core_nwb_icephys#

pydantic model ConfiguredBaseModel#

Bases: BaseModel

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field hdf5_path: str | None = None#

The absolute path that this object is stored in an NWB file

pydantic model LinkML_Meta#

Bases: BaseModel

Extra LinkML Metadata stored as a class attribute

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Fields:
field tree_root: bool = False#
pydantic model PatchClampSeries#

Bases: TimeSeries

An abstract base class for patch-clamp data - stimulus or response, current or voltage.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field comments: str | None = None#

Human-readable comments about the TimeSeries. This second descriptive field can be used to store additional information, or descriptive information if the primary description field is populated with a computer-readable string.

field control: List[int] | None [Optional]#

Numerical labels that apply to each time point in data for the purpose of querying and slicing data by these values. If present, the length of this array should be the same size as the first dimension of data.

field control_description: List[str] | None [Optional]#

Description of each control value. Must be present if control is present. If present, control_description[0] should describe time points where control == 0.

field data: List[float] [Optional]#

Recorded voltage or current.

field description: str | None = None#

Description of the time series.

field gain: float | None = None#

Gain of the recording, in units Volt/Amp (v-clamp) or Volt/Volt (c-clamp).

field name: str [Required]#
field starting_time: TimeSeriesStartingTime | None = None#

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.

field stimulus_description: str | None = None#

Protocol/stimulus name for this patch-clamp dataset.

field sweep_number: int | None = None#

Sweep number, allows to group different PatchClampSeries together.

field sync: TimeSeriesSync | None = None#

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.

field timestamps: List[float] | None [Optional]#

Timestamps for samples stored in data, in seconds, relative to the common experiment master-clock stored in NWBFile.timestamps_reference_time.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=True), frozen=True)#
pydantic model CurrentClampSeries#

Bases: PatchClampSeries

Voltage data from an intracellular current-clamp recording. A corresponding CurrentClampStimulusSeries (stored separately as a stimulus) is used to store the current injected.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field bias_current: float | None = None#

Bias current, in amps.

field bridge_balance: float | None = None#

Bridge balance, in ohms.

field capacitance_compensation: float | None = None#

Capacitance compensation, in farads.

field comments: str | None = None#

Human-readable comments about the TimeSeries. This second descriptive field can be used to store additional information, or descriptive information if the primary description field is populated with a computer-readable string.

field control: List[int] | None [Optional]#

Numerical labels that apply to each time point in data for the purpose of querying and slicing data by these values. If present, the length of this array should be the same size as the first dimension of data.

field control_description: List[str] | None [Optional]#

Description of each control value. Must be present if control is present. If present, control_description[0] should describe time points where control == 0.

field data: CurrentClampSeriesData [Required]#

Recorded voltage.

field description: str | None = None#

Description of the time series.

field gain: float | None = None#

Gain of the recording, in units Volt/Amp (v-clamp) or Volt/Volt (c-clamp).

field name: str [Required]#
field starting_time: TimeSeriesStartingTime | None = None#

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.

field stimulus_description: str | None = None#

Protocol/stimulus name for this patch-clamp dataset.

field sweep_number: int | None = None#

Sweep number, allows to group different PatchClampSeries together.

field sync: TimeSeriesSync | None = None#

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.

field timestamps: List[float] | None [Optional]#

Timestamps for samples stored in data, in seconds, relative to the common experiment master-clock stored in NWBFile.timestamps_reference_time.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=True), frozen=True)#
pydantic model CurrentClampSeriesData#

Bases: ConfiguredBaseModel

Recorded voltage.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field name: Literal['data'] = 'data'#
field unit: str | None = None#

Base unit of measurement for working with the data. which is fixed to ‘volts’. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply ‘data’ by ‘conversion’ and add ‘offset’.

field value: Any [Required]#
linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#
pydantic model IZeroClampSeries#

Bases: 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.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field bias_current: float [Required]#

Bias current, in amps, fixed to 0.0.

field bridge_balance: float [Required]#

Bridge balance, in ohms, fixed to 0.0.

field capacitance_compensation: float [Required]#

Capacitance compensation, in farads, fixed to 0.0.

field comments: str | None = None#

Human-readable comments about the TimeSeries. This second descriptive field can be used to store additional information, or descriptive information if the primary description field is populated with a computer-readable string.

field control: List[int] | None [Optional]#

Numerical labels that apply to each time point in data for the purpose of querying and slicing data by these values. If present, the length of this array should be the same size as the first dimension of data.

field control_description: List[str] | None [Optional]#

Description of each control value. Must be present if control is present. If present, control_description[0] should describe time points where control == 0.

field data: CurrentClampSeriesData [Required]#

Recorded voltage.

field description: str | None = None#

Description of the time series.

field gain: float | None = None#

Gain of the recording, in units Volt/Amp (v-clamp) or Volt/Volt (c-clamp).

field name: str [Required]#
field starting_time: TimeSeriesStartingTime | None = None#

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.

field stimulus_description: str | None = None#

An IZeroClampSeries has no stimulus, so this attribute is automatically set to “N/A”

field sweep_number: int | None = None#

Sweep number, allows to group different PatchClampSeries together.

field sync: TimeSeriesSync | None = None#

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.

field timestamps: List[float] | None [Optional]#

Timestamps for samples stored in data, in seconds, relative to the common experiment master-clock stored in NWBFile.timestamps_reference_time.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=True), frozen=True)#
pydantic model CurrentClampStimulusSeries#

Bases: PatchClampSeries

Stimulus current applied during current clamp recording.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field comments: str | None = None#

Human-readable comments about the TimeSeries. This second descriptive field can be used to store additional information, or descriptive information if the primary description field is populated with a computer-readable string.

field control: List[int] | None [Optional]#

Numerical labels that apply to each time point in data for the purpose of querying and slicing data by these values. If present, the length of this array should be the same size as the first dimension of data.

field control_description: List[str] | None [Optional]#

Description of each control value. Must be present if control is present. If present, control_description[0] should describe time points where control == 0.

field data: CurrentClampStimulusSeriesData [Required]#

Stimulus current applied.

field description: str | None = None#

Description of the time series.

field gain: float | None = None#

Gain of the recording, in units Volt/Amp (v-clamp) or Volt/Volt (c-clamp).

field name: str [Required]#
field starting_time: TimeSeriesStartingTime | None = None#

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.

field stimulus_description: str | None = None#

Protocol/stimulus name for this patch-clamp dataset.

field sweep_number: int | None = None#

Sweep number, allows to group different PatchClampSeries together.

field sync: TimeSeriesSync | None = None#

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.

field timestamps: List[float] | None [Optional]#

Timestamps for samples stored in data, in seconds, relative to the common experiment master-clock stored in NWBFile.timestamps_reference_time.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=True), frozen=True)#
pydantic model CurrentClampStimulusSeriesData#

Bases: ConfiguredBaseModel

Stimulus current applied.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field name: Literal['data'] = 'data'#
field unit: str | None = None#

Base unit of measurement for working with the data. which is fixed to ‘amperes’. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply ‘data’ by ‘conversion’ and add ‘offset’.

field value: Any [Required]#
linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#
pydantic model VoltageClampSeries#

Bases: PatchClampSeries

Current data from an intracellular voltage-clamp recording. A corresponding VoltageClampStimulusSeries (stored separately as a stimulus) is used to store the voltage injected.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field capacitance_fast: VoltageClampSeriesCapacitanceFast | None = None#

Fast capacitance, in farads.

field capacitance_slow: VoltageClampSeriesCapacitanceSlow | None = None#

Slow capacitance, in farads.

field comments: str | None = None#

Human-readable comments about the TimeSeries. This second descriptive field can be used to store additional information, or descriptive information if the primary description field is populated with a computer-readable string.

field control: List[int] | None [Optional]#

Numerical labels that apply to each time point in data for the purpose of querying and slicing data by these values. If present, the length of this array should be the same size as the first dimension of data.

field control_description: List[str] | None [Optional]#

Description of each control value. Must be present if control is present. If present, control_description[0] should describe time points where control == 0.

field data: VoltageClampSeriesData [Required]#

Recorded current.

field description: str | None = None#

Description of the time series.

field gain: float | None = None#

Gain of the recording, in units Volt/Amp (v-clamp) or Volt/Volt (c-clamp).

field name: str [Required]#
field resistance_comp_bandwidth: VoltageClampSeriesResistanceCompBandwidth | None = None#

Resistance compensation bandwidth, in hertz.

field resistance_comp_correction: VoltageClampSeriesResistanceCompCorrection | None = None#

Resistance compensation correction, in percent.

field resistance_comp_prediction: VoltageClampSeriesResistanceCompPrediction | None = None#

Resistance compensation prediction, in percent.

field starting_time: TimeSeriesStartingTime | None = None#

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.

field stimulus_description: str | None = None#

Protocol/stimulus name for this patch-clamp dataset.

field sweep_number: int | None = None#

Sweep number, allows to group different PatchClampSeries together.

field sync: TimeSeriesSync | None = None#

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.

field timestamps: List[float] | None [Optional]#

Timestamps for samples stored in data, in seconds, relative to the common experiment master-clock stored in NWBFile.timestamps_reference_time.

field whole_cell_capacitance_comp: VoltageClampSeriesWholeCellCapacitanceComp | None = None#

Whole cell capacitance compensation, in farads.

field whole_cell_series_resistance_comp: VoltageClampSeriesWholeCellSeriesResistanceComp | None = None#

Whole cell series resistance compensation, in ohms.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=True), frozen=True)#
pydantic model VoltageClampSeriesData#

Bases: ConfiguredBaseModel

Recorded current.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field name: Literal['data'] = 'data'#
field unit: str | None = None#

Base unit of measurement for working with the data. which is fixed to ‘amperes’. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply ‘data’ by ‘conversion’ and add ‘offset’.

field value: Any [Required]#
linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#
pydantic model VoltageClampSeriesCapacitanceFast#

Bases: ConfiguredBaseModel

Fast capacitance, in farads.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field name: Literal['capacitance_fast'] = 'capacitance_fast'#
field unit: str | None = None#

Unit of measurement for capacitance_fast, which is fixed to ‘farads’.

field value: float [Required]#
linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#
pydantic model VoltageClampSeriesCapacitanceSlow#

Bases: ConfiguredBaseModel

Slow capacitance, in farads.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field name: Literal['capacitance_slow'] = 'capacitance_slow'#
field unit: str | None = None#

Unit of measurement for capacitance_fast, which is fixed to ‘farads’.

field value: float [Required]#
linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#
pydantic model VoltageClampSeriesResistanceCompBandwidth#

Bases: ConfiguredBaseModel

Resistance compensation bandwidth, in hertz.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field name: Literal['resistance_comp_bandwidth'] = 'resistance_comp_bandwidth'#
field unit: str | None = None#

Unit of measurement for resistance_comp_bandwidth, which is fixed to ‘hertz’.

field value: float [Required]#
linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#
pydantic model VoltageClampSeriesResistanceCompCorrection#

Bases: ConfiguredBaseModel

Resistance compensation correction, in percent.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field name: Literal['resistance_comp_correction'] = 'resistance_comp_correction'#
field unit: str | None = None#

Unit of measurement for resistance_comp_correction, which is fixed to ‘percent’.

field value: float [Required]#
linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#
pydantic model VoltageClampSeriesResistanceCompPrediction#

Bases: ConfiguredBaseModel

Resistance compensation prediction, in percent.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field name: Literal['resistance_comp_prediction'] = 'resistance_comp_prediction'#
field unit: str | None = None#

Unit of measurement for resistance_comp_prediction, which is fixed to ‘percent’.

field value: float [Required]#
linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#
pydantic model VoltageClampSeriesWholeCellCapacitanceComp#

Bases: ConfiguredBaseModel

Whole cell capacitance compensation, in farads.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field name: Literal['whole_cell_capacitance_comp'] = 'whole_cell_capacitance_comp'#
field unit: str | None = None#

Unit of measurement for whole_cell_capacitance_comp, which is fixed to ‘farads’.

field value: float [Required]#
linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#
pydantic model VoltageClampSeriesWholeCellSeriesResistanceComp#

Bases: ConfiguredBaseModel

Whole cell series resistance compensation, in ohms.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field name: Literal['whole_cell_series_resistance_comp'] = 'whole_cell_series_resistance_comp'#
field unit: str | None = None#

Unit of measurement for whole_cell_series_resistance_comp, which is fixed to ‘ohms’.

field value: float [Required]#
linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#
pydantic model VoltageClampStimulusSeries#

Bases: PatchClampSeries

Stimulus voltage applied during a voltage clamp recording.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field comments: str | None = None#

Human-readable comments about the TimeSeries. This second descriptive field can be used to store additional information, or descriptive information if the primary description field is populated with a computer-readable string.

field control: List[int] | None [Optional]#

Numerical labels that apply to each time point in data for the purpose of querying and slicing data by these values. If present, the length of this array should be the same size as the first dimension of data.

field control_description: List[str] | None [Optional]#

Description of each control value. Must be present if control is present. If present, control_description[0] should describe time points where control == 0.

field data: VoltageClampStimulusSeriesData [Required]#

Stimulus voltage applied.

field description: str | None = None#

Description of the time series.

field gain: float | None = None#

Gain of the recording, in units Volt/Amp (v-clamp) or Volt/Volt (c-clamp).

field name: str [Required]#
field starting_time: TimeSeriesStartingTime | None = None#

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.

field stimulus_description: str | None = None#

Protocol/stimulus name for this patch-clamp dataset.

field sweep_number: int | None = None#

Sweep number, allows to group different PatchClampSeries together.

field sync: TimeSeriesSync | None = None#

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.

field timestamps: List[float] | None [Optional]#

Timestamps for samples stored in data, in seconds, relative to the common experiment master-clock stored in NWBFile.timestamps_reference_time.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=True), frozen=True)#
pydantic model VoltageClampStimulusSeriesData#

Bases: ConfiguredBaseModel

Stimulus voltage applied.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field name: Literal['data'] = 'data'#
field unit: str | None = None#

Base unit of measurement for working with the data. which is fixed to ‘volts’. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply ‘data’ by ‘conversion’ and add ‘offset’.

field value: Any [Required]#
linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#
pydantic model IntracellularElectrode#

Bases: NWBContainer

An intracellular electrode and its metadata.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field cell_id: str | None = None#

unique ID of the cell

field description: str [Required]#

Description of electrode (e.g., whole-cell, sharp, etc.).

field filtering: str | None = None#

Electrode specific filtering.

field initial_access_resistance: str | None = None#

Initial access resistance.

field location: str | None = None#

Location of the electrode. Specify the area, layer, comments on estimation of area/layer, stereotaxic coordinates if in vivo, etc. Use standard atlas names for anatomical regions when possible.

field name: str [Required]#
field resistance: str | None = None#

Electrode resistance, in ohms.

field seal: str | None = None#

Information about seal used for recording.

field slice: str | None = None#

Information about slice used for recording.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=True), frozen=True)#
pydantic model SweepTable#

Bases: 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.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field colnames: str | None = None#

The names of the columns in this table. This should be used to specify an order to the columns.

field description: str | None = None#

Description of what is in this dynamic table.

field id: List[int] [Optional]#

Array of unique identifiers for the rows of this dynamic table.

field name: str [Required]#
field series: List[PatchClampSeries] | None [Optional]#

The PatchClampSeries with the sweep number in that row.

field series_index: SweepTableSeriesIndex [Required]#

Index for series.

field sweep_number: List[int] | None [Optional]#

Sweep number of the PatchClampSeries in that row.

field vector_data: List[VectorData] | None [Optional]#

Vector columns, including index columns, of this dynamic table.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=True), frozen=True)#
pydantic model SweepTableSeriesIndex#

Bases: VectorIndex

Index for series.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field array: NDArray[Shape['* dim0'], Any] | NDArray[Shape['* dim0, * dim1'], Any] | NDArray[Shape['* dim0, * dim1, * dim2'], Any] | NDArray[Shape['* dim0, * dim1, * dim2, * dim3'], Any] | None = None#
field description: str | None = None#

Description of what these vectors represent.

field name: Literal['series_index'] = 'series_index'#
field target: VectorData | None = None#

Reference to the target dataset that this index applies to.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#
pydantic model IntracellularElectrodesTable#

Bases: DynamicTable

Table for storing intracellular electrode related metadata.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field colnames: str | None = None#

The names of the columns in this table. This should be used to specify an order to the columns.

field description: str | None = None#

Description of what is in this dynamic table.

field electrode: List[IntracellularElectrode] | None [Optional]#

Column for storing the reference to the intracellular electrode.

field id: List[int] [Optional]#

Array of unique identifiers for the rows of this dynamic table.

field name: str [Required]#
field vector_data: List[VectorData] | None [Optional]#

Vector columns, including index columns, of this dynamic table.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=True), frozen=True)#
pydantic model IntracellularStimuliTable#

Bases: DynamicTable

Table for storing intracellular stimulus related metadata.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field colnames: str | None = None#

The names of the columns in this table. This should be used to specify an order to the columns.

field description: str | None = None#

Description of what is in this dynamic table.

field id: List[int] [Optional]#

Array of unique identifiers for the rows of this dynamic table.

field name: str [Required]#
field stimulus: IntracellularStimuliTableStimulus [Required]#

Column storing the reference to the recorded stimulus for the recording (rows).

field vector_data: List[VectorData] | None [Optional]#

Vector columns, including index columns, of this dynamic table.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=True), frozen=True)#
pydantic model IntracellularStimuliTableStimulus#

Bases: TimeSeriesReferenceVectorData

Column storing the reference to the recorded stimulus for the recording (rows).

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field array: NDArray[Shape['* dim0'], Any] | NDArray[Shape['* dim0, * dim1'], Any] | NDArray[Shape['* dim0, * dim1, * dim2'], Any] | NDArray[Shape['* dim0, * dim1, * dim2, * dim3'], Any] | None = None#
field description: str | None = None#

Description of what these vectors represent.

field name: Literal['stimulus'] = 'stimulus'#
linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#
pydantic model IntracellularResponsesTable#

Bases: DynamicTable

Table for storing intracellular response related metadata.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field colnames: str | None = None#

The names of the columns in this table. This should be used to specify an order to the columns.

field description: str | None = None#

Description of what is in this dynamic table.

field id: List[int] [Optional]#

Array of unique identifiers for the rows of this dynamic table.

field name: str [Required]#
field response: IntracellularResponsesTableResponse [Required]#

Column storing the reference to the recorded response for the recording (rows)

field vector_data: List[VectorData] | None [Optional]#

Vector columns, including index columns, of this dynamic table.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=True), frozen=True)#
pydantic model IntracellularResponsesTableResponse#

Bases: TimeSeriesReferenceVectorData

Column storing the reference to the recorded response for the recording (rows)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field array: NDArray[Shape['* dim0'], Any] | NDArray[Shape['* dim0, * dim1'], Any] | NDArray[Shape['* dim0, * dim1, * dim2'], Any] | NDArray[Shape['* dim0, * dim1, * dim2, * dim3'], Any] | None = None#
field description: str | None = None#

Description of what these vectors represent.

field name: Literal['response'] = 'response'#
linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#
pydantic model IntracellularRecordingsTable#

Bases: 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.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field children: Dict[str, DynamicTable] | None [Optional]#
field colnames: str | None = None#

The names of the columns in this table. This should be used to specify an order to the columns.

field description: str | None = None#

Description of the contents of this table. Inherited from AlignedDynamicTable and overwritten here to fix the value of the attribute.

field electrodes: IntracellularElectrodesTable [Required]#

Table for storing intracellular electrode related metadata.

field id: List[int] [Optional]#

Array of unique identifiers for the rows of this dynamic table.

field name: Literal['intracellular_recordings'] = 'intracellular_recordings'#
field responses: IntracellularResponsesTable [Required]#

Table for storing intracellular response related metadata.

field stimuli: IntracellularStimuliTable [Required]#

Table for storing intracellular stimulus related metadata.

field vector_data: List[VectorData] | None [Optional]#

Vector columns, including index columns, of this dynamic table.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=True), frozen=True)#
pydantic model SimultaneousRecordingsTable#

Bases: DynamicTable

A table for grouping different intracellular recordings from the IntracellularRecordingsTable table together that were recorded simultaneously from different electrodes.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field colnames: str | None = None#

The names of the columns in this table. This should be used to specify an order to the columns.

field description: str | None = None#

Description of what is in this dynamic table.

field id: List[int] [Optional]#

Array of unique identifiers for the rows of this dynamic table.

field name: Literal['simultaneous_recordings'] = 'simultaneous_recordings'#
field recordings: SimultaneousRecordingsTableRecordings [Required]#

A reference to one or more rows in the IntracellularRecordingsTable table.

field recordings_index: SimultaneousRecordingsTableRecordingsIndex [Required]#

Index dataset for the recordings column.

field vector_data: List[VectorData] | None [Optional]#

Vector columns, including index columns, of this dynamic table.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=True), frozen=True)#
pydantic model SimultaneousRecordingsTableRecordings#

Bases: DynamicTableRegion

A reference to one or more rows in the IntracellularRecordingsTable table.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field array: NDArray[Shape['* dim0'], Any] | NDArray[Shape['* dim0, * dim1'], Any] | NDArray[Shape['* dim0, * dim1, * dim2'], Any] | NDArray[Shape['* dim0, * dim1, * dim2, * dim3'], Any] | None = None#
field description: str | None = None#

Description of what this table region points to.

field name: Literal['recordings'] = 'recordings'#
field table: IntracellularRecordingsTable | None = None#

Reference to the IntracellularRecordingsTable table that this table region applies to. This specializes the attribute inherited from DynamicTableRegion to fix the type of table that can be referenced here.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#
pydantic model SimultaneousRecordingsTableRecordingsIndex#

Bases: VectorIndex

Index dataset for the recordings column.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field array: NDArray[Shape['* dim0'], Any] | NDArray[Shape['* dim0, * dim1'], Any] | NDArray[Shape['* dim0, * dim1, * dim2'], Any] | NDArray[Shape['* dim0, * dim1, * dim2, * dim3'], Any] | None = None#
field description: str | None = None#

Description of what these vectors represent.

field name: Literal['recordings_index'] = 'recordings_index'#
field target: VectorData | None = None#

Reference to the target dataset that this index applies to.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#
pydantic model SequentialRecordingsTable#

Bases: 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.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field colnames: str | None = None#

The names of the columns in this table. This should be used to specify an order to the columns.

field description: str | None = None#

Description of what is in this dynamic table.

field id: List[int] [Optional]#

Array of unique identifiers for the rows of this dynamic table.

field name: Literal['sequential_recordings'] = 'sequential_recordings'#
field simultaneous_recordings: SequentialRecordingsTableSimultaneousRecordings [Required]#

A reference to one or more rows in the SimultaneousRecordingsTable table.

field simultaneous_recordings_index: SequentialRecordingsTableSimultaneousRecordingsIndex [Required]#

Index dataset for the simultaneous_recordings column.

field stimulus_type: List[str] | None [Optional]#

The type of stimulus used for the sequential recording.

field vector_data: List[VectorData] | None [Optional]#

Vector columns, including index columns, of this dynamic table.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=True), frozen=True)#
pydantic model SequentialRecordingsTableSimultaneousRecordings#

Bases: DynamicTableRegion

A reference to one or more rows in the SimultaneousRecordingsTable table.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field array: NDArray[Shape['* dim0'], Any] | NDArray[Shape['* dim0, * dim1'], Any] | NDArray[Shape['* dim0, * dim1, * dim2'], Any] | NDArray[Shape['* dim0, * dim1, * dim2, * dim3'], Any] | None = None#
field description: str | None = None#

Description of what this table region points to.

field name: Literal['simultaneous_recordings'] = 'simultaneous_recordings'#
field table: SimultaneousRecordingsTable | None = None#

Reference to the SimultaneousRecordingsTable table that this table region applies to. This specializes the attribute inherited from DynamicTableRegion to fix the type of table that can be referenced here.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#
pydantic model SequentialRecordingsTableSimultaneousRecordingsIndex#

Bases: VectorIndex

Index dataset for the simultaneous_recordings column.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field array: NDArray[Shape['* dim0'], Any] | NDArray[Shape['* dim0, * dim1'], Any] | NDArray[Shape['* dim0, * dim1, * dim2'], Any] | NDArray[Shape['* dim0, * dim1, * dim2, * dim3'], Any] | None = None#
field description: str | None = None#

Description of what these vectors represent.

field name: Literal['simultaneous_recordings_index'] = 'simultaneous_recordings_index'#
field target: VectorData | None = None#

Reference to the target dataset that this index applies to.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#
pydantic model RepetitionsTable#

Bases: 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.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field colnames: str | None = None#

The names of the columns in this table. This should be used to specify an order to the columns.

field description: str | None = None#

Description of what is in this dynamic table.

field id: List[int] [Optional]#

Array of unique identifiers for the rows of this dynamic table.

field name: Literal['repetitions'] = 'repetitions'#
field sequential_recordings: RepetitionsTableSequentialRecordings [Required]#

A reference to one or more rows in the SequentialRecordingsTable table.

field sequential_recordings_index: RepetitionsTableSequentialRecordingsIndex [Required]#

Index dataset for the sequential_recordings column.

field vector_data: List[VectorData] | None [Optional]#

Vector columns, including index columns, of this dynamic table.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=True), frozen=True)#
pydantic model RepetitionsTableSequentialRecordings#

Bases: DynamicTableRegion

A reference to one or more rows in the SequentialRecordingsTable table.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field array: NDArray[Shape['* dim0'], Any] | NDArray[Shape['* dim0, * dim1'], Any] | NDArray[Shape['* dim0, * dim1, * dim2'], Any] | NDArray[Shape['* dim0, * dim1, * dim2, * dim3'], Any] | None = None#
field description: str | None = None#

Description of what this table region points to.

field name: Literal['sequential_recordings'] = 'sequential_recordings'#
field table: SequentialRecordingsTable | None = None#

Reference to the SequentialRecordingsTable table that this table region applies to. This specializes the attribute inherited from DynamicTableRegion to fix the type of table that can be referenced here.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#
pydantic model RepetitionsTableSequentialRecordingsIndex#

Bases: VectorIndex

Index dataset for the sequential_recordings column.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field array: NDArray[Shape['* dim0'], Any] | NDArray[Shape['* dim0, * dim1'], Any] | NDArray[Shape['* dim0, * dim1, * dim2'], Any] | NDArray[Shape['* dim0, * dim1, * dim2, * dim3'], Any] | None = None#
field description: str | None = None#

Description of what these vectors represent.

field name: Literal['sequential_recordings_index'] = 'sequential_recordings_index'#
field target: VectorData | None = None#

Reference to the target dataset that this index applies to.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#
pydantic model ExperimentalConditionsTable#

Bases: DynamicTable

A table for grouping different intracellular recording repetitions together that belong to the same experimental condition.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field colnames: str | None = None#

The names of the columns in this table. This should be used to specify an order to the columns.

field description: str | None = None#

Description of what is in this dynamic table.

field id: List[int] [Optional]#

Array of unique identifiers for the rows of this dynamic table.

field name: Literal['experimental_conditions'] = 'experimental_conditions'#
field repetitions: ExperimentalConditionsTableRepetitions [Required]#

A reference to one or more rows in the RepetitionsTable table.

field repetitions_index: ExperimentalConditionsTableRepetitionsIndex [Required]#

Index dataset for the repetitions column.

field vector_data: List[VectorData] | None [Optional]#

Vector columns, including index columns, of this dynamic table.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=True), frozen=True)#
pydantic model ExperimentalConditionsTableRepetitions#

Bases: DynamicTableRegion

A reference to one or more rows in the RepetitionsTable table.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field array: NDArray[Shape['* dim0'], Any] | NDArray[Shape['* dim0, * dim1'], Any] | NDArray[Shape['* dim0, * dim1, * dim2'], Any] | NDArray[Shape['* dim0, * dim1, * dim2, * dim3'], Any] | None = None#
field description: str | None = None#

Description of what this table region points to.

field name: Literal['repetitions'] = 'repetitions'#
field table: RepetitionsTable | None = None#

Reference to the RepetitionsTable table that this table region applies to. This specializes the attribute inherited from DynamicTableRegion to fix the type of table that can be referenced here.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#
pydantic model ExperimentalConditionsTableRepetitionsIndex#

Bases: VectorIndex

Index dataset for the repetitions column.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field array: NDArray[Shape['* dim0'], Any] | NDArray[Shape['* dim0, * dim1'], Any] | NDArray[Shape['* dim0, * dim1, * dim2'], Any] | NDArray[Shape['* dim0, * dim1, * dim2, * dim3'], Any] | None = None#
field description: str | None = None#

Description of what these vectors represent.

field name: Literal['repetitions_index'] = 'repetitions_index'#
field target: VectorData | None = None#

Reference to the target dataset that this index applies to.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)#