Contextualization

Entity Matching

These APIs will return as soon as possible, deferring a blocking wait until the last moment. Nevertheless, they can block for a long time awaiting results.

Fit Entity Matching Model

Fit entity matching model.

Note

All users on this CDF subscription with assets read-all and entitymatching read-all and write-all capabilities in the project, are able to access the data sent to this endpoint.

Parameters

sources (Sequence[dict | CogniteResource]) – entities to match from, should have an ‘id’ field. Tolerant to passing more than is needed or used (e.g. json dump of time series list). Metadata fields are automatically flattened to “metadata.key” entries, such that they can be used in match_fields.
targets (Sequence[dict | CogniteResource]) – entities to match to, should have an ‘id’ field. Tolerant to passing more than is needed or used.
true_matches (Sequence[dict | tuple[int | str, int | str]] | None) – Known valid matches given as a list of dicts with keys ‘sourceId’, ‘sourceExternalId’, ‘targetId’, ‘targetExternalId’). If omitted, uses an unsupervised model. A tuple can be used instead of the dictionary for convenience, interpreted as id/externalId based on type.
match_fields (dict | Sequence[tuple[str, str]] | None) – List of (from,to) keys to use in matching. Default in the API is [(‘name’,’name’)]. Also accepts {“source”: .., “target”: ..}.
feature_type (str | None) – feature type that defines the combination of features used, see API docs for details.
classifier (str | None) – classifier used in training.
ignore_missing_fields (bool) – whether missing data in match_fields should return error or be filled in with an empty string.
name (str | None) – Optional user-defined name of model.
description (str | None) – Optional user-defined description of model.
external_id (str | None) – Optional external id. Must be unique within the project.

Returns

Resulting queued model.

Return type

EntityMatchingModel

Re-fit Entity Matching Model

Re-fits an entity matching model, using the combination of the old and new true matches.

Note

All users on this CDF subscription with assets read-all and entitymatching read-all and write-all capabilities in the project, are able to access the data sent to this endpoint.

Parameters

true_matches (Sequence[dict | tuple[int | str, int | str]]) – Updated known valid matches given as a list of dicts with keys ‘fromId’, ‘fromExternalId’, ‘toId’, ‘toExternalId’). A tuple can be used instead of the dictionary for convenience, interpreted as id/externalId based on type.
id (int | None) – ids of the model to use.
external_id (str | None) – external ids of the model to use.

Returns

new model refitted to true_matches.

Return type

EntityMatchingModel

Retrieve Entity Matching Models

EntityMatchingAPI.retrieve(id: int | None = None, external_id: str | None = None) → EntityMatchingModel | None

Retrieve model

Parameters

id (int | None) – id of the model to retrieve.
external_id (str | None) – external id of the model to retrieve.

Returns

Model requested.

Return type

EntityMatchingModel | None

EntityMatchingAPI.retrieve_multiple(ids: Sequence[int] | None = None, external_ids: Sequence[str] | None = None) → EntityMatchingModelList

Retrieve models

Parameters

ids (Sequence[int] | None) – ids of the model to retrieve.
external_ids (Sequence[str] | None) – external ids of the model to retrieve.

Returns

Models requested.

Return type

EntityMatchingModelList

List models

Parameters

name (str | None) – Optional user-defined name of model.
description (str | None) – Optional user-defined description of model.
original_id (int | None) – id of the original model for models that were created with refit.
feature_type (str | None) – feature type that defines the combination of features used.
classifier (str | None) – classifier used in training.
limit (int | None) – Maximum number of items to return. Defaults to 25. Set to -1, float(“inf”) or None to return all items.

Returns

List of models.

Return type

EntityMatchingModelList

Delete Entity Matching Models

EntityMatchingAPI.delete(id: int | Sequence[int] | None = None, external_id: str | Sequence[str] | None = None) → None

Delete models

Parameters

id (int | Sequence[int] | None) – Id or list of ids
external_id (str | Sequence[str] | None) – External ID or list of external ids

Update Entity Matching Models

EntityMatchingAPI.update(item: EntityMatchingModel | EntityMatchingModelUpdate | Sequence[EntityMatchingModel | EntityMatchingModelUpdate]) → EntityMatchingModelList | EntityMatchingModel

Update model

Parameters: item (EntityMatchingModel | EntityMatchingModelUpdate | Sequence[EntityMatchingModel | EntityMatchingModelUpdate]) – Model(s) to update
Returns: No description.
Return type: EntityMatchingModelList | EntityMatchingModel

Predict Using an Entity Matching Model

Predict entity matching.

Warning

Blocks and waits for the model to be ready if it has been recently created.

Note

All users on this CDF subscription with assets read-all and entitymatching read-all and write-all capabilities in the project, are able to access the data sent to this endpoint.

Parameters

sources (Sequence[dict] | None) – entities to match from, does not need an ‘id’ field. Tolerant to passing more than is needed or used (e.g. json dump of time series list). If omitted, will use data from fit.
targets (Sequence[dict] | None) – entities to match to, does not need an ‘id’ field. Tolerant to passing more than is needed or used. If omitted, will use data from fit.
num_matches (int) – number of matches to return for each item.
score_threshold (float | None) – only return matches with a score above this threshold
id (int | None) – ids of the model to use.
external_id (str | None) – external ids of the model to use.

Returns

Object which can be used to wait for and retrieve results.

Return type

ContextualizationJob

Engineering Diagrams

Detect entities in Engineering Diagrams

DiagramsAPI.detect(entities: Sequence[dict | CogniteResource], search_field: str = 'name', partial_match: bool = False, min_tokens: int = 2, file_ids: int | Sequence[int] | None = None, file_external_ids: str | Sequence[str] | None = None, file_references: list[FileReference] | FileReference | None = None, pattern_mode: bool = False, configuration: dict[str, Any] | None = None, *, multiple_jobs: Literal[False]) → DiagramDetectResults

DiagramsAPI.detect(entities: Sequence[dict | CogniteResource], search_field: str = 'name', partial_match: bool = False, min_tokens: int = 2, file_ids: int | Sequence[int] | None = None, file_external_ids: str | Sequence[str] | None = None, file_references: list[FileReference] | FileReference | None = None, pattern_mode: bool = False, configuration: dict[str, Any] | None = None, *, multiple_jobs: Literal[True]) → tuple[DetectJobBundle | None, list[dict[str, Any]]]

DiagramsAPI.detect(entities: Sequence[dict | CogniteResource], search_field: str = 'name', partial_match: bool = False, min_tokens: int = 2, file_ids: int | Sequence[int] | None = None, file_external_ids: str | Sequence[str] | None = None, file_references: list[FileReference] | FileReference | None = None, pattern_mode: bool = False, configuration: dict[str, Any] | None = None) → DiagramDetectResults

Detect entities in a PNID. The results are not written to CDF.

Note

All users on this CDF subscription with assets read-all and files read-all capabilities in the project, are able to access the data sent to this endpoint.

Parameters

entities (Sequence[dict | CogniteResource]) – List of entities to detect
search_field (str) – If entities is a list of dictionaries, this is the key to the values to detect in the PnId
partial_match (bool) – Allow for a partial match (e.g. missing prefix).
min_tokens (int) – Minimal number of tokens a match must be based on
file_ids (int | Sequence[int] | None) – ID of the files, should already be uploaded in the same tenant.
file_external_ids (str | Sequence[str] | None) – File external ids, alternative to file_ids and file_references.
file_references (list[FileReference] | FileReference | None) – File references (id or external_id), and first_page and last_page to specify page ranges per file. Each reference can specify up to 50 pages. Providing a page range will also make the page count of the document a part of the response.
pattern_mode (bool | None) – Only in beta. If True, entities must be provided with a sample field. This enables detecting tags that are similar to the sample, but not necessarily identical. Defaults to None.
configuration (dict[str, Any] | None) – Only in beta. Additional configuration for the detect algorithm, see https://api-docs.cognite.com/20230101-beta/tag/Engineering-diagrams/operation/diagramDetect.
multiple_jobs (bool) – Enables you to publish multiple jobs. If True the method returns a tuple of DetectJobBundle and list of potentially unposted files. If False it will return a single DiagramDetectResults. Defaults to False.

Returns

Resulting queued job or a bundle of jobs and a list of unposted files. Note that the .result property of the job or job bundle will block waiting for results.

Return type

DiagramDetectResults | tuple[DetectJobBundle | None, list[dict[str, Any]]]

Examples

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes.contextualization import FileReference
>>> client = CogniteClient()
>>> detect_job = client.diagrams.detect(
    entities=[{"userDefinedField": "21PT1017","ignoredField": "AA11"}, {"userDefinedField": "21PT1018"}],
    search_field="userDefinedField",
    partial_match=True,
    min_tokens=2,
    file_ids=[101],
    file_external_ids=["Test1"],
    file_references=[
        FileReference(id=20, first_page=1, last_page=10),
        FileReference(external_id="ext_20", first_page=11, last_page=20)
    ],
)
>>> result = detect_job.result
>>> print(result)
<code>
{
    'items': [
        {'fileId': 101, 'annotations': []},
        {'fileExternalId': 'Test1', 'fileId: 1, 'annotations': []},
        {'fileId': 20, 'fileExternalId': 'ext_20', 'annotations': [], 'pageCount': 17},
        {
            'fileId': 20,
            'fileExternalId': 'ext_20',
            'annotations': [
                {
                    'text': '21PT1017',
                    'entities': [{"userDefinedField": "21PT1017","ignoredField": "AA11"}],
                    'region': {
                        'page': 12,
                        'shape': 'rectangle',
                        'vertices': [
                            {'x': 0.01, 'y': 0.01},
                            {'x': 0.01, 'y': 0.02},
                            {'x': 0.02, 'y': 0.02},
                            {'x': 0.02, 'y': 0.01}
                        ]
                    }
                }
            ],
            'pageCount': 17
        }
    ]
}
</code>

Convert to an interactive SVG where the provided annotations are highlighted

DiagramsAPI.convert(detect_job: DiagramDetectResults) → DiagramConvertResults

Convert a P&ID to interactive SVGs where the provided annotations are highlighted.

Parameters: detect_job (DiagramDetectResults) – detect job
Returns: Resulting queued job. Note that .result property of this job will block waiting for results.
Return type: DiagramConvertResults

Examples

>>> from cognite.client import CogniteClient
>>> client = CogniteClient()
>>> detect_job = client.diagrams.detect(...)
>>> client.diagrams.convert(detect_job=detect_job)

Vision

The Vision API enable extraction of information from imagery data based on their visual content. For example, you can can extract features such as text, asset tags or industrial objects from images using this service.

Quickstart

Start an asynchronous job to extract information from image files stored in CDF:

from cognite.client import CogniteClient
from cognite.client.data_classes.contextualization import VisionFeature

c = CogniteClient()
extract_job = c.vision.extract(
    features=[VisionFeature.ASSET_TAG_DETECTION, VisionFeature.PEOPLE_DETECTION],
    file_ids=[1, 2],
)

The returned job object, extract_job, can be used to retrieve the status of the job and the prediction results once the job is completed. Wait for job completion and get the parsed results:

extract_job.wait_for_completion()
for item in extract_job.items:
    predictions = item.predictions
    # do something with the predictions

Save the prediction results in CDF as Annotations:

extract_job.save_predictions()

Note

Prediction results are stored in CDF as Annotations using the images.* annotation types. In particular, text detections are stored as images.TextRegion, asset tag detections are stored as images.AssetLink, while other detections are stored as images.ObjectDetection.

Tweaking the parameters of a feature extractor:

from cognite.client.data_classes.contextualization import FeatureParameters, TextDetectionParameters

extract_job = c.vision.extract(
    features=VisionFeature.TEXT_DETECTION,
    file_ids=[1, 2],
    parameters=FeatureParameters(text_detection_parameters=TextDetectionParameters(threshold=0.9))
    # or
    # parameters = {"textDetectionParameters": {"threshold": 0.9}}
)

Extract

VisionAPI.extract(features: VisionFeature | list[VisionFeature], file_ids: list[int] | None = None, file_external_ids: list[str] | None = None, parameters: FeatureParameters | None = None) → VisionExtractJob

Start an asynchronous job to extract features from image files.

Parameters

features (VisionFeature | list[VisionFeature]) – The feature(s) to extract from the provided image files.
file_ids (list[int] | None) – IDs of the image files to analyze. The images must already be uploaded in the same CDF project.
file_external_ids (list[str] | None) – The external file ids of the image files to analyze.
parameters (FeatureParameters | None) – No description.

Returns

Resulting queued job, which can be used to retrieve the status of the job or the prediction results if the job is finished. Note that .result property of this job will wait for the job to finish and returns the results.

Return type

VisionExtractJob

Examples

Start a job, wait for completion and then get the parsed results:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes.contextualization import VisionFeature
>>> c = CogniteClient()
>>> extract_job = c.vision.extract(features=VisionFeature.ASSET_TAG_DETECTION, file_ids=[1])
>>> extract_job.wait_for_completion()
>>> for item in extract_job.items:
...     predictions = item.predictions
...     # do something with the predictions
>>> # Save predictions in CDF using Annotations API:
>>> extract_job.save_predictions()

Get vision extract job

VisionAPI.get_extract_job(job_id: int) → VisionExtractJob

Retrieve an existing extract job by ID.

Parameters: job_id (int) – ID of an existing feature extraction job.
Returns: Vision extract job, which can be used to retrieve the status of the job or the prediction results if the job is finished. Note that .result property of this job will wait for the job to finish and returns the results.
Return type: VisionExtractJob

Examples

Retrieve a vision extract job by ID:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> extract_job = c.vision.get_extract_job(job_id=1)
>>> extract_job.wait_for_completion()
>>> for item in extract_job.items:
...     predictions = item.predictions
...     # do something with the predictions

Contextualization Data Classes

class cognite.client.data_classes.contextualization.AssetTagDetectionParameters(threshold: 'float | None' = None, partial_match: 'bool | None' = None, asset_subtree_ids: 'list[int] | None' = None)

Bases: VisionResource, ThresholdParameter

asset_subtree_ids: list[int] | None = None

dump(camel_case: bool = False) → dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A dictionary representation of the instance.
Return type: dict[str, Any]

partial_match: bool | None = None

threshold: float | None = None

to_pandas(camel_case: bool = False) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Parameters: camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)
Returns: The dataframe.
Return type: pandas.DataFrame

Bases: CogniteResource

Data class for the result of a contextualization job.

dump(camel_case: bool = False) → dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A dictionary representation of the instance.
Return type: dict[str, Any]

property result: dict[str, Any]: Waits for the job to finish and returns the results.

to_pandas(expand: Sequence[str] = ('metadata',), ignore: list[str] | None = None, camel_case: bool = False) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Parameters

expand (Sequence[str]) – List of row keys to expand, only works if the value is a Dict. Will expand metadata by default.
ignore (list[str] | None) – List of row keys to not include when converting to a data frame.
camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)

Returns

The dataframe.

Return type

pandas.DataFrame

update_status() → str: Updates the model status and returns it

wait_for_completion(timeout: int | None = None, interval: int = 1) → None

Waits for job completion. This is generally not needed to call directly, as .result will do so automatically.

Parameters

timeout (int | None) – Time out after this many seconds. (None means wait indefinitely)
interval (int) – Poll status every this many seconds.

Raises

ModelFailedException – The model fit failed.

class cognite.client.data_classes.contextualization.ContextualizationJobList(resources: Collection[Any], cognite_client: CogniteClient | None = None)

Bases: CogniteResourceList[ContextualizationJob]

append(item): S.append(value) – append value to the end of the sequence

clear() → None -- remove all items from S

copy()

count(value) → integer -- return number of occurrences of value

dump(camel_case: bool = False) → list[dict[str, Any]]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A list of dicts representing the instance.
Return type: list[dict[str, Any]]

extend(other: Collection[Any]) → None: S.extend(iterable) – extend sequence by appending elements from the iterable

get(id: int | None = None, external_id: str | None = None) → T_CogniteResource | None

Get an item from this list by id or external_id.

Parameters

id (int | None) – The id of the item to get.
external_id (str | None) – The external_id of the item to get.

Returns

The requested item

Return type

T_CogniteResource | None

index(value[, start[, stop]]) → integer -- return first index of value.

Raises ValueError if the value is not present.

Supporting start and stop arguments is optional, but recommended.

insert(i, item): S.insert(index, value) – insert value before index

pop([index]) → item -- remove and return item at index (default last).: Raise IndexError if list is empty or index is out of range.

remove(item): S.remove(value) – remove first occurrence of value. Raise ValueError if the value is not present.

reverse(): S.reverse() – reverse IN PLACE

sort(*args, **kwds)

to_pandas(camel_case: bool = False, expand_metadata: bool = False, metadata_prefix: str = 'metadata.') → pandas.DataFrame

Convert the instance into a pandas DataFrame. Note that if the metadata column is expanded and there are keys in the metadata that already exist in the DataFrame, then an error will be raised by pd.join.

Parameters

camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)
expand_metadata (bool) – Expand the metadata column into separate columns.
metadata_prefix (str) – Prefix to use for metadata columns.

Returns

The Cognite resource as a dataframe.

Return type

pandas.DataFrame

class cognite.client.data_classes.contextualization.ContextualizationJobType(value)

Bases: Enum

An enumeration.

DIAGRAMS = 'diagrams'

ENTITY_MATCHING = 'entity_matching'

VISION = 'vision'

class cognite.client.data_classes.contextualization.DetectJobBundle(job_ids: list[int], cognite_client: CogniteClient | None = None)

Bases: object

fetch_results() → list[dict[str, Any]]

property result: tuple[list[dict[str, Any]], list[dict[str, Any]]]: Waits for the job to finish and returns the results.

wait_for_completion(timeout: int | None = None) → None

Waits for all jobs to complete, generally not needed to call as it is called by result.

Parameters: timeout (int | None) – Time out after this many seconds. (None means wait indefinitely)

class cognite.client.data_classes.contextualization.DiagramConvertItem(file_id: int | None = None, file_external_id: str | None = None, results: list | None = None, cognite_client: CogniteClient | None = None)

Bases: CogniteResource

dump(camel_case: bool = False) → dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A dictionary representation of the instance.
Return type: dict[str, Any]

property pages: DiagramConvertPageList

to_pandas(camel_case: bool = False) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Parameters: camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)
Returns: The dataframe.
Return type: pandas.DataFrame

class cognite.client.data_classes.contextualization.DiagramConvertPage(page: int | None = None, png_url: str | None = None, svg_url: str | None = None, cognite_client: CogniteClient | None = None)

Bases: CogniteResource

dump(camel_case: bool = False) → dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A dictionary representation of the instance.
Return type: dict[str, Any]

to_pandas(expand: Sequence[str] = ('metadata',), ignore: list[str] | None = None, camel_case: bool = False) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Parameters

expand (Sequence[str]) – List of row keys to expand, only works if the value is a Dict. Will expand metadata by default.
ignore (list[str] | None) – List of row keys to not include when converting to a data frame.
camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)

Returns

The dataframe.

Return type

pandas.DataFrame

class cognite.client.data_classes.contextualization.DiagramConvertPageList(resources: Collection[Any], cognite_client: CogniteClient | None = None)

Bases: CogniteResourceList[DiagramConvertPage]

append(item): S.append(value) – append value to the end of the sequence

clear() → None -- remove all items from S

copy()

count(value) → integer -- return number of occurrences of value

dump(camel_case: bool = False) → list[dict[str, Any]]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A list of dicts representing the instance.
Return type: list[dict[str, Any]]

extend(other: Collection[Any]) → None: S.extend(iterable) – extend sequence by appending elements from the iterable

get(id: int | None = None, external_id: str | None = None) → T_CogniteResource | None

Get an item from this list by id or external_id.

Parameters

id (int | None) – The id of the item to get.
external_id (str | None) – The external_id of the item to get.

Returns

The requested item

Return type

T_CogniteResource | None

index(value[, start[, stop]]) → integer -- return first index of value.

Raises ValueError if the value is not present.

Supporting start and stop arguments is optional, but recommended.

insert(i, item): S.insert(index, value) – insert value before index

pop([index]) → item -- remove and return item at index (default last).: Raise IndexError if list is empty or index is out of range.

remove(item): S.remove(value) – remove first occurrence of value. Raise ValueError if the value is not present.

reverse(): S.reverse() – reverse IN PLACE

sort(*args, **kwds)

to_pandas(camel_case: bool = False, expand_metadata: bool = False, metadata_prefix: str = 'metadata.') → pandas.DataFrame

Convert the instance into a pandas DataFrame. Note that if the metadata column is expanded and there are keys in the metadata that already exist in the DataFrame, then an error will be raised by pd.join.

Parameters

camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)
expand_metadata (bool) – Expand the metadata column into separate columns.
metadata_prefix (str) – Prefix to use for metadata columns.

Returns

The Cognite resource as a dataframe.

Return type

pandas.DataFrame

class cognite.client.data_classes.contextualization.DiagramConvertResults(**kwargs: Any)

Bases: ContextualizationJob

dump(camel_case: bool = False) → dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A dictionary representation of the instance.
Return type: dict[str, Any]

property items: list[DiagramConvertItem] | None: returns a list of all results by file

property result: dict[str, Any]: Waits for the job to finish and returns the results.

to_pandas(expand: Sequence[str] = ('metadata',), ignore: list[str] | None = None, camel_case: bool = False) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Parameters

expand (Sequence[str]) – List of row keys to expand, only works if the value is a Dict. Will expand metadata by default.
ignore (list[str] | None) – List of row keys to not include when converting to a data frame.
camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)

Returns

The dataframe.

Return type

pandas.DataFrame

update_status() → str: Updates the model status and returns it

wait_for_completion(timeout: int | None = None, interval: int = 1) → None

Waits for job completion. This is generally not needed to call directly, as .result will do so automatically.

Parameters

timeout (int | None) – Time out after this many seconds. (None means wait indefinitely)
interval (int) – Poll status every this many seconds.

Raises

ModelFailedException – The model fit failed.

Bases: CogniteResource

dump(camel_case: bool = False) → dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A dictionary representation of the instance.
Return type: dict[str, Any]

to_pandas(camel_case: bool = False) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Parameters: camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)
Returns: The dataframe.
Return type: pandas.DataFrame

class cognite.client.data_classes.contextualization.DiagramDetectResults(**kwargs: Any)

Bases: ContextualizationJob

convert() → DiagramConvertResults: Convert a P&ID to an interactive SVG where the provided annotations are highlighted

dump(camel_case: bool = False) → dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A dictionary representation of the instance.
Return type: dict[str, Any]

property errors: list[str]: Returns a list of all error messages across files

property items: list[DiagramDetectItem] | None: Returns a list of all results by file

property result: dict[str, Any]: Waits for the job to finish and returns the results.

to_pandas(expand: Sequence[str] = ('metadata',), ignore: list[str] | None = None, camel_case: bool = False) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Parameters

expand (Sequence[str]) – List of row keys to expand, only works if the value is a Dict. Will expand metadata by default.
ignore (list[str] | None) – List of row keys to not include when converting to a data frame.
camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)

Returns

The dataframe.

Return type

pandas.DataFrame

update_status() → str: Updates the model status and returns it

wait_for_completion(timeout: int | None = None, interval: int = 1) → None

Waits for job completion. This is generally not needed to call directly, as .result will do so automatically.

Parameters

timeout (int | None) – Time out after this many seconds. (None means wait indefinitely)
interval (int) – Poll status every this many seconds.

Raises

ModelFailedException – The model fit failed.

class cognite.client.data_classes.contextualization.DialGaugeDetection(threshold: 'float | None' = None, min_level: 'float | None' = None, max_level: 'float | None' = None, dead_angle: 'float | None' = None, non_linear_angle: 'float | None' = None)

Bases: VisionResource, ThresholdParameter

dead_angle: float | None = None

dump(camel_case: bool = False) → dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A dictionary representation of the instance.
Return type: dict[str, Any]

max_level: float | None = None

min_level: float | None = None

non_linear_angle: float | None = None

threshold: float | None = None

to_pandas(camel_case: bool = False) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Parameters: camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)
Returns: The dataframe.
Return type: pandas.DataFrame

class cognite.client.data_classes.contextualization.DigitalGaugeDetection(threshold: 'float | None' = None, comma_position: 'int | None' = None, min_num_digits: 'int | None' = None, max_num_digits: 'int | None' = None)

Bases: VisionResource, ThresholdParameter

comma_position: int | None = None

dump(camel_case: bool = False) → dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A dictionary representation of the instance.
Return type: dict[str, Any]

max_num_digits: int | None = None

min_num_digits: int | None = None

threshold: float | None = None

to_pandas(camel_case: bool = False) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Parameters: camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)
Returns: The dataframe.
Return type: pandas.DataFrame

Bases: CogniteResource

Entity matching model. See the fit method for the meaning of these fields.

dump(camel_case: bool = False) → dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A dictionary representation of the instance.
Return type: dict[str, Any]

predict(sources: list[dict] | None = None, targets: list[dict] | None = None, num_matches: int = 1, score_threshold: float | None = None) → ContextualizationJob

Predict entity matching. NB. blocks and waits for the model to be ready if it has been recently created.

Parameters

sources (list[dict] | None) – entities to match from, does not need an ‘id’ field. Tolerant to passing more than is needed or used (e.g. json dump of time series list). If omitted, will use data from fit.
targets (list[dict] | None) – entities to match to, does not need an ‘id’ field. Tolerant to passing more than is needed or used. If omitted, will use data from fit.
num_matches (int) – number of matches to return for each item.
score_threshold (float | None) – only return matches with a score above this threshold

Returns

object which can be used to wait for and retrieve results.

Return type

ContextualizationJob

refit(true_matches: Sequence[dict | tuple[int | str, int | str]]) → EntityMatchingModel

Re-fits an entity matching model, using the combination of the old and new true matches.

Parameters: true_matches (Sequence[dict | tuple[int | str, int | str]]) – Updated known valid matches given as a list of dicts with keys ‘fromId’, ‘fromExternalId’, ‘toId’, ‘toExternalId’). A tuple can be used instead of the dictionary for convenience, interpreted as id/externalId based on type.
Returns: new model refitted to true_matches.
Return type: EntityMatchingModel

to_pandas(expand: Sequence[str] = ('metadata',), ignore: list[str] | None = None, camel_case: bool = False) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Parameters

expand (Sequence[str]) – List of row keys to expand, only works if the value is a Dict. Will expand metadata by default.
ignore (list[str] | None) – List of row keys to not include when converting to a data frame.
camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)

Returns

The dataframe.

Return type

pandas.DataFrame

update_status() → str: Updates the model status and returns it

wait_for_completion(timeout: int | None = None, interval: int = 1) → None

Waits for model completion. This is generally not needed to call directly, as .result will do so automatically.

Parameters

timeout (int | None) – Time out after this many seconds. (None means wait indefinitely)
interval (int) – Poll status every this many seconds.

Raises

ModelFailedException – The model fit failed.

class cognite.client.data_classes.contextualization.EntityMatchingModelList(resources: Collection[Any], cognite_client: CogniteClient | None = None)

Bases: CogniteResourceList[EntityMatchingModel]

append(item): S.append(value) – append value to the end of the sequence

clear() → None -- remove all items from S

copy()

count(value) → integer -- return number of occurrences of value

dump(camel_case: bool = False) → list[dict[str, Any]]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A list of dicts representing the instance.
Return type: list[dict[str, Any]]

extend(other: Collection[Any]) → None: S.extend(iterable) – extend sequence by appending elements from the iterable

get(id: int | None = None, external_id: str | None = None) → T_CogniteResource | None

Get an item from this list by id or external_id.

Parameters

id (int | None) – The id of the item to get.
external_id (str | None) – The external_id of the item to get.

Returns

The requested item

Return type

T_CogniteResource | None

index(value[, start[, stop]]) → integer -- return first index of value.

Raises ValueError if the value is not present.

Supporting start and stop arguments is optional, but recommended.

insert(i, item): S.insert(index, value) – insert value before index

pop([index]) → item -- remove and return item at index (default last).: Raise IndexError if list is empty or index is out of range.

remove(item): S.remove(value) – remove first occurrence of value. Raise ValueError if the value is not present.

reverse(): S.reverse() – reverse IN PLACE

sort(*args, **kwds)

to_pandas(camel_case: bool = False, expand_metadata: bool = False, metadata_prefix: str = 'metadata.') → pandas.DataFrame

Convert the instance into a pandas DataFrame. Note that if the metadata column is expanded and there are keys in the metadata that already exist in the DataFrame, then an error will be raised by pd.join.

Parameters

camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)
expand_metadata (bool) – Expand the metadata column into separate columns.
metadata_prefix (str) – Prefix to use for metadata columns.

Returns

The Cognite resource as a dataframe.

Return type

pandas.DataFrame

class cognite.client.data_classes.contextualization.EntityMatchingModelUpdate(id: int | None = None, external_id: str | None = None)

Bases: CogniteUpdate

Changes applied to entity matching model

Parameters

id (int) – A server-generated ID for the object.
external_id (str) – The external ID provided by the client. Must be unique for the resource type.

property description: _PrimitiveUpdate

dump(camel_case: bool = True) → dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – No description.
Returns: A dictionary representation of the instance.
Return type: dict[str, Any]

property name: _PrimitiveUpdate

class cognite.client.data_classes.contextualization.FeatureParameters(text_detection_parameters: 'TextDetectionParameters | None' = None, asset_tag_detection_parameters: 'AssetTagDetectionParameters | None' = None, people_detection_parameters: 'PeopleDetectionParameters | None' = None, industrial_object_detection_parameters: 'IndustrialObjectDetectionParameters | None' = None, personal_protective_equipment_detection_parameters: 'PersonalProtectiveEquipmentDetectionParameters | None' = None, digital_gauge_detection_parameters: 'DigitalGaugeDetection | None' = None, dial_gauge_detection_parameters: 'DialGaugeDetection | None' = None, level_gauge_detection_parameters: 'LevelGaugeDetection | None' = None, valve_detection_parameters: 'ValveDetection | None' = None)

Bases: VisionResource

asset_tag_detection_parameters: AssetTagDetectionParameters | None = None

dial_gauge_detection_parameters: DialGaugeDetection | None = None

digital_gauge_detection_parameters: DigitalGaugeDetection | None = None

dump(camel_case: bool = False) → dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A dictionary representation of the instance.
Return type: dict[str, Any]

industrial_object_detection_parameters: IndustrialObjectDetectionParameters | None = None

level_gauge_detection_parameters: LevelGaugeDetection | None = None

people_detection_parameters: PeopleDetectionParameters | None = None

personal_protective_equipment_detection_parameters: PersonalProtectiveEquipmentDetectionParameters | None = None

text_detection_parameters: TextDetectionParameters | None = None

to_pandas(camel_case: bool = False) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Parameters: camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)
Returns: The dataframe.
Return type: pandas.DataFrame

valve_detection_parameters: ValveDetection | None = None

class cognite.client.data_classes.contextualization.FileReference(file_id: int | None = None, file_external_id: str | None = None, first_page: int | None = None, last_page: int | None = None)

Bases: object

to_api_item() → dict[str, str | int | dict[str, int]]

class cognite.client.data_classes.contextualization.IndustrialObjectDetectionParameters(threshold: 'float | None' = None)

Bases: VisionResource, ThresholdParameter

dump(camel_case: bool = False) → dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A dictionary representation of the instance.
Return type: dict[str, Any]

threshold: float | None = None

to_pandas(camel_case: bool = False) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Parameters: camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)
Returns: The dataframe.
Return type: pandas.DataFrame

class cognite.client.data_classes.contextualization.JobStatus(value)

Bases: Enum

An enumeration.

COLLECTING = 'Collecting'

COMPLETED = 'Completed'

DISTRIBUTED = 'Distributed'

DISTRIBUTING = 'Distributing'

FAILED = 'Failed'

QUEUED = 'Queued'

RUNNING = 'Running'

is_finished() → bool

is_not_finished() → bool

class cognite.client.data_classes.contextualization.LevelGaugeDetection(threshold: 'float | None' = None, min_level: 'float | None' = None, max_level: 'float | None' = None)

Bases: VisionResource, ThresholdParameter

dump(camel_case: bool = False) → dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A dictionary representation of the instance.
Return type: dict[str, Any]

max_level: float | None = None

min_level: float | None = None

threshold: float | None = None

to_pandas(camel_case: bool = False) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Parameters: camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)
Returns: The dataframe.
Return type: pandas.DataFrame

class cognite.client.data_classes.contextualization.PeopleDetectionParameters(threshold: 'float | None' = None)

Bases: VisionResource, ThresholdParameter

dump(camel_case: bool = False) → dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A dictionary representation of the instance.
Return type: dict[str, Any]

threshold: float | None = None

to_pandas(camel_case: bool = False) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Parameters: camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)
Returns: The dataframe.
Return type: pandas.DataFrame

class cognite.client.data_classes.contextualization.PersonalProtectiveEquipmentDetectionParameters(threshold: 'float | None' = None)

Bases: VisionResource, ThresholdParameter

dump(camel_case: bool = False) → dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A dictionary representation of the instance.
Return type: dict[str, Any]

threshold: float | None = None

to_pandas(camel_case: bool = False) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Parameters: camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)
Returns: The dataframe.
Return type: pandas.DataFrame

class cognite.client.data_classes.contextualization.ResourceReference(id: int | None = None, external_id: str | None = None, cognite_client: None | CogniteClient = None)

Bases: CogniteResource

dump(camel_case: bool = False) → dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A dictionary representation of the instance.
Return type: dict[str, Any]

to_pandas(expand: Sequence[str] = ('metadata',), ignore: list[str] | None = None, camel_case: bool = False) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Parameters

expand (Sequence[str]) – List of row keys to expand, only works if the value is a Dict. Will expand metadata by default.
ignore (list[str] | None) – List of row keys to not include when converting to a data frame.
camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)

Returns

The dataframe.

Return type

pandas.DataFrame

class cognite.client.data_classes.contextualization.ResourceReferenceList(resources: Collection[Any], cognite_client: CogniteClient | None = None)

Bases: CogniteResourceList[ResourceReference]

append(item): S.append(value) – append value to the end of the sequence

clear() → None -- remove all items from S

copy()

count(value) → integer -- return number of occurrences of value

dump(camel_case: bool = False) → list[dict[str, Any]]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A list of dicts representing the instance.
Return type: list[dict[str, Any]]

extend(other: Collection[Any]) → None: S.extend(iterable) – extend sequence by appending elements from the iterable

get(id: int | None = None, external_id: str | None = None) → T_CogniteResource | None

Get an item from this list by id or external_id.

Parameters

id (int | None) – The id of the item to get.
external_id (str | None) – The external_id of the item to get.

Returns

The requested item

Return type

T_CogniteResource | None

index(value[, start[, stop]]) → integer -- return first index of value.

Raises ValueError if the value is not present.

Supporting start and stop arguments is optional, but recommended.

insert(i, item): S.insert(index, value) – insert value before index

pop([index]) → item -- remove and return item at index (default last).: Raise IndexError if list is empty or index is out of range.

remove(item): S.remove(value) – remove first occurrence of value. Raise ValueError if the value is not present.

reverse(): S.reverse() – reverse IN PLACE

sort(*args, **kwds)

to_pandas(camel_case: bool = False, expand_metadata: bool = False, metadata_prefix: str = 'metadata.') → pandas.DataFrame

Convert the instance into a pandas DataFrame. Note that if the metadata column is expanded and there are keys in the metadata that already exist in the DataFrame, then an error will be raised by pd.join.

Parameters

camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)
expand_metadata (bool) – Expand the metadata column into separate columns.
metadata_prefix (str) – Prefix to use for metadata columns.

Returns

The Cognite resource as a dataframe.

Return type

pandas.DataFrame

class cognite.client.data_classes.contextualization.TextDetectionParameters(threshold: 'float | None' = None)

Bases: VisionResource, ThresholdParameter

dump(camel_case: bool = False) → dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A dictionary representation of the instance.
Return type: dict[str, Any]

threshold: float | None = None

to_pandas(camel_case: bool = False) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Parameters: camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)
Returns: The dataframe.
Return type: pandas.DataFrame

class cognite.client.data_classes.contextualization.ThresholdParameter(threshold: 'float | None' = None)

Bases: object

threshold: float | None = None

class cognite.client.data_classes.contextualization.ValveDetection(threshold: 'float | None' = None)

Bases: VisionResource, ThresholdParameter

dump(camel_case: bool = False) → dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A dictionary representation of the instance.
Return type: dict[str, Any]

threshold: float | None = None

to_pandas(camel_case: bool = False) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Parameters: camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)
Returns: The dataframe.
Return type: pandas.DataFrame

class cognite.client.data_classes.contextualization.VisionExtractItem(file_id: int | None = None, predictions: dict[str, Any] | None = None, file_external_id: str | None = None, error_message: str | None = None, cognite_client: CogniteClient | None = None)

Bases: CogniteResource

Data class for storing predictions for a single image file

dump(camel_case: bool = False) → dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A dictionary representation of the instance.
Return type: dict[str, Any]

to_pandas(expand: Sequence[str] = ('metadata',), ignore: list[str] | None = None, camel_case: bool = False) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Parameters

expand (Sequence[str]) – List of row keys to expand, only works if the value is a Dict. Will expand metadata by default.
ignore (list[str] | None) – List of row keys to not include when converting to a data frame.
camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)

Returns

The dataframe.

Return type

pandas.DataFrame

class cognite.client.data_classes.contextualization.VisionExtractJob(*args: Any, **kwargs: Any)

Bases: VisionJob

dump(camel_case: bool = False) → dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A dictionary representation of the instance.
Return type: dict[str, Any]

property errors: list[str]: Returns a list of all error messages across files

property items: list[VisionExtractItem] | None: Returns a list of all predictions by file

property result: dict[str, Any]: Waits for the job to finish and returns the results.

save_predictions(creating_user: str | None = None, creating_app: str | None = None, creating_app_version: str | None = None) → Annotation | AnnotationList

Saves all predictions made by the feature extractors in CDF using the Annotations API. See https://docs.cognite.com/api/v1/#operation/annotationsSuggest

Parameters

creating_user (str | None) – (str, optional): A username, or email, or name. This is not checked nor enforced. If the value is None, it means the annotation was created by a service.
creating_app (str | None) – The name of the app from which this annotation was created. Defaults to ‘cognite-sdk-python’.
creating_app_version (str | None) – The version of the app that created this annotation. Must be a valid semantic versioning (SemVer) string. Defaults to client version.

Returns

(suggested) annotation(s) stored in CDF.

Return type

Annotation | AnnotationList

to_pandas(expand: Sequence[str] = ('metadata',), ignore: list[str] | None = None, camel_case: bool = False) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Parameters

expand (Sequence[str]) – List of row keys to expand, only works if the value is a Dict. Will expand metadata by default.
ignore (list[str] | None) – List of row keys to not include when converting to a data frame.
camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)

Returns

The dataframe.

Return type

pandas.DataFrame

update_status() → str: Updates the model status and returns it

wait_for_completion(timeout: int | None = None, interval: int = 1) → None

Waits for job completion. This is generally not needed to call directly, as .result will do so automatically.

Parameters

timeout (int | None) – Time out after this many seconds. (None means wait indefinitely)
interval (int) – Poll status every this many seconds.

Raises

ModelFailedException – The model fit failed.

class cognite.client.data_classes.contextualization.VisionExtractPredictions(text_predictions: 'list[TextRegion] | None' = None, asset_tag_predictions: 'list[AssetLink] | None' = None, industrial_object_predictions: 'list[ObjectDetection] | None' = None, people_predictions: 'list[ObjectDetection] | None' = None, personal_protective_equipment_predictions: 'list[ObjectDetection] | None' = None, digital_gauge_predictions: 'list[ObjectDetection] | None' = None, dial_gauge_predictions: 'list[KeypointCollectionWithObjectDetection] | None' = None, level_gauge_predictions: 'list[KeypointCollectionWithObjectDetection] | None' = None, valve_predictions: 'list[KeypointCollectionWithObjectDetection] | None' = None)

Bases: VisionResource

asset_tag_predictions: list[AssetLink] | None = None

dial_gauge_predictions: list[KeypointCollectionWithObjectDetection] | None = None

digital_gauge_predictions: list[ObjectDetection] | None = None

dump(camel_case: bool = False) → dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A dictionary representation of the instance.
Return type: dict[str, Any]

industrial_object_predictions: list[ObjectDetection] | None = None

level_gauge_predictions: list[KeypointCollectionWithObjectDetection] | None = None

people_predictions: list[ObjectDetection] | None = None

personal_protective_equipment_predictions: list[ObjectDetection] | None = None

text_predictions: list[TextRegion] | None = None

to_pandas(camel_case: bool = False) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Parameters: camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)
Returns: The dataframe.
Return type: pandas.DataFrame

valve_predictions: list[KeypointCollectionWithObjectDetection] | None = None

class cognite.client.data_classes.contextualization.VisionFeature(value)

Bases: str, Enum

An enumeration.

ASSET_TAG_DETECTION = 'AssetTagDetection'

DIAL_GAUGE_DETECTION = 'DialGaugeDetection'

DIGITAL_GAUGE_DETECTION = 'DigitalGaugeDetection'

INDUSTRIAL_OBJECT_DETECTION = 'IndustrialObjectDetection'

LEVEL_GAUGE_DETECTION = 'LevelGaugeDetection'

PEOPLE_DETECTION = 'PeopleDetection'

PERSONAL_PROTECTIVE_EQUIPMENT_DETECTION = 'PersonalProtectiveEquipmentDetection'

TEXT_DETECTION = 'TextDetection'

VALVE_DETECTION = 'ValveDetection'

classmethod beta_features() → set[VisionFeature]: Returns a set of VisionFeature’s that are currently in beta

Bases: ContextualizationJob

dump(camel_case: bool = False) → dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A dictionary representation of the instance.
Return type: dict[str, Any]

property result: dict[str, Any]: Waits for the job to finish and returns the results.

to_pandas(expand: Sequence[str] = ('metadata',), ignore: list[str] | None = None, camel_case: bool = False) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Parameters

expand (Sequence[str]) – List of row keys to expand, only works if the value is a Dict. Will expand metadata by default.
ignore (list[str] | None) – List of row keys to not include when converting to a data frame.
camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)

Returns

The dataframe.

Return type

pandas.DataFrame

update_status() → str: Updates the model status and returns it

wait_for_completion(timeout: int | None = None, interval: int = 1) → None

Waits for job completion. This is generally not needed to call directly, as .result will do so automatically.

Parameters

timeout (int | None) – Time out after this many seconds. (None means wait indefinitely)
interval (int) – Poll status every this many seconds.

Raises

ModelFailedException – The model fit failed.

class cognite.client.data_classes.annotation_types.images.AssetLink(text: 'str', text_region: 'BoundingBox', asset_ref: 'CdfResourceRef', confidence: 'float | None' = None)

Bases: VisionResource

asset_ref: CdfResourceRef

confidence: float | None = None

dump(camel_case: bool = False) → dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A dictionary representation of the instance.
Return type: dict[str, Any]

text: str

text_region: BoundingBox

to_pandas(camel_case: bool = False) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Parameters: camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)
Returns: The dataframe.
Return type: pandas.DataFrame

class cognite.client.data_classes.annotation_types.images.KeypointCollection(label: 'str', keypoints: 'dict[str, Keypoint]', attributes: 'dict[str, Attribute] | None' = None, confidence: 'float | None' = None)

Bases: VisionResource

attributes: dict[str, Attribute] | None = None

confidence: float | None = None

dump(camel_case: bool = False) → dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A dictionary representation of the instance.
Return type: dict[str, Any]

keypoints: dict[str, Keypoint]

label: str

to_pandas(camel_case: bool = False) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Parameters: camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)
Returns: The dataframe.
Return type: pandas.DataFrame

class cognite.client.data_classes.annotation_types.images.KeypointCollectionWithObjectDetection(object_detection: 'ObjectDetection', keypoint_collection: 'KeypointCollection')

Bases: VisionResource

dump(camel_case: bool = False) → dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A dictionary representation of the instance.
Return type: dict[str, Any]

keypoint_collection: KeypointCollection

object_detection: ObjectDetection

to_pandas(camel_case: bool = False) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Parameters: camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)
Returns: The dataframe.
Return type: pandas.DataFrame

class cognite.client.data_classes.annotation_types.images.ObjectDetection(label: 'str', confidence: 'float | None' = None, attributes: 'dict[str, Attribute] | None' = None, bounding_box: 'BoundingBox | None' = None, polygon: 'Polygon | None' = None, polyline: 'Polyline | None' = None)

Bases: VisionResource

attributes: dict[str, Attribute] | None = None

bounding_box: BoundingBox | None = None

confidence: float | None = None

dump(camel_case: bool = False) → dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A dictionary representation of the instance.
Return type: dict[str, Any]

label: str

polygon: Polygon | None = None

polyline: Polyline | None = None

to_pandas(camel_case: bool = False) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Parameters: camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)
Returns: The dataframe.
Return type: pandas.DataFrame

class cognite.client.data_classes.annotation_types.images.TextRegion(text: 'str', text_region: 'BoundingBox', confidence: 'float | None' = None)

Bases: VisionResource

confidence: float | None = None

dump(camel_case: bool = False) → dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A dictionary representation of the instance.
Return type: dict[str, Any]

text: str

text_region: BoundingBox

to_pandas(camel_case: bool = False) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Parameters: camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)
Returns: The dataframe.
Return type: pandas.DataFrame

class cognite.client.data_classes.annotation_types.primitives.Attribute(type: "Literal[('boolean', 'numerical')]", value: 'bool | float', description: 'str | None' = None)

Bases: VisionResource

description: str | None = None

dump(camel_case: bool = False) → dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A dictionary representation of the instance.
Return type: dict[str, Any]

to_pandas(camel_case: bool = False) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Parameters: camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)
Returns: The dataframe.
Return type: pandas.DataFrame

type: Literal['boolean', 'numerical']

value: bool | float

class cognite.client.data_classes.annotation_types.primitives.BoundingBox(x_min: 'float', x_max: 'float', y_min: 'float', y_max: 'float')

Bases: VisionResource

dump(camel_case: bool = False) → dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A dictionary representation of the instance.
Return type: dict[str, Any]

to_pandas(camel_case: bool = False) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Parameters: camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)
Returns: The dataframe.
Return type: pandas.DataFrame

x_max: float

x_min: float

y_max: float

y_min: float

class cognite.client.data_classes.annotation_types.primitives.CdfResourceRef(id: 'int | None' = None, external_id: 'str | None' = None)

Bases: VisionResource

dump(camel_case: bool = False) → dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A dictionary representation of the instance.
Return type: dict[str, Any]

external_id: str | None = None

id: int | None = None

to_pandas(camel_case: bool = False) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Parameters: camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)
Returns: The dataframe.
Return type: pandas.DataFrame

class cognite.client.data_classes.annotation_types.primitives.Keypoint(point: 'Point', confidence: 'float | None' = None)

Bases: VisionResource

confidence: float | None = None

dump(camel_case: bool = False) → dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A dictionary representation of the instance.
Return type: dict[str, Any]

point: Point

to_pandas(camel_case: bool = False) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Parameters: camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)
Returns: The dataframe.
Return type: pandas.DataFrame

class cognite.client.data_classes.annotation_types.primitives.Point(x: 'float', y: 'float')

Bases: VisionResource

dump(camel_case: bool = False) → dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A dictionary representation of the instance.
Return type: dict[str, Any]

to_pandas(camel_case: bool = False) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Parameters: camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)
Returns: The dataframe.
Return type: pandas.DataFrame

x: float

y: float

class cognite.client.data_classes.annotation_types.primitives.Polygon(vertices: 'list[Point]')

Bases: VisionResource

dump(camel_case: bool = False) → dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A dictionary representation of the instance.
Return type: dict[str, Any]

to_pandas(camel_case: bool = False) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Parameters: camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)
Returns: The dataframe.
Return type: pandas.DataFrame

vertices: list[Point]

class cognite.client.data_classes.annotation_types.primitives.Polyline(vertices: 'list[Point]')

Bases: VisionResource

dump(camel_case: bool = False) → dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A dictionary representation of the instance.
Return type: dict[str, Any]

to_pandas(camel_case: bool = False) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Parameters: camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)
Returns: The dataframe.
Return type: pandas.DataFrame

vertices: list[Point]

class cognite.client.data_classes.annotation_types.primitives.VisionResource(cognite_client: CogniteClient | None = None)

Bases: CogniteResource

dump(camel_case: bool = False) → dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters: camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns: A dictionary representation of the instance.
Return type: dict[str, Any]

to_pandas(camel_case: bool = False) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Parameters: camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)
Returns: The dataframe.
Return type: pandas.DataFrame