Read

cognite.neat._session._read.CDFReadAPI

Bases: BaseReadAPI

Reads from CDF Data Models. Use the .data_model() method to load a CDF Data Model to the knowledge graph.

data_model(data_model_id)

Reads a Data Model from CDF to the knowledge graph.

Parameters:

Name	Type	Description	Default
data_model_id	DataModelIdentifier	Tuple of strings with the id of a CDF Data Model.	required

Example

neat.read.cdf.data_model(("example_data_model_space", "EXAMPLE_DATA_MODEL", "v1"))

graph(data_model_id, instance_space=None)

Reads a knowledge graph from Cognite Data Fusion (CDF).

Parameters:

Name	Type	Description	Default
data_model_id	DataModelIdentifier	Tuple of strings with the id of a CDF Data Model.	required
instance_space	str | SequenceNotStr[str] | None	The instance spaces to extract. If None, all instance spaces are extracted.	None

Returns:

Name	Type	Description
IssueList	IssueList	A list of issues that occurred during the extraction.

cognite.neat._session._read.CDFClassicAPI

Bases: BaseReadAPI

Reads from the Classic Data Model from CDF. Use the .graph() method to load CDF core resources to the knowledge graph.

graph(root_asset_external_id, limit_per_type=None)

Reads the classic knowledge graph from CDF.

The Classic Graph consists of the following core resource type.

Classic Node CDF Resources

• Assets
• TimeSeries
• Sequences
• Events
• Files

All the classic node CDF resources can have one or more connections to one or more assets. This will match a direct relationship in the data modeling of CDF.

In addition, you have relationships between the classic node CDF resources. This matches an edge in the data modeling of CDF.

Finally, you have labels and data sets that to organize the graph. In which data sets have a similar, but different, role as a space in data modeling. While labels can be compared to node types in data modeling, used to quickly filter and find nodes/edges.

This extractor will extract the classic CDF graph into Neat starting from either a data set or a root asset.

It works as follows

1. Extract all core nodes (assets, time series, sequences, events, files) filtered by the given data set or root asset.
2. Extract all relationships starting from any of the extracted core nodes.
3. Extract all core nodes that are targets of the relationships that are not already extracted.
4. Extract all labels that are connected to the extracted core nodes/relationships.
5. Extract all data sets that are connected to the extracted core nodes/relationships.

Parameters:

Name	Type	Description	Default
root_asset_external_id	str	The external id of the root asset	required
limit_per_type	int | None	The maximum number of nodes to extract per core node type. If None, all nodes are extracted.	None

Returns:

Name	Type	Description
IssueList	IssueList	A list of issues that occurred during the extraction.

Example

neat.read.cdf.graph("root_asset_external_id")

cognite.neat._session._read.ExcelReadAPI

Bases: BaseReadAPI

Reads a Neat Excel Rules sheet to the graph store. The rules sheet may stem from an Information architect, or a DMS Architect.

Parameters:

Name	Type	Description	Default
io		file path to the Excel sheet	required

Example

neat.read.excel("information_or_dms_rules_sheet.xlsx")

__call__(io)

Reads a Neat Excel Rules sheet to the graph store. The rules sheet may stem from an Information architect, or a DMS Architect.

Parameters:

Name	Type	Description	Default
io	Any	file path to the Excel sheet	required

cognite.neat._session._read.YamlReadAPI

Bases: BaseReadAPI

__call__(io, format='neat')

Reads a yaml with either neat rules, or several toolkit yaml files to import Data Model(s) into NeatSession.

Parameters:

Name	Type	Description	Default
io	Any	File path to the Yaml file in the case of "neat" yaml, or path to a zip folder or directory with several Yaml files in the case of "toolkit".	required
format	Literal['neat', 'toolkit']	The format of the yaml file(s). Can be either "neat" or "toolkit".	'neat'

Example

neat.read.yaml("path_to_toolkit_yamls")

cognite.neat._session._read.CSVReadAPI

Bases: BaseReadAPI

Reads a csv that contains a column to use as primary key which will be the unique identifier for the type of data you want to read in. Ex. a csv can hold information about assets, and their identifiers are specified in a "ASSET_TAG" column.

Parameters:

Name	Type	Description	Default
io		file path or url to the csv	required
type		string that specifies what type of data the csv contains. For instance "Asset" or "Equipment"	required
primary_key		string name of the column that should be used as the unique identifier for each row of data	required

Example

type_described_in_table = "Turbine"
column_with_identifier = "UNIQUE_TAG_NAME"
neat.read.csv("url_or_path_to_csv_file", type=type_described_in_table, primary_key=column_with_identifier)

cognite.neat._session._read.XMLReadAPI

Bases: BaseReadAPI

Reads an XML file that is either of DEXPI or AML format.

Parameters:

Name	Type	Description	Default
io		file path or url to the XML	required
format		can be either "dexpi" or "aml" are the currenly supported XML source types.	required

dexpi(io)

Reads a DEXPI file into the NeatSession.

Parameters:

Name	Type	Description	Default
io	Any	file path or url to the DEXPI file	required

Example

neat.read.xml.dexpi("url_or_path_to_dexpi_file")

aml(io)

Reads an AML file into NeatSession.

Parameters:

Name	Type	Description	Default
io	Any	file path or url to the AML file	required

Example

neat.read.xml.aml("url_or_path_to_aml_file")

cognite.neat._session._read.RDFReadAPI

Bases: BaseReadAPI

Reads an RDF source into NeatSession. Supported sources are "ontology" or "imf".

Parameters:

Name	Type	Description	Default
io		file path or url to the RDF source	required

ontology(io)

Reads an OWL ontology source into NeatSession.

Parameters:

Name	Type	Description	Default
io	Any	file path or url to the OWL file	required

Example

neat.read.rdf.ontology("url_or_path_to_owl_source")

imf(io)

Reads IMF Types provided as SHACL shapes into NeatSession.

Parameters:

Name	Type	Description	Default
io	Any	file path or url to the IMF file	required

Example

neat.read.rdf.imf("url_or_path_to_imf_source")