first draft linkml schema

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sneakers-the-rat 2023-08-16 23:47:35 -07:00
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commit e8adb4f88c
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docs/notes/linkml.md Normal file
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# LinkML
Features of LinkML to keep in mind while writing the schema.
- [Defining slots](https://linkml.io/linkml/schemas/advanced.html#defining-slots) - slots that
can be used to infer class identity - would be useful for inferring types in all the recursive
shit
- [multidimensional arrays](https://linkml.io/linkml/howtos/multidimensional-arrays.html)
- [units](https://linkml.io/linkml-model/docs/unit/)
- See also [modeling measurements](https://linkml.io/linkml/howtos/model-measurements.html)
- modeling conditional presence https://github.com/linkml/linkml-model/issues/126
## Python dataclass problems
- Generator doesn't seem to honor the `slot_usage` property - eg. for the `Schema` class,
`doc` is marked as required despite the requirement being removed

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@ -118,7 +118,7 @@ we need to map:
- `neurodata_type_inc` -> `is_a` - `neurodata_type_inc` -> `is_a`
- Groups: - Groups:
- Slots: Lots of properties are reused in the nwb spec, and LinkML lets us separate these out as slots - Slots: Lots of properties are reused in the nwb spec, and LinkML lets us separate these out as slots
- `quantity` needs a manual map - `quantity` needs a manual map to linkML's cardinality property
- dims, shape, and dtypes: these should have been just attributes rather than put in the spec - dims, shape, and dtypes: these should have been just attributes rather than put in the spec
language, so we'll just make an Array class and use that. language, so we'll just make an Array class and use that.
- dims and shape should probably be a dictionary so you don't need a zillion nulls, eg rather than - dims and shape should probably be a dictionary so you don't need a zillion nulls, eg rather than

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@ -1,7 +0,0 @@
# Example data object
---
entries:
- id: example:Namespaces001
name: foo bar
primary_email: foo.bar@example.com
age_in_years: 33

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@ -1,5 +0,0 @@
# Examples of use of nwb_schema_language
This folder contains example data conforming to nwb_schema_language
The source for these is in [src/data](../src/data/examples)

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@ -1,23 +1,104 @@
type NamedThing type AnyType
{ {
id: Uriorcurie! }
type Attribute implements DtypeMixin
{
name: String!
dims: [String]
shape: [String]
value: AnyType
defaultValue: AnyType
doc: String!
required: Boolean
dtype: String
}
type CompoundDtype
{
name: String!
doc: String!
dtype: FlatDtype!
}
type Dataset implements DtypeMixin, NamingMixin
{
neurodataTypeDef: String
neurodataTypeInc: String
name: String name: String
description: String defaultName: String
dims: [String]
shape: [String]
value: AnyType
defaultValue: AnyType
doc: String!
quantity: String
linkable: Boolean
attributes: [Attribute]
dtype: String
}
interface DtypeMixin
{
dtype: String
}
type Group implements NamingMixin
{
neurodataTypeDef: String
neurodataTypeInc: String
name: String
defaultName: String
doc: String!
quantity: String
linkable: Boolean
attributes: [Attribute]
datasets: [Dataset]
groups: [Group]
links: [Link]
}
type Link
{
name: String
doc: String!
targetType: String!
quantity: String
}
type Namespace
{
doc: String!
name: String!
fullName: String
version: String!
date: Date
author: [String]!
contact: [String]!
schema: [Schema]
} }
type Namespaces type Namespaces
{ {
id: Uriorcurie! namespaces: [Namespace]
name: String
description: String
primaryEmail: String
birthDate: Date
ageInYears: Integer
vitalStatus: PersonStatus
} }
type NamespacesCollection interface NamingMixin
{ {
entries: [Namespaces] }
type ReferenceDtype
{
targetType: String!
reftype: ReftypeOptions
}
type Schema
{
source: String
namespace: String
doc: String!
title: String
neurodataTypes: [String]
} }

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@ -1,42 +1,49 @@
{ {
"comments": { "comments": {
"description": "Auto generated by LinkML jsonld context generator", "description": "Auto generated by LinkML jsonld context generator",
"generation_date": "2023-08-16T15:57:40", "generation_date": "2023-08-16T23:21:36",
"source": "nwb_schema_language.yaml" "source": "nwb_schema_language.yaml"
}, },
"@context": { "@context": {
"PATO": {
"@id": "http://purl.obolibrary.org/obo/PATO_",
"@prefix": true
},
"biolink": "https://w3id.org/biolink/",
"example": "https://example.org/",
"linkml": "https://w3id.org/linkml/", "linkml": "https://w3id.org/linkml/",
"nwb_schema_language": "https://w3id.org/p2p_ld/nwb-schema-language/", "nwb_schema_language": "https://w3id.org/p2p_ld/nwb-schema-language/",
"schema": "http://schema.org/", "schema": {
"skos": "http://www.w3.org/2004/02/skos/core#",
"@vocab": "https://w3id.org/p2p_ld/nwb-schema-language/",
"age_in_years": {
"@type": "xsd:integer"
},
"birth_date": {
"@type": "xsd:date",
"@id": "schema:birthDate"
},
"description": {
"@id": "schema:description"
},
"id": "@id",
"name": {
"@id": "schema:name"
},
"entries": {
"@type": "@id" "@type": "@id"
}, },
"primary_email": { "skos": "http://www.w3.org/2004/02/skos/core#",
"@vocab": "https://w3id.org/p2p_ld/nwb-schema-language/",
"attributes": {
"@type": "@id"
},
"author": {
"@id": "schema:author"
},
"contact": {
"@id": "schema:email" "@id": "schema:email"
}, },
"vital_status": { "datasets": {
"@type": "@id"
},
"date": {
"@type": "xsd:date",
"@id": "schema:dateModified"
},
"default_value": {
"@type": "@id"
},
"groups": {
"@type": "@id"
},
"linkable": {
"@type": "xsd:boolean"
},
"links": {
"@type": "@id"
},
"namespaces": {
"@type": "@id"
},
"reftype": {
"@context": { "@context": {
"@vocab": "@null", "@vocab": "@null",
"text": "skos:notation", "text": "skos:notation",
@ -44,8 +51,14 @@
"meaning": "@id" "meaning": "@id"
} }
}, },
"NamedThing": { "required": {
"@id": "schema:Thing" "@type": "xsd:boolean"
},
"value": {
"@type": "@id"
},
"AnyType": {
"@id": "linkml:Any"
} }
} }
} }

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@ -1,160 +1,571 @@
{ {
"$defs": { "$defs": {
"NamedThing": { "AnyType": {
"additionalProperties": true,
"description": "",
"title": "AnyType",
"type": "object"
},
"Attribute": {
"additionalProperties": false, "additionalProperties": false,
"description": "A generic grouping for any identifiable entity", "description": "",
"properties": { "properties": {
"description": { "default_value": {
"description": "A human-readable description for a thing", "$ref": "#/$defs/AnyType",
"description": "Optional default value for variable-valued attributes."
},
"dims": {
"items": {
"type": "string"
},
"type": "array"
},
"doc": {
"description": "Description of corresponding object.",
"type": "string" "type": "string"
}, },
"id": { "dtype": {
"description": "A unique identifier for a thing", "oneOf": [
{
"$ref": "#/$defs/FlatDtype"
},
{
"$ref": "#/$defs/CompoundDtype"
},
{
"$ref": "#/$defs/ReferenceDtype"
}
],
"type": "string" "type": "string"
}, },
"name": { "name": {
"description": "A human-readable name for a thing", "type": "string"
},
"required": {
"description": "Optional boolean key describing whether the attribute is required. Default value is True.",
"type": "boolean"
},
"shape": {
"items": {
"oneOf": [
{
"minimum": 1,
"type": "integer"
},
{
"const": "null"
}
],
"type": "string"
},
"type": "array"
},
"value": {
"$ref": "#/$defs/AnyType",
"description": "Optional constant, fixed value for the attribute."
}
},
"required": [
"name",
"doc"
],
"title": "Attribute",
"type": "object"
},
"CompoundDtype": {
"additionalProperties": false,
"description": "",
"properties": {
"doc": {
"description": "Description of corresponding object.",
"type": "string"
},
"dtype": {
"$ref": "#/$defs/FlatDtype"
},
"name": {
"type": "string" "type": "string"
} }
}, },
"required": [ "required": [
"id" "name",
"doc",
"dtype"
], ],
"title": "NamedThing", "title": "CompoundDtype",
"type": "object"
},
"Dataset": {
"additionalProperties": false,
"description": "",
"properties": {
"attributes": {
"items": {
"$ref": "#/$defs/Attribute"
},
"type": "array"
},
"default_name": {
"type": "string"
},
"default_value": {
"$ref": "#/$defs/AnyType",
"description": "Optional default value for variable-valued attributes."
},
"dims": {
"items": {
"type": "string"
},
"type": "array"
},
"doc": {
"description": "Description of corresponding object.",
"type": "string"
},
"dtype": {
"oneOf": [
{
"$ref": "#/$defs/FlatDtype"
},
{
"$ref": "#/$defs/CompoundDtype"
},
{
"$ref": "#/$defs/ReferenceDtype"
}
],
"type": "string"
},
"linkable": {
"type": "boolean"
},
"name": {
"type": "string"
},
"neurodata_type_def": {
"type": "string"
},
"neurodata_type_inc": {
"type": "string"
},
"quantity": {
"anyOf": [
{
"minimum": 1,
"type": "integer"
},
{
"$ref": "#/$defs/QuantityEnum"
}
],
"type": "string"
},
"shape": {
"items": {
"oneOf": [
{
"minimum": 1,
"type": "integer"
},
{
"const": "null"
}
],
"type": "string"
},
"type": "array"
},
"value": {
"$ref": "#/$defs/AnyType",
"description": "Optional constant, fixed value for the attribute."
}
},
"required": [
"doc"
],
"title": "Dataset",
"type": "object"
},
"FlatDtype": {
"description": "",
"enum": [
"float",
"float32",
"double",
"float64",
"long",
"int64",
"int",
"int32",
"int16",
"short",
"int8",
"uint",
"uint32",
"uint16",
"uint8",
"uint64",
"numeric",
"text",
"utf",
"utf8",
"utf-8",
"ascii",
"bool",
"isodatetime"
],
"title": "FlatDtype",
"type": "string"
},
"Group": {
"additionalProperties": false,
"description": "",
"properties": {
"attributes": {
"items": {
"$ref": "#/$defs/Attribute"
},
"type": "array"
},
"datasets": {
"items": {
"$ref": "#/$defs/Dataset"
},
"type": "array"
},
"default_name": {
"type": "string"
},
"doc": {
"description": "Description of corresponding object.",
"type": "string"
},
"groups": {
"items": {
"$ref": "#/$defs/Group"
},
"type": "array"
},
"linkable": {
"type": "boolean"
},
"links": {
"items": {
"$ref": "#/$defs/Link"
},
"type": "array"
},
"name": {
"type": "string"
},
"neurodata_type_def": {
"type": "string"
},
"neurodata_type_inc": {
"type": "string"
},
"quantity": {
"anyOf": [
{
"minimum": 1,
"type": "integer"
},
{
"$ref": "#/$defs/QuantityEnum"
}
],
"type": "string"
}
},
"required": [
"doc"
],
"title": "Group",
"type": "object"
},
"Link": {
"additionalProperties": false,
"description": "",
"properties": {
"doc": {
"description": "Description of corresponding object.",
"type": "string"
},
"name": {
"type": "string"
},
"quantity": {
"anyOf": [
{
"minimum": 1,
"type": "integer"
},
{
"$ref": "#/$defs/QuantityEnum"
}
],
"type": "string"
},
"target_type": {
"anyOf": [
{
"$ref": "#/$defs/Dataset"
},
{
"$ref": "#/$defs/Group"
}
],
"description": "Describes the neurodata_type of the target that the reference points to",
"type": "string"
}
},
"required": [
"doc",
"target_type"
],
"title": "Link",
"type": "object"
},
"Namespace": {
"additionalProperties": false,
"description": "",
"properties": {
"author": {
"description": "List of strings with the names of the authors of the namespace.",
"items": {
"type": "string"
},
"type": "array"
},
"contact": {
"description": "List of strings with the contact information for the authors. Ordering of the contacts should match the ordering of the authors.",
"items": {
"type": "string"
},
"type": "array"
},
"date": {
"description": "Date that a namespace was last modified or released",
"format": "date",
"type": "string"
},
"doc": {
"description": "Description of corresponding object.",
"type": "string"
},
"full_name": {
"description": "Optional string with extended full name for the namespace.",
"type": "string"
},
"name": {
"type": "string"
},
"schema": {
"description": "List of the schema to be included in this namespace.",
"items": {
"$ref": "#/$defs/Schema"
},
"type": "array"
},
"version": {
"pattern": "^(0|[1-9]\\d*)\\.(0|[1-9]\\d*)\\.(0|[1-9]\\d*)(?:-((?:0|[1-9]\\d*|\\d*[a-zA-Z-][0-9a-zA-Z-]*)(?:\\.(?:0|[1-9]\\d*|\\d*[a-zA-Z-][0-9a-zA-Z-]*))*))?(?:\\+([0-9a-zA-Z-]+(?:\\.[0-9a-zA-Z-]+)*))?$",
"type": "string"
}
},
"required": [
"doc",
"name",
"version",
"author",
"contact"
],
"title": "Namespace",
"type": "object" "type": "object"
}, },
"Namespaces": { "Namespaces": {
"additionalProperties": false, "additionalProperties": false,
"description": "Represents a Namespaces", "description": "",
"properties": { "properties": {
"age_in_years": { "namespaces": {
"description": "Number of years since birth", "items": {
"type": "integer" "$ref": "#/$defs/Namespace"
},
"type": "array"
}
},
"title": "Namespaces",
"type": "object"
},
"QuantityEnum": {
"description": "",
"enum": [
"*",
"?",
"+",
"zero_or_many",
"one_or_many",
"zero_or_one"
],
"title": "QuantityEnum",
"type": "string"
},
"ReferenceDtype": {
"additionalProperties": false,
"description": "",
"properties": {
"reftype": {
"$ref": "#/$defs/ReftypeOptions",
"description": "describes the kind of reference"
}, },
"birth_date": { "target_type": {
"description": "Date on which a person is born", "anyOf": [
"format": "date", {
"$ref": "#/$defs/Dataset"
},
{
"$ref": "#/$defs/Group"
}
],
"description": "Describes the neurodata_type of the target that the reference points to",
"type": "string" "type": "string"
},
"description": {
"description": "A human-readable description for a thing",
"type": "string"
},
"id": {
"description": "A unique identifier for a thing",
"type": "string"
},
"name": {
"description": "A human-readable name for a thing",
"type": "string"
},
"primary_email": {
"description": "The main email address of a person",
"pattern": "^\\S+@[\\S+\\.]+\\S+",
"type": "string"
},
"vital_status": {
"$ref": "#/$defs/PersonStatus",
"description": "living or dead status"
} }
}, },
"required": [ "required": [
"id" "target_type"
], ],
"title": "Namespaces", "title": "ReferenceDtype",
"type": "object" "type": "object"
}, },
"NamespacesCollection": { "ReftypeOptions": {
"additionalProperties": false,
"description": "A holder for Namespaces objects",
"properties": {
"entries": {
"additionalProperties": {
"anyOf": [
{
"$ref": "#/$defs/Namespaces__identifier_optional"
},
{
"type": "null"
}
]
},
"type": "object"
}
},
"title": "NamespacesCollection",
"type": "object"
},
"Namespaces__identifier_optional": {
"additionalProperties": false,
"description": "Represents a Namespaces",
"properties": {
"age_in_years": {
"description": "Number of years since birth",
"type": "integer"
},
"birth_date": {
"description": "Date on which a person is born",
"format": "date",
"type": "string"
},
"description": {
"description": "A human-readable description for a thing",
"type": "string"
},
"id": {
"description": "A unique identifier for a thing",
"type": "string"
},
"name": {
"description": "A human-readable name for a thing",
"type": "string"
},
"primary_email": {
"description": "The main email address of a person",
"pattern": "^\\S+@[\\S+\\.]+\\S+",
"type": "string"
},
"vital_status": {
"$ref": "#/$defs/PersonStatus",
"description": "living or dead status"
}
},
"required": [],
"title": "Namespaces",
"type": "object"
},
"PersonStatus": {
"description": "", "description": "",
"enum": [ "enum": [
"ALIVE", "ref",
"DEAD", "reference",
"UNKNOWN" "object",
"region"
], ],
"title": "PersonStatus", "title": "ReftypeOptions",
"type": "string" "type": "string"
},
"Schema": {
"additionalProperties": false,
"allOf": [
{
"if": {
"properties": {
"namespace": {}
},
"required": [
"namespace"
]
},
"then": {
"properties": {
"source": {}
},
"required": [
"source"
]
}
},
{
"if": {
"properties": {
"source": {}
},
"required": [
"source"
]
},
"then": {
"properties": {
"namespace": {}
},
"required": [
"namespace"
]
}
},
{
"if": {
"properties": {
"namespace": {}
},
"required": [
"namespace"
]
},
"then": {
"properties": {
"source": {}
},
"required": [
"source"
]
}
},
{
"if": {
"properties": {
"source": {}
},
"required": [
"source"
]
},
"then": {
"properties": {
"namespace": {}
},
"required": [
"namespace"
]
}
}
],
"description": "",
"properties": {
"doc": {
"description": "Description of corresponding object.",
"type": "string"
},
"namespace": {
"description": "describes a named reference to another namespace. In contrast to source, this is a reference by name to a known namespace (i.e., the namespace is resolved during the build and must point to an already existing namespace). This mechanism is used to allow, e.g., extension of a core namespace (here the NWB core namespace) without requiring hard paths to the files describing the core namespace. Either source or namespace must be specified, but not both.",
"type": "string"
},
"neurodata_types": {
"description": "an optional list of strings indicating which data types should be included from the given specification source or namespace. The default is null indicating that all data types should be included.",
"items": {
"anyOf": [
{
"$ref": "#/$defs/Dataset"
},
{
"$ref": "#/$defs/Group"
}
],
"type": "string"
},
"type": "array"
},
"source": {
"description": "describes the name of the YAML (or JSON) file with the schema specification. The schema files should be located in the same folder as the namespace file.",
"pattern": ".*\\.(yml|yaml|json)",
"type": "string"
},
"title": {
"description": "a descriptive title for a file for documentation purposes.",
"type": "string"
}
},
"title": "Schema",
"type": "object"
} }
}, },
"$id": "https://w3id.org/p2p_ld/nwb-schema-language", "$id": "https://w3id.org/p2p_ld/nwb-schema-language",
"$schema": "http://json-schema.org/draft-07/schema#", "$schema": "http://json-schema.org/draft-07/schema#",
"additionalProperties": true, "additionalProperties": true,
"description": "A holder for Namespaces objects",
"metamodel_version": "1.7.0", "metamodel_version": "1.7.0",
"properties": {
"entries": {
"additionalProperties": {
"anyOf": [
{
"$ref": "#/$defs/Namespaces__identifier_optional"
},
{
"type": "null"
}
]
},
"type": "object"
}
},
"title": "nwb-schema-language", "title": "nwb-schema-language",
"type": "object", "type": "object",
"version": null "version": null

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@ -1,11 +1,8 @@
{ {
"PATO": "http://purl.obolibrary.org/obo/PATO_",
"biolink": "https://w3id.org/biolink/",
"example": "https://example.org/",
"linkml": "https://w3id.org/linkml/", "linkml": "https://w3id.org/linkml/",
"nwb_schema_language": "https://w3id.org/p2p_ld/nwb-schema-language/", "nwb_schema_language": "https://w3id.org/p2p_ld/nwb-schema-language/",
"schema": "http://schema.org/", "schema": "http://schema.org/",
"NamedThing": { "AnyType": {
"@id": "schema:Thing" "@id": "linkml:Any"
} }
} }

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@ -1,23 +1,82 @@
// A generic grouping for any identifiable entity message Attribute
message NamedThing
{ {
uriorcurie id = 0
string name = 0 string name = 0
string description = 0 repeated string dims = 0
repeated string shape = 0
anyType value = 0
anyType defaultValue = 0
string doc = 0
boolean required = 0
string dtype = 0
}
message CompoundDtype
{
string name = 0
string doc = 0
flatDtype dtype = 0
}
message Dataset
{
string neurodataTypeDef = 0
string neurodataTypeInc = 0
string name = 0
string defaultName = 0
repeated string dims = 0
repeated string shape = 0
anyType value = 0
anyType defaultValue = 0
string doc = 0
string quantity = 0
boolean linkable = 0
repeated attribute attributes = 0
string dtype = 0
}
message Group
{
string neurodataTypeDef = 0
string neurodataTypeInc = 0
string name = 0
string defaultName = 0
string doc = 0
string quantity = 0
boolean linkable = 0
repeated attribute attributes = 0
repeated dataset datasets = 0
repeated group groups = 0
repeated link links = 0
}
message Link
{
string name = 0
string doc = 0
string targetType = 0
string quantity = 0
}
message Namespace
{
string doc = 0
string name = 0
string fullName = 0
string version = 0
date date = 0
repeated string author = 0
repeated string contact = 0
repeated schema schema = 0
} }
// Represents a Namespaces
message Namespaces message Namespaces
{ {
uriorcurie id = 0 repeated namespace namespaces = 0
string name = 0
string description = 0
string primaryEmail = 0
date birthDate = 0
integer ageInYears = 0
personStatus vitalStatus = 0
} }
// A holder for Namespaces objects message ReferenceDtype
message NamespacesCollection
{ {
repeated namespaces entries = 0 string targetType = 0
reftypeOptions reftype = 0
}
message Schema
{
string source = 0
string namespace = 0
string doc = 0
string title = 0
repeated string neurodataTypes = 0
} }

View file

@ -43,31 +43,137 @@ linkml:Jsonpath xsd:string
linkml:Sparqlpath xsd:string linkml:Sparqlpath xsd:string
<NamedThing> ( <AnyType> CLOSED {
CLOSED { ( $<AnyType_tes> rdf:type . * ;
( $<NamedThing_tes> ( schema1:name @linkml:String ? ; rdf:type [ linkml:Any ] ?
schema1:description @linkml:String ? )
) ; }
rdf:type [ schema1:Thing ]
) <Attribute> CLOSED {
} OR @<Namespaces> ( $<Attribute_tes> ( &<DtypeMixin_tes> ;
) rdf:type [ <DtypeMixin> ] ? ;
<name> @linkml:String ;
<dims> @linkml:String * ;
<shape> @linkml:String * ;
<value> @<AnyType> ? ;
<default_value> @<AnyType> ? ;
<doc> @linkml:String ;
<required> @linkml:Boolean ? ;
<dtype> @linkml:String ?
) ;
rdf:type [ <Attribute> ] ?
)
}
<CompoundDtype> CLOSED {
( $<CompoundDtype_tes> ( <name> @linkml:String ;
<doc> @linkml:String ;
<dtype> @<FlatDtype>
) ;
rdf:type [ <CompoundDtype> ] ?
)
}
<Dataset> CLOSED {
( $<Dataset_tes> ( &<DtypeMixin_tes> ;
rdf:type [ <DtypeMixin> ] ? ;
&<NamingMixin_tes> ;
rdf:type [ <NamingMixin> ] ? ;
<neurodata_type_def> @linkml:String ? ;
<neurodata_type_inc> @linkml:String ? ;
<name> @linkml:String ? ;
<default_name> @linkml:String ? ;
<dims> @linkml:String * ;
<shape> @linkml:String * ;
<value> @<AnyType> ? ;
<default_value> @<AnyType> ? ;
<doc> @linkml:String ;
<quantity> @linkml:String ? ;
<linkable> @linkml:Boolean ? ;
<attributes> @<Attribute> * ;
<dtype> @linkml:String ?
) ;
rdf:type [ <Dataset> ] ?
)
}
<DtypeMixin> {
( $<DtypeMixin_tes> <dtype> @linkml:String ? ;
rdf:type [ <DtypeMixin> ] ?
)
}
<Group> CLOSED {
( $<Group_tes> ( &<NamingMixin_tes> ;
rdf:type [ <NamingMixin> ] ? ;
<neurodata_type_def> @linkml:String ? ;
<neurodata_type_inc> @linkml:String ? ;
<name> @linkml:String ? ;
<default_name> @linkml:String ? ;
<doc> @linkml:String ;
<quantity> @linkml:String ? ;
<linkable> @linkml:Boolean ? ;
<attributes> @<Attribute> * ;
<datasets> @<Dataset> * ;
<groups> @<Group> * ;
<links> @<Link> *
) ;
rdf:type [ <Group> ] ?
)
}
<Link> CLOSED {
( $<Link_tes> ( <name> @linkml:String ? ;
<doc> @linkml:String ;
<target_type> @linkml:String ;
<quantity> @linkml:String ?
) ;
rdf:type [ <Link> ] ?
)
}
<Namespace> CLOSED {
( $<Namespace_tes> ( <doc> @linkml:String ;
<name> @linkml:String ;
<full_name> @linkml:String ? ;
<version> @linkml:String ;
schema1:dateModified @linkml:Date ? ;
schema1:author @linkml:String + ;
schema1:email @linkml:String + ;
<schema> @<Schema> *
) ;
rdf:type [ <Namespace> ] ?
)
}
<Namespaces> CLOSED { <Namespaces> CLOSED {
( $<Namespaces_tes> ( &<NamedThing_tes> ; ( $<Namespaces_tes> <namespaces> @<Namespace> * ;
rdf:type [ schema1:Thing ] ? ; rdf:type [ <Namespaces> ] ?
schema1:email @linkml:String ? ; )
schema1:birthDate @linkml:Date ? ; }
<age_in_years> @linkml:Integer ? ;
<vital_status> @<PersonStatus> ? <NamingMixin> {
( $<NamingMixin_tes> rdf:type . * ;
rdf:type [ <NamingMixin> ] ?
)
}
<ReferenceDtype> CLOSED {
( $<ReferenceDtype_tes> ( <target_type> @linkml:String ;
<reftype> @<ReftypeOptions> ?
) ; ) ;
rdf:type [ <Namespaces> ] rdf:type [ <ReferenceDtype> ] ?
) )
} }
<NamespacesCollection> CLOSED { <Schema> CLOSED {
( $<NamespacesCollection_tes> <entries> @<Namespaces> * ; ( $<Schema_tes> ( <source> @linkml:String ? ;
rdf:type [ <NamespacesCollection> ] ? <namespace> @linkml:String ? ;
<doc> @linkml:String ;
<title> @linkml:String ? ;
<neurodata_types> @linkml:String *
) ;
rdf:type [ <Schema> ] ?
) )
} }

View file

@ -1,24 +1,92 @@
CREATE TABLE "NamedThing" ( CREATE TABLE "Attribute" (
id TEXT NOT NULL, name TEXT NOT NULL,
dims TEXT,
shape TEXT,
value TEXT,
default_value TEXT,
doc TEXT NOT NULL,
required BOOLEAN,
dtype TEXT,
PRIMARY KEY (name, dims, shape, value, default_value, doc, required, dtype)
);
CREATE TABLE "CompoundDtype" (
name TEXT NOT NULL,
doc TEXT NOT NULL,
dtype VARCHAR(11) NOT NULL,
PRIMARY KEY (name, doc, dtype)
);
CREATE TABLE "Dataset" (
neurodata_type_def TEXT,
neurodata_type_inc TEXT,
name TEXT, name TEXT,
description TEXT, default_name TEXT,
PRIMARY KEY (id) dims TEXT,
shape TEXT,
value TEXT,
default_value TEXT,
doc TEXT NOT NULL,
quantity TEXT,
linkable BOOLEAN,
attributes TEXT,
dtype TEXT,
PRIMARY KEY (neurodata_type_def, neurodata_type_inc, name, default_name, dims, shape, value, default_value, doc, quantity, linkable, attributes, dtype)
);
CREATE TABLE "Group" (
neurodata_type_def TEXT,
neurodata_type_inc TEXT,
name TEXT,
default_name TEXT,
doc TEXT NOT NULL,
quantity TEXT,
linkable BOOLEAN,
attributes TEXT,
datasets TEXT,
groups TEXT,
links TEXT,
PRIMARY KEY (neurodata_type_def, neurodata_type_inc, name, default_name, doc, quantity, linkable, attributes, datasets, groups, links)
);
CREATE TABLE "Link" (
name TEXT,
doc TEXT NOT NULL,
target_type TEXT NOT NULL,
quantity TEXT,
PRIMARY KEY (name, doc, target_type, quantity)
);
CREATE TABLE "Namespace" (
doc TEXT NOT NULL,
name TEXT NOT NULL,
full_name TEXT,
version TEXT NOT NULL,
date DATE,
author TEXT NOT NULL,
contact TEXT NOT NULL,
schema TEXT,
PRIMARY KEY (doc, name, full_name, version, date, author, contact, schema)
); );
CREATE TABLE "Namespaces" ( CREATE TABLE "Namespaces" (
id TEXT NOT NULL, namespaces TEXT,
name TEXT, PRIMARY KEY (namespaces)
description TEXT,
primary_email TEXT,
birth_date DATE,
age_in_years INTEGER,
vital_status VARCHAR(7),
PRIMARY KEY (id)
); );
CREATE TABLE "NamespacesCollection" ( CREATE TABLE "ReferenceDtype" (
entries TEXT, target_type TEXT NOT NULL,
PRIMARY KEY (entries) reftype VARCHAR(9),
PRIMARY KEY (target_type, reftype)
);
CREATE TABLE "Schema" (
source TEXT,
namespace TEXT,
doc TEXT NOT NULL,
title TEXT,
neurodata_types TEXT,
PRIMARY KEY (source, namespace, doc, title, neurodata_types)
); );

View file

@ -5,6 +5,9 @@ description = "Translation of the nwb-schema-language to LinkML"
authors = ["Jonny Saunders <j@nny.fyi>"] authors = ["Jonny Saunders <j@nny.fyi>"]
license = "GNU GPL v3.0" license = "GNU GPL v3.0"
readme = "README.md" readme = "README.md"
packages = [
{ include = "nwb_schema_language", from="src"}
]
include = ["README.md", "src/nwb_schema_language/schema", "project"] include = ["README.md", "src/nwb_schema_language/schema", "project"]
[tool.poetry.dependencies] [tool.poetry.dependencies]

View file

@ -1,7 +0,0 @@
# Example data object
---
entries:
- id: example:Namespaces001
name: foo bar
primary_email: foo.bar@example.com
age_in_years: 33

View file

@ -0,0 +1,264 @@
datasets:
- neurodata_type_def: NWBData
neurodata_type_inc: Data
doc: An abstract data type for a dataset.
- neurodata_type_def: TimeSeriesReferenceVectorData
neurodata_type_inc: VectorData
default_name: timeseries
dtype:
- name: idx_start
dtype: int32
doc: Start index into the TimeSeries 'data' and 'timestamp' datasets of the referenced
TimeSeries. The first dimension of those arrays is always time.
- name: count
dtype: int32
doc: Number of data samples available in this time series, during this epoch
- name: timeseries
dtype:
target_type: TimeSeries
reftype: object
doc: The TimeSeries that this index applies to
doc: 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.
- neurodata_type_def: Image
neurodata_type_inc: NWBData
dtype: numeric
dims:
- - x
- y
- - x
- y
- r, g, b
- - x
- y
- r, g, b, a
shape:
- - null
- null
- - null
- null
- 3
- - null
- null
- 4
doc: 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)).
attributes:
- name: resolution
dtype: float32
doc: Pixel resolution of the image, in pixels per centimeter.
required: false
- name: description
dtype: text
doc: Description of the image.
required: false
- neurodata_type_def: ImageReferences
neurodata_type_inc: NWBData
dtype:
target_type: Image
reftype: object
dims:
- num_images
shape:
- null
doc: Ordered dataset of references to Image objects.
groups:
- neurodata_type_def: NWBContainer
neurodata_type_inc: Container
doc: An abstract data type for a generic container storing collections of data and
metadata. Base type for all data and metadata containers.
- neurodata_type_def: NWBDataInterface
neurodata_type_inc: NWBContainer
doc: An abstract data type for a generic container storing collections of data,
as opposed to metadata.
- neurodata_type_def: TimeSeries
neurodata_type_inc: NWBDataInterface
doc: General purpose time series.
attributes:
- name: description
dtype: text
default_value: no description
doc: Description of the time series.
required: false
- name: comments
dtype: text
default_value: no comments
doc: 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.
required: false
datasets:
- name: data
dims:
- - num_times
- - num_times
- num_DIM2
- - num_times
- num_DIM2
- num_DIM3
- - num_times
- num_DIM2
- num_DIM3
- num_DIM4
shape:
- - null
- - null
- null
- - null
- null
- null
- - null
- null
- null
- null
doc: 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.
attributes:
- name: conversion
dtype: float32
default_value: 1.0
doc: Scalar to multiply each element in data to convert it to the specified 'unit'.
If the data are stored in acquisition system units or other units
that require a conversion to be interpretable, multiply the data by 'conversion'
to convert the data to the specified 'unit'. e.g. if the data acquisition system
stores values in this object as signed 16-bit integers (int16 range
-32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data
acquisition system gain is 8000X, then the 'conversion' multiplier to get from
raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.
required: false
- name: offset
dtype: float32
default_value: 0.0
doc: Scalar to add to the data after scaling by 'conversion' to finalize its coercion
to the specified 'unit'. Two common examples of this include (a) data stored in an
unsigned type that requires a shift after scaling to re-center the data,
and (b) specialized recording devices that naturally cause a scalar offset with
respect to the true units.
required: false
- name: resolution
dtype: float32
default_value: -1.0
doc: Smallest meaningful difference between values in data, stored in the specified
by unit, e.g., the change in value of the least significant bit, or a larger
number if signal noise is known to be present. If unknown, use -1.0.
required: false
- name: unit
dtype: text
doc: Base unit of measurement for working with the data. Actual stored values are
not necessarily stored in these units. To access the data in these units,
multiply 'data' by 'conversion' and add 'offset'.
- name: continuity
dtype: text
doc: Optionally describe the continuity of the data. Can be "continuous", "instantaneous", or
"step". For example, a voltage trace would be "continuous", because samples
are recorded from a continuous process. An array of lick times would be "instantaneous",
because the data represents distinct moments in time. Times of image presentations would be
"step" because the picture remains the same until the next timepoint. This field is optional,
but is useful in providing information about the underlying data. It may inform the way this
data is interpreted, the way it is visualized, and what analysis methods are applicable.
required: false
- name: starting_time
dtype: float64
doc: 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.
quantity: '?'
attributes:
- name: rate
dtype: float32
doc: Sampling rate, in Hz.
- name: unit
dtype: text
value: seconds
doc: Unit of measurement for time, which is fixed to 'seconds'.
- name: timestamps
dtype: float64
dims:
- num_times
shape:
- null
doc: Timestamps for samples stored in data, in seconds, relative to the
common experiment master-clock stored in NWBFile.timestamps_reference_time.
quantity: '?'
attributes:
- name: interval
dtype: int32
value: 1
doc: Value is '1'
- name: unit
dtype: text
value: seconds
doc: Unit of measurement for timestamps, which is fixed to 'seconds'.
- name: control
dtype: uint8
dims:
- num_times
shape:
- null
doc: 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.
quantity: '?'
- name: control_description
dtype: text
dims:
- num_control_values
shape:
- null
doc: Description of each control value. Must be present if control is present.
If present, control_description[0] should describe time points where control == 0.
quantity: '?'
groups:
- name: sync
doc: 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.
quantity: '?'
- neurodata_type_def: ProcessingModule
neurodata_type_inc: NWBContainer
doc: A collection of processed data.
attributes:
- name: description
dtype: text
doc: Description of this collection of processed data.
groups:
- neurodata_type_inc: NWBDataInterface
doc: Data objects stored in this collection.
quantity: '*'
- neurodata_type_inc: DynamicTable
doc: Tables stored in this collection.
quantity: '*'
- neurodata_type_def: Images
neurodata_type_inc: NWBDataInterface
default_name: Images
doc: A collection of images with an optional way to specify the order of the images
using the "order_of_images" dataset. An order must be specified if the images are
referenced by index, e.g., from an IndexSeries.
attributes:
- name: description
dtype: text
doc: Description of this collection of images.
datasets:
- neurodata_type_inc: Image
doc: Images stored in this collection.
quantity: '+'
- name: order_of_images
neurodata_type_inc: ImageReferences
doc: Ordered dataset of references to Image objects stored in the parent group.
Each Image object in the Images group should be stored once and only once, so
the dataset should have the same length as the number of images.
quantity: '?'

View file

@ -0,0 +1,124 @@
groups:
- neurodata_type_def: SpatialSeries
neurodata_type_inc: TimeSeries
doc: "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."
datasets:
- name: data
dtype: numeric
dims:
- - num_times
- - num_times
- x
- - num_times
- x,y
- - num_times
- x,y,z
shape:
- - null
- - null
- 1
- - null
- 2
- - null
- 3
doc: 1-D or 2-D array storing position or direction relative to some reference frame.
attributes:
- name: unit
dtype: text
default_value: meters
doc: Base unit of measurement for working with the data. The default value
is 'meters'. Actual stored values are not necessarily stored in these units.
To access the data in these units, multiply 'data' by 'conversion' and add 'offset'.
required: false
- name: reference_frame
dtype: text
doc: Description defining what exactly 'straight-ahead' means.
quantity: '?'
- neurodata_type_def: BehavioralEpochs
neurodata_type_inc: NWBDataInterface
default_name: BehavioralEpochs
doc: 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.
groups:
- neurodata_type_inc: IntervalSeries
doc: IntervalSeries object containing start and stop times of epochs.
quantity: '*'
- neurodata_type_def: BehavioralEvents
neurodata_type_inc: NWBDataInterface
default_name: BehavioralEvents
doc: TimeSeries for storing behavioral events. See description of <a href="#BehavioralEpochs">BehavioralEpochs</a>
for more details.
groups:
- neurodata_type_inc: TimeSeries
doc: TimeSeries object containing behavioral events.
quantity: '*'
- neurodata_type_def: BehavioralTimeSeries
neurodata_type_inc: NWBDataInterface
default_name: BehavioralTimeSeries
doc: TimeSeries for storing Behavoioral time series data. See description of <a href="#BehavioralEpochs">BehavioralEpochs</a>
for more details.
groups:
- neurodata_type_inc: TimeSeries
doc: TimeSeries object containing continuous behavioral data.
quantity: '*'
- neurodata_type_def: PupilTracking
neurodata_type_inc: NWBDataInterface
default_name: PupilTracking
doc: Eye-tracking data, representing pupil size.
groups:
- neurodata_type_inc: TimeSeries
doc: TimeSeries object containing time series data on pupil size.
quantity: '+'
- neurodata_type_def: EyeTracking
neurodata_type_inc: NWBDataInterface
default_name: EyeTracking
doc: Eye-tracking data, representing direction of gaze.
groups:
- neurodata_type_inc: SpatialSeries
doc: SpatialSeries object containing data measuring direction of gaze.
quantity: '*'
- neurodata_type_def: CompassDirection
neurodata_type_inc: NWBDataInterface
default_name: CompassDirection
doc: 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.
groups:
- neurodata_type_inc: SpatialSeries
doc: SpatialSeries object containing direction of gaze travel.
quantity: '*'
- neurodata_type_def: Position
neurodata_type_inc: NWBDataInterface
default_name: Position
doc: Position data, whether along the x, x/y or x/y/z axis.
groups:
- neurodata_type_inc: SpatialSeries
doc: SpatialSeries object containing position data.
quantity: '+'

View file

@ -0,0 +1,14 @@
groups:
- neurodata_type_def: Device
neurodata_type_inc: NWBContainer
doc: Metadata about a data acquisition device, e.g., recording system, electrode, microscope.
attributes:
- name: description
dtype: text
doc: Description of the device (e.g., model, firmware version, processing software version, etc.)
as free-form text.
required: false
- name: manufacturer
dtype: text
doc: The name of the manufacturer of the device.
required: false

View file

@ -0,0 +1,333 @@
groups:
- neurodata_type_def: ElectricalSeries
neurodata_type_inc: TimeSeries
doc: 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.
attributes:
- name: filtering
dtype: text
doc: Filtering applied to all channels of the data. For example, if this ElectricalSeries represents
high-pass-filtered data (also known as AP Band), then this value could be "High-pass 4-pole Bessel filter
at 500 Hz". If this ElectricalSeries represents low-pass-filtered LFP data and the type of filter is unknown,
then this value could be "Low-pass filter at 300 Hz". If a non-standard filter type is used, provide as much
detail about the filter properties as possible.
required: false
datasets:
- name: data
dtype: numeric
dims:
- - num_times
- - num_times
- num_channels
- - num_times
- num_channels
- num_samples
shape:
- - null
- - null
- null
- - null
- null
- null
doc: Recorded voltage data.
attributes:
- name: unit
dtype: text
value: volts
doc: Base unit of measurement for working with the data. This value is fixed to
'volts'. Actual stored values are not necessarily stored in these units. To
access the data in these units, multiply 'data' by 'conversion', followed by
'channel_conversion' (if present), and then add 'offset'.
- name: electrodes
neurodata_type_inc: DynamicTableRegion
doc: DynamicTableRegion pointer to the electrodes that this time series was generated from.
- name: channel_conversion
dtype: float32
dims:
- num_channels
shape:
- null
doc: Channel-specific conversion factor. Multiply the data in the 'data' dataset by these
values along the channel axis (as indicated by axis attribute) AND by the global
conversion factor in the 'conversion' attribute of 'data' to get the data values in
Volts, i.e, data in Volts = data * data.conversion * channel_conversion. This
approach allows for both global and per-channel data conversion factors needed
to support the storage of electrical recordings as native values generated by data
acquisition systems. If this dataset is not present, then there is no channel-specific
conversion factor, i.e. it is 1 for all channels.
quantity: '?'
attributes:
- name: axis
dtype: int32
value: 1
doc: The zero-indexed axis of the 'data' dataset that the channel-specific conversion
factor corresponds to. This value is fixed to 1.
- neurodata_type_def: SpikeEventSeries
neurodata_type_inc: ElectricalSeries
doc: "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)."
datasets:
- name: data
dtype: numeric
dims:
- - num_events
- num_samples
- - num_events
- num_channels
- num_samples
shape:
- - null
- null
- - null
- null
- null
doc: Spike waveforms.
attributes:
- name: unit
dtype: text
value: volts
doc: Unit of measurement for waveforms, which is fixed to 'volts'.
- name: timestamps
dtype: float64
dims:
- num_times
shape:
- null
doc: Timestamps for samples stored in data, in seconds, relative to the
common experiment master-clock stored in NWBFile.timestamps_reference_time.
Timestamps are required for the events. Unlike for TimeSeries, timestamps are
required for SpikeEventSeries and are thus re-specified here.
attributes:
- name: interval
dtype: int32
value: 1
doc: Value is '1'
- name: unit
dtype: text
value: seconds
doc: Unit of measurement for timestamps, which is fixed to 'seconds'.
- neurodata_type_def: FeatureExtraction
neurodata_type_inc: NWBDataInterface
default_name: FeatureExtraction
doc: Features, such as PC1 and PC2, that are extracted from signals stored in a
SpikeEventSeries or other source.
datasets:
- name: description
dtype: text
dims:
- num_features
shape:
- null
doc: Description of features (eg, ''PC1'') for each of the extracted features.
- name: features
dtype: float32
dims:
- num_events
- num_channels
- num_features
shape:
- null
- null
- null
doc: Multi-dimensional array of features extracted from each event.
- name: times
dtype: float64
dims:
- num_events
shape:
- null
doc: Times of events that features correspond to (can be a link).
- name: electrodes
neurodata_type_inc: DynamicTableRegion
doc: DynamicTableRegion pointer to the electrodes that this time series was generated from.
- neurodata_type_def: EventDetection
neurodata_type_inc: NWBDataInterface
default_name: EventDetection
doc: Detected spike events from voltage trace(s).
datasets:
- name: detection_method
dtype: text
doc: Description of how events were detected, such as voltage threshold, or dV/dT
threshold, as well as relevant values.
- name: source_idx
dtype: int32
dims:
- num_events
shape:
- null
doc: Indices (zero-based) into source ElectricalSeries::data array corresponding
to time of event. ''description'' should define what is meant by time of
event (e.g., .25 ms before action potential peak, zero-crossing time, etc).
The index points to each event from the raw data.
- name: times
dtype: float64
dims:
- num_events
shape:
- null
doc: Timestamps of events, in seconds.
attributes:
- name: unit
dtype: text
value: seconds
doc: Unit of measurement for event times, which is fixed to 'seconds'.
links:
- name: source_electricalseries
target_type: ElectricalSeries
doc: Link to the ElectricalSeries that this data was calculated from. Metadata
about electrodes and their position can be read from that ElectricalSeries so
it's not necessary to include that information here.
- neurodata_type_def: EventWaveform
neurodata_type_inc: NWBDataInterface
default_name: EventWaveform
doc: 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.
groups:
- neurodata_type_inc: SpikeEventSeries
doc: SpikeEventSeries object(s) containing detected spike event waveforms.
quantity: '*'
- neurodata_type_def: FilteredEphys
neurodata_type_inc: NWBDataInterface
default_name: FilteredEphys
doc: 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.
groups:
- neurodata_type_inc: ElectricalSeries
doc: ElectricalSeries object(s) containing filtered electrophysiology data.
quantity: '+'
- neurodata_type_def: LFP
neurodata_type_inc: NWBDataInterface
default_name: LFP
doc: 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.
groups:
- neurodata_type_inc: ElectricalSeries
doc: ElectricalSeries object(s) containing LFP data for one or more channels.
quantity: '+'
- neurodata_type_def: ElectrodeGroup
neurodata_type_inc: NWBContainer
doc: A physical grouping of electrodes, e.g. a shank of an array.
attributes:
- name: description
dtype: text
doc: Description of this electrode group.
- name: location
dtype: text
doc: Location of electrode group. Specify the area, layer, comments on estimation
of area/layer, etc. Use standard atlas names for anatomical regions when possible.
datasets:
- name: position
dtype:
- name: x
dtype: float32
doc: x coordinate
- name: y
dtype: float32
doc: y coordinate
- name: z
dtype: float32
doc: z coordinate
doc: stereotaxic or common framework coordinates
quantity: '?'
links:
- name: device
target_type: Device
doc: Link to the device that was used to record from this electrode group.
# The types below have been deprecated
- neurodata_type_def: ClusterWaveforms
neurodata_type_inc: NWBDataInterface
default_name: ClusterWaveforms
doc: 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.
datasets:
- name: waveform_filtering
dtype: text
doc: Filtering applied to data before generating mean/sd
- name: waveform_mean
dtype: float32
dims:
- num_clusters
- num_samples
shape:
- null
- null
doc: The mean waveform for each cluster, using the same indices for each wave
as cluster numbers in the associated Clustering module (i.e, cluster 3 is in
array slot [3]). Waveforms corresponding to gaps in cluster sequence should
be empty (e.g., zero- filled)
- name: waveform_sd
dtype: float32
dims:
- num_clusters
- num_samples
shape:
- null
- null
doc: Stdev of waveforms for each cluster, using the same indices as in mean
links:
- name: clustering_interface
target_type: Clustering
doc: Link to Clustering interface that was the source of the clustered data
- neurodata_type_def: Clustering
neurodata_type_inc: NWBDataInterface
default_name: Clustering
doc: DEPRECATED Clustered spike data, whether from automatic clustering tools (e.g.,
klustakwik) or as a result of manual sorting.
datasets:
- name: description
dtype: text
doc: Description of clusters or clustering, (e.g. cluster 0 is noise, clusters
curated using Klusters, etc)
- name: num
dtype: int32
dims:
- num_events
shape:
- null
doc: Cluster number of each event
- name: peak_over_rms
dtype: float32
dims:
- num_clusters
shape:
- null
doc: Maximum ratio of waveform peak to RMS on any channel in the cluster (provides
a basic clustering metric).
- name: times
dtype: float64
dims:
- num_events
shape:
- null
doc: Times of clustered events, in seconds. This may be a link to times field
in associated FeatureExtraction module.

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groups:
- neurodata_type_def: TimeIntervals
neurodata_type_inc: DynamicTable
doc: A container for aggregating epoch data and the TimeSeries that each epoch applies
to.
datasets:
- name: start_time
neurodata_type_inc: VectorData
dtype: float32
doc: Start time of epoch, in seconds.
- name: stop_time
neurodata_type_inc: VectorData
dtype: float32
doc: Stop time of epoch, in seconds.
- name: tags
neurodata_type_inc: VectorData
dtype: text
doc: User-defined tags that identify or categorize events.
quantity: '?'
- name: tags_index
neurodata_type_inc: VectorIndex
doc: Index for tags.
quantity: '?'
- name: timeseries
neurodata_type_inc: TimeSeriesReferenceVectorData
doc: An index into a TimeSeries object.
quantity: '?'
- name: timeseries_index
neurodata_type_inc: VectorIndex
doc: Index for timeseries.
quantity: '?'

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groups:
- neurodata_type_def: NWBFile
neurodata_type_inc: NWBContainer
name: root
doc: An NWB file storing cellular-based neurophysiology data from a single
experimental session.
attributes:
- name: nwb_version
dtype: text
value: "2.6.0"
doc: File version string. Use semantic versioning, e.g. 1.2.1. This will be the
name of the format with trailing major, minor and patch numbers.
datasets:
- name: file_create_date
dtype: isodatetime
dims:
- num_modifications
shape:
- null
doc: 'A record of the date the file was created and of subsequent modifications.
The date is stored in UTC with local timezone offset as ISO 8601
extended formatted strings: 2018-09-28T14:43:54.123+02:00. Dates stored in
UTC end in "Z" with no timezone offset. Date accuracy is up to milliseconds.
The file can be created after the experiment was run, so this may differ from
the experiment start time. Each modification to the nwb file adds a new entry
to the array.'
- name: identifier
dtype: text
doc: A unique text identifier for the file. For example, concatenated lab name,
file creation date/time and experimentalist, or a hash of these and/or other
values. The goal is that the string should be unique to all other files.
- name: session_description
dtype: text
doc: A description of the experimental session and data in the file.
- name: session_start_time
dtype: isodatetime
doc: 'Date and time of the experiment/session start. The date is stored
in UTC with local timezone offset as ISO 8601 extended formatted string:
2018-09-28T14:43:54.123+02:00.
Dates stored in UTC end in "Z" with no timezone offset. Date accuracy is
up to milliseconds.'
- name: timestamps_reference_time
dtype: isodatetime
doc: 'Date and time corresponding to time zero of all timestamps. The
date is stored in UTC with local timezone offset as ISO 8601 extended formatted
string: 2018-09-28T14:43:54.123+02:00. Dates stored in UTC end in "Z" with
no timezone offset. Date accuracy is up to milliseconds. All times stored
in the file use this time as reference (i.e., time zero).'
groups:
- name: acquisition
doc: Data streams recorded from the system, including ephys, ophys, tracking,
etc. This group should be read-only after the experiment is completed and
timestamps are corrected to a common timebase. The data stored here may be links
to raw data stored in external NWB files. This will allow keeping bulky raw
data out of the file while preserving the option of keeping some/all in the
file. Acquired data includes tracking and experimental data streams
(i.e., everything measured from the system). If bulky data is stored in the /acquisition
group, the data can exist in a separate NWB file that is linked to by the file
being used for processing and analysis.
groups:
- neurodata_type_inc: NWBDataInterface
doc: Acquired, raw data.
quantity: '*'
- neurodata_type_inc: DynamicTable
doc: Tabular data that is relevant to acquisition
quantity: '*'
- name: analysis
doc: Lab-specific and custom scientific analysis of data. There is no defined
format for the content of this group - the format is up to the individual user/lab.
To facilitate sharing analysis data between labs, the contents here
should be stored in standard types (e.g., neurodata_types) and appropriately documented.
The file can store lab-specific and custom data analysis without
restriction on its form or schema, reducing data formatting restrictions on
end users. Such data should be placed in the analysis group. The analysis data
should be documented so that it could be shared with other labs.
groups:
- neurodata_type_inc: NWBContainer
doc: Custom analysis results.
quantity: '*'
- neurodata_type_inc: DynamicTable
doc: Tabular data that is relevant to data stored in analysis
quantity: '*'
- name: scratch
doc: 'A place to store one-off analysis results. Data placed here is not intended for
sharing. By placing data here, users acknowledge that there is no guarantee that
their data meets any standard.'
quantity: '?'
groups:
- neurodata_type_inc: NWBContainer
doc: Any one-off containers
quantity: '*'
- neurodata_type_inc: DynamicTable
doc: Any one-off tables
quantity: '*'
datasets:
- neurodata_type_inc: ScratchData
doc: Any one-off datasets
quantity: '*'
- name: processing
doc: "The home for ProcessingModules. These modules perform intermediate analysis\
\ of data that is necessary to perform before scientific analysis. Examples\
\ include spike clustering, extracting position from tracking data, stitching\
\ together image slices. ProcessingModules can be large\
\ and express many data sets from relatively complex analysis (e.g., spike detection\
\ and clustering) or small, representing extraction of position information\
\ from tracking video, or even binary lick/no-lick decisions. Common software\
\ tools (e.g., klustakwik, MClust) are expected to read/write data here. \
\ 'Processing' refers to intermediate analysis of the acquired data to make\
\ it more amenable to scientific analysis."
groups:
- neurodata_type_inc: ProcessingModule
doc: Intermediate analysis of acquired data.
quantity: '*'
- name: stimulus
doc: '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.'
groups:
- name: presentation
doc: Stimuli presented during the experiment.
groups:
- neurodata_type_inc: TimeSeries
doc: TimeSeries objects containing data of presented stimuli.
quantity: '*'
- name: templates
doc: 'Template stimuli. Timestamps in templates are based on stimulus
design and are relative to the beginning of the stimulus. When templates are
used, the stimulus instances must convert presentation times to the experiment`s
time reference frame.'
groups:
- neurodata_type_inc: TimeSeries
doc: TimeSeries objects containing template data of presented stimuli.
quantity: '*'
- neurodata_type_inc: Images
doc: Images objects containing images of presented stimuli.
quantity: '*'
- name: general
doc: "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."
datasets:
- name: data_collection
dtype: text
doc: Notes about data collection and analysis.
quantity: '?'
- name: experiment_description
dtype: text
doc: General description of the experiment.
quantity: '?'
- name: experimenter
dtype: text
doc: Name of person(s) who performed the experiment. Can also specify roles
of different people involved.
quantity: '?'
dims:
- num_experimenters
shape:
- null
- name: institution
dtype: text
doc: Institution(s) where experiment was performed.
quantity: '?'
- name: keywords
dtype: text
dims:
- num_keywords
shape:
- null
doc: Terms to search over.
quantity: '?'
- name: lab
dtype: text
doc: Laboratory where experiment was performed.
quantity: '?'
- name: notes
dtype: text
doc: Notes about the experiment.
quantity: '?'
- name: pharmacology
dtype: text
doc: Description of drugs used, including how and when they were administered.
Anesthesia(s), painkiller(s), etc., plus dosage, concentration, etc.
quantity: '?'
- name: protocol
dtype: text
doc: Experimental protocol, if applicable. e.g., include IACUC protocol number.
quantity: '?'
- name: related_publications
dtype: text
doc: Publication information. PMID, DOI, URL, etc.
dims:
- num_publications
shape:
- null
quantity: '?'
- name: session_id
dtype: text
doc: Lab-specific ID for the session.
quantity: '?'
- name: slices
dtype: text
doc: Description of slices, including information about preparation thickness,
orientation, temperature, and bath solution.
quantity: '?'
- name: source_script
dtype: text
doc: Script file or link to public source code used to create this NWB file.
quantity: '?'
attributes:
- name: file_name
dtype: text
doc: Name of script file.
- name: stimulus
dtype: text
doc: Notes about stimuli, such as how and where they were presented.
quantity: '?'
- name: surgery
dtype: text
doc: Narrative description about surgery/surgeries, including date(s) and who
performed surgery.
quantity: '?'
- name: virus
dtype: text
doc: Information about virus(es) used in experiments, including virus ID, source,
date made, injection location, volume, etc.
quantity: '?'
groups:
- neurodata_type_inc: LabMetaData
doc: Place-holder than can be extended so that lab-specific meta-data can be
placed in /general.
quantity: '*'
- name: devices
doc: Description of hardware devices used during experiment, e.g., monitors,
ADC boards, microscopes, etc.
quantity: '?'
groups:
- neurodata_type_inc: Device
doc: Data acquisition devices.
quantity: '*'
- name: subject
neurodata_type_inc: Subject
doc: Information about the animal or person from which the data was measured.
quantity: '?'
- name: extracellular_ephys
doc: Metadata related to extracellular electrophysiology.
quantity: '?'
groups:
- neurodata_type_inc: ElectrodeGroup
doc: Physical group of electrodes.
quantity: '*'
- name: electrodes
neurodata_type_inc: DynamicTable
doc: A table of all electrodes (i.e. channels) used for recording.
quantity: '?'
datasets:
- name: x
neurodata_type_inc: VectorData
dtype: float32
doc: x coordinate of the channel location in the brain (+x is posterior).
quantity: '?'
- name: y
neurodata_type_inc: VectorData
dtype: float32
doc: y coordinate of the channel location in the brain (+y is inferior).
quantity: '?'
- name: z
neurodata_type_inc: VectorData
dtype: float32
doc: z coordinate of the channel location in the brain (+z is right).
quantity: '?'
- name: imp
neurodata_type_inc: VectorData
dtype: float32
doc: Impedance of the channel, in ohms.
quantity: '?'
- name: location
neurodata_type_inc: VectorData
dtype: text
doc: Location of the electrode (channel). 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.
- name: filtering
neurodata_type_inc: VectorData
dtype: text
doc: Description of hardware filtering, including the filter name and frequency cutoffs.
quantity: '?'
- name: group
neurodata_type_inc: VectorData
dtype:
target_type: ElectrodeGroup
reftype: object
doc: Reference to the ElectrodeGroup this electrode is a part of.
- name: group_name
neurodata_type_inc: VectorData
dtype: text
doc: Name of the ElectrodeGroup this electrode is a part of.
- name: rel_x
neurodata_type_inc: VectorData
dtype: float32
doc: x coordinate in electrode group
quantity: '?'
- name: rel_y
neurodata_type_inc: VectorData
dtype: float32
doc: y coordinate in electrode group
quantity: '?'
- name: rel_z
neurodata_type_inc: VectorData
dtype: float32
doc: z coordinate in electrode group
quantity: '?'
- name: reference
neurodata_type_inc: VectorData
dtype: text
doc: Description of the reference electrode and/or reference scheme used for this electrode, e.g.,
"stainless steel skull screw" or "online common average referencing".
quantity: '?'
- name: intracellular_ephys
doc: Metadata related to intracellular electrophysiology.
quantity: '?'
datasets:
- name: filtering
dtype: text
doc: '[DEPRECATED] Use IntracellularElectrode.filtering instead. Description
of filtering used. Includes filtering type and parameters, frequency fall-off,
etc. If this changes between TimeSeries, filter description should be stored
as a text attribute for each TimeSeries.'
quantity: '?'
groups:
- neurodata_type_inc: IntracellularElectrode
doc: An intracellular electrode.
quantity: '*'
- name: sweep_table
neurodata_type_inc: SweepTable
doc: '[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.'
quantity: '?'
- name: intracellular_recordings
neurodata_type_inc: IntracellularRecordingsTable
doc: 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 are recorded
as 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.
quantity: '?'
- name: simultaneous_recordings
neurodata_type_inc: SimultaneousRecordingsTable
doc: A table for grouping different intracellular recordings from the IntracellularRecordingsTable
table together that were recorded simultaneously from different electrodes
quantity: '?'
- name: sequential_recordings
neurodata_type_inc: SequentialRecordingsTable
doc: A table for grouping different sequential recordings from the SimultaneousRecordingsTable
table together. This is typically used to group together sequential recordings
where the a sequence of stimuli of the same type with varying parameters
have been presented in a sequence.
quantity: '?'
- name: repetitions
neurodata_type_inc: RepetitionsTable
doc: 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.
quantity: '?'
- name: experimental_conditions
neurodata_type_inc: ExperimentalConditionsTable
doc: A table for grouping different intracellular recording repetitions together
that belong to the same experimental experimental_conditions.
quantity: '?'
- name: optogenetics
doc: Metadata describing optogenetic stimuluation.
quantity: '?'
groups:
- neurodata_type_inc: OptogeneticStimulusSite
doc: An optogenetic stimulation site.
quantity: '*'
- name: optophysiology
doc: Metadata related to optophysiology.
quantity: '?'
groups:
- neurodata_type_inc: ImagingPlane
doc: An imaging plane.
quantity: '*'
- name: intervals
doc: Experimental intervals, whether that be logically distinct sub-experiments
having a particular scientific goal, trials (see trials subgroup) during an
experiment, or epochs (see epochs subgroup) deriving from analysis of data.
quantity: '?'
groups:
- name: epochs
neurodata_type_inc: TimeIntervals
doc: Divisions in time marking experimental stages or sub-divisions of a single
recording session.
quantity: '?'
- name: trials
neurodata_type_inc: TimeIntervals
doc: Repeated experimental events that have a logical grouping.
quantity: '?'
- name: invalid_times
neurodata_type_inc: TimeIntervals
doc: Time intervals that should be removed from analysis.
quantity: '?'
- neurodata_type_inc: TimeIntervals
doc: Optional additional table(s) for describing other experimental time intervals.
quantity: '*'
- name: units
neurodata_type_inc: Units
doc: Data about sorted spike units.
quantity: '?'
- neurodata_type_def: LabMetaData
neurodata_type_inc: NWBContainer
doc: Lab-specific meta-data.
- neurodata_type_def: Subject
neurodata_type_inc: NWBContainer
doc: Information about the animal or person from which the data was measured.
datasets:
- name: age
dtype: text
doc: Age of subject. Can be supplied instead of 'date_of_birth'.
quantity: '?'
attributes:
- name: reference
doc: "Age is with reference to this event. Can be 'birth' or
'gestational'. If reference is omitted, 'birth' is implied."
dtype: text
required: false
default_value: birth
- name: date_of_birth
dtype: isodatetime
doc: Date of birth of subject. Can be supplied instead of 'age'.
quantity: '?'
- name: description
dtype: text
doc: Description of subject and where subject came from (e.g., breeder, if
animal).
quantity: '?'
- name: genotype
dtype: text
doc: Genetic strain. If absent, assume Wild Type (WT).
quantity: '?'
- name: sex
dtype: text
doc: Gender of subject.
quantity: '?'
- name: species
dtype: text
doc: Species of subject.
quantity: '?'
- name: strain
dtype: text
doc: Strain of subject.
quantity: '?'
- name: subject_id
dtype: text
doc: ID of animal/person used/participating in experiment (lab convention).
quantity: '?'
- name: weight
dtype: text
doc: Weight at time of experiment, at time of surgery and at other important
times.
quantity: '?'
datasets:
- neurodata_type_def: ScratchData
neurodata_type_inc: NWBData
doc: Any one-off datasets
attributes:
- name: notes
doc: 'Any notes the user has about the dataset being stored'
dtype: text

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groups:
- neurodata_type_def: PatchClampSeries
neurodata_type_inc: TimeSeries
doc: An abstract base class for patch-clamp data - stimulus or response,
current or voltage.
attributes:
- name: stimulus_description
dtype: text
doc: Protocol/stimulus name for this patch-clamp dataset.
- name: sweep_number
dtype: uint32
doc: Sweep number, allows to group different PatchClampSeries together.
required: false
datasets:
- name: data
dtype: numeric
dims:
- num_times
shape:
- null
doc: Recorded voltage or current.
attributes:
- name: unit
dtype: text
doc: Base unit of measurement for working with the data. Actual stored values are
not necessarily stored in these units. To access the data in these units,
multiply 'data' by 'conversion' and add 'offset'.
- name: gain
dtype: float32
doc: Gain of the recording, in units Volt/Amp (v-clamp) or Volt/Volt (c-clamp).
quantity: '?'
links:
- name: electrode
target_type: IntracellularElectrode
doc: Link to IntracellularElectrode object that describes the electrode that was
used to apply or record this data.
- neurodata_type_def: CurrentClampSeries
neurodata_type_inc: PatchClampSeries
doc: Voltage data from an intracellular current-clamp recording. A
corresponding CurrentClampStimulusSeries (stored separately as a stimulus) is
used to store the current injected.
datasets:
- name: data
doc: Recorded voltage.
attributes:
- name: unit
dtype: text
value: volts
doc: 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'.
- name: bias_current
dtype: float32
doc: Bias current, in amps.
quantity: '?'
- name: bridge_balance
dtype: float32
doc: Bridge balance, in ohms.
quantity: '?'
- name: capacitance_compensation
dtype: float32
doc: Capacitance compensation, in farads.
quantity: '?'
- neurodata_type_def: IZeroClampSeries
neurodata_type_inc: CurrentClampSeries
doc: 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.
attributes:
- name: stimulus_description
dtype: text
doc: An IZeroClampSeries has no stimulus, so this attribute is automatically set to "N/A"
value: N/A
datasets:
- name: bias_current
dtype: float32
value: 0.0
doc: Bias current, in amps, fixed to 0.0.
- name: bridge_balance
dtype: float32
value: 0.0
doc: Bridge balance, in ohms, fixed to 0.0.
- name: capacitance_compensation
dtype: float32
value: 0.0
doc: Capacitance compensation, in farads, fixed to 0.0.
- neurodata_type_def: CurrentClampStimulusSeries
neurodata_type_inc: PatchClampSeries
doc: Stimulus current applied during current clamp recording.
datasets:
- name: data
doc: Stimulus current applied.
attributes:
- name: unit
dtype: text
value: amperes
doc: 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'.
- neurodata_type_def: VoltageClampSeries
neurodata_type_inc: PatchClampSeries
doc: Current data from an intracellular voltage-clamp recording. A
corresponding VoltageClampStimulusSeries (stored separately as a stimulus) is
used to store the voltage injected.
datasets:
- name: data
doc: Recorded current.
attributes:
- name: unit
dtype: text
value: amperes
doc: 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'.
- name: capacitance_fast
dtype: float32
doc: Fast capacitance, in farads.
quantity: '?'
attributes:
- name: unit
dtype: text
value: farads
doc: Unit of measurement for capacitance_fast, which is fixed to 'farads'.
- name: capacitance_slow
dtype: float32
doc: Slow capacitance, in farads.
quantity: '?'
attributes:
- name: unit
dtype: text
value: farads
doc: Unit of measurement for capacitance_fast, which is fixed to 'farads'.
- name: resistance_comp_bandwidth
dtype: float32
doc: Resistance compensation bandwidth, in hertz.
quantity: '?'
attributes:
- name: unit
dtype: text
value: hertz
doc: Unit of measurement for resistance_comp_bandwidth, which is fixed to 'hertz'.
- name: resistance_comp_correction
dtype: float32
doc: Resistance compensation correction, in percent.
quantity: '?'
attributes:
- name: unit
dtype: text
value: percent
doc: Unit of measurement for resistance_comp_correction, which is fixed to 'percent'.
- name: resistance_comp_prediction
dtype: float32
doc: Resistance compensation prediction, in percent.
quantity: '?'
attributes:
- name: unit
dtype: text
value: percent
doc: Unit of measurement for resistance_comp_prediction, which is fixed to 'percent'.
- name: whole_cell_capacitance_comp
dtype: float32
doc: Whole cell capacitance compensation, in farads.
quantity: '?'
attributes:
- name: unit
dtype: text
value: farads
doc: Unit of measurement for whole_cell_capacitance_comp, which is fixed to 'farads'.
- name: whole_cell_series_resistance_comp
dtype: float32
doc: Whole cell series resistance compensation, in ohms.
quantity: '?'
attributes:
- name: unit
dtype: text
value: ohms
doc: Unit of measurement for whole_cell_series_resistance_comp, which is fixed to 'ohms'.
- neurodata_type_def: VoltageClampStimulusSeries
neurodata_type_inc: PatchClampSeries
doc: Stimulus voltage applied during a voltage clamp recording.
datasets:
- name: data
doc: Stimulus voltage applied.
attributes:
- name: unit
dtype: text
value: volts
doc: 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'.
- neurodata_type_def: IntracellularElectrode
neurodata_type_inc: NWBContainer
doc: An intracellular electrode and its metadata.
datasets:
- name: cell_id
dtype: text
doc: unique ID of the cell
quantity: '?'
- name: description
dtype: text
doc: Description of electrode (e.g., whole-cell, sharp, etc.).
- name: filtering
dtype: text
doc: Electrode specific filtering.
quantity: '?'
- name: initial_access_resistance
dtype: text
doc: Initial access resistance.
quantity: '?'
- name: location
dtype: text
doc: 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.
quantity: '?'
- name: resistance
dtype: text
doc: Electrode resistance, in ohms.
quantity: '?'
- name: seal
dtype: text
doc: Information about seal used for recording.
quantity: '?'
- name: slice
dtype: text
doc: Information about slice used for recording.
quantity: '?'
links:
- name: device
target_type: Device
doc: Device that was used to record from this electrode.
- neurodata_type_def: SweepTable
neurodata_type_inc: DynamicTable
doc: '[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.'
datasets:
- name: sweep_number
neurodata_type_inc: VectorData
dtype: uint32
doc: Sweep number of the PatchClampSeries in that row.
- name: series
neurodata_type_inc: VectorData
dtype:
target_type: PatchClampSeries
reftype: object
doc: The PatchClampSeries with the sweep number in that row.
- name: series_index
neurodata_type_inc: VectorIndex
doc: Index for series.
- neurodata_type_def: IntracellularElectrodesTable
neurodata_type_inc: DynamicTable
doc: Table for storing intracellular electrode related metadata.
attributes:
- name: description
dtype: text
value: Table for storing intracellular electrode related metadata.
doc: Description of what is in this dynamic table.
datasets:
- name: electrode
neurodata_type_inc: VectorData
dtype:
target_type: IntracellularElectrode
reftype: object
doc: Column for storing the reference to the intracellular electrode.
- neurodata_type_def: IntracellularStimuliTable
neurodata_type_inc: DynamicTable
doc: Table for storing intracellular stimulus related metadata.
attributes:
- name: description
dtype: text
value: Table for storing intracellular stimulus related metadata.
doc: Description of what is in this dynamic table.
datasets:
- name: stimulus
neurodata_type_inc: TimeSeriesReferenceVectorData
doc: Column storing the reference to the recorded stimulus for the recording (rows).
- neurodata_type_def: IntracellularResponsesTable
neurodata_type_inc: DynamicTable
doc: Table for storing intracellular response related metadata.
attributes:
- name: description
dtype: text
value: Table for storing intracellular response related metadata.
doc: Description of what is in this dynamic table.
datasets:
- name: response
neurodata_type_inc: TimeSeriesReferenceVectorData
doc: Column storing the reference to the recorded response for the recording (rows)
- neurodata_type_def: IntracellularRecordingsTable
neurodata_type_inc: AlignedDynamicTable
name: intracellular_recordings
doc: 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.
attributes:
- name: description
dtype: text
value: A table to group together a stimulus and response from a single electrode
and a single simultaneous recording and for storing metadata about the intracellular
recording.
doc: Description of the contents of this table. Inherited from AlignedDynamicTable
and overwritten here to fix the value of the attribute.
groups:
- name: electrodes
neurodata_type_inc: IntracellularElectrodesTable
doc: Table for storing intracellular electrode related metadata.
- name: stimuli
neurodata_type_inc: IntracellularStimuliTable
doc: Table for storing intracellular stimulus related metadata.
- name: responses
neurodata_type_inc: IntracellularResponsesTable
doc: Table for storing intracellular response related metadata.
- neurodata_type_def: SimultaneousRecordingsTable
neurodata_type_inc: DynamicTable
name: simultaneous_recordings
doc: A table for grouping different intracellular recordings from the IntracellularRecordingsTable
table together that were recorded simultaneously from different electrodes.
datasets:
- name: recordings
neurodata_type_inc: DynamicTableRegion
doc: A reference to one or more rows in the IntracellularRecordingsTable table.
attributes:
- name: table
dtype:
target_type: IntracellularRecordingsTable
reftype: object
doc: 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.
- name: recordings_index
neurodata_type_inc: VectorIndex
doc: Index dataset for the recordings column.
- neurodata_type_def: SequentialRecordingsTable
neurodata_type_inc: DynamicTable
name: sequential_recordings
doc: 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.
datasets:
- name: simultaneous_recordings
neurodata_type_inc: DynamicTableRegion
doc: A reference to one or more rows in the SimultaneousRecordingsTable table.
attributes:
- name: table
dtype:
target_type: SimultaneousRecordingsTable
reftype: object
doc: 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.
- name: simultaneous_recordings_index
neurodata_type_inc: VectorIndex
doc: Index dataset for the simultaneous_recordings column.
- name: stimulus_type
neurodata_type_inc: VectorData
dtype: text
doc: The type of stimulus used for the sequential recording.
- neurodata_type_def: RepetitionsTable
neurodata_type_inc: DynamicTable
name: repetitions
doc: 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.
datasets:
- name: sequential_recordings
neurodata_type_inc: DynamicTableRegion
doc: A reference to one or more rows in the SequentialRecordingsTable table.
attributes:
- name: table
dtype:
target_type: SequentialRecordingsTable
reftype: object
doc: 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.
- name: sequential_recordings_index
neurodata_type_inc: VectorIndex
doc: Index dataset for the sequential_recordings column.
- neurodata_type_def: ExperimentalConditionsTable
neurodata_type_inc: DynamicTable
name: experimental_conditions
doc: A table for grouping different intracellular recording repetitions together
that belong to the same experimental condition.
datasets:
- name: repetitions
neurodata_type_inc: DynamicTableRegion
doc: A reference to one or more rows in the RepetitionsTable table.
attributes:
- name: table
dtype:
target_type: RepetitionsTable
reftype: object
doc: 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.
- name: repetitions_index
neurodata_type_inc: VectorIndex
doc: Index dataset for the repetitions column.

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datasets:
- neurodata_type_def: GrayscaleImage
neurodata_type_inc: Image
dims:
- x
- y
shape:
- null
- null
doc: A grayscale image.
dtype: numeric
- neurodata_type_def: RGBImage
neurodata_type_inc: Image
dims:
- x
- y
- r, g, b
shape:
- null
- null
- 3
doc: A color image.
dtype: numeric
- neurodata_type_def: RGBAImage
neurodata_type_inc: Image
dims:
- x
- y
- r, g, b, a
shape:
- null
- null
- 4
doc: A color image with transparency.
dtype: numeric
groups:
- neurodata_type_def: ImageSeries
neurodata_type_inc: TimeSeries
doc: 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].
datasets:
- name: data
dtype: numeric
dims:
- - frame
- x
- y
- - frame
- x
- y
- z
shape:
- - null
- null
- null
- - null
- null
- null
- null
doc: Binary data representing images across frames. If data are stored in an external
file, this should be an empty 3D array.
- name: dimension
dtype: int32
dims:
- rank
shape:
- null
doc: Number of pixels on x, y, (and z) axes.
quantity: '?'
- name: external_file
dtype: text
dims:
- num_files
shape:
- null
doc: 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.
quantity: '?'
attributes:
- name: starting_frame
dtype: int32
dims:
- num_files
shape:
- null
doc: Each external image may contain one or more consecutive frames of the full
ImageSeries. This attribute serves as an index to indicate which frames each file
contains, to facilitate random access. The 'starting_frame' attribute, hence,
contains a list of frame numbers within the full ImageSeries of the first frame
of each file listed in the parent 'external_file' dataset. Zero-based indexing is
used (hence, the first element will always be zero). For example, if the
'external_file' dataset has three paths to files and the first file has 5 frames,
the second file has 10 frames, and the third file has 20 frames, then this
attribute will have values [0, 5, 15]. If there is a single external file that
holds all of the frames of the ImageSeries (and so there is a single element in
the 'external_file' dataset), then this attribute should have value [0].
- name: format
dtype: text
default_value: raw
doc: Format of image. If this is 'external', then the attribute 'external_file'
contains the path information to the image files. If this is 'raw', then the raw
(single-channel) binary data is stored in the 'data' dataset. If this attribute
is not present, then the default format='raw' case is assumed.
quantity: '?'
links:
- name: device
target_type: Device
doc: Link to the Device object that was used to capture these images.
quantity: '?'
- neurodata_type_def: ImageMaskSeries
neurodata_type_inc: ImageSeries
doc: 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.
links:
- name: masked_imageseries
target_type: ImageSeries
doc: Link to ImageSeries object that this image mask is applied to.
- neurodata_type_def: OpticalSeries
neurodata_type_inc: ImageSeries
doc: 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.
datasets:
- name: distance
dtype: float32
doc: Distance from camera/monitor to target/eye.
quantity: '?'
- name: field_of_view
dtype: float32
dims:
- - width, height
- - width, height, depth
shape:
- - 2
- - 3
doc: Width, height and depth of image, or imaged area, in meters.
quantity: '?'
- name: data
dtype: numeric
dims:
- - frame
- x
- y
- - frame
- x
- y
- r, g, b
shape:
- - null
- null
- null
- - null
- null
- null
- 3
doc: Images presented to subject, either grayscale or RGB
- name: orientation
dtype: text
doc: Description of image relative to some reference frame (e.g., which way is
up). Must also specify frame of reference.
quantity: '?'
- neurodata_type_def: IndexSeries
neurodata_type_inc: TimeSeries
doc: Stores indices to image frames stored in an ImageSeries. The purpose of the
IndexSeries is to allow a static image stack to be stored in an Images
object, 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
Images object, and the timestamps array indicates when that image
was displayed.
datasets:
- name: data
dtype: uint32
dims:
- num_times
shape:
- null
doc: Index of the image (using zero-indexing) in the linked Images object.
attributes:
- name: conversion
dtype: float32
doc: This field is unused by IndexSeries.
required: false
- name: resolution
dtype: float32
doc: This field is unused by IndexSeries.
required: false
- name: offset
dtype: float32
doc: This field is unused by IndexSeries.
required: false
- name: unit
dtype: text
value: N/A
doc: This field is unused by IndexSeries and has the value N/A.
links:
- name: indexed_timeseries
target_type: ImageSeries
doc: Link to ImageSeries object containing images that are indexed. Use of this link
is discouraged and will be deprecated. Link to an Images type instead.
quantity: '?'
- name: indexed_images
target_type: Images
doc: Link to Images object containing an ordered set of images that are indexed. The Images object
must contain a 'ordered_images' dataset specifying the order of the images in the Images type.
quantity: '?'

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groups:
- neurodata_type_def: AbstractFeatureSeries
neurodata_type_inc: TimeSeries
doc: 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.
datasets:
- name: data
dtype: numeric
dims:
- - num_times
- - num_times
- num_features
shape:
- - null
- - null
- null
doc: Values of each feature at each time.
attributes:
- name: unit
dtype: text
default_value: see 'feature_units'
doc: Since there can be different units for different features, store the units
in 'feature_units'. The default value for this attribute is "see 'feature_units'".
required: false
- name: feature_units
dtype: text
dims:
- num_features
shape:
- null
doc: Units of each feature.
quantity: '?'
- name: features
dtype: text
dims:
- num_features
shape:
- null
doc: Description of the features represented in TimeSeries::data.
- neurodata_type_def: AnnotationSeries
neurodata_type_inc: TimeSeries
doc: 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.
datasets:
- name: data
dtype: text
dims:
- num_times
shape:
- null
doc: Annotations made during an experiment.
attributes:
- name: resolution
dtype: float32
value: -1.0
doc: Smallest meaningful difference between values in data. Annotations have
no units, so the value is fixed to -1.0.
- name: unit
dtype: text
value: n/a
doc: Base unit of measurement for working with the data. Annotations have
no units, so the value is fixed to 'n/a'.
- neurodata_type_def: IntervalSeries
neurodata_type_inc: TimeSeries
doc: 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.
datasets:
- name: data
dtype: int8
dims:
- num_times
shape:
- null
doc: Use values >0 if interval started, <0 if interval ended.
attributes:
- name: resolution
dtype: float32
value: -1.0
doc: Smallest meaningful difference between values in data. Annotations have
no units, so the value is fixed to -1.0.
- name: unit
dtype: text
value: n/a
doc: Base unit of measurement for working with the data. Annotations have
no units, so the value is fixed to 'n/a'.
- neurodata_type_def: DecompositionSeries
neurodata_type_inc: TimeSeries
doc: Spectral analysis of a time series, e.g. of an LFP or a speech signal.
datasets:
- name: data
dtype: numeric
dims:
- num_times
- num_channels
- num_bands
shape:
- null
- null
- null
doc: Data decomposed into frequency bands.
attributes:
- name: unit
dtype: text
default_value: no unit
doc: Base unit of measurement for working with the data. Actual stored values are
not necessarily stored in these units. To access the data in these units,
multiply 'data' by 'conversion'.
- name: metric
dtype: text
doc: The metric used, e.g. phase, amplitude, power.
- name: source_channels
neurodata_type_inc: DynamicTableRegion
doc: DynamicTableRegion pointer to the channels that this decomposition series was generated from.
quantity: '?'
groups:
- name: bands
neurodata_type_inc: DynamicTable
doc: Table for describing the bands that this series was generated from. There
should be one row in this table for each band.
datasets:
- name: band_name
neurodata_type_inc: VectorData
dtype: text
doc: Name of the band, e.g. theta.
- name: band_limits
neurodata_type_inc: VectorData
dtype: float32
dims:
- num_bands
- low, high
shape:
- null
- 2
doc: Low and high limit of each band in Hz. If it is a Gaussian filter, use
2 SD on either side of the center.
- name: band_mean
neurodata_type_inc: VectorData
dtype: float32
dims:
- num_bands
shape:
- null
doc: The mean Gaussian filters, in Hz.
- name: band_stdev
neurodata_type_inc: VectorData
dtype: float32
dims:
- num_bands
shape:
- null
doc: The standard deviation of Gaussian filters, in Hz.
links:
- name: source_timeseries
target_type: TimeSeries
doc: Link to TimeSeries object that this data was calculated from. Metadata about
electrodes and their position can be read from that ElectricalSeries so it is
not necessary to store that information here.
quantity: '?'
- neurodata_type_def: Units
neurodata_type_inc: DynamicTable
default_name: Units
doc: Data about spiking units. Event times of observed units (e.g. cell, synapse,
etc.) should be concatenated and stored in spike_times.
datasets:
- name: spike_times_index
neurodata_type_inc: VectorIndex
doc: Index into the spike_times dataset.
quantity: '?'
- name: spike_times
neurodata_type_inc: VectorData
dtype: float64
doc: Spike times for each unit in seconds.
quantity: '?'
attributes:
- name: resolution
dtype: float64
doc: The smallest possible difference between two spike times. Usually 1 divided by the acquisition sampling rate
from which spike times were extracted, but could be larger if the acquisition time series was downsampled or
smaller if the acquisition time series was smoothed/interpolated and it is possible for the spike time to be
between samples.
required: false
- name: obs_intervals_index
neurodata_type_inc: VectorIndex
doc: Index into the obs_intervals dataset.
quantity: '?'
- name: obs_intervals
neurodata_type_inc: VectorData
dtype: float64
dims:
- num_intervals
- start|end
shape:
- null
- 2
doc: Observation intervals for each unit.
quantity: '?'
- name: electrodes_index
neurodata_type_inc: VectorIndex
doc: Index into electrodes.
quantity: '?'
- name: electrodes
neurodata_type_inc: DynamicTableRegion
doc: Electrode that each spike unit came from, specified using a DynamicTableRegion.
quantity: '?'
- name: electrode_group
neurodata_type_inc: VectorData
dtype:
target_type: ElectrodeGroup
reftype: object
doc: Electrode group that each spike unit came from.
quantity: '?'
- name: waveform_mean
neurodata_type_inc: VectorData
dtype: float32
dims:
- - num_units
- num_samples
- - num_units
- num_samples
- num_electrodes
shape:
- - null
- null
- - null
- null
- null
doc: Spike waveform mean for each spike unit.
quantity: '?'
attributes:
- name: sampling_rate
dtype: float32
doc: Sampling rate, in hertz.
required: false
- name: unit
dtype: text
value: volts
doc: Unit of measurement. This value is fixed to 'volts'.
required: false
- name: waveform_sd
neurodata_type_inc: VectorData
dtype: float32
dims:
- - num_units
- num_samples
- - num_units
- num_samples
- num_electrodes
shape:
- - null
- null
- - null
- null
- null
doc: Spike waveform standard deviation for each spike unit.
quantity: '?'
attributes:
- name: sampling_rate
dtype: float32
doc: Sampling rate, in hertz.
required: false
- name: unit
dtype: text
value: volts
doc: Unit of measurement. This value is fixed to 'volts'.
required: false
- name: waveforms
neurodata_type_inc: VectorData
dtype: numeric
dims:
- num_waveforms
- num_samples
shape:
- null
- null
doc: "Individual waveforms for each spike on each electrode. This is a doubly indexed column. The 'waveforms_index'
column indexes which waveforms in this column belong to the same spike event for a given unit, where each waveform
was recorded from a different electrode. The 'waveforms_index_index' column indexes the 'waveforms_index' column
to indicate which spike events belong to a given unit. For example, if the
'waveforms_index_index' column has values [2, 5, 6], then the first 2 elements of the 'waveforms_index' column
correspond to the 2 spike events of the first unit, the next 3 elements of the 'waveforms_index' column correspond
to the 3 spike events of the second unit, and the next 1 element of the 'waveforms_index' column corresponds to
the 1 spike event of the third unit. If the 'waveforms_index' column has values [3, 6, 8, 10, 12, 13], then
the first 3 elements of the 'waveforms' column contain the 3 spike waveforms that were recorded from 3 different
electrodes for the first spike time of the first unit. See
https://nwb-schema.readthedocs.io/en/stable/format_description.html#doubly-ragged-arrays for a graphical
representation of this example. When there is only one electrode for each unit (i.e., each spike time is
associated with a single waveform), then the 'waveforms_index' column will have values 1, 2, ..., N, where N is
the number of spike events. The number of electrodes for each spike event should be the same within a given unit.
The 'electrodes' column should be used to indicate which electrodes are associated with each unit, and the order
of the waveforms within a given unit x spike event should be in the same order as the electrodes referenced in
the 'electrodes' column of this table. The number of samples for each waveform must be the same."
quantity: '?'
attributes:
- name: sampling_rate
dtype: float32
doc: Sampling rate, in hertz.
required: false
- name: unit
dtype: text
value: volts
doc: Unit of measurement. This value is fixed to 'volts'.
required: false
- name: waveforms_index
neurodata_type_inc: VectorIndex
doc: Index into the waveforms dataset. One value for every spike event. See 'waveforms' for more detail.
quantity: '?'
- name: waveforms_index_index
neurodata_type_inc: VectorIndex
doc: Index into the waveforms_index dataset. One value for every unit (row in the table). See 'waveforms' for more
detail.
quantity: '?'

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@ -0,0 +1,60 @@
namespaces:
- name: core
doc: NWB namespace
author:
- Andrew Tritt
- Oliver Ruebel
- Ryan Ly
- Ben Dichter
- Keith Godfrey
- Jeff Teeters
contact:
- ajtritt@lbl.gov
- oruebel@lbl.gov
- rly@lbl.gov
- bdichter@lbl.gov
- keithg@alleninstitute.org
- jteeters@berkeley.edu
full_name: NWB core
schema:
- namespace: hdmf-common
- doc: This source module contains base data types used throughout the NWB data
format.
source: nwb.base.yaml
title: Base data types
- doc: This source module contains neurodata_types for device data.
source: nwb.device.yaml
title: Devices
- doc: This source module contains neurodata_types for epoch data.
source: nwb.epoch.yaml
title: Epochs
- doc: This source module contains neurodata_types for image data.
source: nwb.image.yaml
title: Image data
- doc: Main NWB file specification.
source: nwb.file.yaml
title: NWB file
- doc: Miscellaneous types.
source: nwb.misc.yaml
title: Miscellaneous neurodata_types.
- doc: This source module contains neurodata_types for behavior data.
source: nwb.behavior.yaml
title: Behavior
- doc: This source module contains neurodata_types for extracellular electrophysiology
data.
source: nwb.ecephys.yaml
title: Extracellular electrophysiology
- doc: This source module contains neurodata_types for intracellular electrophysiology
data.
source: nwb.icephys.yaml
title: Intracellular electrophysiology
- doc: This source module contains neurodata_types for opto-genetics data.
source: nwb.ogen.yaml
title: Optogenetics
- doc: This source module contains neurodata_types for optical physiology data.
source: nwb.ophys.yaml
title: Optical physiology
- doc: This source module contains neurodata_type for retinotopy data.
source: nwb.retinotopy.yaml
title: Retinotopy
version: "2.6.0-alpha"

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@ -0,0 +1,42 @@
groups:
- neurodata_type_def: OptogeneticSeries
neurodata_type_inc: TimeSeries
doc: An optogenetic stimulus.
datasets:
- name: data
dtype: numeric
dims:
- num_times
shape:
- null
doc: Applied power for optogenetic stimulus, in watts.
attributes:
- name: unit
dtype: text
value: watts
doc: Unit of measurement for data, which is fixed to 'watts'.
links:
- name: site
target_type: OptogeneticStimulusSite
doc: Link to OptogeneticStimulusSite object that describes the site to which this
stimulus was applied.
- neurodata_type_def: OptogeneticStimulusSite
neurodata_type_inc: NWBContainer
doc: A site of optogenetic stimulation.
datasets:
- name: description
dtype: text
doc: Description of stimulation site.
- name: excitation_lambda
dtype: float32
doc: Excitation wavelength, in nm.
- name: location
dtype: text
doc: 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.
links:
- name: device
target_type: Device
doc: Device that generated the stimulus.

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@ -0,0 +1,360 @@
groups:
- neurodata_type_def: OnePhotonSeries
neurodata_type_inc: ImageSeries
doc: Image stack recorded over time from 1-photon microscope.
attributes:
- name: pmt_gain
dtype: float32
doc: Photomultiplier gain.
required: false
- name: scan_line_rate
dtype: float32
doc: Lines imaged per second. This is also stored in /general/optophysiology but
is kept here as it is useful information for analysis, and so good to be stored
w/ the actual data.
required: false
- name: exposure_time
dtype: float32
doc: Exposure time of the sample; often the inverse of the frequency.
required: false
- name: binning
dtype: uint8
doc: Amount of pixels combined into 'bins'; could be 1, 2, 4, 8, etc.
required: false
- name: power
dtype: float32
doc: Power of the excitation in mW, if known.
required: false
- name: intensity
dtype: float32
doc: Intensity of the excitation in mW/mm^2, if known.
required: false
links:
- name: imaging_plane
target_type: ImagingPlane
doc: Link to ImagingPlane object from which this TimeSeries data was generated.
- neurodata_type_def: TwoPhotonSeries
neurodata_type_inc: ImageSeries
doc: Image stack recorded over time from 2-photon microscope.
attributes:
- name: pmt_gain
dtype: float32
doc: Photomultiplier gain.
required: false
- name: scan_line_rate
dtype: float32
doc: Lines imaged per second. This is also stored in /general/optophysiology but
is kept here as it is useful information for analysis, and so good to be stored
w/ the actual data.
required: false
datasets:
- name: field_of_view
dtype: float32
dims:
- - width|height
- - width|height|depth
shape:
- - 2
- - 3
doc: Width, height and depth of image, or imaged area, in meters.
quantity: '?'
links:
- name: imaging_plane
target_type: ImagingPlane
doc: Link to ImagingPlane object from which this TimeSeries data was generated.
- neurodata_type_def: RoiResponseSeries
neurodata_type_inc: TimeSeries
doc: ROI responses over an imaging plane. The first dimension represents time.
The second dimension, if present, represents ROIs.
datasets:
- name: data
dtype: numeric
dims:
- - num_times
- - num_times
- num_ROIs
shape:
- - null
- - null
- null
doc: Signals from ROIs.
- name: rois
neurodata_type_inc: DynamicTableRegion
doc: DynamicTableRegion referencing into an ROITable containing information on the ROIs
stored in this timeseries.
- neurodata_type_def: DfOverF
neurodata_type_inc: NWBDataInterface
default_name: DfOverF
doc: 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).
groups:
- neurodata_type_inc: RoiResponseSeries
doc: RoiResponseSeries object(s) containing dF/F for a ROI.
quantity: '+'
- neurodata_type_def: Fluorescence
neurodata_type_inc: NWBDataInterface
default_name: Fluorescence
doc: 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).
groups:
- neurodata_type_inc: RoiResponseSeries
doc: RoiResponseSeries object(s) containing fluorescence data for a ROI.
quantity: '+'
- neurodata_type_def: ImageSegmentation
neurodata_type_inc: NWBDataInterface
default_name: ImageSegmentation
doc: 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.
groups:
- neurodata_type_inc: PlaneSegmentation
doc: Results from image segmentation of a specific imaging plane.
quantity: '+'
- neurodata_type_def: PlaneSegmentation
neurodata_type_inc: DynamicTable
doc: Results from image segmentation of a specific imaging plane.
datasets:
- name: image_mask
neurodata_type_inc: VectorData
dims:
- - num_roi
- num_x
- num_y
- - num_roi
- num_x
- num_y
- num_z
shape:
- - null
- null
- null
- - null
- null
- null
- null
doc: ROI masks for each ROI. Each image mask is the size of the original imaging
plane (or volume) and members of the ROI are finite non-zero.
quantity: '?'
- name: pixel_mask_index
neurodata_type_inc: VectorIndex
doc: Index into pixel_mask.
quantity: '?'
- name: pixel_mask
neurodata_type_inc: VectorData
dtype:
- name: x
dtype: uint32
doc: Pixel x-coordinate.
- name: y
dtype: uint32
doc: Pixel y-coordinate.
- name: weight
dtype: float32
doc: Weight of the pixel.
doc: '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'
quantity: '?'
- name: voxel_mask_index
neurodata_type_inc: VectorIndex
doc: Index into voxel_mask.
quantity: '?'
- name: voxel_mask
neurodata_type_inc: VectorData
dtype:
- name: x
dtype: uint32
doc: Voxel x-coordinate.
- name: y
dtype: uint32
doc: Voxel y-coordinate.
- name: z
dtype: uint32
doc: Voxel z-coordinate.
- name: weight
dtype: float32
doc: Weight of the voxel.
doc: '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'
quantity: '?'
groups:
- name: reference_images
doc: Image stacks that the segmentation masks apply to.
groups:
- neurodata_type_inc: ImageSeries
doc: One or more image stacks that the masks apply to (can be one-element
stack).
quantity: '*'
links:
- name: imaging_plane
target_type: ImagingPlane
doc: Link to ImagingPlane object from which this data was generated.
- neurodata_type_def: ImagingPlane
neurodata_type_inc: NWBContainer
doc: An imaging plane and its metadata.
datasets:
- name: description
dtype: text
doc: Description of the imaging plane.
quantity: '?'
- name: excitation_lambda
dtype: float32
doc: Excitation wavelength, in nm.
- name: imaging_rate
dtype: float32
doc: Rate that images are acquired, in Hz. If the corresponding TimeSeries is present, the rate should be stored
there instead.
quantity: '?'
- name: indicator
dtype: text
doc: Calcium indicator.
- name: location
dtype: text
doc: Location of the imaging plane. 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.
- name: manifold
dtype: float32
dims:
- - height
- width
- x, y, z
- - height
- width
- depth
- x, y, z
shape:
- - null
- null
- 3
- - null
- null
- null
- 3
doc: "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."
quantity: '?'
attributes:
- name: conversion
dtype: float32
default_value: 1.0
doc: Scalar to multiply each element in data to convert it to the specified 'unit'.
If the data are stored in acquisition system units or other units
that require a conversion to be interpretable, multiply the data by 'conversion'
to convert the data to the specified 'unit'. e.g. if the data acquisition system
stores values in this object as pixels from x = -500 to 499, y = -500 to 499
that correspond to a 2 m x 2 m range, then the 'conversion' multiplier to get
from raw data acquisition pixel units to meters is 2/1000.
required: false
- name: unit
dtype: text
default_value: meters
doc: Base unit of measurement for working with the data. The default value is 'meters'.
required: false
- name: origin_coords
dtype: float32
dims:
- - x, y
- - x, y, z
shape:
- - 2
- - 3
doc: 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).
quantity: '?'
attributes:
- name: unit
dtype: text
default_value: meters
doc: Measurement units for origin_coords. The default value is 'meters'.
- name: grid_spacing
dtype: float32
dims:
- - x, y
- - x, y, z
shape:
- - 2
- - 3
doc: 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.
quantity: '?'
attributes:
- name: unit
dtype: text
default_value: meters
doc: Measurement units for grid_spacing. The default value is 'meters'.
- name: reference_frame
dtype: text
doc: Describes reference frame of origin_coords and grid_spacing.
For example, this can be a text description of the anatomical location and orientation of the grid
defined by origin_coords and grid_spacing or the vectors needed to transform or rotate the grid to
a common anatomical axis (e.g., AP/DV/ML). This field is necessary to interpret origin_coords and grid_spacing.
If origin_coords and grid_spacing are not present, then this field is not required.
For example, if the microscope takes 10 x 10 x 2 images, where the first value of the data matrix
(index (0, 0, 0)) corresponds to (-1.2, -0.6, -2) mm relative to bregma, the spacing between pixels is 0.2 mm in
x, 0.2 mm in y and 0.5 mm in z, and larger numbers in x means more anterior, larger numbers in y means more
rightward, and larger numbers in z means more ventral, then enter the following --
origin_coords = (-1.2, -0.6, -2)
grid_spacing = (0.2, 0.2, 0.5)
reference_frame = "Origin coordinates are relative to bregma. First dimension corresponds to anterior-posterior
axis (larger index = more anterior). Second dimension corresponds to medial-lateral axis (larger index = more
rightward). Third dimension corresponds to dorsal-ventral axis (larger index = more ventral)."
quantity: '?'
groups:
- neurodata_type_inc: OpticalChannel
doc: An optical channel used to record from an imaging plane.
quantity: '+'
links:
- name: device
target_type: Device
doc: Link to the Device object that was used to record from this electrode.
- neurodata_type_def: OpticalChannel
neurodata_type_inc: NWBContainer
doc: An optical channel used to record from an imaging plane.
datasets:
- name: description
dtype: text
doc: Description or other notes about the channel.
- name: emission_lambda
dtype: float32
doc: Emission wavelength for channel, in nm.
- neurodata_type_def: MotionCorrection
neurodata_type_inc: NWBDataInterface
default_name: MotionCorrection
doc: '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).'
groups:
- neurodata_type_inc: CorrectedImageStack
doc: Reuslts from motion correction of an image stack.
quantity: '+'
- neurodata_type_def: CorrectedImageStack
neurodata_type_inc: NWBDataInterface
doc: Reuslts from motion correction of an image stack.
groups:
- name: corrected
neurodata_type_inc: ImageSeries
doc: Image stack with frames shifted to the common coordinates.
- name: xy_translation
neurodata_type_inc: TimeSeries
doc: Stores the x,y delta necessary to align each frame to the common coordinates,
for example, to align each frame to a reference image.
links:
- name: original
target_type: ImageSeries
doc: Link to ImageSeries object that is being registered.

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@ -0,0 +1,234 @@
groups:
- neurodata_type_def: ImagingRetinotopy
neurodata_type_inc: NWBDataInterface
default_name: ImagingRetinotopy
doc: '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).'
datasets:
- name: axis_1_phase_map
dtype: float32
dims:
- num_rows
- num_cols
shape:
- null
- null
doc: Phase response to stimulus on the first measured axis.
attributes:
- name: dimension
dtype: int32
dims:
- num_rows, num_cols
shape:
- 2
doc: 'Number of rows and columns in the image. NOTE: row, column representation
is equivalent to height, width.'
- name: field_of_view
dtype: float32
dims:
- height, width
shape:
- 2
doc: Size of viewing area, in meters.
- name: unit
dtype: text
doc: Unit that axis data is stored in (e.g., degrees).
- name: axis_1_power_map
dtype: float32
dims:
- num_rows
- num_cols
shape:
- null
- null
doc: 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.
quantity: '?'
attributes:
- name: dimension
dtype: int32
dims:
- num_rows, num_cols
shape:
- 2
doc: 'Number of rows and columns in the image. NOTE: row, column representation
is equivalent to height, width.'
- name: field_of_view
dtype: float32
dims:
- height, width
shape:
- 2
doc: Size of viewing area, in meters.
- name: unit
dtype: text
doc: Unit that axis data is stored in (e.g., degrees).
- name: axis_2_phase_map
dtype: float32
dims:
- num_rows
- num_cols
shape:
- null
- null
doc: Phase response to stimulus on the second measured axis.
attributes:
- name: dimension
dtype: int32
dims:
- num_rows, num_cols
shape:
- 2
doc: 'Number of rows and columns in the image. NOTE: row, column representation
is equivalent to height, width.'
- name: field_of_view
dtype: float32
dims:
- height, width
shape:
- 2
doc: Size of viewing area, in meters.
- name: unit
dtype: text
doc: Unit that axis data is stored in (e.g., degrees).
- name: axis_2_power_map
dtype: float32
dims:
- num_rows
- num_cols
shape:
- null
- null
doc: 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.
quantity: '?'
attributes:
- name: dimension
dtype: int32
dims:
- num_rows, num_cols
shape:
- 2
doc: 'Number of rows and columns in the image. NOTE: row, column representation
is equivalent to height, width.'
- name: field_of_view
dtype: float32
dims:
- height, width
shape:
- 2
doc: Size of viewing area, in meters.
- name: unit
dtype: text
doc: Unit that axis data is stored in (e.g., degrees).
- name: axis_descriptions
dtype: text
dims:
- axis_1, axis_2
shape:
- 2
doc: Two-element array describing the contents of the two response axis fields.
Description should be something like ['altitude', 'azimuth'] or '['radius',
'theta'].
- name: focal_depth_image
dtype: uint16
dims:
- num_rows
- num_cols
shape:
- null
- null
doc: 'Gray-scale image taken with same settings/parameters (e.g., focal depth,
wavelength) as data collection. Array format: [rows][columns].'
quantity: '?'
attributes:
- name: bits_per_pixel
dtype: int32
doc: Number of bits used to represent each value. This is necessary to determine
maximum (white) pixel value.
- name: dimension
dtype: int32
dims:
- num_rows, num_cols
shape:
- 2
doc: 'Number of rows and columns in the image. NOTE: row, column representation
is equivalent to height, width.'
- name: field_of_view
dtype: float32
dims:
- height, width
shape:
- 2
doc: Size of viewing area, in meters.
- name: focal_depth
dtype: float32
doc: Focal depth offset, in meters.
- name: format
dtype: text
doc: Format of image. Right now only 'raw' is supported.
- name: sign_map
dtype: float32
dims:
- num_rows
- num_cols
shape:
- null
- null
doc: Sine of the angle between the direction of the gradient in axis_1 and axis_2.
quantity: '?'
attributes:
- name: dimension
dtype: int32
dims:
- num_rows, num_cols
shape:
- 2
doc: 'Number of rows and columns in the image. NOTE: row, column representation
is equivalent to height, width.'
- name: field_of_view
dtype: float32
dims:
- height, width
shape:
- 2
doc: Size of viewing area, in meters.
- name: vasculature_image
dtype: uint16
dims:
- num_rows
- num_cols
shape:
- null
- null
doc: 'Gray-scale anatomical image of cortical surface. Array structure: [rows][columns]'
attributes:
- name: bits_per_pixel
dtype: int32
doc: Number of bits used to represent each value. This is necessary to determine
maximum (white) pixel value
- name: dimension
dtype: int32
dims:
- num_rows, num_cols
shape:
- 2
doc: 'Number of rows and columns in the image. NOTE: row, column representation
is equivalent to height, width.'
- name: field_of_view
dtype: float32
dims:
- height, width
shape:
- 2
doc: Size of viewing area, in meters.
- name: format
dtype: text
doc: Format of image. Right now only 'raw' is supported.

View file

@ -1,5 +1,5 @@
# Auto generated from nwb_schema_language.yaml by pythongen.py version: 0.0.1 # Auto generated from nwb_schema_language.yaml by pythongen.py version: 0.0.1
# Generation date: 2023-08-16T15:57:41 # Generation date: 2023-08-16T23:21:37
# Schema: nwb-schema-language # Schema: nwb-schema-language
# #
# id: https://w3id.org/p2p_ld/nwb-schema-language # id: https://w3id.org/p2p_ld/nwb-schema-language
@ -21,8 +21,8 @@ from linkml_runtime.utils.formatutils import camelcase, underscore, sfx
from linkml_runtime.utils.enumerations import EnumDefinitionImpl from linkml_runtime.utils.enumerations import EnumDefinitionImpl
from rdflib import Namespace, URIRef from rdflib import Namespace, URIRef
from linkml_runtime.utils.curienamespace import CurieNamespace from linkml_runtime.utils.curienamespace import CurieNamespace
from linkml_runtime.linkml_model.types import Date, Integer, String, Uriorcurie from linkml_runtime.linkml_model.types import Boolean, Date, String
from linkml_runtime.utils.metamodelcore import URIorCURIE, XSDDate from linkml_runtime.utils.metamodelcore import Bool, XSDDate
metamodel_version = "1.7.0" metamodel_version = "1.7.0"
version = None version = None
@ -31,9 +31,6 @@ version = None
dataclasses._init_fn = dataclasses_init_fn_with_kwargs dataclasses._init_fn = dataclasses_init_fn_with_kwargs
# Namespaces # Namespaces
PATO = CurieNamespace('PATO', 'http://purl.obolibrary.org/obo/PATO_')
BIOLINK = CurieNamespace('biolink', 'https://w3id.org/biolink/')
EXAMPLE = CurieNamespace('example', 'https://example.org/')
LINKML = CurieNamespace('linkml', 'https://w3id.org/linkml/') LINKML = CurieNamespace('linkml', 'https://w3id.org/linkml/')
NWB_SCHEMA_LANGUAGE = CurieNamespace('nwb_schema_language', 'https://w3id.org/p2p_ld/nwb-schema-language/') NWB_SCHEMA_LANGUAGE = CurieNamespace('nwb_schema_language', 'https://w3id.org/p2p_ld/nwb-schema-language/')
SCHEMA = CurieNamespace('schema', 'http://schema.org/') SCHEMA = CurieNamespace('schema', 'http://schema.org/')
@ -43,50 +40,71 @@ DEFAULT_ = NWB_SCHEMA_LANGUAGE
# Types # Types
# Class references # Class references
class NamedThingId(URIorCURIE):
pass
class NamespacesId(NamedThingId):
pass
@dataclass @dataclass
class NamedThing(YAMLRoot): class Namespace(YAMLRoot):
"""
A generic grouping for any identifiable entity
"""
_inherited_slots: ClassVar[List[str]] = [] _inherited_slots: ClassVar[List[str]] = []
class_class_uri: ClassVar[URIRef] = SCHEMA.Thing class_class_uri: ClassVar[URIRef] = NWB_SCHEMA_LANGUAGE.Namespace
class_class_curie: ClassVar[str] = "schema:Thing" class_class_curie: ClassVar[str] = "nwb_schema_language:Namespace"
class_name: ClassVar[str] = "NamedThing" class_name: ClassVar[str] = "Namespace"
class_model_uri: ClassVar[URIRef] = NWB_SCHEMA_LANGUAGE.NamedThing class_model_uri: ClassVar[URIRef] = NWB_SCHEMA_LANGUAGE.Namespace
id: Union[str, NamedThingId] = None doc: str = None
name: Optional[str] = None name: str = None
description: Optional[str] = None version: str = None
author: Union[str, List[str]] = None
contact: Union[str, List[str]] = None
full_name: Optional[str] = None
date: Optional[Union[str, XSDDate]] = None
schema: Optional[Union[Union[dict, "Schema"], List[Union[dict, "Schema"]]]] = empty_list()
def __post_init__(self, *_: List[str], **kwargs: Dict[str, Any]): def __post_init__(self, *_: List[str], **kwargs: Dict[str, Any]):
if self._is_empty(self.id): if self._is_empty(self.doc):
self.MissingRequiredField("id") self.MissingRequiredField("doc")
if not isinstance(self.id, NamedThingId): if not isinstance(self.doc, str):
self.id = NamedThingId(self.id) self.doc = str(self.doc)
if self.name is not None and not isinstance(self.name, str): if self._is_empty(self.name):
self.MissingRequiredField("name")
if not isinstance(self.name, str):
self.name = str(self.name) self.name = str(self.name)
if self.description is not None and not isinstance(self.description, str): if self._is_empty(self.version):
self.description = str(self.description) self.MissingRequiredField("version")
if not isinstance(self.version, str):
self.version = str(self.version)
if self._is_empty(self.author):
self.MissingRequiredField("author")
if not isinstance(self.author, list):
self.author = [self.author] if self.author is not None else []
self.author = [v if isinstance(v, str) else str(v) for v in self.author]
if self._is_empty(self.contact):
self.MissingRequiredField("contact")
if not isinstance(self.contact, list):
self.contact = [self.contact] if self.contact is not None else []
self.contact = [v if isinstance(v, str) else str(v) for v in self.contact]
if self.full_name is not None and not isinstance(self.full_name, str):
self.full_name = str(self.full_name)
if self.date is not None and not isinstance(self.date, XSDDate):
self.date = XSDDate(self.date)
print(self.schema)
if not isinstance(self.schema, list):
self.schema = [self.schema] if self.schema is not None else []
self.schema = [v if isinstance(v, Schema) else Schema(**as_dict(v)) for v in self.schema]
super().__post_init__(**kwargs) super().__post_init__(**kwargs)
@dataclass @dataclass
class Namespaces(NamedThing): class Namespaces(YAMLRoot):
"""
Represents a Namespaces
"""
_inherited_slots: ClassVar[List[str]] = [] _inherited_slots: ClassVar[List[str]] = []
class_class_uri: ClassVar[URIRef] = NWB_SCHEMA_LANGUAGE.Namespaces class_class_uri: ClassVar[URIRef] = NWB_SCHEMA_LANGUAGE.Namespaces
@ -94,100 +112,579 @@ class Namespaces(NamedThing):
class_name: ClassVar[str] = "Namespaces" class_name: ClassVar[str] = "Namespaces"
class_model_uri: ClassVar[URIRef] = NWB_SCHEMA_LANGUAGE.Namespaces class_model_uri: ClassVar[URIRef] = NWB_SCHEMA_LANGUAGE.Namespaces
id: Union[str, NamespacesId] = None namespaces: Optional[Union[Union[dict, Namespace], List[Union[dict, Namespace]]]] = empty_list()
primary_email: Optional[str] = None
birth_date: Optional[Union[str, XSDDate]] = None
age_in_years: Optional[int] = None
vital_status: Optional[Union[str, "PersonStatus"]] = None
def __post_init__(self, *_: List[str], **kwargs: Dict[str, Any]): def __post_init__(self, *_: List[str], **kwargs: Dict[str, Any]):
if self._is_empty(self.id): self._normalize_inlined_as_dict(slot_name="namespaces", slot_type=Namespace, key_name="doc", keyed=False)
self.MissingRequiredField("id")
if not isinstance(self.id, NamespacesId):
self.id = NamespacesId(self.id)
if self.primary_email is not None and not isinstance(self.primary_email, str):
self.primary_email = str(self.primary_email)
if self.birth_date is not None and not isinstance(self.birth_date, XSDDate):
self.birth_date = XSDDate(self.birth_date)
if self.age_in_years is not None and not isinstance(self.age_in_years, int):
self.age_in_years = int(self.age_in_years)
if self.vital_status is not None and not isinstance(self.vital_status, PersonStatus):
self.vital_status = PersonStatus(self.vital_status)
super().__post_init__(**kwargs) super().__post_init__(**kwargs)
@dataclass @dataclass
class NamespacesCollection(YAMLRoot): class Schema(YAMLRoot):
"""
A holder for Namespaces objects
"""
_inherited_slots: ClassVar[List[str]] = [] _inherited_slots: ClassVar[List[str]] = []
class_class_uri: ClassVar[URIRef] = NWB_SCHEMA_LANGUAGE.NamespacesCollection class_class_uri: ClassVar[URIRef] = NWB_SCHEMA_LANGUAGE.Schema
class_class_curie: ClassVar[str] = "nwb_schema_language:NamespacesCollection" class_class_curie: ClassVar[str] = "nwb_schema_language:Schema"
class_name: ClassVar[str] = "NamespacesCollection" class_name: ClassVar[str] = "Schema"
class_model_uri: ClassVar[URIRef] = NWB_SCHEMA_LANGUAGE.NamespacesCollection class_model_uri: ClassVar[URIRef] = NWB_SCHEMA_LANGUAGE.Schema
entries: Optional[Union[Dict[Union[str, NamespacesId], Union[dict, Namespaces]], List[Union[dict, Namespaces]]]] = empty_dict() doc: str = None
source: Optional[str] = None
namespace: Optional[str] = None
title: Optional[str] = None
neurodata_types: Optional[Union[str, List[str]]] = empty_list()
def __post_init__(self, *_: List[str], **kwargs: Dict[str, Any]): def __post_init__(self, *_: List[str], **kwargs: Dict[str, Any]):
self._normalize_inlined_as_dict(slot_name="entries", slot_type=Namespaces, key_name="id", keyed=True) if self._is_empty(self.doc):
self.MissingRequiredField("doc")
if not isinstance(self.doc, str):
self.doc = str(self.doc)
if self.source is not None and not isinstance(self.source, str):
self.source = str(self.source)
if self.namespace is not None and not isinstance(self.namespace, str):
self.namespace = str(self.namespace)
if self.title is not None and not isinstance(self.title, str):
self.title = str(self.title)
if not isinstance(self.neurodata_types, list):
self.neurodata_types = [self.neurodata_types] if self.neurodata_types is not None else []
self.neurodata_types = [v if isinstance(v, str) else str(v) for v in self.neurodata_types]
super().__post_init__(**kwargs) super().__post_init__(**kwargs)
# Enumerations @dataclass
class PersonStatus(EnumDefinitionImpl): class Group(YAMLRoot):
_inherited_slots: ClassVar[List[str]] = []
ALIVE = PermissibleValue( class_class_uri: ClassVar[URIRef] = NWB_SCHEMA_LANGUAGE.Group
text="ALIVE", class_class_curie: ClassVar[str] = "nwb_schema_language:Group"
description="the person is living", class_name: ClassVar[str] = "Group"
meaning=PATO["0001421"]) class_model_uri: ClassVar[URIRef] = NWB_SCHEMA_LANGUAGE.Group
DEAD = PermissibleValue(
text="DEAD", doc: str = None
description="the person is deceased", neurodata_type_def: Optional[str] = None
meaning=PATO["0001422"]) neurodata_type_inc: Optional[str] = None
UNKNOWN = PermissibleValue( name: Optional[str] = None
text="UNKNOWN", default_name: Optional[str] = None
description="the vital status is not known") quantity: Optional[str] = 1
linkable: Optional[Union[bool, Bool]] = None
attributes: Optional[Union[Union[dict, "Attribute"], List[Union[dict, "Attribute"]]]] = empty_list()
datasets: Optional[Union[Union[dict, "Dataset"], List[Union[dict, "Dataset"]]]] = empty_list()
groups: Optional[Union[Union[dict, "Group"], List[Union[dict, "Group"]]]] = empty_list()
links: Optional[Union[Union[dict, "Link"], List[Union[dict, "Link"]]]] = empty_list()
def __post_init__(self, *_: List[str], **kwargs: Dict[str, Any]):
if self._is_empty(self.doc):
self.MissingRequiredField("doc")
if not isinstance(self.doc, str):
self.doc = str(self.doc)
if self.neurodata_type_def is not None and not isinstance(self.neurodata_type_def, str):
self.neurodata_type_def = str(self.neurodata_type_def)
if self.neurodata_type_inc is not None and not isinstance(self.neurodata_type_inc, str):
self.neurodata_type_inc = str(self.neurodata_type_inc)
if self.name is not None and not isinstance(self.name, str):
self.name = str(self.name)
if self.default_name is not None and not isinstance(self.default_name, str):
self.default_name = str(self.default_name)
if self.quantity is not None and not isinstance(self.quantity, str):
self.quantity = str(self.quantity)
if self.linkable is not None and not isinstance(self.linkable, Bool):
self.linkable = Bool(self.linkable)
self._normalize_inlined_as_dict(slot_name="attributes", slot_type=Attribute, key_name="name", keyed=False)
self._normalize_inlined_as_dict(slot_name="datasets", slot_type=Dataset, key_name="doc", keyed=False)
self._normalize_inlined_as_dict(slot_name="groups", slot_type=Group, key_name="doc", keyed=False)
self._normalize_inlined_as_dict(slot_name="links", slot_type=Link, key_name="doc", keyed=False)
super().__post_init__(**kwargs)
@dataclass
class Attribute(YAMLRoot):
_inherited_slots: ClassVar[List[str]] = []
class_class_uri: ClassVar[URIRef] = NWB_SCHEMA_LANGUAGE.Attribute
class_class_curie: ClassVar[str] = "nwb_schema_language:Attribute"
class_name: ClassVar[str] = "Attribute"
class_model_uri: ClassVar[URIRef] = NWB_SCHEMA_LANGUAGE.Attribute
name: str = None
doc: str = None
dims: Optional[Union[str, List[str]]] = empty_list()
shape: Optional[Union[str, List[str]]] = empty_list()
value: Optional[Union[dict, "AnyType"]] = None
default_value: Optional[Union[dict, "AnyType"]] = None
required: Optional[Union[bool, Bool]] = True
dtype: Optional[str] = None
def __post_init__(self, *_: List[str], **kwargs: Dict[str, Any]):
if self._is_empty(self.name):
self.MissingRequiredField("name")
if not isinstance(self.name, str):
self.name = str(self.name)
if self._is_empty(self.doc):
self.MissingRequiredField("doc")
if not isinstance(self.doc, str):
self.doc = str(self.doc)
if not isinstance(self.dims, list):
self.dims = [self.dims] if self.dims is not None else []
self.dims = [v if isinstance(v, str) else str(v) for v in self.dims]
if not isinstance(self.shape, list):
self.shape = [self.shape] if self.shape is not None else []
self.shape = [v if isinstance(v, str) else str(v) for v in self.shape]
if self.required is not None and not isinstance(self.required, Bool):
self.required = Bool(self.required)
if self.dtype is not None and not isinstance(self.dtype, str):
self.dtype = str(self.dtype)
super().__post_init__(**kwargs)
@dataclass
class Link(YAMLRoot):
_inherited_slots: ClassVar[List[str]] = []
class_class_uri: ClassVar[URIRef] = NWB_SCHEMA_LANGUAGE.Link
class_class_curie: ClassVar[str] = "nwb_schema_language:Link"
class_name: ClassVar[str] = "Link"
class_model_uri: ClassVar[URIRef] = NWB_SCHEMA_LANGUAGE.Link
doc: str = None
target_type: str = None
name: Optional[str] = None
quantity: Optional[str] = 1
def __post_init__(self, *_: List[str], **kwargs: Dict[str, Any]):
if self._is_empty(self.doc):
self.MissingRequiredField("doc")
if not isinstance(self.doc, str):
self.doc = str(self.doc)
if self._is_empty(self.target_type):
self.MissingRequiredField("target_type")
if not isinstance(self.target_type, str):
self.target_type = str(self.target_type)
if self.name is not None and not isinstance(self.name, str):
self.name = str(self.name)
if self.quantity is not None and not isinstance(self.quantity, str):
self.quantity = str(self.quantity)
super().__post_init__(**kwargs)
@dataclass
class Dataset(YAMLRoot):
_inherited_slots: ClassVar[List[str]] = []
class_class_uri: ClassVar[URIRef] = NWB_SCHEMA_LANGUAGE.Dataset
class_class_curie: ClassVar[str] = "nwb_schema_language:Dataset"
class_name: ClassVar[str] = "Dataset"
class_model_uri: ClassVar[URIRef] = NWB_SCHEMA_LANGUAGE.Dataset
doc: str = None
neurodata_type_def: Optional[str] = None
neurodata_type_inc: Optional[str] = None
name: Optional[str] = None
default_name: Optional[str] = None
dims: Optional[Union[str, List[str]]] = empty_list()
shape: Optional[Union[str, List[str]]] = empty_list()
value: Optional[Union[dict, "AnyType"]] = None
default_value: Optional[Union[dict, "AnyType"]] = None
quantity: Optional[str] = 1
linkable: Optional[Union[bool, Bool]] = None
attributes: Optional[Union[Union[dict, Attribute], List[Union[dict, Attribute]]]] = empty_list()
dtype: Optional[str] = None
def __post_init__(self, *_: List[str], **kwargs: Dict[str, Any]):
if self._is_empty(self.doc):
self.MissingRequiredField("doc")
if not isinstance(self.doc, str):
self.doc = str(self.doc)
if self.neurodata_type_def is not None and not isinstance(self.neurodata_type_def, str):
self.neurodata_type_def = str(self.neurodata_type_def)
if self.neurodata_type_inc is not None and not isinstance(self.neurodata_type_inc, str):
self.neurodata_type_inc = str(self.neurodata_type_inc)
if self.name is not None and not isinstance(self.name, str):
self.name = str(self.name)
if self.default_name is not None and not isinstance(self.default_name, str):
self.default_name = str(self.default_name)
if not isinstance(self.dims, list):
self.dims = [self.dims] if self.dims is not None else []
self.dims = [v if isinstance(v, str) else str(v) for v in self.dims]
if not isinstance(self.shape, list):
self.shape = [self.shape] if self.shape is not None else []
self.shape = [v if isinstance(v, str) else str(v) for v in self.shape]
if self.quantity is not None and not isinstance(self.quantity, str):
self.quantity = str(self.quantity)
if self.linkable is not None and not isinstance(self.linkable, Bool):
self.linkable = Bool(self.linkable)
self._normalize_inlined_as_dict(slot_name="attributes", slot_type=Attribute, key_name="name", keyed=False)
if self.dtype is not None and not isinstance(self.dtype, str):
self.dtype = str(self.dtype)
super().__post_init__(**kwargs)
@dataclass
class ReferenceDtype(YAMLRoot):
_inherited_slots: ClassVar[List[str]] = []
class_class_uri: ClassVar[URIRef] = NWB_SCHEMA_LANGUAGE.ReferenceDtype
class_class_curie: ClassVar[str] = "nwb_schema_language:ReferenceDtype"
class_name: ClassVar[str] = "ReferenceDtype"
class_model_uri: ClassVar[URIRef] = NWB_SCHEMA_LANGUAGE.ReferenceDtype
target_type: str = None
reftype: Optional[Union[str, "ReftypeOptions"]] = None
def __post_init__(self, *_: List[str], **kwargs: Dict[str, Any]):
if self._is_empty(self.target_type):
self.MissingRequiredField("target_type")
if not isinstance(self.target_type, str):
self.target_type = str(self.target_type)
if self.reftype is not None and not isinstance(self.reftype, ReftypeOptions):
self.reftype = ReftypeOptions(self.reftype)
super().__post_init__(**kwargs)
@dataclass
class CompoundDtype(YAMLRoot):
_inherited_slots: ClassVar[List[str]] = []
class_class_uri: ClassVar[URIRef] = NWB_SCHEMA_LANGUAGE.CompoundDtype
class_class_curie: ClassVar[str] = "nwb_schema_language:CompoundDtype"
class_name: ClassVar[str] = "CompoundDtype"
class_model_uri: ClassVar[URIRef] = NWB_SCHEMA_LANGUAGE.CompoundDtype
name: str = None
doc: str = None
dtype: Union[str, "FlatDtype"] = None
def __post_init__(self, *_: List[str], **kwargs: Dict[str, Any]):
if self._is_empty(self.name):
self.MissingRequiredField("name")
if not isinstance(self.name, str):
self.name = str(self.name)
if self._is_empty(self.doc):
self.MissingRequiredField("doc")
if not isinstance(self.doc, str):
self.doc = str(self.doc)
if self._is_empty(self.dtype):
self.MissingRequiredField("dtype")
if not isinstance(self.dtype, FlatDtype):
self.dtype = FlatDtype(self.dtype)
super().__post_init__(**kwargs)
@dataclass
class DtypeMixin(YAMLRoot):
_inherited_slots: ClassVar[List[str]] = []
class_class_uri: ClassVar[URIRef] = NWB_SCHEMA_LANGUAGE.DtypeMixin
class_class_curie: ClassVar[str] = "nwb_schema_language:DtypeMixin"
class_name: ClassVar[str] = "DtypeMixin"
class_model_uri: ClassVar[URIRef] = NWB_SCHEMA_LANGUAGE.DtypeMixin
dtype: Optional[str] = None
def __post_init__(self, *_: List[str], **kwargs: Dict[str, Any]):
if self.dtype is not None and not isinstance(self.dtype, str):
self.dtype = str(self.dtype)
super().__post_init__(**kwargs)
class NamingMixin(YAMLRoot):
"""
require either neurodata_type_def or name to be present
"""
_inherited_slots: ClassVar[List[str]] = []
class_class_uri: ClassVar[URIRef] = NWB_SCHEMA_LANGUAGE.NamingMixin
class_class_curie: ClassVar[str] = "nwb_schema_language:NamingMixin"
class_name: ClassVar[str] = "NamingMixin"
class_model_uri: ClassVar[URIRef] = NWB_SCHEMA_LANGUAGE.NamingMixin
AnyType = Any
# Enumerations
class ReftypeOptions(EnumDefinitionImpl):
ref = PermissibleValue(
text="ref",
description="Reference to another group or dataset of the given target_type")
reference = PermissibleValue(
text="reference",
description="Reference to another group or dataset of the given target_type")
object = PermissibleValue(
text="object",
description="Reference to another group or dataset of the given target_type")
region = PermissibleValue(
text="region",
description="Reference to a region (i.e. subset) of another dataset of the given target_type")
_defn = EnumDefinition( _defn = EnumDefinition(
name="PersonStatus", name="ReftypeOptions",
) )
class QuantityEnum(EnumDefinitionImpl):
zero_or_many = PermissibleValue(
text="zero_or_many",
description="Zero or more instances, equivalent to *")
one_or_many = PermissibleValue(
text="one_or_many",
description="One or more instances, equivalent to +")
zero_or_one = PermissibleValue(
text="zero_or_one",
description="Zero or one instances, equivalent to ?")
_defn = EnumDefinition(
name="QuantityEnum",
)
@classmethod
def _addvals(cls):
setattr(cls, "*",
PermissibleValue(
text="*",
description="Zero or more instances, equivalent to zero_or_many"))
setattr(cls, "?",
PermissibleValue(
text="?",
description="Zero or one instances, equivalent to zero_or_one"))
setattr(cls, "+",
PermissibleValue(
text="+",
description="One or more instances, equivalent to one_or_many"))
class FlatDtype(EnumDefinitionImpl):
float = PermissibleValue(
text="float",
description="single precision floating point (32 bit)")
float32 = PermissibleValue(
text="float32",
description="single precision floating point (32 bit)")
double = PermissibleValue(
text="double",
description="double precision floating point (64 bit)")
float64 = PermissibleValue(
text="float64",
description="double precision floating point (64 bit)")
long = PermissibleValue(
text="long",
description="signed 64 bit integer")
int64 = PermissibleValue(
text="int64",
description="signed 64 bit integer")
int = PermissibleValue(
text="int",
description="signed 32 bit integer")
int32 = PermissibleValue(
text="int32",
description="signed 32 bit integer")
int16 = PermissibleValue(
text="int16",
description="signed 16 bit integer")
short = PermissibleValue(
text="short",
description="signed 16 bit integer")
int8 = PermissibleValue(
text="int8",
description="signed 8 bit integer")
uint = PermissibleValue(
text="uint",
description="unsigned 32 bit integer")
uint32 = PermissibleValue(
text="uint32",
description="unsigned 32 bit integer")
uint16 = PermissibleValue(
text="uint16",
description="unsigned 16 bit integer")
uint8 = PermissibleValue(
text="uint8",
description="unsigned 8 bit integer")
uint64 = PermissibleValue(
text="uint64",
description="unsigned 64 bit integer")
numeric = PermissibleValue(
text="numeric",
description="any numeric type (i.e., any int, uint, float)")
text = PermissibleValue(
text="text",
description="8-bit Unicode")
utf = PermissibleValue(
text="utf",
description="8-bit Unicode")
utf8 = PermissibleValue(
text="utf8",
description="8-bit Unicode")
ascii = PermissibleValue(
text="ascii",
description="ASCII text")
bool = PermissibleValue(
text="bool",
description="8 bit integer with valid values 0 or 1")
isodatetime = PermissibleValue(
text="isodatetime",
description="ISO 8601 datetime string")
_defn = EnumDefinition(
name="FlatDtype",
)
@classmethod
def _addvals(cls):
setattr(cls, "utf-8",
PermissibleValue(
text="utf-8",
description="8-bit Unicode"))
# Slots # Slots
class slots: class slots:
pass pass
slots.id = Slot(uri=SCHEMA.identifier, name="id", curie=SCHEMA.curie('identifier'), slots.doc = Slot(uri=NWB_SCHEMA_LANGUAGE.doc, name="doc", curie=NWB_SCHEMA_LANGUAGE.curie('doc'),
model_uri=NWB_SCHEMA_LANGUAGE.id, domain=None, range=URIRef) model_uri=NWB_SCHEMA_LANGUAGE.doc, domain=None, range=str)
slots.name = Slot(uri=SCHEMA.name, name="name", curie=SCHEMA.curie('name'), slots.name = Slot(uri=NWB_SCHEMA_LANGUAGE.name, name="name", curie=NWB_SCHEMA_LANGUAGE.curie('name'),
model_uri=NWB_SCHEMA_LANGUAGE.name, domain=None, range=Optional[str]) model_uri=NWB_SCHEMA_LANGUAGE.name, domain=None, range=Optional[str])
slots.description = Slot(uri=SCHEMA.description, name="description", curie=SCHEMA.curie('description'), slots.full_name = Slot(uri=NWB_SCHEMA_LANGUAGE.full_name, name="full_name", curie=NWB_SCHEMA_LANGUAGE.curie('full_name'),
model_uri=NWB_SCHEMA_LANGUAGE.description, domain=None, range=Optional[str]) model_uri=NWB_SCHEMA_LANGUAGE.full_name, domain=None, range=Optional[str])
slots.primary_email = Slot(uri=SCHEMA.email, name="primary_email", curie=SCHEMA.curie('email'), slots.version = Slot(uri=NWB_SCHEMA_LANGUAGE.version, name="version", curie=NWB_SCHEMA_LANGUAGE.curie('version'),
model_uri=NWB_SCHEMA_LANGUAGE.primary_email, domain=None, range=Optional[str]) model_uri=NWB_SCHEMA_LANGUAGE.version, domain=None, range=str,
pattern=re.compile(r'^(0|[1-9]\d*)\.(0|[1-9]\d*)\.(0|[1-9]\d*)(?:-((?:0|[1-9]\d*|\d*[a-zA-Z-][0-9a-zA-Z-]*)(?:\.(?:0|[1-9]\d*|\d*[a-zA-Z-][0-9a-zA-Z-]*))*))?(?:\+([0-9a-zA-Z-]+(?:\.[0-9a-zA-Z-]+)*))?$'))
slots.birth_date = Slot(uri=SCHEMA.birthDate, name="birth_date", curie=SCHEMA.curie('birthDate'), slots.date = Slot(uri=SCHEMA.dateModified, name="date", curie=SCHEMA.curie('dateModified'),
model_uri=NWB_SCHEMA_LANGUAGE.birth_date, domain=None, range=Optional[Union[str, XSDDate]]) model_uri=NWB_SCHEMA_LANGUAGE.date, domain=None, range=Optional[Union[str, XSDDate]])
slots.age_in_years = Slot(uri=NWB_SCHEMA_LANGUAGE.age_in_years, name="age_in_years", curie=NWB_SCHEMA_LANGUAGE.curie('age_in_years'), slots.author = Slot(uri=SCHEMA.author, name="author", curie=SCHEMA.curie('author'),
model_uri=NWB_SCHEMA_LANGUAGE.age_in_years, domain=None, range=Optional[int]) model_uri=NWB_SCHEMA_LANGUAGE.author, domain=None, range=Union[str, List[str]])
slots.vital_status = Slot(uri=NWB_SCHEMA_LANGUAGE.vital_status, name="vital_status", curie=NWB_SCHEMA_LANGUAGE.curie('vital_status'), slots.contact = Slot(uri=SCHEMA.email, name="contact", curie=SCHEMA.curie('email'),
model_uri=NWB_SCHEMA_LANGUAGE.vital_status, domain=None, range=Optional[Union[str, "PersonStatus"]]) model_uri=NWB_SCHEMA_LANGUAGE.contact, domain=None, range=Union[str, List[str]])
slots.namespacesCollection__entries = Slot(uri=NWB_SCHEMA_LANGUAGE.entries, name="namespacesCollection__entries", curie=NWB_SCHEMA_LANGUAGE.curie('entries'), slots.schema = Slot(uri=NWB_SCHEMA_LANGUAGE.schema, name="schema", curie=NWB_SCHEMA_LANGUAGE.curie('schema'),
model_uri=NWB_SCHEMA_LANGUAGE.namespacesCollection__entries, domain=None, range=Optional[Union[Dict[Union[str, NamespacesId], Union[dict, Namespaces]], List[Union[dict, Namespaces]]]]) model_uri=NWB_SCHEMA_LANGUAGE.schema, domain=None, range=Optional[Union[Union[dict, Schema], List[Union[dict, Schema]]]])
slots.Namespaces_primary_email = Slot(uri=SCHEMA.email, name="Namespaces_primary_email", curie=SCHEMA.curie('email'), slots.source = Slot(uri=NWB_SCHEMA_LANGUAGE.source, name="source", curie=NWB_SCHEMA_LANGUAGE.curie('source'),
model_uri=NWB_SCHEMA_LANGUAGE.Namespaces_primary_email, domain=Namespaces, range=Optional[str], model_uri=NWB_SCHEMA_LANGUAGE.source, domain=None, range=Optional[str],
pattern=re.compile(r'^\S+@[\S+\.]+\S+')) pattern=re.compile(r'.*\.(yml|yaml|json)'))
slots.namespace = Slot(uri=NWB_SCHEMA_LANGUAGE.namespace, name="namespace", curie=NWB_SCHEMA_LANGUAGE.curie('namespace'),
model_uri=NWB_SCHEMA_LANGUAGE.namespace, domain=None, range=Optional[str])
slots.namespaces = Slot(uri=NWB_SCHEMA_LANGUAGE.namespaces, name="namespaces", curie=NWB_SCHEMA_LANGUAGE.curie('namespaces'),
model_uri=NWB_SCHEMA_LANGUAGE.namespaces, domain=None, range=Optional[Union[Union[dict, Namespace], List[Union[dict, Namespace]]]])
slots.neurodata_types = Slot(uri=NWB_SCHEMA_LANGUAGE.neurodata_types, name="neurodata_types", curie=NWB_SCHEMA_LANGUAGE.curie('neurodata_types'),
model_uri=NWB_SCHEMA_LANGUAGE.neurodata_types, domain=None, range=Optional[Union[str, List[str]]])
slots.title = Slot(uri=NWB_SCHEMA_LANGUAGE.title, name="title", curie=NWB_SCHEMA_LANGUAGE.curie('title'),
model_uri=NWB_SCHEMA_LANGUAGE.title, domain=None, range=Optional[str])
slots.neurodata_type_def = Slot(uri=NWB_SCHEMA_LANGUAGE.neurodata_type_def, name="neurodata_type_def", curie=NWB_SCHEMA_LANGUAGE.curie('neurodata_type_def'),
model_uri=NWB_SCHEMA_LANGUAGE.neurodata_type_def, domain=None, range=Optional[str])
slots.neurodata_type_inc = Slot(uri=NWB_SCHEMA_LANGUAGE.neurodata_type_inc, name="neurodata_type_inc", curie=NWB_SCHEMA_LANGUAGE.curie('neurodata_type_inc'),
model_uri=NWB_SCHEMA_LANGUAGE.neurodata_type_inc, domain=None, range=Optional[str])
slots.default_name = Slot(uri=NWB_SCHEMA_LANGUAGE.default_name, name="default_name", curie=NWB_SCHEMA_LANGUAGE.curie('default_name'),
model_uri=NWB_SCHEMA_LANGUAGE.default_name, domain=None, range=Optional[str])
slots.quantity = Slot(uri=NWB_SCHEMA_LANGUAGE.quantity, name="quantity", curie=NWB_SCHEMA_LANGUAGE.curie('quantity'),
model_uri=NWB_SCHEMA_LANGUAGE.quantity, domain=None, range=Optional[str])
slots.linkable = Slot(uri=NWB_SCHEMA_LANGUAGE.linkable, name="linkable", curie=NWB_SCHEMA_LANGUAGE.curie('linkable'),
model_uri=NWB_SCHEMA_LANGUAGE.linkable, domain=None, range=Optional[Union[bool, Bool]])
slots.attributes = Slot(uri=NWB_SCHEMA_LANGUAGE.attributes, name="attributes", curie=NWB_SCHEMA_LANGUAGE.curie('attributes'),
model_uri=NWB_SCHEMA_LANGUAGE.attributes, domain=None, range=Optional[Union[Union[dict, Attribute], List[Union[dict, Attribute]]]])
slots.datasets = Slot(uri=NWB_SCHEMA_LANGUAGE.datasets, name="datasets", curie=NWB_SCHEMA_LANGUAGE.curie('datasets'),
model_uri=NWB_SCHEMA_LANGUAGE.datasets, domain=None, range=Optional[Union[Union[dict, Dataset], List[Union[dict, Dataset]]]])
slots.groups = Slot(uri=NWB_SCHEMA_LANGUAGE.groups, name="groups", curie=NWB_SCHEMA_LANGUAGE.curie('groups'),
model_uri=NWB_SCHEMA_LANGUAGE.groups, domain=None, range=Optional[Union[Union[dict, Group], List[Union[dict, Group]]]])
slots.links = Slot(uri=NWB_SCHEMA_LANGUAGE.links, name="links", curie=NWB_SCHEMA_LANGUAGE.curie('links'),
model_uri=NWB_SCHEMA_LANGUAGE.links, domain=None, range=Optional[Union[Union[dict, Link], List[Union[dict, Link]]]])
slots.dtype = Slot(uri=NWB_SCHEMA_LANGUAGE.dtype, name="dtype", curie=NWB_SCHEMA_LANGUAGE.curie('dtype'),
model_uri=NWB_SCHEMA_LANGUAGE.dtype, domain=None, range=Optional[str])
slots.dims = Slot(uri=NWB_SCHEMA_LANGUAGE.dims, name="dims", curie=NWB_SCHEMA_LANGUAGE.curie('dims'),
model_uri=NWB_SCHEMA_LANGUAGE.dims, domain=None, range=Optional[Union[str, List[str]]])
slots.shape = Slot(uri=NWB_SCHEMA_LANGUAGE.shape, name="shape", curie=NWB_SCHEMA_LANGUAGE.curie('shape'),
model_uri=NWB_SCHEMA_LANGUAGE.shape, domain=None, range=Optional[Union[str, List[str]]])
slots.value = Slot(uri=NWB_SCHEMA_LANGUAGE.value, name="value", curie=NWB_SCHEMA_LANGUAGE.curie('value'),
model_uri=NWB_SCHEMA_LANGUAGE.value, domain=None, range=Optional[Union[dict, AnyType]])
slots.default_value = Slot(uri=NWB_SCHEMA_LANGUAGE.default_value, name="default_value", curie=NWB_SCHEMA_LANGUAGE.curie('default_value'),
model_uri=NWB_SCHEMA_LANGUAGE.default_value, domain=None, range=Optional[Union[dict, AnyType]])
slots.required = Slot(uri=NWB_SCHEMA_LANGUAGE.required, name="required", curie=NWB_SCHEMA_LANGUAGE.curie('required'),
model_uri=NWB_SCHEMA_LANGUAGE.required, domain=None, range=Optional[Union[bool, Bool]])
slots.target_type = Slot(uri=NWB_SCHEMA_LANGUAGE.target_type, name="target_type", curie=NWB_SCHEMA_LANGUAGE.curie('target_type'),
model_uri=NWB_SCHEMA_LANGUAGE.target_type, domain=None, range=str)
slots.reftype = Slot(uri=NWB_SCHEMA_LANGUAGE.reftype, name="reftype", curie=NWB_SCHEMA_LANGUAGE.curie('reftype'),
model_uri=NWB_SCHEMA_LANGUAGE.reftype, domain=None, range=Optional[Union[str, "ReftypeOptions"]])
slots.Namespace_name = Slot(uri=NWB_SCHEMA_LANGUAGE.name, name="Namespace_name", curie=NWB_SCHEMA_LANGUAGE.curie('name'),
model_uri=NWB_SCHEMA_LANGUAGE.Namespace_name, domain=Namespace, range=str)
slots.Schema_doc = Slot(uri=NWB_SCHEMA_LANGUAGE.doc, name="Schema_doc", curie=NWB_SCHEMA_LANGUAGE.curie('doc'),
model_uri=NWB_SCHEMA_LANGUAGE.Schema_doc, domain=Schema, range=str)
slots.Attribute_name = Slot(uri=NWB_SCHEMA_LANGUAGE.name, name="Attribute_name", curie=NWB_SCHEMA_LANGUAGE.curie('name'),
model_uri=NWB_SCHEMA_LANGUAGE.Attribute_name, domain=Attribute, range=str)
slots.CompoundDtype_name = Slot(uri=NWB_SCHEMA_LANGUAGE.name, name="CompoundDtype_name", curie=NWB_SCHEMA_LANGUAGE.curie('name'),
model_uri=NWB_SCHEMA_LANGUAGE.CompoundDtype_name, domain=CompoundDtype, range=str)
slots.CompoundDtype_dtype = Slot(uri=NWB_SCHEMA_LANGUAGE.dtype, name="CompoundDtype_dtype", curie=NWB_SCHEMA_LANGUAGE.curie('dtype'),
model_uri=NWB_SCHEMA_LANGUAGE.CompoundDtype_dtype, domain=CompoundDtype, range=Union[str, "FlatDtype"])

View file

@ -11,86 +11,373 @@ see_also:
prefixes: prefixes:
nwb_schema_language: https://w3id.org/p2p_ld/nwb-schema-language/ nwb_schema_language: https://w3id.org/p2p_ld/nwb-schema-language/
linkml: https://w3id.org/linkml/ linkml: https://w3id.org/linkml/
biolink: https://w3id.org/biolink/
schema: http://schema.org/ schema: http://schema.org/
PATO: http://purl.obolibrary.org/obo/PATO_
example: https://example.org/
default_prefix: nwb_schema_language default_prefix: nwb_schema_language
default_range: string default_range: string
imports: imports:
- linkml:types - linkml:types
classes: settings:
email: "\\S+@\\S+{\\.\\w}+"
protected_string: "^[A-Za-z_][A-Za-z0-9_]*$"
NamedThing: classes:
description: >- Namespace:
A generic grouping for any identifiable entity
slots: slots:
- id - doc
- name - name
- description - full_name
class_uri: schema:Thing - version
- date
- author
- contact
- schema
slot_usage:
name:
required: true
Namespaces: Namespaces:
is_a: NamedThing
description: >-
Represents a Namespaces
slots: slots:
- primary_email - namespaces
- birth_date
- age_in_years Schema:
- vital_status slots:
slot_usage: - source
primary_email: - namespace
pattern: "^\\S+@[\\S+\\.]+\\S+" - doc
- title
- neurodata_types
slot_usage:
doc:
required: false
rules:
- preconditions: {slot_conditions: { namespace: { value_presence: ABSENT }}}
postconditions: {slot_conditions: { source: {required: true }}}
description: If namespace is absent, source is required
- preconditions: {slot_conditions: { source: { value_presence: ABSENT }}}
postconditions: {slot_conditions: { namespace: {required: true }}}
description: If source is absent, namespace is required.
- preconditions: { slot_conditions: { namespace: { value_presence: PRESENT }}}
postconditions: { slot_conditions: { source: { value_presence: ABSENT }}}
description: If namespace is present, source is cannot be
- preconditions: { slot_conditions: { source: { value_presence: PRESENT }}}
postconditions: { slot_conditions: { namespace: { value_presence: ABSENT }}}
description: If source is present, namespace cannot be.
Group:
mixins:
- NamingMixin
slots:
- neurodata_type_def
- neurodata_type_inc
- name
- default_name
- doc
- quantity
- linkable
- attributes
- datasets
- groups
- links
Attribute:
mixins:
- DtypeMixin
slots:
- name
- dims
- shape
- value
- default_value
- doc
- required
slot_usage:
name:
required: true
Link:
slots:
- name
- doc
- target_type
- quantity
Dataset:
mixins:
- DtypeMixin
- NamingMixin
slots:
- neurodata_type_def
- neurodata_type_inc
- name
- default_name
- dims
- shape
- value
- default_value
- doc
- quantity
- linkable
- attributes
ReferenceDtype:
slots:
- target_type
- reftype
CompoundDtype:
slots:
- name
- doc
- dtype
slot_usage:
name:
required: true
dtype:
required: true
range: FlatDtype
DtypeMixin:
mixin: true
slots:
- dtype
rules:
- preconditions:
slot_conditions:
dtype:
range: CompoundDtype
postconditions:
slot_conditions:
dtype:
multivalued: true
NamingMixin:
mixin: true
description: require either neurodata_type_def or name to be present
rules:
- preconditions: { slot_conditions: { neurodata_type_def: { value_presence: ABSENT } } }
postconditions: { slot_conditions: { name: { required: true } } }
description: If not defining a new type, a name is required
- preconditions: { slot_conditions: { name: { value_presence: ABSENT } } }
postconditions: { slot_conditions: { neurodata_type_def: { required: true } } }
description: If a name is not given, must be defining a new type
AnyType:
class_uri: linkml:Any
NamespacesCollection:
tree_root: true
description: >-
A holder for Namespaces objects
attributes:
entries:
range: Namespaces
multivalued: true
inlined: true
slots: slots:
id: # namespaces
identifier: true doc:
slot_uri: schema:identifier required: true
range: uriorcurie description: Description of corresponding object.
description: A unique identifier for a thing
name: name:
slot_uri: schema:name structured_pattern:
description: A human-readable name for a thing syntax: "{protected_string}"
description: interpolated: true
slot_uri: schema:description full_name:
description: A human-readable description for a thing description: Optional string with extended full name for the namespace.
primary_email: version:
slot_uri: schema:email required: true
description: The main email address of a person pattern: "^(0|[1-9]\\d*)\\.(0|[1-9]\\d*)\\.(0|[1-9]\\d*)(?:-((?:0|[1-9]\\d*|\\d*[a-zA-Z-][0-9a-zA-Z-]*)(?:\\.(?:0|[1-9]\\d*|\\d*[a-zA-Z-][0-9a-zA-Z-]*))*))?(?:\\+([0-9a-zA-Z-]+(?:\\.[0-9a-zA-Z-]+)*))?$"
birth_date: date:
slot_uri: schema:birthDate
range: date range: date
description: Date on which a person is born slot_uri: schema:dateModified
age_in_years: description: Date that a namespace was last modified or released
range: integer examples:
description: Number of years since birth - value: 2017-04-25 17:14:13
vital_status: author:
range: PersonStatus multivalued: true
description: living or dead status required: true
slot_uri: schema:author
description: List of strings with the names of the authors of the namespace.
contact:
multivalued: true
required: true
slot_uri: schema:email
structured_pattern:
syntax: "{email}"
interpolated: true
description: List of strings with the contact information for the authors. Ordering of the contacts should match the ordering of the authors.
schema:
multivalued: true
range: Schema
description: List of the schema to be included in this namespace.
inlined_as_list: true
# schema
source:
description: describes the name of the YAML (or JSON) file with the schema specification. The schema files should be located in the same folder as the namespace file.
pattern: ".*\\.(yml|yaml|json)"
namespace:
description: describes a named reference to another namespace. In contrast to source, this is a reference by name to a known namespace (i.e., the namespace is resolved during the build and must point to an already existing namespace). This mechanism is used to allow, e.g., extension of a core namespace (here the NWB core namespace) without requiring hard paths to the files describing the core namespace. Either source or namespace must be specified, but not both.
namespaces:
multivalued: true
range: Namespace
neurodata_types:
multivalued: true
any_of:
- range: Dataset
- range: Group
description: an optional list of strings indicating which data types should be included from the given specification source or namespace. The default is null indicating that all data types should be included.
title:
description: a descriptive title for a file for documentation purposes.
# groups
neurodata_type_def:
structured_pattern:
syntax: "{protected_string}"
interpolated: true
neurodata_type_inc:
structured_pattern:
syntax: "{protected_string}"
interpolated: true
default_name:
structured_pattern:
syntax: "{protected_string}"
interpolated: true
quantity:
any_of:
- range: integer
minimum_value: 1
- range: QuantityEnum
ifabsent: int(1)
todos:
- logic to check that the corresponding class can only be implemented quantity times.
linkable:
range: boolean
# Recursive properties
attributes:
range: Attribute
multivalued: true
datasets:
range: Dataset
multivalued: true
groups:
range: Group
multivalued: true
links:
range: Link
multivalued: true
# attributes
dtype:
exactly_one_of:
- range: FlatDtype
- range: CompoundDtype
- range: ReferenceDtype
dims:
multivalued: true
range: string
todos:
- Can't quite figure out how to allow an array of arrays
shape:
multivalued: true
exactly_one_of:
- range: integer
minimum_value: 1
- equals_string: "null"
todos:
- Can't quite figure out how to allow an array of arrays
value:
range: AnyType
description: Optional constant, fixed value for the attribute.
default_value:
range: AnyType
description: Optional default value for variable-valued attributes.
required:
range: boolean
description: Optional boolean key describing whether the attribute is required. Default value is True.
ifabsent: "true"
# links
target_type:
description: Describes the neurodata_type of the target that the reference points
to
required: true
any_of:
- range: Dataset
- range: Group
reftype:
description: describes the kind of reference
range: reftype_options
enums: enums:
PersonStatus: reftype_options:
permissible_values: permissible_values:
ALIVE: ref: { description: Reference to another group or dataset of the given target_type }
description: the person is living reference: { description: Reference to another group or dataset of the given target_type }
meaning: PATO:0001421 object: { description: Reference to another group or dataset of the given target_type }
DEAD: region: { description: Reference to a region (i.e. subset) of another dataset of the given target_type}
description: the person is deceased QuantityEnum:
meaning: PATO:0001422 permissible_values:
UNKNOWN: "*":
description: the vital status is not known description: Zero or more instances, equivalent to zero_or_many
todos: "?":
- map this to an ontology description: Zero or one instances, equivalent to zero_or_one
"+":
description: One or more instances, equivalent to one_or_many
"zero_or_many":
description: Zero or more instances, equivalent to *
"one_or_many":
description: One or more instances, equivalent to +
"zero_or_one":
description: Zero or one instances, equivalent to ?
FlatDtype:
permissible_values:
"float":
description: single precision floating point (32 bit)
"float32":
description: single precision floating point (32 bit)
"double":
description: double precision floating point (64 bit)
"float64":
description: double precision floating point (64 bit)
"long":
description: signed 64 bit integer
"int64":
description: signed 64 bit integer
"int":
description: signed 32 bit integer
"int32":
description: signed 32 bit integer
"int16":
description: signed 16 bit integer
"short":
description: signed 16 bit integer
"int8":
description: signed 8 bit integer
"uint":
description: unsigned 32 bit integer
"uint32":
description: unsigned 32 bit integer
"uint16":
description: unsigned 16 bit integer
"uint8":
description: unsigned 8 bit integer
"uint64":
description: unsigned 64 bit integer
"numeric":
description: any numeric type (i.e., any int, uint, float)
"text":
description: 8-bit Unicode
"utf":
description: 8-bit Unicode
"utf8":
description: 8-bit Unicode
"utf-8":
description: 8-bit Unicode
"ascii":
description: ASCII text
"bool":
description: 8 bit integer with valid values 0 or 1
"isodatetime":
description: ISO 8601 datetime string
examples:
- value: 2018-09-28T14:43:54.123+02:00

View file

@ -7,7 +7,7 @@ from linkml_runtime.loaders import yaml_loader
from nwb_schema_language.datamodel.nwb_schema_language import Namespaces from nwb_schema_language.datamodel.nwb_schema_language import Namespaces
ROOT = os.path.join(os.path.dirname(__file__), '..') ROOT = os.path.join(os.path.dirname(__file__), '..')
DATA_DIR = os.path.join(ROOT, "src", "data", "examples") DATA_DIR = os.path.join(ROOT, "src", "data", "tests")
EXAMPLE_FILES = glob.glob(os.path.join(DATA_DIR, '*.yaml')) EXAMPLE_FILES = glob.glob(os.path.join(DATA_DIR, '*.yaml'))
@ -15,8 +15,8 @@ EXAMPLE_FILES = glob.glob(os.path.join(DATA_DIR, '*.yaml'))
class TestData(unittest.TestCase): class TestData(unittest.TestCase):
"""Test data and datamodel.""" """Test data and datamodel."""
def test_data(self): def test_namespaces(self):
"""Date test.""" """Date test."""
for path in EXAMPLE_FILES: namespace_file = [f for f in EXAMPLE_FILES if 'namespace.yaml' in f][0]
obj = yaml_loader.load(path, target_class=Namespaces) obj = yaml_loader.load(namespace_file, target_class=Namespaces)
assert obj assert obj