optional[float] = Field(
|
||||
None, description="""Pixel resolution of the image, in pixels per centimeter."""
|
||||
|
|
@ -116,7 +101,6 @@ class RGBAImage(Image):
|
|||
A color image with transparency.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
resolution: Optional[float] = Field(
|
||||
None, description="""Pixel resolution of the image, in pixels per centimeter."""
|
||||
|
|
@ -138,7 +122,6 @@ class ImageSeries(TimeSeries):
|
|||
General image data that is common between acquisition and stimulus time series. Sometimes the image data is stored in the file in a raw format while other times it will be stored as a series of external image files in the host file system. The data field will either be binary data, if the data is stored in the NWB file, or empty, if the data is stored in an external image stack. [frame][x][y] or [frame][x][y][z].
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: Optional[
|
||||
Union[
|
||||
|
|
@ -191,7 +174,6 @@ 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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["external_file"] = Field("external_file")
|
||||
starting_frame: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -205,7 +187,6 @@ class ImageMaskSeries(ImageSeries):
|
|||
An alpha mask that is applied to a presented visual stimulus. The 'data' array contains an array of mask values that are applied to the displayed image. Mask values are stored as RGBA. Mask can vary with time. The timestamps array indicates the starting time of a mask, and that mask pattern continues until it's explicitly changed.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: Optional[
|
||||
Union[
|
||||
|
|
@ -258,7 +239,6 @@ class OpticalSeries(ImageSeries):
|
|||
Image data that is presented or recorded. A stimulus template movie will be stored only as an image. When the image is presented as stimulus, additional data is required, such as field of view (e.g., how much of the visual field the image covers, or how what is the area of the target being imaged). If the OpticalSeries represents acquired imaging data, orientation is also important.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
distance: Optional[float] = Field(
|
||||
None, description="""Distance from camera/monitor to target/eye."""
|
||||
|
|
@ -327,7 +307,6 @@ 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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: NDArray[Shape["* num_times"], int] = Field(
|
||||
..., description="""Index of the frame in the referenced ImageSeries."""
|
||||
|
|
@ -359,15 +338,3 @@ class IndexSeries(TimeSeries):
|
|||
None,
|
||||
description="""Lab-specific time and sync information as provided directly from hardware devices and that is necessary for aligning all acquired time information to a common timebase. The timestamp array stores time in the common timebase. This group will usually only be populated in TimeSeries that are stored external to the NWB file, in files storing raw data. Once timestamp data is calculated, the contents of 'sync' are mostly for archival purposes.""",
|
||||
)
|
||||
|
||||
|
||||
# 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()
|
||||
ImageSeriesExternalFile.model_rebuild()
|
||||
ImageMaskSeries.model_rebuild()
|
||||
OpticalSeries.model_rebuild()
|
||||
IndexSeries.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -32,13 +32,6 @@ version = "None"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -56,13 +49,3 @@ class ConfiguredBaseModel(BaseModel):
|
|||
self.array[i] = value
|
||||
else:
|
||||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
|
|
|
|||
|
|
@ -27,16 +27,16 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from .core_nwb_base import TimeSeries, TimeSeriesSync, TimeSeriesStartingTime
|
||||
from .core_nwb_ecephys import ElectrodeGroup
|
||||
|
||||
from ...hdmf_common.v1_5_0.hdmf_common_table import (
|
||||
DynamicTableRegion,
|
||||
VectorIndex,
|
||||
DynamicTable,
|
||||
VectorData,
|
||||
VectorIndex,
|
||||
)
|
||||
|
||||
from .core_nwb_ecephys import ElectrodeGroup
|
||||
from .core_nwb_base import TimeSeriesSync, TimeSeriesStartingTime, TimeSeries
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
|
|
@ -44,13 +44,6 @@ version = "2.3.0"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -70,18 +63,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class AbstractFeatureSeries(TimeSeries):
|
||||
"""
|
||||
Abstract features, such as quantitative descriptions of sensory stimuli. The TimeSeries::data field is a 2D array, storing those features (e.g., for visual grating stimulus this might be orientation, spatial frequency and contrast). Null stimuli (eg, uniform gray) can be marked as being an independent feature (eg, 1.0 for gray, 0.0 for actual stimulus) or by storing NaNs for feature values, or through use of the TimeSeries::control fields. A set of features is considered to persist until the next set of features is defined. The final set of features stored should be the null set. This is useful when storing the raw stimulus is impractical.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Values of each feature at each time.""")
|
||||
feature_units: Optional[NDArray[Shape["* num_features"], str]] = Field(
|
||||
|
|
@ -125,7 +111,6 @@ class AbstractFeatureSeriesData(ConfiguredBaseModel):
|
|||
Values of each feature at each time.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -144,7 +129,6 @@ class AnnotationSeries(TimeSeries):
|
|||
Stores user annotations made during an experiment. The data[] field stores a text array, and timestamps are stored for each annotation (ie, interval=1). This is largely an alias to a standard TimeSeries storing a text array but that is identifiable as storing annotations in a machine-readable way.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: NDArray[Shape["* num_times"], str] = Field(
|
||||
..., description="""Annotations made during an experiment."""
|
||||
|
|
@ -183,7 +167,6 @@ 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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: NDArray[Shape["* num_times"], int] = Field(
|
||||
..., description="""Use values >0 if interval started, <0 if interval ended."""
|
||||
|
|
@ -222,7 +205,6 @@ class DecompositionSeries(TimeSeries):
|
|||
Spectral analysis of a time series, e.g. of an LFP or a speech signal.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Data decomposed into frequency bands.""")
|
||||
metric: str = Field(
|
||||
|
|
@ -270,7 +252,6 @@ class DecompositionSeriesData(ConfiguredBaseModel):
|
|||
Data decomposed into frequency bands.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -286,7 +267,6 @@ class DecompositionSeriesSourceChannels(DynamicTableRegion):
|
|||
DynamicTableRegion pointer to the channels that this decomposition series was generated from.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["source_channels"] = Field("source_channels")
|
||||
table: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -310,7 +290,6 @@ class DecompositionSeriesBands(DynamicTable):
|
|||
Table for describing the bands that this series was generated from. There should be one row in this table for each band.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["bands"] = Field("bands")
|
||||
band_name: Optional[List[str] | str] = Field(
|
||||
default_factory=list, description="""Name of the band, e.g. theta."""
|
||||
|
|
@ -347,7 +326,6 @@ class Units(DynamicTable):
|
|||
Data about spiking units. Event times of observed units (e.g. cell, synapse, etc.) should be concatenated and stored in spike_times.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("Units")
|
||||
spike_times_index: Optional[str] = Field(
|
||||
None, description="""Index into the spike_times dataset."""
|
||||
|
|
@ -422,7 +400,6 @@ class UnitsSpikeTimesIndex(VectorIndex):
|
|||
Index into the spike_times dataset.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["spike_times_index"] = Field("spike_times_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -446,7 +423,6 @@ class UnitsSpikeTimes(VectorData):
|
|||
Spike times for each unit.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["spike_times"] = Field("spike_times")
|
||||
resolution: Optional[float] = Field(
|
||||
None,
|
||||
|
|
@ -470,7 +446,6 @@ class UnitsObsIntervalsIndex(VectorIndex):
|
|||
Index into the obs_intervals dataset.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["obs_intervals_index"] = Field("obs_intervals_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -494,7 +469,6 @@ class UnitsElectrodesIndex(VectorIndex):
|
|||
Index into electrodes.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["electrodes_index"] = Field("electrodes_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -518,7 +492,6 @@ class UnitsElectrodes(DynamicTableRegion):
|
|||
Electrode that each spike unit came from, specified using a DynamicTableRegion.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["electrodes"] = Field("electrodes")
|
||||
table: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -542,7 +515,6 @@ class UnitsWaveformsIndex(VectorIndex):
|
|||
Index into the waveforms dataset. One value for every spike event. See 'waveforms' for more detail.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["waveforms_index"] = Field("waveforms_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -566,7 +538,6 @@ class UnitsWaveformsIndexIndex(VectorIndex):
|
|||
Index into the waveforms_index dataset. One value for every unit (row in the table). See 'waveforms' for more detail.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["waveforms_index_index"] = Field("waveforms_index_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -583,23 +554,3 @@ class UnitsWaveformsIndexIndex(VectorIndex):
|
|||
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()
|
||||
IntervalSeries.model_rebuild()
|
||||
DecompositionSeries.model_rebuild()
|
||||
DecompositionSeriesData.model_rebuild()
|
||||
DecompositionSeriesSourceChannels.model_rebuild()
|
||||
DecompositionSeriesBands.model_rebuild()
|
||||
Units.model_rebuild()
|
||||
UnitsSpikeTimesIndex.model_rebuild()
|
||||
UnitsSpikeTimes.model_rebuild()
|
||||
UnitsObsIntervalsIndex.model_rebuild()
|
||||
UnitsElectrodesIndex.model_rebuild()
|
||||
UnitsElectrodes.model_rebuild()
|
||||
UnitsWaveformsIndex.model_rebuild()
|
||||
UnitsWaveformsIndexIndex.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -29,9 +29,9 @@ if TYPE_CHECKING:
|
|||
|
||||
from .core_nwb_base import (
|
||||
TimeSeriesSync,
|
||||
TimeSeries,
|
||||
NWBContainer,
|
||||
TimeSeriesStartingTime,
|
||||
TimeSeries,
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -40,13 +40,6 @@ version = "2.3.0"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -66,18 +59,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class OptogeneticSeries(TimeSeries):
|
||||
"""
|
||||
An optogenetic stimulus.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: NDArray[Shape["* num_times"], float] = Field(
|
||||
..., description="""Applied power for optogenetic stimulus, in watts."""
|
||||
|
|
@ -116,7 +102,6 @@ class OptogeneticStimulusSite(NWBContainer):
|
|||
A site of optogenetic stimulation.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: str = Field(..., description="""Description of stimulation site.""")
|
||||
excitation_lambda: float = Field(
|
||||
|
|
@ -126,9 +111,3 @@ class OptogeneticStimulusSite(NWBContainer):
|
|||
...,
|
||||
description="""Location of the stimulation site. 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.""",
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
OptogeneticSeries.model_rebuild()
|
||||
OptogeneticStimulusSite.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -28,18 +28,18 @@ if TYPE_CHECKING:
|
|||
|
||||
|
||||
from .core_nwb_base import (
|
||||
TimeSeries,
|
||||
TimeSeriesSync,
|
||||
NWBDataInterface,
|
||||
TimeSeriesStartingTime,
|
||||
NWBContainer,
|
||||
TimeSeriesSync,
|
||||
TimeSeriesStartingTime,
|
||||
NWBDataInterface,
|
||||
TimeSeries,
|
||||
)
|
||||
|
||||
from ...hdmf_common.v1_5_0.hdmf_common_table import (
|
||||
DynamicTableRegion,
|
||||
VectorIndex,
|
||||
DynamicTable,
|
||||
VectorData,
|
||||
VectorIndex,
|
||||
)
|
||||
|
||||
from .core_nwb_image import ImageSeries, ImageSeriesExternalFile
|
||||
|
|
@ -50,13 +50,6 @@ version = "2.3.0"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -76,18 +69,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class TwoPhotonSeries(ImageSeries):
|
||||
"""
|
||||
Image stack recorded over time from 2-photon microscope.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
pmt_gain: Optional[float] = Field(None, description="""Photomultiplier gain.""")
|
||||
scan_line_rate: Optional[float] = Field(
|
||||
|
|
@ -154,7 +140,6 @@ class RoiResponseSeries(TimeSeries):
|
|||
ROI responses over an imaging plane. The first dimension represents time. The second dimension, if present, represents ROIs.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: Union[
|
||||
NDArray[Shape["* num_times"], float],
|
||||
|
|
@ -198,7 +183,6 @@ class RoiResponseSeriesRois(DynamicTableRegion):
|
|||
DynamicTableRegion referencing into an ROITable containing information on the ROIs stored in this timeseries.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["rois"] = Field("rois")
|
||||
table: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -222,7 +206,6 @@ 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).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[RoiResponseSeries] | RoiResponseSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -234,7 +217,6 @@ class Fluorescence(NWBDataInterface):
|
|||
Fluorescence information about a region of interest (ROI). Storage hierarchy of fluorescence should be the same as for segmentation (ie, same names for ROIs and for image planes).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[RoiResponseSeries] | RoiResponseSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -246,7 +228,6 @@ class ImageSegmentation(NWBDataInterface):
|
|||
Stores pixels in an image that represent different regions of interest (ROIs) or masks. All segmentation for a given imaging plane is stored together, with storage for multiple imaging planes (masks) supported. Each ROI is stored in its own subgroup, with the ROI group containing both a 2D mask and a list of pixels that make up this mask. Segments can also be used for masking neuropil. If segmentation is allowed to change with time, a new imaging plane (or module) is required and ROI names should remain consistent between them.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[PlaneSegmentation] | PlaneSegmentation] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -258,7 +239,6 @@ class PlaneSegmentation(DynamicTable):
|
|||
Results from image segmentation of a specific imaging plane.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
image_mask: Optional[
|
||||
Union[
|
||||
|
|
@ -309,7 +289,6 @@ class PlaneSegmentationPixelMaskIndex(VectorIndex):
|
|||
Index into pixel_mask.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["pixel_mask_index"] = Field("pixel_mask_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -333,7 +312,6 @@ class PlaneSegmentationPixelMask(VectorData):
|
|||
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
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["pixel_mask"] = Field("pixel_mask")
|
||||
x: Optional[int] = Field(None, description="""Pixel x-coordinate.""")
|
||||
y: Optional[int] = Field(None, description="""Pixel y-coordinate.""")
|
||||
|
|
@ -356,7 +334,6 @@ class PlaneSegmentationVoxelMaskIndex(VectorIndex):
|
|||
Index into voxel_mask.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["voxel_mask_index"] = Field("voxel_mask_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -380,7 +357,6 @@ class PlaneSegmentationVoxelMask(VectorData):
|
|||
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
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["voxel_mask"] = Field("voxel_mask")
|
||||
x: Optional[int] = Field(None, description="""Voxel x-coordinate.""")
|
||||
y: Optional[int] = Field(None, description="""Voxel y-coordinate.""")
|
||||
|
|
@ -404,7 +380,6 @@ class ImagingPlane(NWBContainer):
|
|||
An imaging plane and its metadata.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[OpticalChannel] | OpticalChannel] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -416,7 +391,6 @@ class OpticalChannel(NWBContainer):
|
|||
An optical channel used to record from an imaging plane.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: str = Field(
|
||||
..., description="""Description or other notes about the channel."""
|
||||
|
|
@ -431,7 +405,6 @@ class MotionCorrection(NWBDataInterface):
|
|||
An image stack where all frames are shifted (registered) to a common coordinate system, to account for movement and drift between frames. Note: each frame at each point in time is assumed to be 2-D (has only x & y dimensions).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[CorrectedImageStack] | CorrectedImageStack] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -443,7 +416,6 @@ class CorrectedImageStack(NWBDataInterface):
|
|||
Reuslts from motion correction of an image stack.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
corrected: str = Field(
|
||||
...,
|
||||
|
|
@ -453,22 +425,3 @@ class CorrectedImageStack(NWBDataInterface):
|
|||
...,
|
||||
description="""Stores the x,y delta necessary to align each frame to the common coordinates, for example, to align each frame to a reference image.""",
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
TwoPhotonSeries.model_rebuild()
|
||||
RoiResponseSeries.model_rebuild()
|
||||
RoiResponseSeriesRois.model_rebuild()
|
||||
DfOverF.model_rebuild()
|
||||
Fluorescence.model_rebuild()
|
||||
ImageSegmentation.model_rebuild()
|
||||
PlaneSegmentation.model_rebuild()
|
||||
PlaneSegmentationPixelMaskIndex.model_rebuild()
|
||||
PlaneSegmentationPixelMask.model_rebuild()
|
||||
PlaneSegmentationVoxelMaskIndex.model_rebuild()
|
||||
PlaneSegmentationVoxelMask.model_rebuild()
|
||||
ImagingPlane.model_rebuild()
|
||||
OpticalChannel.model_rebuild()
|
||||
MotionCorrection.model_rebuild()
|
||||
CorrectedImageStack.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -35,13 +35,6 @@ version = "2.3.0"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -61,18 +54,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class ImagingRetinotopy(NWBDataInterface):
|
||||
"""
|
||||
Intrinsic signal optical imaging or widefield imaging for measuring retinotopy. Stores orthogonal maps (e.g., altitude/azimuth; radius/theta) of responses to specific stimuli and a combined polarity map from which to identify visual areas. This group does not store the raw responses imaged during retinotopic mapping or the stimuli presented, but rather the resulting phase and power maps after applying a Fourier transform on the averaged responses. Note: for data consistency, all images and arrays are stored in the format [row][column] and [row, col], which equates to [y][x]. Field of view and dimension arrays may appear backward (i.e., y before x).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("ImagingRetinotopy")
|
||||
axis_1_phase_map: str = Field(
|
||||
..., description="""Phase response to stimulus on the first measured axis."""
|
||||
|
|
@ -111,7 +97,6 @@ class ImagingRetinotopyAxis1PhaseMap(ConfiguredBaseModel):
|
|||
Phase response to stimulus on the first measured axis.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["axis_1_phase_map"] = Field("axis_1_phase_map")
|
||||
dimension: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -131,7 +116,6 @@ class ImagingRetinotopyAxis1PowerMap(ConfiguredBaseModel):
|
|||
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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["axis_1_power_map"] = Field("axis_1_power_map")
|
||||
dimension: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -151,7 +135,6 @@ class ImagingRetinotopyAxis2PhaseMap(ConfiguredBaseModel):
|
|||
Phase response to stimulus on the second measured axis.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["axis_2_phase_map"] = Field("axis_2_phase_map")
|
||||
dimension: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -171,7 +154,6 @@ class ImagingRetinotopyAxis2PowerMap(ConfiguredBaseModel):
|
|||
Power response on the second measured axis. Response is scaled so 0.0 is no power in the response and 1.0 is maximum relative power.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["axis_2_power_map"] = Field("axis_2_power_map")
|
||||
dimension: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -191,7 +173,6 @@ class ImagingRetinotopyFocalDepthImage(ConfiguredBaseModel):
|
|||
Gray-scale image taken with same settings/parameters (e.g., focal depth, wavelength) as data collection. Array format: [rows][columns].
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["focal_depth_image"] = Field("focal_depth_image")
|
||||
bits_per_pixel: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -218,7 +199,6 @@ class ImagingRetinotopySignMap(ConfiguredBaseModel):
|
|||
Sine of the angle between the direction of the gradient in axis_1 and axis_2.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["sign_map"] = Field("sign_map")
|
||||
dimension: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -235,7 +215,6 @@ class ImagingRetinotopyVasculatureImage(ConfiguredBaseModel):
|
|||
Gray-scale anatomical image of cortical surface. Array structure: [rows][columns]
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["vasculature_image"] = Field("vasculature_image")
|
||||
bits_per_pixel: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -252,15 +231,3 @@ class ImagingRetinotopyVasculatureImage(ConfiguredBaseModel):
|
|||
None, description="""Format of image. Right now only 'raw' is supported."""
|
||||
)
|
||||
array: Optional[NDArray[Shape["* num_rows, * num_cols"], int]] = Field(None)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
ImagingRetinotopy.model_rebuild()
|
||||
ImagingRetinotopyAxis1PhaseMap.model_rebuild()
|
||||
ImagingRetinotopyAxis1PowerMap.model_rebuild()
|
||||
ImagingRetinotopyAxis2PhaseMap.model_rebuild()
|
||||
ImagingRetinotopyAxis2PowerMap.model_rebuild()
|
||||
ImagingRetinotopyFocalDepthImage.model_rebuild()
|
||||
ImagingRetinotopySignMap.model_rebuild()
|
||||
ImagingRetinotopyVasculatureImage.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -205,13 +205,6 @@ version = "2.3.0"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -229,13 +222,3 @@ class ConfiguredBaseModel(BaseModel):
|
|||
self.array[i] = value
|
||||
else:
|
||||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
|
|
|
|||
|
|
@ -27,9 +27,9 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from ...hdmf_common.v1_5_0.hdmf_common_table import DynamicTable, VectorData
|
||||
from ...hdmf_common.v1_5_0.hdmf_common_base import Data, Container
|
||||
|
||||
from ...hdmf_common.v1_5_0.hdmf_common_base import Container, Data
|
||||
from ...hdmf_common.v1_5_0.hdmf_common_table import DynamicTable, VectorData
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
|
|
@ -37,13 +37,6 @@ version = "2.4.0"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -63,18 +56,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class NWBData(Data):
|
||||
"""
|
||||
An abstract data type for a dataset.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
|
||||
|
||||
|
|
@ -83,7 +69,6 @@ class TimeSeriesReferenceVectorData(VectorData):
|
|||
Column storing references to a TimeSeries (rows). For each TimeSeries this VectorData column stores the start_index and count to indicate the range in time to be selected as well as an object reference to the TimeSeries.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("timeseries")
|
||||
idx_start: int = Field(
|
||||
...,
|
||||
|
|
@ -114,7 +99,6 @@ class Image(NWBData):
|
|||
An abstract data type for an image. Shape can be 2-D (x, y), or 3-D where the third dimension can have three or four elements, e.g. (x, y, (r, g, b)) or (x, y, (r, g, b, a)).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
resolution: Optional[float] = Field(
|
||||
None, description="""Pixel resolution of the image, in pixels per centimeter."""
|
||||
|
|
@ -136,7 +120,6 @@ class NWBContainer(Container):
|
|||
An abstract data type for a generic container storing collections of data and metadata. Base type for all data and metadata containers.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
|
||||
|
||||
|
|
@ -145,7 +128,6 @@ class NWBDataInterface(NWBContainer):
|
|||
An abstract data type for a generic container storing collections of data, as opposed to metadata.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
|
||||
|
||||
|
|
@ -154,7 +136,6 @@ class TimeSeries(NWBDataInterface):
|
|||
General purpose time series.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: Optional[str] = Field(
|
||||
None, description="""Description of the time series."""
|
||||
|
|
@ -194,7 +175,6 @@ class TimeSeriesData(ConfiguredBaseModel):
|
|||
Data values. Data can be in 1-D, 2-D, 3-D, or 4-D. The first dimension should always represent time. This can also be used to store binary data (e.g., image frames). This can also be a link to data stored in an external file.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
conversion: Optional[float] = Field(
|
||||
None,
|
||||
|
|
@ -227,7 +207,6 @@ class TimeSeriesStartingTime(ConfiguredBaseModel):
|
|||
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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["starting_time"] = Field("starting_time")
|
||||
rate: Optional[float] = Field(None, description="""Sampling rate, in Hz.""")
|
||||
unit: Optional[str] = Field(
|
||||
|
|
@ -242,7 +221,6 @@ class TimeSeriesSync(ConfiguredBaseModel):
|
|||
Lab-specific time and sync information as provided directly from hardware devices and that is necessary for aligning all acquired time information to a common timebase. The timestamp array stores time in the common timebase. This group will usually only be populated in TimeSeries that are stored external to the NWB file, in files storing raw data. Once timestamp data is calculated, the contents of 'sync' are mostly for archival purposes.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["sync"] = Field("sync")
|
||||
|
||||
|
||||
|
|
@ -251,7 +229,6 @@ class ProcessingModule(NWBContainer):
|
|||
A collection of processed data.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[
|
||||
List[Union[BaseModel, DynamicTable, NWBDataInterface]]
|
||||
| Union[BaseModel, DynamicTable, NWBDataInterface]
|
||||
|
|
@ -264,7 +241,6 @@ class Images(NWBDataInterface):
|
|||
A collection of images.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("Images")
|
||||
description: Optional[str] = Field(
|
||||
None, description="""Description of this collection of images."""
|
||||
|
|
@ -272,18 +248,3 @@ class Images(NWBDataInterface):
|
|||
image: List[str] | str = Field(
|
||||
default_factory=list, description="""Images stored in this collection."""
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
NWBData.model_rebuild()
|
||||
TimeSeriesReferenceVectorData.model_rebuild()
|
||||
Image.model_rebuild()
|
||||
NWBContainer.model_rebuild()
|
||||
NWBDataInterface.model_rebuild()
|
||||
TimeSeries.model_rebuild()
|
||||
TimeSeriesData.model_rebuild()
|
||||
TimeSeriesStartingTime.model_rebuild()
|
||||
TimeSeriesSync.model_rebuild()
|
||||
ProcessingModule.model_rebuild()
|
||||
Images.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -28,9 +28,9 @@ if TYPE_CHECKING:
|
|||
|
||||
|
||||
from .core_nwb_base import (
|
||||
NWBDataInterface,
|
||||
TimeSeries,
|
||||
TimeSeriesStartingTime,
|
||||
NWBDataInterface,
|
||||
TimeSeriesSync,
|
||||
)
|
||||
|
||||
|
|
@ -42,13 +42,6 @@ version = "2.4.0"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -68,18 +61,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class SpatialSeries(TimeSeries):
|
||||
"""
|
||||
Direction, e.g., of gaze or travel, or position. The TimeSeries::data field is a 2D array storing position or direction relative to some reference frame. Array structure: [num measurements] [num dimensions]. Each SpatialSeries has a text dataset reference_frame that indicates the zero-position, or the zero-axes for direction. For example, if representing gaze direction, 'straight-ahead' might be a specific pixel on the monitor, or some other point in space. For position data, the 0,0 point might be the top-left corner of an enclosure, as viewed from the tracking camera. The unit of data will indicate how to interpret SpatialSeries values.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(
|
||||
...,
|
||||
|
|
@ -123,7 +109,6 @@ class SpatialSeriesData(ConfiguredBaseModel):
|
|||
1-D or 2-D array storing position or direction relative to some reference frame.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -142,7 +127,6 @@ class BehavioralEpochs(NWBDataInterface):
|
|||
TimeSeries for storing behavioral epochs. The objective of this and the other two Behavioral interfaces (e.g. BehavioralEvents and BehavioralTimeSeries) is to provide generic hooks for software tools/scripts. This allows a tool/script to take the output one specific interface (e.g., UnitTimes) and plot that data relative to another data modality (e.g., behavioral events) without having to define all possible modalities in advance. Declaring one of these interfaces means that one or more TimeSeries of the specified type is published. These TimeSeries should reside in a group having the same name as the interface. For example, if a BehavioralTimeSeries interface is declared, the module will have one or more TimeSeries defined in the module sub-group 'BehavioralTimeSeries'. BehavioralEpochs should use IntervalSeries. BehavioralEvents is used for irregular events. BehavioralTimeSeries is for continuous data.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[IntervalSeries] | IntervalSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -154,7 +138,6 @@ class BehavioralEvents(NWBDataInterface):
|
|||
TimeSeries for storing behavioral events. See description of <a href=\"#BehavioralEpochs\">BehavioralEpochs</a> for more details.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[TimeSeries] | TimeSeries] = Field(default_factory=dict)
|
||||
name: str = Field(...)
|
||||
|
||||
|
|
@ -164,7 +147,6 @@ class BehavioralTimeSeries(NWBDataInterface):
|
|||
TimeSeries for storing Behavoioral time series data. See description of <a href=\"#BehavioralEpochs\">BehavioralEpochs</a> for more details.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[TimeSeries] | TimeSeries] = Field(default_factory=dict)
|
||||
name: str = Field(...)
|
||||
|
||||
|
|
@ -174,7 +156,6 @@ class PupilTracking(NWBDataInterface):
|
|||
Eye-tracking data, representing pupil size.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[TimeSeries] | TimeSeries] = Field(default_factory=dict)
|
||||
name: str = Field(...)
|
||||
|
||||
|
|
@ -184,7 +165,6 @@ class EyeTracking(NWBDataInterface):
|
|||
Eye-tracking data, representing direction of gaze.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[SpatialSeries] | SpatialSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -196,7 +176,6 @@ class CompassDirection(NWBDataInterface):
|
|||
With a CompassDirection interface, a module publishes a SpatialSeries object representing a floating point value for theta. The SpatialSeries::reference_frame field should indicate what direction corresponds to 0 and which is the direction of rotation (this should be clockwise). The si_unit for the SpatialSeries should be radians or degrees.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[SpatialSeries] | SpatialSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -208,21 +187,7 @@ class Position(NWBDataInterface):
|
|||
Position data, whether along the x, x/y or x/y/z axis.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[SpatialSeries] | SpatialSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
name: str = Field(...)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
SpatialSeries.model_rebuild()
|
||||
SpatialSeriesData.model_rebuild()
|
||||
BehavioralEpochs.model_rebuild()
|
||||
BehavioralEvents.model_rebuild()
|
||||
BehavioralTimeSeries.model_rebuild()
|
||||
PupilTracking.model_rebuild()
|
||||
EyeTracking.model_rebuild()
|
||||
CompassDirection.model_rebuild()
|
||||
Position.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -35,13 +35,6 @@ version = "2.4.0"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -61,18 +54,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class Device(NWBContainer):
|
||||
"""
|
||||
Metadata about a data acquisition device, e.g., recording system, electrode, microscope.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -81,8 +67,3 @@ class Device(NWBContainer):
|
|||
manufacturer: Optional[str] = Field(
|
||||
None, description="""The name of the manufacturer of the device."""
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
Device.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -27,29 +27,22 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from ...hdmf_common.v1_5_0.hdmf_common_table import DynamicTableRegion, DynamicTable
|
||||
|
||||
from .core_nwb_base import (
|
||||
NWBContainer,
|
||||
TimeSeriesStartingTime,
|
||||
NWBDataInterface,
|
||||
TimeSeries,
|
||||
TimeSeriesStartingTime,
|
||||
NWBContainer,
|
||||
TimeSeriesSync,
|
||||
)
|
||||
|
||||
from ...hdmf_common.v1_5_0.hdmf_common_table import DynamicTableRegion, DynamicTable
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
version = "2.4.0"
|
||||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -69,18 +62,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class ElectricalSeries(TimeSeries):
|
||||
"""
|
||||
A time series of acquired voltage data from extracellular recordings. The data field is an int or float array storing data in volts. The first dimension should always represent time. The second dimension, if present, should represent channels.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
filtering: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -133,7 +119,6 @@ class ElectricalSeriesElectrodes(DynamicTableRegion):
|
|||
DynamicTableRegion pointer to the electrodes that this time series was generated from.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["electrodes"] = Field("electrodes")
|
||||
table: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -157,7 +142,6 @@ 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).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: Union[
|
||||
NDArray[Shape["* num_events, * num_channels, * num_samples"], float],
|
||||
|
|
@ -209,7 +193,6 @@ class FeatureExtraction(NWBDataInterface):
|
|||
Features, such as PC1 and PC2, that are extracted from signals stored in a SpikeEventSeries or other source.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("FeatureExtraction")
|
||||
description: NDArray[Shape["* num_features"], str] = Field(
|
||||
...,
|
||||
|
|
@ -236,7 +219,6 @@ class FeatureExtractionElectrodes(DynamicTableRegion):
|
|||
DynamicTableRegion pointer to the electrodes that this time series was generated from.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["electrodes"] = Field("electrodes")
|
||||
table: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -260,7 +242,6 @@ class EventDetection(NWBDataInterface):
|
|||
Detected spike events from voltage trace(s).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("EventDetection")
|
||||
detection_method: str = Field(
|
||||
...,
|
||||
|
|
@ -280,7 +261,6 @@ class EventWaveform(NWBDataInterface):
|
|||
Represents either the waveforms of detected events, as extracted from a raw data trace in /acquisition, or the event waveforms that were stored during experiment acquisition.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[SpikeEventSeries] | SpikeEventSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -292,7 +272,6 @@ class FilteredEphys(NWBDataInterface):
|
|||
Electrophysiology data from one or more channels that has been subjected to filtering. Examples of filtered data include Theta and Gamma (LFP has its own interface). FilteredEphys modules publish an ElectricalSeries for each filtered channel or set of channels. The name of each ElectricalSeries is arbitrary but should be informative. The source of the filtered data, whether this is from analysis of another time series or as acquired by hardware, should be noted in each's TimeSeries::description field. There is no assumed 1::1 correspondence between filtered ephys signals and electrodes, as a single signal can apply to many nearby electrodes, and one electrode may have different filtered (e.g., theta and/or gamma) signals represented. Filter properties should be noted in the ElectricalSeries 'filtering' attribute.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[ElectricalSeries] | ElectricalSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -304,7 +283,6 @@ class LFP(NWBDataInterface):
|
|||
LFP data from one or more channels. The electrode map in each published ElectricalSeries will identify which channels are providing LFP data. Filter properties should be noted in the ElectricalSeries 'filtering' attribute.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[ElectricalSeries] | ElectricalSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -316,7 +294,6 @@ class ElectrodeGroup(NWBContainer):
|
|||
A physical grouping of electrodes, e.g. a shank of an array.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: Optional[str] = Field(
|
||||
None, description="""Description of this electrode group."""
|
||||
|
|
@ -335,7 +312,6 @@ class ElectrodeGroupPosition(ConfiguredBaseModel):
|
|||
stereotaxic or common framework coordinates
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["position"] = Field("position")
|
||||
x: Optional[float] = Field(None, description="""x coordinate""")
|
||||
y: Optional[float] = Field(None, description="""y coordinate""")
|
||||
|
|
@ -347,7 +323,6 @@ class ClusterWaveforms(NWBDataInterface):
|
|||
DEPRECATED The mean waveform shape, including standard deviation, of the different clusters. Ideally, the waveform analysis should be performed on data that is only high-pass filtered. This is a separate module because it is expected to require updating. For example, IMEC probes may require different storage requirements to store/display mean waveforms, requiring a new interface or an extension of this one.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("ClusterWaveforms")
|
||||
waveform_filtering: str = Field(
|
||||
..., description="""Filtering applied to data before generating mean/sd"""
|
||||
|
|
@ -367,7 +342,6 @@ class Clustering(NWBDataInterface):
|
|||
DEPRECATED Clustered spike data, whether from automatic clustering tools (e.g., klustakwik) or as a result of manual sorting.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("Clustering")
|
||||
description: str = Field(
|
||||
...,
|
||||
|
|
@ -384,20 +358,3 @@ class Clustering(NWBDataInterface):
|
|||
...,
|
||||
description="""Times of clustered events, in seconds. This may be a link to times field in associated FeatureExtraction module.""",
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
ElectricalSeries.model_rebuild()
|
||||
ElectricalSeriesElectrodes.model_rebuild()
|
||||
SpikeEventSeries.model_rebuild()
|
||||
FeatureExtraction.model_rebuild()
|
||||
FeatureExtractionElectrodes.model_rebuild()
|
||||
EventDetection.model_rebuild()
|
||||
EventWaveform.model_rebuild()
|
||||
FilteredEphys.model_rebuild()
|
||||
LFP.model_rebuild()
|
||||
ElectrodeGroup.model_rebuild()
|
||||
ElectrodeGroupPosition.model_rebuild()
|
||||
ClusterWaveforms.model_rebuild()
|
||||
Clustering.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -27,27 +27,20 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from .core_nwb_base import TimeSeries
|
||||
|
||||
from ...hdmf_common.v1_5_0.hdmf_common_table import (
|
||||
DynamicTable,
|
||||
VectorIndex,
|
||||
DynamicTable,
|
||||
VectorData,
|
||||
)
|
||||
|
||||
from .core_nwb_base import TimeSeries
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
version = "2.4.0"
|
||||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -67,18 +60,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class TimeIntervals(DynamicTable):
|
||||
"""
|
||||
A container for aggregating epoch data and the TimeSeries that each epoch applies to.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
start_time: Optional[List[float] | float] = Field(
|
||||
default_factory=list, description="""Start time of epoch, in seconds."""
|
||||
|
|
@ -119,7 +105,6 @@ class TimeIntervalsTagsIndex(VectorIndex):
|
|||
Index for tags.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["tags_index"] = Field("tags_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -143,7 +128,6 @@ class TimeIntervalsTimeseries(VectorData):
|
|||
An index into a TimeSeries object.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["timeseries"] = Field("timeseries")
|
||||
idx_start: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -174,7 +158,6 @@ class TimeIntervalsTimeseriesIndex(VectorIndex):
|
|||
Index for timeseries.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["timeseries_index"] = Field("timeseries_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -191,11 +174,3 @@ class TimeIntervalsTimeseriesIndex(VectorIndex):
|
|||
NDArray[Shape["* dim0, * dim1, * dim2, * dim3"], Any],
|
||||
]
|
||||
] = Field(None)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
TimeIntervals.model_rebuild()
|
||||
TimeIntervalsTagsIndex.model_rebuild()
|
||||
TimeIntervalsTimeseries.model_rebuild()
|
||||
TimeIntervalsTimeseriesIndex.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -27,37 +27,37 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from .core_nwb_ophys import ImagingPlane
|
||||
|
||||
from .core_nwb_base import (
|
||||
TimeSeries,
|
||||
NWBDataInterface,
|
||||
ProcessingModule,
|
||||
NWBContainer,
|
||||
NWBData,
|
||||
from .core_nwb_icephys import (
|
||||
SweepTable,
|
||||
SimultaneousRecordingsTable,
|
||||
SequentialRecordingsTable,
|
||||
RepetitionsTable,
|
||||
ExperimentalConditionsTable,
|
||||
IntracellularElectrode,
|
||||
IntracellularRecordingsTable,
|
||||
)
|
||||
|
||||
from .core_nwb_epoch import TimeIntervals
|
||||
|
||||
from ...hdmf_common.v1_5_0.hdmf_common_table import DynamicTable, VectorData
|
||||
from .core_nwb_ecephys import ElectrodeGroup
|
||||
|
||||
from .core_nwb_icephys import (
|
||||
RepetitionsTable,
|
||||
IntracellularElectrode,
|
||||
SequentialRecordingsTable,
|
||||
IntracellularRecordingsTable,
|
||||
SweepTable,
|
||||
ExperimentalConditionsTable,
|
||||
SimultaneousRecordingsTable,
|
||||
from .core_nwb_base import (
|
||||
NWBContainer,
|
||||
ProcessingModule,
|
||||
NWBData,
|
||||
NWBDataInterface,
|
||||
TimeSeries,
|
||||
)
|
||||
|
||||
from .core_nwb_ogen import OptogeneticStimulusSite
|
||||
from .core_nwb_device import Device
|
||||
|
||||
from .core_nwb_misc import Units
|
||||
|
||||
from .core_nwb_ecephys import ElectrodeGroup
|
||||
from .core_nwb_ogen import OptogeneticStimulusSite
|
||||
|
||||
from .core_nwb_device import Device
|
||||
from ...hdmf_common.v1_5_0.hdmf_common_table import DynamicTable, VectorData
|
||||
|
||||
from .core_nwb_ophys import ImagingPlane
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
|
|
@ -65,13 +65,6 @@ version = "2.4.0"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -91,18 +84,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class ScratchData(NWBData):
|
||||
"""
|
||||
Any one-off datasets
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
notes: Optional[str] = Field(
|
||||
None, description="""Any notes the user has about the dataset being stored"""
|
||||
|
|
@ -114,7 +100,6 @@ class NWBFile(NWBContainer):
|
|||
An NWB:N file storing cellular-based neurophysiology data from a single experimental session.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: Literal["root"] = Field("root")
|
||||
nwb_version: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -185,7 +170,6 @@ class NWBFileStimulus(ConfiguredBaseModel):
|
|||
Data pushed into the system (eg, video stimulus, sound, voltage, etc) and secondary representations of that data (eg, measurements of something used as a stimulus). This group should be made read-only after experiment complete and timestamps are corrected to common timebase. Stores both presented stimuli and stimulus templates, the latter in case the same stimulus is presented multiple times, or is pulled from an external stimulus library. Stimuli are here defined as any signal that is pushed into the system as part of the experiment (eg, sound, video, voltage, etc). Many different experiments can use the same stimuli, and stimuli can be re-used during an experiment. The stimulus group is organized so that one version of template stimuli can be stored and these be used multiple times. These templates can exist in the present file or can be linked to a remote library file.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["stimulus"] = Field("stimulus")
|
||||
presentation: Optional[List[TimeSeries] | TimeSeries] = Field(
|
||||
default_factory=dict, description="""Stimuli presented during the experiment."""
|
||||
|
|
@ -201,7 +185,6 @@ class NWBFileGeneral(ConfiguredBaseModel):
|
|||
Experimental metadata, including protocol, notes and description of hardware device(s). The metadata stored in this section should be used to describe the experiment. Metadata necessary for interpreting the data is stored with the data. General experimental metadata, including animal strain, experimental protocols, experimenter, devices, etc, are stored under 'general'. Core metadata (e.g., that required to interpret data fields) is stored with the data itself, and implicitly defined by the file specification (e.g., time is in seconds). The strategy used here for storing non-core metadata is to use free-form text fields, such as would appear in sentences or paragraphs from a Methods section. Metadata fields are text to enable them to be more general, for example to represent ranges instead of numerical values. Machine-readable metadata is stored as attributes to these free-form datasets. All entries in the below table are to be included when data is present. Unused groups (e.g., intracellular_ephys in an optophysiology experiment) should not be created unless there is data to store within them.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["general"] = Field("general")
|
||||
data_collection: Optional[str] = Field(
|
||||
None, description="""Notes about data collection and analysis."""
|
||||
|
|
@ -291,7 +274,6 @@ class NWBFileGeneralSourceScript(ConfiguredBaseModel):
|
|||
Script file or link to public source code used to create this NWB file.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["source_script"] = Field("source_script")
|
||||
file_name: Optional[str] = Field(None, description="""Name of script file.""")
|
||||
value: str = Field(...)
|
||||
|
|
@ -302,7 +284,6 @@ class NWBFileGeneralExtracellularEphys(ConfiguredBaseModel):
|
|||
Metadata related to extracellular electrophysiology.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["extracellular_ephys"] = Field("extracellular_ephys")
|
||||
electrode_group: Optional[List[str] | str] = Field(
|
||||
default_factory=list, description="""Physical group of electrodes."""
|
||||
|
|
@ -318,7 +299,6 @@ class NWBFileGeneralExtracellularEphysElectrodes(DynamicTable):
|
|||
A table of all electrodes (i.e. channels) used for recording.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["electrodes"] = Field("electrodes")
|
||||
x: Optional[List[float] | float] = Field(
|
||||
default_factory=list,
|
||||
|
|
@ -386,7 +366,6 @@ class NWBFileGeneralIntracellularEphys(ConfiguredBaseModel):
|
|||
Metadata related to intracellular electrophysiology.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["intracellular_ephys"] = Field("intracellular_ephys")
|
||||
filtering: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -426,7 +405,6 @@ class LabMetaData(NWBContainer):
|
|||
Lab-specific meta-data.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
|
||||
|
||||
|
|
@ -435,7 +413,6 @@ class Subject(NWBContainer):
|
|||
Information about the animal or person from which the data was measured.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
age: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -463,17 +440,3 @@ class Subject(NWBContainer):
|
|||
None,
|
||||
description="""Weight at time of experiment, at time of surgery and at other important times.""",
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
ScratchData.model_rebuild()
|
||||
NWBFile.model_rebuild()
|
||||
NWBFileStimulus.model_rebuild()
|
||||
NWBFileGeneral.model_rebuild()
|
||||
NWBFileGeneralSourceScript.model_rebuild()
|
||||
NWBFileGeneralExtracellularEphys.model_rebuild()
|
||||
NWBFileGeneralExtracellularEphysElectrodes.model_rebuild()
|
||||
NWBFileGeneralIntracellularEphys.model_rebuild()
|
||||
LabMetaData.model_rebuild()
|
||||
Subject.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -27,20 +27,20 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from .core_nwb_base import (
|
||||
TimeSeries,
|
||||
TimeSeriesSync,
|
||||
TimeSeriesStartingTime,
|
||||
NWBContainer,
|
||||
TimeSeriesReferenceVectorData,
|
||||
from ...hdmf_common.v1_5_0.hdmf_common_table import (
|
||||
AlignedDynamicTable,
|
||||
DynamicTableRegion,
|
||||
VectorIndex,
|
||||
DynamicTable,
|
||||
VectorData,
|
||||
)
|
||||
|
||||
from ...hdmf_common.v1_5_0.hdmf_common_table import (
|
||||
DynamicTableRegion,
|
||||
DynamicTable,
|
||||
AlignedDynamicTable,
|
||||
VectorData,
|
||||
VectorIndex,
|
||||
from .core_nwb_base import (
|
||||
NWBContainer,
|
||||
TimeSeriesSync,
|
||||
TimeSeriesStartingTime,
|
||||
TimeSeriesReferenceVectorData,
|
||||
TimeSeries,
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -49,13 +49,6 @@ version = "2.4.0"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -75,18 +68,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class PatchClampSeries(TimeSeries):
|
||||
"""
|
||||
An abstract base class for patch-clamp data - stimulus or response, current or voltage.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
stimulus_description: Optional[str] = Field(
|
||||
None, description="""Protocol/stimulus name for this patch-clamp dataset."""
|
||||
|
|
@ -134,7 +120,6 @@ class PatchClampSeriesData(ConfiguredBaseModel):
|
|||
Recorded voltage or current.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -148,7 +133,6 @@ class CurrentClampSeries(PatchClampSeries):
|
|||
Voltage data from an intracellular current-clamp recording. A corresponding CurrentClampStimulusSeries (stored separately as a stimulus) is used to store the current injected.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Recorded voltage.""")
|
||||
bias_current: Optional[float] = Field(
|
||||
|
|
@ -205,7 +189,6 @@ class CurrentClampSeriesData(ConfiguredBaseModel):
|
|||
Recorded voltage.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -219,7 +202,6 @@ class IZeroClampSeries(CurrentClampSeries):
|
|||
Voltage data from an intracellular recording when all current and amplifier settings are off (i.e., CurrentClampSeries fields will be zero). There is no CurrentClampStimulusSeries associated with an IZero series because the amplifier is disconnected and no stimulus can reach the cell.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
stimulus_description: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -277,7 +259,6 @@ class CurrentClampStimulusSeries(PatchClampSeries):
|
|||
Stimulus current applied during current clamp recording.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Stimulus current applied.""")
|
||||
stimulus_description: Optional[str] = Field(
|
||||
|
|
@ -325,7 +306,6 @@ class CurrentClampStimulusSeriesData(ConfiguredBaseModel):
|
|||
Stimulus current applied.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -339,7 +319,6 @@ class VoltageClampSeries(PatchClampSeries):
|
|||
Current data from an intracellular voltage-clamp recording. A corresponding VoltageClampStimulusSeries (stored separately as a stimulus) is used to store the voltage injected.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Recorded current.""")
|
||||
capacitance_fast: Optional[str] = Field(
|
||||
|
|
@ -408,7 +387,6 @@ class VoltageClampSeriesData(ConfiguredBaseModel):
|
|||
Recorded current.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -422,7 +400,6 @@ class VoltageClampSeriesCapacitanceFast(ConfiguredBaseModel):
|
|||
Fast capacitance, in farads.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["capacitance_fast"] = Field("capacitance_fast")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -436,7 +413,6 @@ class VoltageClampSeriesCapacitanceSlow(ConfiguredBaseModel):
|
|||
Slow capacitance, in farads.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["capacitance_slow"] = Field("capacitance_slow")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -450,7 +426,6 @@ class VoltageClampSeriesResistanceCompBandwidth(ConfiguredBaseModel):
|
|||
Resistance compensation bandwidth, in hertz.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["resistance_comp_bandwidth"] = Field("resistance_comp_bandwidth")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -464,7 +439,6 @@ class VoltageClampSeriesResistanceCompCorrection(ConfiguredBaseModel):
|
|||
Resistance compensation correction, in percent.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["resistance_comp_correction"] = Field("resistance_comp_correction")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -478,7 +452,6 @@ class VoltageClampSeriesResistanceCompPrediction(ConfiguredBaseModel):
|
|||
Resistance compensation prediction, in percent.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["resistance_comp_prediction"] = Field("resistance_comp_prediction")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -492,7 +465,6 @@ class VoltageClampSeriesWholeCellCapacitanceComp(ConfiguredBaseModel):
|
|||
Whole cell capacitance compensation, in farads.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["whole_cell_capacitance_comp"] = Field("whole_cell_capacitance_comp")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -506,7 +478,6 @@ class VoltageClampSeriesWholeCellSeriesResistanceComp(ConfiguredBaseModel):
|
|||
Whole cell series resistance compensation, in ohms.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["whole_cell_series_resistance_comp"] = Field(
|
||||
"whole_cell_series_resistance_comp"
|
||||
)
|
||||
|
|
@ -522,7 +493,6 @@ class VoltageClampStimulusSeries(PatchClampSeries):
|
|||
Stimulus voltage applied during a voltage clamp recording.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Stimulus voltage applied.""")
|
||||
stimulus_description: Optional[str] = Field(
|
||||
|
|
@ -570,7 +540,6 @@ class VoltageClampStimulusSeriesData(ConfiguredBaseModel):
|
|||
Stimulus voltage applied.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -584,7 +553,6 @@ class IntracellularElectrode(NWBContainer):
|
|||
An intracellular electrode and its metadata.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: str = Field(
|
||||
...,
|
||||
|
|
@ -616,7 +584,6 @@ class SweepTable(DynamicTable):
|
|||
[DEPRECATED] Table used to group different PatchClampSeries. SweepTable is being replaced by IntracellularRecordingsTable and SimultaneousRecordingsTable tables. Additional SequentialRecordingsTable, RepetitionsTable, and ExperimentalConditions tables provide enhanced support for experiment metadata.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
sweep_number: Optional[List[int] | int] = Field(
|
||||
default_factory=list,
|
||||
|
|
@ -649,7 +616,6 @@ class SweepTableSeriesIndex(VectorIndex):
|
|||
Index for series.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["series_index"] = Field("series_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -673,7 +639,6 @@ class IntracellularElectrodesTable(DynamicTable):
|
|||
Table for storing intracellular electrode related metadata.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: Optional[str] = Field(
|
||||
None, description="""Description of what is in this dynamic table."""
|
||||
|
|
@ -701,7 +666,6 @@ class IntracellularStimuliTable(DynamicTable):
|
|||
Table for storing intracellular stimulus related metadata.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: Optional[str] = Field(
|
||||
None, description="""Description of what is in this dynamic table."""
|
||||
|
|
@ -729,7 +693,6 @@ class IntracellularStimuliTableStimulus(TimeSeriesReferenceVectorData):
|
|||
Column storing the reference to the recorded stimulus for the recording (rows).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["stimulus"] = Field("stimulus")
|
||||
idx_start: int = Field(
|
||||
...,
|
||||
|
|
@ -760,7 +723,6 @@ class IntracellularResponsesTable(DynamicTable):
|
|||
Table for storing intracellular response related metadata.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: Optional[str] = Field(
|
||||
None, description="""Description of what is in this dynamic table."""
|
||||
|
|
@ -788,7 +750,6 @@ class IntracellularResponsesTableResponse(TimeSeriesReferenceVectorData):
|
|||
Column storing the reference to the recorded response for the recording (rows)
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["response"] = Field("response")
|
||||
idx_start: int = Field(
|
||||
...,
|
||||
|
|
@ -819,7 +780,6 @@ class IntracellularRecordingsTable(AlignedDynamicTable):
|
|||
A table to group together a stimulus and response from a single electrode and a single simultaneous recording. Each row in the table represents a single recording consisting typically of a stimulus and a corresponding response. In some cases, however, only a stimulus or a response is recorded as part of an experiment. In this case, both the stimulus and response will point to the same TimeSeries while the idx_start and count of the invalid column will be set to -1, thus, indicating that no values have been recorded for the stimulus or response, respectively. Note, a recording MUST contain at least a stimulus or a response. Typically the stimulus and response are PatchClampSeries. However, the use of AD/DA channels that are not associated to an electrode is also common in intracellular electrophysiology, in which case other TimeSeries may be used.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: Literal["intracellular_recordings"] = Field("intracellular_recordings")
|
||||
description: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -859,7 +819,6 @@ class SimultaneousRecordingsTable(DynamicTable):
|
|||
A table for grouping different intracellular recordings from the IntracellularRecordingsTable table together that were recorded simultaneously from different electrodes.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: Literal["simultaneous_recordings"] = Field("simultaneous_recordings")
|
||||
recordings: str = Field(
|
||||
...,
|
||||
|
|
@ -890,7 +849,6 @@ class SimultaneousRecordingsTableRecordings(DynamicTableRegion):
|
|||
A reference to one or more rows in the IntracellularRecordingsTable table.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["recordings"] = Field("recordings")
|
||||
table: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -914,7 +872,6 @@ class SimultaneousRecordingsTableRecordingsIndex(VectorIndex):
|
|||
Index dataset for the recordings column.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["recordings_index"] = Field("recordings_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -938,7 +895,6 @@ class SequentialRecordingsTable(DynamicTable):
|
|||
A table for grouping different sequential recordings from the SimultaneousRecordingsTable table together. This is typically used to group together sequential recordings where a sequence of stimuli of the same type with varying parameters have been presented in a sequence.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: Literal["sequential_recordings"] = Field("sequential_recordings")
|
||||
simultaneous_recordings: str = Field(
|
||||
...,
|
||||
|
|
@ -973,7 +929,6 @@ class SequentialRecordingsTableSimultaneousRecordings(DynamicTableRegion):
|
|||
A reference to one or more rows in the SimultaneousRecordingsTable table.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["simultaneous_recordings"] = Field("simultaneous_recordings")
|
||||
table: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -997,7 +952,6 @@ class SequentialRecordingsTableSimultaneousRecordingsIndex(VectorIndex):
|
|||
Index dataset for the simultaneous_recordings column.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["simultaneous_recordings_index"] = Field(
|
||||
"simultaneous_recordings_index"
|
||||
)
|
||||
|
|
@ -1023,7 +977,6 @@ class RepetitionsTable(DynamicTable):
|
|||
A table for grouping different sequential intracellular recordings together. With each SequentialRecording typically representing a particular type of stimulus, the RepetitionsTable table is typically used to group sets of stimuli applied in sequence.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: Literal["repetitions"] = Field("repetitions")
|
||||
sequential_recordings: str = Field(
|
||||
...,
|
||||
|
|
@ -1054,7 +1007,6 @@ class RepetitionsTableSequentialRecordings(DynamicTableRegion):
|
|||
A reference to one or more rows in the SequentialRecordingsTable table.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["sequential_recordings"] = Field("sequential_recordings")
|
||||
table: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -1078,7 +1030,6 @@ class RepetitionsTableSequentialRecordingsIndex(VectorIndex):
|
|||
Index dataset for the sequential_recordings column.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["sequential_recordings_index"] = Field("sequential_recordings_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -1102,7 +1053,6 @@ class ExperimentalConditionsTable(DynamicTable):
|
|||
A table for grouping different intracellular recording repetitions together that belong to the same experimental condition.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: Literal["experimental_conditions"] = Field("experimental_conditions")
|
||||
repetitions: str = Field(
|
||||
...,
|
||||
|
|
@ -1133,7 +1083,6 @@ class ExperimentalConditionsTableRepetitions(DynamicTableRegion):
|
|||
A reference to one or more rows in the RepetitionsTable table.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["repetitions"] = Field("repetitions")
|
||||
table: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -1157,7 +1106,6 @@ class ExperimentalConditionsTableRepetitionsIndex(VectorIndex):
|
|||
Index dataset for the repetitions column.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["repetitions_index"] = Field("repetitions_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -1174,46 +1122,3 @@ class ExperimentalConditionsTableRepetitionsIndex(VectorIndex):
|
|||
NDArray[Shape["* dim0, * dim1, * dim2, * dim3"], Any],
|
||||
]
|
||||
] = Field(None)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
PatchClampSeries.model_rebuild()
|
||||
PatchClampSeriesData.model_rebuild()
|
||||
CurrentClampSeries.model_rebuild()
|
||||
CurrentClampSeriesData.model_rebuild()
|
||||
IZeroClampSeries.model_rebuild()
|
||||
CurrentClampStimulusSeries.model_rebuild()
|
||||
CurrentClampStimulusSeriesData.model_rebuild()
|
||||
VoltageClampSeries.model_rebuild()
|
||||
VoltageClampSeriesData.model_rebuild()
|
||||
VoltageClampSeriesCapacitanceFast.model_rebuild()
|
||||
VoltageClampSeriesCapacitanceSlow.model_rebuild()
|
||||
VoltageClampSeriesResistanceCompBandwidth.model_rebuild()
|
||||
VoltageClampSeriesResistanceCompCorrection.model_rebuild()
|
||||
VoltageClampSeriesResistanceCompPrediction.model_rebuild()
|
||||
VoltageClampSeriesWholeCellCapacitanceComp.model_rebuild()
|
||||
VoltageClampSeriesWholeCellSeriesResistanceComp.model_rebuild()
|
||||
VoltageClampStimulusSeries.model_rebuild()
|
||||
VoltageClampStimulusSeriesData.model_rebuild()
|
||||
IntracellularElectrode.model_rebuild()
|
||||
SweepTable.model_rebuild()
|
||||
SweepTableSeriesIndex.model_rebuild()
|
||||
IntracellularElectrodesTable.model_rebuild()
|
||||
IntracellularStimuliTable.model_rebuild()
|
||||
IntracellularStimuliTableStimulus.model_rebuild()
|
||||
IntracellularResponsesTable.model_rebuild()
|
||||
IntracellularResponsesTableResponse.model_rebuild()
|
||||
IntracellularRecordingsTable.model_rebuild()
|
||||
SimultaneousRecordingsTable.model_rebuild()
|
||||
SimultaneousRecordingsTableRecordings.model_rebuild()
|
||||
SimultaneousRecordingsTableRecordingsIndex.model_rebuild()
|
||||
SequentialRecordingsTable.model_rebuild()
|
||||
SequentialRecordingsTableSimultaneousRecordings.model_rebuild()
|
||||
SequentialRecordingsTableSimultaneousRecordingsIndex.model_rebuild()
|
||||
RepetitionsTable.model_rebuild()
|
||||
RepetitionsTableSequentialRecordings.model_rebuild()
|
||||
RepetitionsTableSequentialRecordingsIndex.model_rebuild()
|
||||
ExperimentalConditionsTable.model_rebuild()
|
||||
ExperimentalConditionsTableRepetitions.model_rebuild()
|
||||
ExperimentalConditionsTableRepetitionsIndex.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from .core_nwb_base import Image, TimeSeries, TimeSeriesStartingTime, TimeSeriesSync
|
||||
from .core_nwb_base import Image, TimeSeriesStartingTime, TimeSeries, TimeSeriesSync
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
|
|
@ -35,13 +35,6 @@ version = "2.4.0"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -61,18 +54,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class GrayscaleImage(Image):
|
||||
"""
|
||||
A grayscale image.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
resolution: Optional[float] = Field(
|
||||
None, description="""Pixel resolution of the image, in pixels per centimeter."""
|
||||
|
|
@ -94,7 +80,6 @@ class RGBImage(Image):
|
|||
A color image.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
resolution: Optional[float] = Field(
|
||||
None, description="""Pixel resolution of the image, in pixels per centimeter."""
|
||||
|
|
@ -116,7 +101,6 @@ class RGBAImage(Image):
|
|||
A color image with transparency.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
resolution: Optional[float] = Field(
|
||||
None, description="""Pixel resolution of the image, in pixels per centimeter."""
|
||||
|
|
@ -138,7 +122,6 @@ class ImageSeries(TimeSeries):
|
|||
General image data that is common between acquisition and stimulus time series. Sometimes the image data is stored in the file in a raw format while other times it will be stored as a series of external image files in the host file system. The data field will either be binary data, if the data is stored in the NWB file, or empty, if the data is stored in an external image stack. [frame][x][y] or [frame][x][y][z].
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: Union[
|
||||
NDArray[Shape["* frame, * x, * y"], float],
|
||||
|
|
@ -192,7 +175,6 @@ 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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["external_file"] = Field("external_file")
|
||||
starting_frame: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -206,7 +188,6 @@ class ImageMaskSeries(ImageSeries):
|
|||
An alpha mask that is applied to a presented visual stimulus. The 'data' array contains an array of mask values that are applied to the displayed image. Mask values are stored as RGBA. Mask can vary with time. The timestamps array indicates the starting time of a mask, and that mask pattern continues until it's explicitly changed.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: Union[
|
||||
NDArray[Shape["* frame, * x, * y"], float],
|
||||
|
|
@ -260,7 +241,6 @@ class OpticalSeries(ImageSeries):
|
|||
Image data that is presented or recorded. A stimulus template movie will be stored only as an image. When the image is presented as stimulus, additional data is required, such as field of view (e.g., how much of the visual field the image covers, or how what is the area of the target being imaged). If the OpticalSeries represents acquired imaging data, orientation is also important.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
distance: Optional[float] = Field(
|
||||
None, description="""Distance from camera/monitor to target/eye."""
|
||||
|
|
@ -329,7 +309,6 @@ 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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: NDArray[Shape["* num_times"], int] = Field(
|
||||
..., description="""Index of the frame in the referenced ImageSeries."""
|
||||
|
|
@ -361,15 +340,3 @@ class IndexSeries(TimeSeries):
|
|||
None,
|
||||
description="""Lab-specific time and sync information as provided directly from hardware devices and that is necessary for aligning all acquired time information to a common timebase. The timestamp array stores time in the common timebase. This group will usually only be populated in TimeSeries that are stored external to the NWB file, in files storing raw data. Once timestamp data is calculated, the contents of 'sync' are mostly for archival purposes.""",
|
||||
)
|
||||
|
||||
|
||||
# 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()
|
||||
ImageSeriesExternalFile.model_rebuild()
|
||||
ImageMaskSeries.model_rebuild()
|
||||
OpticalSeries.model_rebuild()
|
||||
IndexSeries.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -32,13 +32,6 @@ version = "None"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -56,13 +49,3 @@ class ConfiguredBaseModel(BaseModel):
|
|||
self.array[i] = value
|
||||
else:
|
||||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
|
|
|
|||
|
|
@ -27,16 +27,16 @@ if TYPE_CHECKING:
|
|||
import numpy as np
|
||||
|
||||
|
||||
from .core_nwb_base import TimeSeries, TimeSeriesSync, TimeSeriesStartingTime
|
||||
from .core_nwb_ecephys import ElectrodeGroup
|
||||
|
||||
from ...hdmf_common.v1_5_0.hdmf_common_table import (
|
||||
DynamicTableRegion,
|
||||
VectorIndex,
|
||||
DynamicTable,
|
||||
VectorData,
|
||||
VectorIndex,
|
||||
)
|
||||
|
||||
from .core_nwb_ecephys import ElectrodeGroup
|
||||
from .core_nwb_base import TimeSeriesSync, TimeSeriesStartingTime, TimeSeries
|
||||
|
||||
|
||||
metamodel_version = "None"
|
||||
|
|
@ -44,13 +44,6 @@ version = "2.4.0"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -70,18 +63,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class AbstractFeatureSeries(TimeSeries):
|
||||
"""
|
||||
Abstract features, such as quantitative descriptions of sensory stimuli. The TimeSeries::data field is a 2D array, storing those features (e.g., for visual grating stimulus this might be orientation, spatial frequency and contrast). Null stimuli (eg, uniform gray) can be marked as being an independent feature (eg, 1.0 for gray, 0.0 for actual stimulus) or by storing NaNs for feature values, or through use of the TimeSeries::control fields. A set of features is considered to persist until the next set of features is defined. The final set of features stored should be the null set. This is useful when storing the raw stimulus is impractical.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Values of each feature at each time.""")
|
||||
feature_units: Optional[NDArray[Shape["* num_features"], str]] = Field(
|
||||
|
|
@ -125,7 +111,6 @@ class AbstractFeatureSeriesData(ConfiguredBaseModel):
|
|||
Values of each feature at each time.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -144,7 +129,6 @@ class AnnotationSeries(TimeSeries):
|
|||
Stores user annotations made during an experiment. The data[] field stores a text array, and timestamps are stored for each annotation (ie, interval=1). This is largely an alias to a standard TimeSeries storing a text array but that is identifiable as storing annotations in a machine-readable way.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: NDArray[Shape["* num_times"], str] = Field(
|
||||
..., description="""Annotations made during an experiment."""
|
||||
|
|
@ -183,7 +167,6 @@ 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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: NDArray[Shape["* num_times"], int] = Field(
|
||||
..., description="""Use values >0 if interval started, <0 if interval ended."""
|
||||
|
|
@ -222,7 +205,6 @@ class DecompositionSeries(TimeSeries):
|
|||
Spectral analysis of a time series, e.g. of an LFP or a speech signal.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: str = Field(..., description="""Data decomposed into frequency bands.""")
|
||||
metric: str = Field(
|
||||
|
|
@ -270,7 +252,6 @@ class DecompositionSeriesData(ConfiguredBaseModel):
|
|||
Data decomposed into frequency bands.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["data"] = Field("data")
|
||||
unit: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -286,7 +267,6 @@ class DecompositionSeriesSourceChannels(DynamicTableRegion):
|
|||
DynamicTableRegion pointer to the channels that this decomposition series was generated from.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["source_channels"] = Field("source_channels")
|
||||
table: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -310,7 +290,6 @@ class DecompositionSeriesBands(DynamicTable):
|
|||
Table for describing the bands that this series was generated from. There should be one row in this table for each band.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["bands"] = Field("bands")
|
||||
band_name: Optional[List[str] | str] = Field(
|
||||
default_factory=list, description="""Name of the band, e.g. theta."""
|
||||
|
|
@ -347,7 +326,6 @@ class Units(DynamicTable):
|
|||
Data about spiking units. Event times of observed units (e.g. cell, synapse, etc.) should be concatenated and stored in spike_times.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("Units")
|
||||
spike_times_index: Optional[str] = Field(
|
||||
None, description="""Index into the spike_times dataset."""
|
||||
|
|
@ -422,7 +400,6 @@ class UnitsSpikeTimesIndex(VectorIndex):
|
|||
Index into the spike_times dataset.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["spike_times_index"] = Field("spike_times_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -446,7 +423,6 @@ class UnitsSpikeTimes(VectorData):
|
|||
Spike times for each unit.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["spike_times"] = Field("spike_times")
|
||||
resolution: Optional[float] = Field(
|
||||
None,
|
||||
|
|
@ -470,7 +446,6 @@ class UnitsObsIntervalsIndex(VectorIndex):
|
|||
Index into the obs_intervals dataset.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["obs_intervals_index"] = Field("obs_intervals_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -494,7 +469,6 @@ class UnitsElectrodesIndex(VectorIndex):
|
|||
Index into electrodes.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["electrodes_index"] = Field("electrodes_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -518,7 +492,6 @@ class UnitsElectrodes(DynamicTableRegion):
|
|||
Electrode that each spike unit came from, specified using a DynamicTableRegion.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["electrodes"] = Field("electrodes")
|
||||
table: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -542,7 +515,6 @@ class UnitsWaveformsIndex(VectorIndex):
|
|||
Index into the waveforms dataset. One value for every spike event. See 'waveforms' for more detail.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["waveforms_index"] = Field("waveforms_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -566,7 +538,6 @@ class UnitsWaveformsIndexIndex(VectorIndex):
|
|||
Index into the waveforms_index dataset. One value for every unit (row in the table). See 'waveforms' for more detail.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["waveforms_index_index"] = Field("waveforms_index_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -583,23 +554,3 @@ class UnitsWaveformsIndexIndex(VectorIndex):
|
|||
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()
|
||||
IntervalSeries.model_rebuild()
|
||||
DecompositionSeries.model_rebuild()
|
||||
DecompositionSeriesData.model_rebuild()
|
||||
DecompositionSeriesSourceChannels.model_rebuild()
|
||||
DecompositionSeriesBands.model_rebuild()
|
||||
Units.model_rebuild()
|
||||
UnitsSpikeTimesIndex.model_rebuild()
|
||||
UnitsSpikeTimes.model_rebuild()
|
||||
UnitsObsIntervalsIndex.model_rebuild()
|
||||
UnitsElectrodesIndex.model_rebuild()
|
||||
UnitsElectrodes.model_rebuild()
|
||||
UnitsWaveformsIndex.model_rebuild()
|
||||
UnitsWaveformsIndexIndex.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -29,9 +29,9 @@ if TYPE_CHECKING:
|
|||
|
||||
from .core_nwb_base import (
|
||||
TimeSeriesSync,
|
||||
TimeSeries,
|
||||
NWBContainer,
|
||||
TimeSeriesStartingTime,
|
||||
TimeSeries,
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -40,13 +40,6 @@ version = "2.4.0"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -66,18 +59,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class OptogeneticSeries(TimeSeries):
|
||||
"""
|
||||
An optogenetic stimulus.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: NDArray[Shape["* num_times"], float] = Field(
|
||||
..., description="""Applied power for optogenetic stimulus, in watts."""
|
||||
|
|
@ -116,7 +102,6 @@ class OptogeneticStimulusSite(NWBContainer):
|
|||
A site of optogenetic stimulation.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: str = Field(..., description="""Description of stimulation site.""")
|
||||
excitation_lambda: float = Field(
|
||||
|
|
@ -126,9 +111,3 @@ class OptogeneticStimulusSite(NWBContainer):
|
|||
...,
|
||||
description="""Location of the stimulation site. 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.""",
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
OptogeneticSeries.model_rebuild()
|
||||
OptogeneticStimulusSite.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -28,18 +28,18 @@ if TYPE_CHECKING:
|
|||
|
||||
|
||||
from .core_nwb_base import (
|
||||
TimeSeries,
|
||||
TimeSeriesSync,
|
||||
NWBDataInterface,
|
||||
TimeSeriesStartingTime,
|
||||
NWBContainer,
|
||||
TimeSeriesSync,
|
||||
TimeSeriesStartingTime,
|
||||
NWBDataInterface,
|
||||
TimeSeries,
|
||||
)
|
||||
|
||||
from ...hdmf_common.v1_5_0.hdmf_common_table import (
|
||||
DynamicTableRegion,
|
||||
VectorIndex,
|
||||
DynamicTable,
|
||||
VectorData,
|
||||
VectorIndex,
|
||||
)
|
||||
|
||||
from .core_nwb_image import ImageSeries, ImageSeriesExternalFile
|
||||
|
|
@ -50,13 +50,6 @@ version = "2.4.0"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -76,18 +69,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class TwoPhotonSeries(ImageSeries):
|
||||
"""
|
||||
Image stack recorded over time from 2-photon microscope.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
pmt_gain: Optional[float] = Field(None, description="""Photomultiplier gain.""")
|
||||
scan_line_rate: Optional[float] = Field(
|
||||
|
|
@ -155,7 +141,6 @@ class RoiResponseSeries(TimeSeries):
|
|||
ROI responses over an imaging plane. The first dimension represents time. The second dimension, if present, represents ROIs.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
data: Union[
|
||||
NDArray[Shape["* num_times"], float],
|
||||
|
|
@ -199,7 +184,6 @@ class RoiResponseSeriesRois(DynamicTableRegion):
|
|||
DynamicTableRegion referencing into an ROITable containing information on the ROIs stored in this timeseries.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["rois"] = Field("rois")
|
||||
table: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -223,7 +207,6 @@ 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).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[RoiResponseSeries] | RoiResponseSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -235,7 +218,6 @@ class Fluorescence(NWBDataInterface):
|
|||
Fluorescence information about a region of interest (ROI). Storage hierarchy of fluorescence should be the same as for segmentation (ie, same names for ROIs and for image planes).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[RoiResponseSeries] | RoiResponseSeries] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -247,7 +229,6 @@ class ImageSegmentation(NWBDataInterface):
|
|||
Stores pixels in an image that represent different regions of interest (ROIs) or masks. All segmentation for a given imaging plane is stored together, with storage for multiple imaging planes (masks) supported. Each ROI is stored in its own subgroup, with the ROI group containing both a 2D mask and a list of pixels that make up this mask. Segments can also be used for masking neuropil. If segmentation is allowed to change with time, a new imaging plane (or module) is required and ROI names should remain consistent between them.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[PlaneSegmentation] | PlaneSegmentation] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -259,7 +240,6 @@ class PlaneSegmentation(DynamicTable):
|
|||
Results from image segmentation of a specific imaging plane.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
image_mask: Optional[
|
||||
Union[
|
||||
|
|
@ -310,7 +290,6 @@ class PlaneSegmentationPixelMaskIndex(VectorIndex):
|
|||
Index into pixel_mask.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["pixel_mask_index"] = Field("pixel_mask_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -334,7 +313,6 @@ class PlaneSegmentationPixelMask(VectorData):
|
|||
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
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["pixel_mask"] = Field("pixel_mask")
|
||||
x: Optional[int] = Field(None, description="""Pixel x-coordinate.""")
|
||||
y: Optional[int] = Field(None, description="""Pixel y-coordinate.""")
|
||||
|
|
@ -357,7 +335,6 @@ class PlaneSegmentationVoxelMaskIndex(VectorIndex):
|
|||
Index into voxel_mask.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["voxel_mask_index"] = Field("voxel_mask_index")
|
||||
target: Optional[str] = Field(
|
||||
None,
|
||||
|
|
@ -381,7 +358,6 @@ class PlaneSegmentationVoxelMask(VectorData):
|
|||
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
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["voxel_mask"] = Field("voxel_mask")
|
||||
x: Optional[int] = Field(None, description="""Voxel x-coordinate.""")
|
||||
y: Optional[int] = Field(None, description="""Voxel y-coordinate.""")
|
||||
|
|
@ -405,7 +381,6 @@ class ImagingPlane(NWBContainer):
|
|||
An imaging plane and its metadata.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[OpticalChannel] | OpticalChannel] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -417,7 +392,6 @@ class OpticalChannel(NWBContainer):
|
|||
An optical channel used to record from an imaging plane.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
description: str = Field(
|
||||
..., description="""Description or other notes about the channel."""
|
||||
|
|
@ -432,7 +406,6 @@ class MotionCorrection(NWBDataInterface):
|
|||
An image stack where all frames are shifted (registered) to a common coordinate system, to account for movement and drift between frames. Note: each frame at each point in time is assumed to be 2-D (has only x & y dimensions).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
children: Optional[List[CorrectedImageStack] | CorrectedImageStack] = Field(
|
||||
default_factory=dict
|
||||
)
|
||||
|
|
@ -444,7 +417,6 @@ class CorrectedImageStack(NWBDataInterface):
|
|||
Reuslts from motion correction of an image stack.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field(...)
|
||||
corrected: str = Field(
|
||||
...,
|
||||
|
|
@ -454,22 +426,3 @@ class CorrectedImageStack(NWBDataInterface):
|
|||
...,
|
||||
description="""Stores the x,y delta necessary to align each frame to the common coordinates, for example, to align each frame to a reference image.""",
|
||||
)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
TwoPhotonSeries.model_rebuild()
|
||||
RoiResponseSeries.model_rebuild()
|
||||
RoiResponseSeriesRois.model_rebuild()
|
||||
DfOverF.model_rebuild()
|
||||
Fluorescence.model_rebuild()
|
||||
ImageSegmentation.model_rebuild()
|
||||
PlaneSegmentation.model_rebuild()
|
||||
PlaneSegmentationPixelMaskIndex.model_rebuild()
|
||||
PlaneSegmentationPixelMask.model_rebuild()
|
||||
PlaneSegmentationVoxelMaskIndex.model_rebuild()
|
||||
PlaneSegmentationVoxelMask.model_rebuild()
|
||||
ImagingPlane.model_rebuild()
|
||||
OpticalChannel.model_rebuild()
|
||||
MotionCorrection.model_rebuild()
|
||||
CorrectedImageStack.model_rebuild()
|
||||
|
|
|
|||
|
|
@ -35,13 +35,6 @@ version = "2.4.0"
|
|||
|
||||
|
||||
class ConfiguredBaseModel(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
validate_assignment=True,
|
||||
validate_default=True,
|
||||
extra="allow",
|
||||
arbitrary_types_allowed=True,
|
||||
use_enum_values=True,
|
||||
)
|
||||
hdf5_path: Optional[str] = Field(
|
||||
None, description="The absolute path that this object is stored in an NWB file"
|
||||
)
|
||||
|
|
@ -61,18 +54,11 @@ class ConfiguredBaseModel(BaseModel):
|
|||
super().__setitem__(i, value)
|
||||
|
||||
|
||||
class LinkML_Meta(BaseModel):
|
||||
"""Extra LinkML Metadata stored as a class attribute"""
|
||||
|
||||
tree_root: bool = False
|
||||
|
||||
|
||||
class ImagingRetinotopy(NWBDataInterface):
|
||||
"""
|
||||
Intrinsic signal optical imaging or widefield imaging for measuring retinotopy. Stores orthogonal maps (e.g., altitude/azimuth; radius/theta) of responses to specific stimuli and a combined polarity map from which to identify visual areas. This group does not store the raw responses imaged during retinotopic mapping or the stimuli presented, but rather the resulting phase and power maps after applying a Fourier transform on the averaged responses. Note: for data consistency, all images and arrays are stored in the format [row][column] and [row, col], which equates to [y][x]. Field of view and dimension arrays may appear backward (i.e., y before x).
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
|
||||
name: str = Field("ImagingRetinotopy")
|
||||
axis_1_phase_map: str = Field(
|
||||
..., description="""Phase response to stimulus on the first measured axis."""
|
||||
|
|
@ -111,7 +97,6 @@ class ImagingRetinotopyAxis1PhaseMap(ConfiguredBaseModel):
|
|||
Phase response to stimulus on the first measured axis.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["axis_1_phase_map"] = Field("axis_1_phase_map")
|
||||
dimension: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -131,7 +116,6 @@ class ImagingRetinotopyAxis1PowerMap(ConfiguredBaseModel):
|
|||
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.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["axis_1_power_map"] = Field("axis_1_power_map")
|
||||
dimension: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -151,7 +135,6 @@ class ImagingRetinotopyAxis2PhaseMap(ConfiguredBaseModel):
|
|||
Phase response to stimulus on the second measured axis.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["axis_2_phase_map"] = Field("axis_2_phase_map")
|
||||
dimension: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -171,7 +154,6 @@ class ImagingRetinotopyAxis2PowerMap(ConfiguredBaseModel):
|
|||
Power response on the second measured axis. Response is scaled so 0.0 is no power in the response and 1.0 is maximum relative power.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["axis_2_power_map"] = Field("axis_2_power_map")
|
||||
dimension: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -191,7 +173,6 @@ class ImagingRetinotopyFocalDepthImage(ConfiguredBaseModel):
|
|||
Gray-scale image taken with same settings/parameters (e.g., focal depth, wavelength) as data collection. Array format: [rows][columns].
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["focal_depth_image"] = Field("focal_depth_image")
|
||||
bits_per_pixel: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -218,7 +199,6 @@ class ImagingRetinotopySignMap(ConfiguredBaseModel):
|
|||
Sine of the angle between the direction of the gradient in axis_1 and axis_2.
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["sign_map"] = Field("sign_map")
|
||||
dimension: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -235,7 +215,6 @@ class ImagingRetinotopyVasculatureImage(ConfiguredBaseModel):
|
|||
Gray-scale anatomical image of cortical surface. Array structure: [rows][columns]
|
||||
"""
|
||||
|
||||
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
|
||||
name: Literal["vasculature_image"] = Field("vasculature_image")
|
||||
bits_per_pixel: Optional[int] = Field(
|
||||
None,
|
||||
|
|
@ -252,15 +231,3 @@ class ImagingRetinotopyVasculatureImage(ConfiguredBaseModel):
|
|||
None, description="""Format of image. Right now only 'raw' is supported."""
|
||||
)
|
||||
array: Optional[NDArray[Shape["* num_rows, * num_cols"], int]] = Field(None)
|
||||
|
||||
|
||||
# Model rebuild
|
||||
# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model
|
||||
ImagingRetinotopy.model_rebuild()
|
||||
ImagingRetinotopyAxis1PhaseMap.model_rebuild()
|
||||
ImagingRetinotopyAxis1PowerMap.model_rebuild()
|
||||
ImagingRetinotopyAxis2PhaseMap.model_rebuild()
|
||||
ImagingRetinotopyAxis2PowerMap.model_rebuild()
|
||||
ImagingRetinotopyFocalDepthImage.model_rebuild()
|
||||
ImagingRetinotopySignMap.model_rebuild()
|
||||
ImagingRetinotopyVasculatureImage.model_rebuild()
|
||||
|
|
|
|||
Some files were not shown because too many files have changed in this diff Show more
Loading…
Reference in a new issue