Importers
cognite.neat._rules.importers
#
BaseImporter
#
Bases: ABC
, Generic[T_InputRules]
BaseImporter class which all importers inherit from.
Source code in cognite/neat/_rules/importers/_base.py
DMSImporter
#
Bases: BaseImporter[DMSInputRules]
Imports a Data Model from Cognite Data Fusion.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
schema |
DMSSchema
|
The schema containing the data model. |
required |
read_issues |
Sequence[NeatIssue] | None
|
A list of issues that occurred during the import. |
None
|
metadata |
DMSInputMetadata | None
|
Metadata for the data model. |
None
|
ref_metadata |
DMSInputMetadata | None
|
Metadata for the reference data model. |
None
|
Source code in cognite/neat/_rules/importers/_dms2rules.py
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|
from_data_model_id(client, data_model_id, reference_model_id=None)
classmethod
#
Create a DMSImporter ready to convert the given data model to rules.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
client |
CogniteClient
|
Instantiated CogniteClient to retrieve data model. |
required |
reference_model_id |
DataModelIdentifier | None
|
The reference data model to retrieve. This is the data model that the given data model is built on top of, typically, an enterprise data model. |
None
|
data_model_id |
DataModelIdentifier
|
Data Model to retrieve. |
required |
Returns:
Name | Type | Description |
---|---|---|
DMSImporter |
DMSImporter
|
DMSImporter instance |
Source code in cognite/neat/_rules/importers/_dms2rules.py
DTDLImporter
#
Bases: BaseImporter[InformationInputRules]
Importer from Azure Digital Twin - DTDL (Digital Twin Definition Language).
This importer supports DTDL v2.0 and v3.0.
It is recommended to use the class methods from_directory
and from_zip
to create an instance of this class.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
items |
Sequence[DTDLBase]
|
A sequence of DTDLBase objects. |
required |
title |
str
|
Title of the data model. Defaults to None. |
None
|
read_issues |
list[ValidationIssue]
|
A list of issues that occurred during reading. Defaults to None. |
None
|
schema |
SchemaCompleteness
|
Schema completeness. Defaults to SchemaCompleteness.partial. |
partial
|
Source code in cognite/neat/_rules/importers/_dtdl2rules/dtdl_importer.py
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IMFImporter
#
Bases: BaseRDFImporter
Convert SHACL shapes to tables/ transformation rules / Excel file.
Args:
filepath: Path to RDF file containing the SHACL Shapes
Note
Rewrite to fit the SHACL rules we apply OWL Ontologies are information models which completeness varies. As such, constructing functional data model directly will often be impossible, therefore the produced Rules object will be ill formed. To avoid this, neat will automatically attempt to make the imported rules compliant by adding default values for missing information, attaching dangling properties to default containers based on the property type, etc.
One has to be aware that NEAT will be opinionated about how to make the ontology compliant, and that the resulting rules may not be what you expect.
Source code in cognite/neat/_rules/importers/_rdf/_imf2rules/_imf2rules.py
InferenceImporter
#
Bases: BaseRDFImporter
Infers rules from a triple store.
Rules inference through analysis of knowledge graph provided in various formats. Use the factory methods to create a triple store from sources such as RDF files, JSON files, YAML files, XML files, or directly from a graph store.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
issue_list |
IssueList
|
Issue list to store issues |
required |
graph |
Graph
|
Knowledge graph |
required |
max_number_of_instance |
int
|
Maximum number of instances to be used in inference |
required |
prefix |
str
|
Prefix to be used for the inferred model |
required |
Source code in cognite/neat/_rules/importers/_rdf/_inference2rules.py
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OWLImporter
#
Bases: BaseRDFImporter
Convert OWL ontology to tables/ transformation rules / Excel file.
Args:
filepath: Path to OWL ontology
Note
OWL Ontologies are information models which completeness varies. As such, constructing functional data model directly will often be impossible, therefore the produced Rules object will be ill formed. To avoid this, neat will automatically attempt to make the imported rules compliant by adding default values for missing information, attaching dangling properties to default containers based on the property type, etc.
One has to be aware that NEAT will be opinionated about how to make the ontology compliant, and that the resulting rules may not be what you expect.
Source code in cognite/neat/_rules/importers/_rdf/_owl2rules/_owl2rules.py
ExcelImporter
#
Bases: BaseImporter[T_InputRules]
Import rules from an Excel file.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
filepath |
Path
|
The path to the Excel file. |
required |
Source code in cognite/neat/_rules/importers/_spreadsheet2rules.py
GoogleSheetImporter
#
Bases: BaseImporter[T_InputRules]
Import rules from a Google Sheet.
.. warning::
This importer is experimental and may not work as expected.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
sheet_id |
str
|
The Google Sheet ID. |
required |
skiprows |
int
|
The number of rows to skip when reading the Google Sheet. |
1
|
Source code in cognite/neat/_rules/importers/_spreadsheet2rules.py
YAMLImporter
#
Bases: BaseImporter[T_InputRules]
Imports the rules from a YAML file.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
raw_data |
dict[str, Any]
|
The raw data to be imported. |
required |
.. note::
YAML files are typically used for storing rules when checked into version control systems, e.g., git-history.
The advantage of using YAML files over Excel is that tools like git can show the differences between different
versions of the rules.