Endpoints

Overview

The table below shows all the TIM endpoints implemented into the Python Client. For each endpoint a specific function is available to communicate directly to the TIM API. These functions are grouped into classes and are organized along the different sections of the TIM API.

TIM Endpoint	Class	Name	Type
/user-groups	user_groups	list_user_group	GET
/user-groups	user_groups	create_user_group	POST
/user-groups/{id}	user_groups	details_user_group	GET
/user-groups/{id}	user_groups	update_user_group	PUT
/user-groups/{id}	user_groups	delete_user_group	DELETE
/workspaces	workspaces	list_workspace	GET
/workspaces	workspaces	create_workspace	POST
/workspaces/{id}	workspaces	details_workspace	GET
/workspaces/{id}	workspaces	edit_workspace	PATCH
/workspaces/{id}	workspaces	delete_workspace	DELETE
/datasets/csv	datasets	upload_dataset	POST
/datasets/{id}/csv	datasets	update_dataset	PATCH
/datasets	datasets	list_dataset	GET
/datasets	datasets	delete_dataset_list	DELETE
/datasets/{id}	datasets	details_dataset	GET
/datasets/{id}	datasets	edit_dataset_details	PATCH
/datasets/{id}	datasets	delete_dataset	DELETE
/datasets/{id}/log	datasets	dataset_logs	GET
/datasets/{id}/versions/{version}/log	datasets	logs_dataset_version	GET
/datasets/{id}/versions	datasets	list_dataset_versions	GET
/datasets/{id}/versions/{version}	datasets	details_dataset_version	GET
/datasets/{id}/versions/{version}	datasets	delete_dataset_version	DELETE
/datasets/{id}/versions/{version}/status	datasets	status_dataset_version	GET
/datasets/{id}/versions/{version}/slice	datasets	slice_dataset_version	POST
/use-cases	use_cases	list_use_case	GET
/use-cases	use_cases	create_use_case	POST
/use-cases/{id}	use_cases	details_use_case	GET
/use-cases/{id}	use_cases	edit_use_case	PATCH
/use-cases/{id}	use_cases	delete_use_case	DELETE
/experiments	experiments	list_experiment	GET
/experiments	experiments	create_experiment	POST
/experiments/{id}	experiments	details_experiment	GET
/experiments/{id}	experiments	edit_experiment	PATCH
/experiments/{id}	experiments	delete_experiment	DELETE
/forecast-jobs/build-model	forecasting	build_model	POST
/forecast-jobs/upload-model	forecasting	upload_model	POST
/forecast-jobs/{id}/rebuild-model	forecasting	rebuild_model	POST
/forecast-jobs/{id}/retrain-model	forecasting	retrain_model	POST
/forecast-jobs/{id}/predict	forecasting	predict	POST
/forecast-jobs/{id}/rca	forecasting	rca	POST
/forecast-jobs/{id}/what-if	forecasting	what_if	POST
/forecast-jobs	forecasting	job_list	GET
/forecast-jobs	forecasting	delete_job_list	DELETE
/forecast-jobs/{id}	forecasting	job_details	GET
/forecast-jobs/{id}	forecasting	delete_job	DELETE
/forecast-jobs/{id}/copy	forecasting	copy_job	POST
/forecast-jobs/{id}/execute	forecasting	execute	POST
/forecast-jobs/{id}/log	forecasting	job_logs	GET
/forecast-jobs/{id}/status	forecasting	status	GET
/forecast-jobs/{id}/status/collect	forecasting	status_collect	GET
/forecast-jobs/{id}/results/table	forecasting	results_table	GET
/forecast-jobs/{id}/results/production-forecast	forecasting	results_production_forecast	GET
/forecast-jobs/{id}/results/model	forecasting	results_model	GET
/forecast-jobs/{id}/results/accuracies	forecasting	results_accuracies	GET
/forecast-jobs/{id}/results/rca	forecasting	results_rca	GET
/forecast-jobs/results/production-table	forecasting	results_production_table	GET
/forecast-jobs/results/production-accuracies	forecasting	results_production_accuracies	GET
/detection-jobs/build-model/kpi-driven	anomaly_detection	build_kpi_model	POST
/detection-jobs/build-model/system-driven	anomaly_detection	build_system_model	POST
/detection-jobs/build-model/outlier	anomaly_detection	build_outlier_model	POST
/detection-jobs/build-model/drift/kolmogorov-smirnov	anomaly_detection	build_drift_model_kolmogorov_smirnov	POST
/detection-jobs/build-model/drift/jensen-shannon	anomaly_detection	build_drift_model_jensen_shannon	POST
/detection-jobs/upload-model	anomaly_detection	upload_model	POST
/detection-jobs/{id}/rebuild-model/kpi-driven	anomaly_detection	rebuild_kpi_model	POST
/detection-jobs/{id}/detect	anomaly_detection	detect	POST
/detection-jobs/{id}/rca	anomaly_detection	rca	POST
/detection-jobs/{id}/what-if	anomaly_detection	what_if	POST
/detection-jobs	anomaly_detection	jobs_list	GET
/detection-jobs	anomaly_detection	delete_job_list	DELETE
/detection-jobs/{id}	anomaly_detection	job_details	GET
/detection-jobs/{id}	anomaly_detection	delete_job	DELETE
/detection-jobs/{id}/copy	anomaly_detection	copy_job	POST
/detection-jobs/{id}/execute	anomaly_detection	execute	POST
/detection-jobs/{id}/log	anomaly_detection	job_logs	GET
/detection-jobs/{id}/status	anomaly_detection	status	GET
/detection-jobs/{id}/status/collect	anomaly_detection	status_collect	GET
/detection-jobs/{id}/results/table	anomaly_detection	results_table	GET
/detection-jobs/{id}/results/model	anomaly_detection	results_model	GET
/detection-jobs/{id}/results/accuracies	anomaly_detection	results_accuracies	GET
/detection-jobs/{id}/results/rca	anomaly_detection	results_rca	GET
/detection-jobs/results/production-table	anomaly_detection	results_production_table	GET
/detection-jobs/results/production-accuracies	anomaly_detection	results_production_accuracies	GET
/licenses	license	details_license	GET
/licenses/storage	license	storage_license	GET
/users/me	user	details_user	GET
/ops/dataset-calls	telemetry	dataset_calls	GET
/ops/job-calls	telemetry	job_calls	GET

User Groups

This chapter covers all the functions in the "user_groups" class which communicate directly to the endpoints in the User Groups section of the TIM API.

list_user_group

This function allows the user to list all users groups to which the user has access. The user is able to sort the list, offset the start of the list of items and limit the number of items returned from the API.

user_groups_list_user_group = client.user_groups.list_user_group(
    offset = None,
    limit = None,
    sort = None,
)

create_user_group

This function allows the user to create a new user group within TIM. A JSON configuration with details about the new user group is required to use this function.

user_groups_create_user_group = client.user_groups.create_user_group(
    configuration = create_user_group_configuration
)

details_user_group

This function allows the user to extract the details of an existing user group within the TIM Platform. A valid TIM user group ID is required to use this function.

user_groups_details_user_group = client.user_groups.details_user_group(
    id = user_group_id
)

update_user_group

This function allows the user to update the details of an existing user group within the TIM Platform. A valid TIM user group ID and a JSON configuration are required to use this function.

user_groups_update_user_group = client.user_groups.update_user_group(
    id = user_group_id,
    configuration = create_user_group_configuration
)

delete_user_group

This function allows the user to delete an existing user group within the TIM Platform. A valid TIM user group ID is required to use this function.

user_groups_delete_user_group = client.user_groups.delete_user_group(
    id = user_group_id
)

Workspaces

This chapter covers all the functions in the "workspaces" class which communicate directly to the endpoints in the Workspace section of the TIM API.

list_workspace

This function allows the user to list all workspaces to which the user has access. The user is able to sort the list, offset the start of the list of items and limit the number of items returned from the API. The user can also filter workspaces from a specific user group.

workspaces_list_workspace = client.workspaces.list_workspace(
    offset = None,
    limit = None,
    user_group_id = None,
    sort = None
)

create_workspace

This function allows the user to create a new workspace within TIM. A JSON configuration with details about the new workspace is required to use this function.

workspaces_create_workspace = client.workspaces.create_workspace(
    configuration = create_workspace_configuration
)

details_workspace

This function allows the user to extract the details of an existing workspace within the TIM Platform. A valid TIM workspace ID is required to use this function.

workspaces_details_workspace = client.workspaces.details_workspace(
    id = workspace_id
)

edit_workspace

This function allows the user to edit the details of an existing workspace within the TIM Platform. A valid TIM workspace ID and a JSON configuration are required to use this function.

workspaces_update_workspace = client.workspaces.edit_workspace(
    id = workspace_id,
    configuration = edit_workspace_configuration
)

delete_workspace

This function allows the user to delete an existing workspace within the TIM Platform. A valid TIM workspace ID is required to use this function.

workspaces_delete_workspace = client.workspaces.delete_workspace(
    id = workspace_id
)

Datasets

This chapter covers all the functions in the "datasets" class which communicate directly to the endpoints in the Datasets section of the TIM API.

upload_dataset

This function allows the user to upload an new dataset to the TIM Platform. A dataset in the form of a pandas dataframe with the right properties as outlined in the TIM documentation as well as a JSON configuration with details about the dataset are required to use this function.

datasets_upload_dataset = client.datasets.upload_dataset(
    dataset = dataset,
    configuration = upload_dataset_configuration
)

update_dataset

This function allows the user to update an existing dataset in the TIM Platform. The following items are required to use this function:

• A valid TIM dataset ID.
• A dataset in the form of a pandas dataframe with the right properties as outlined in the TIM documentation.
• A JSON configuration with details about the dataset.

datasets_update_dataset = client.datasets.update_dataset(
    id = dataset_id,
    dataset = dataset,
    configuration = update_dataset_configuration
)

list_dataset

This function allows the user to list all datasets to which the user has access. The user is able to sort the list, offset the start of the list of items and limit the number of items returned from the API. The user can also filter datasets from a specific workspace.

datasets_list_dataset = client.datasets.list_dataset(
    offset = None,
    limit = None,
    workspace_id = None,
    sort = None
)

delete_dataset_list

This function allows the user to delete multiple datasets within the TIM Platform. The user is able to sort the list of datasets, offset the start of the list of items and limit the number of datasets deleted within the API. The user can also delete datasets from a specific workspace.

datasets_list_dataset = client.datasets.list_dataset(
    offset = None,
    limit = None,
    workspace_id = None,
    sort = None
)

details_dataset

This function allows the user to extract the details of an existing dataset within the TIM Platform. A valid TIM dataset ID is required to use this function.

datasets_details_dataset = client.datasets.details_dataset(
    id = dataset_id
)

edit_dataset_details

This function allows the user to edit the details of an existing dataset within the TIM Platform. A valid TIM dataset ID and a JSON configuration are required to use this function.

datasets_edit_dataset_details = client.datasets.edit_dataset_details(
    id = dataset_id,
    configuration = edit_dataset_details_configuration
)

delete_dataset

This function allows the user to delete an existing dataset within the TIM Platform. A valid TIM dataset ID is required to use this function.

datasets_delete_dataset = client.datasets.delete_dataset(
    id = dataset_id
)

dataset_logs

This function allows the user to extract the logs of an upload or update of a dataset to the TIM Platform. A valid TIM dataset ID is required to use this function.

datasets_dataset_logs = client.datasets.dataset_logs(
    id = dataset_id
)

logs_dataset_version

This function allows the user to extract the logs of a specific update of a dataset to the TIM Platform. A valid TIM dataset ID and dataset version ID are required to use this function.

datasets_logs_dataset_version = client.datasets.logs_dataset_version(
    id = dataset_id,
    version = dataset_version_id
)

list_dataset_versions

This function allows the user to list all version updates of a TIM dataset. A valid TIM dataset ID is required to use this function.

datasets_list_dataset_versions = client.datasets.list_dataset_versions(
    id = dataset_id
)

details_dataset_version

This function allows the user to extract the details of an existing dataset version within the TIM Platform. A valid TIM dataset ID and dataset version ID are required to use this function.

datasets_dataset_version_status = client.datasets.status_dataset_version(
    id = dataset_id,
    version_id = dataset_version_id
)

delete_dataset_version

This function allows the user to delete a version of an existing dataset within the TIM Platform. A valid TIM dataset ID and dataset version ID are required to use this function.

datasets_delete_dataset_version = client.datasets.delete_dataset_version(
    id = dataset_id,
    version = version_id
)

status_dataset_version

This function allows the user to check the status of a dataset upload or version update to the TIM platform. A valid TIM dataset ID and dataset version ID are required to use this function.

datasets_dataset_version_status = client.datasets.status_dataset_version(
    id = dataset_id,
    version_id = dataset_version_id
)

slice_dataset_version

This function allows the user to get a slice of data from a specific dataset version. A valid TIM dataset ID and dataset version ID are required to use this function.

datasets_dataset_version_slice = client.datasets.slice_dataset_version(
    id = dataset_id,
    version_id = dataset_version_id
)

Use Cases

This chapter covers all the functions in the "use_cases" class which communicate directly to the endpoints in the Use Cases section of the TIM API.

list_use_case

This function allows the user to list all use cases to which the user has access. The user is able to sort the list, offset the start of the list of items and limit the number of items returned from the API. The user can also filter use cases from a specific user group, workspace and/or dataset. Lastly, the user can filter the use cases with panel data.

use_cases_list_use_case = client.use_cases.list_use_case(
    offset = None,
    limit = None,
    user_group_id = None,
    workspace_id = None,
    dataset_id = None,
    sort = None,
    is_panel_data = None,
)

create_use_case

This function allows the user to create a new use case within TIM. A JSON configuration with details about the new use case is required to use this function.

use_cases_create_use_case = client.use_cases.create_use_case(
    configuration = create_use_case_configuration
)

details_use_case

This function allows the user to extract the details of an existing use case within the TIM Platform. A valid TIM use case ID is required to use this function.

use_cases_details_use_case = client.use_cases.details_use_case(
    id = use_case_id
)

edit_use_case

This function allows the user to edit the details of an existing use case within the TIM Platform. A valid TIM use case ID and a JSON configuration are required to use this function.

use_cases_update_use_case = client.use_cases.edit_use_case(
    id = use_case_id,
    configuration = edit_use_case_configuration
)

delete_use_case

This function allows the user to delete an existing use case within the TIM Platform. A valid TIM use case ID is required to use this function.

use_cases_delete_use_case = client.use_cases.delete_use_case(
    id = use_case_id
)

Experiments

This chapter covers all the functions in the "experiments" class which communicate directly to the endpoints in the Experiments section of the TIM API.

list_experiment

This function allows the user to list all experiments to which the user has access. The user is able to sort the list, offset the start of the list of items and limit the number of items returned from the API. The user can also filter experiments from a specific workspace, use case and/or dataset. Lastly, the user can filter the experiments of a specific type (forecasting or anomaly detection).

experiments_list_experiment = client.experiments.list_experiment(
    offset = None,
    limit = None,
    workspace_id = None,
    use_case_id = None,
    dataset_id = None,
    sort = None,
    type = None,
)

create_experiment

This function allows the user to create a new experiment within TIM. A JSON configuration with details about the new experiment is required to use this function.

experiments_create_experiment = client.experiments.create_experiment(
    configuration = create_experiment_configuration
)

details_experiment

This function allows the user to extract the details of an existing experiment within the TIM Platform. A valid TIM experiment ID is required to use this function.

experiments_details_experiment = client.experiments.details_experiment(
    id = experiment_id
)

edit_experiment

This function allows the user to edit the details of an existing experiment within the TIM Platform. A valid TIM experiment ID and a JSON configuration are required to use this function.

experiments_update_experiment = client.experiments.edit_experiment(
    id = experiment_id,
    configuration = edit_experiment_configuration
)

delete_experiment

This function allows the user to delete an existing experiment within the TIM Platform. A valid TIM experiment ID is required to use this function.

experiments_delete_experiment = client.experiments.delete_experiment(
    id = experiment_id
)

Forecasting

This chapter covers all the functions in the "forecasting" class which communicate directly to the endpoints in the Forecasting section of the TIM API.

build_model

This function allows the user to register a new forecasting job of the type "build_model". A JSON configuration with details about the new job is required to use this function.

forecasting_build_model = client.forecasting.build_model(
    configuration = forecast_build_model_configuration
)

upload_model

This function allows the user to register a new forecasting job by uploading a pre-built model. A JSON configuration with details about the new job and containing the pre-built model, is required to use this function.

forecasting_upload_model = client.forecasting.upload_model(
    configuration = forecasting_upload_model_configuration
)

rebuild_model

This function allows the user to register a new forecasting job of the type "rebuild_model". A valid TIM ID from a previously built model and a JSON configuration with details about the new job are required to use this function.

forecasting_rebuild_model = client.forecasting.rebuild_model(
    parent_job_id = forecasting_build_model_id,
    configuration = forecast_rebuild_model_configuration
)

retrain_model

This function allows the user to register a new forecasting job of the type "retrain_model". A valid TIM ID from a previously built model and a JSON configuration with details about the new job are required to use this function.

forecasting_retrain_model = client.forecasting.retrain_model(
    parent_job_id = forecasting_build_model_id,
    configuration = forecast_retrain_model_configuration
)

predict

This function allows the user to register a new forecasting job of the type "predict". A valid TIM ID from a previously built model and a JSON configuration with details about the new job are required to use this function.

forecasting_predict = client.forecasting.predict(
    parent_job_id = forecasting_build_model_id,
    configuration = forecast_predict_configuration
)

rca

This function allows the user to register a new forecasting job of the type "rca". A valid TIM ID from an executed forecasting job is required to use this function.

forecasting_rca = client.forecasting.rca(
    parent_job_id = forecasting_build_model_id
)

what_if

This function allows the user to register a new forecasting job of the type "what-if". A valid TIM ID from an executed forecasting job and a what-if configuration are required to use this function.

forecasting_what_if = client.forecasting.what_if(
    parent_job_id = forecasting_build_model_id,
    configuration = forecasting_what_if_configuration
)

job_list

This function allows the user to list all forecasting jobs to which the user has access. The user is able to sort the list, offset the start of the list of items and limit the number of items returned from the API. The user can also filter jobs from a specific experiment, use case, sequence and/or parent job. Next, the user can filter the jobs of a specific type, status and period of registration.

forecasting_job_list = client.forecasting.job_list(
    offset = None,
    limit = None,
    experiment_id = None,
    use_case_id = None,
    type = None,
    status = None,
    parent_id = None,
    from_datetime = None,
    to_datetime = None,
    sort = None,
    sequence_job_id = None,
)

delete_job_list

This function allows the user to delete multiple jobs within the TIM Platform. The user is able to sort the list of job, offset the start of the list of items and limit the number of jobs deleted within the API. The user can also filter jobs from a specific experiment, use case and/or parent job. Next, the user can filter the jobs of a specific type, status and period of registration.

forecasting_delete_job_list = client.forecasting.delete_job_list(
    experiment_id = None,
    use_case_id = None,
    type = None,
    status = None,
    parent_id = None,
    from_datetime = None,
    to_datetime = None,
)

job_details

This function allows the user to extract the details of an existing job within the TIM Platform. A valid TIM forecasting job ID is required to use this function.

forecasting_job_details= client.forecasting.job_details(
    id = job_id
)

delete_job

This function allows the user to delete an existing job within the TIM Platform. A valid TIM forecasting job ID is required to use this function.

forecasting_delete_job = client.forecasting.delete_job(
    id = job_id
)

copy_job

This function allows the user to copy an existing job within the TIM Platform and register a new one with the same configuration. A valid TIM forecasting job ID is required to use this function.

forecasting_copy_job = client.forecasting.copy_job(
    id = job_id
)

execute

This function allows the user to execute a registered forecasting job. A valid TIM forecasting job ID is required to use this function.

forecasting_status = client.forecasting.status(
    id = job_id
)

job_logs

This function allows the user to collect the logs of an executed forecasting job. A valid TIM forecasting job ID is required to use this function.

forecasting_job_logs = client.forecasting.job_logs(
    id = job_id
)

status

This function allows the user to check the status of an executed forecasting job. A valid TIM forecasting job ID is required to use this function.

forecasting_status = client.forecasting.status(
    id = job_id
)

status_collect

This function allows the user to collect the status history of an executed forecasting job. A valid TIM forecasting job ID is required to use this function.

forecasting_status_collect = client.forecasting.status_collect(
    id = job_id
)

results_table

This function allows the user to collect the results table of a forecasting job containing the forecasted values and additional useful information. The user can filter for specific forecast types and model indices. A valid and finished TIM forecasting job ID is required to use this function.

forecasting_results_table = client.forecasting.results_table(
    id = job_id,
    forecast_type = None,
    model_index = None
)

results_production_forecast

This function allows the user to collect the results table of a forecasting job containing only the forecasted values of the type "Production" and a timestamp. This gives the user a lean way of collecting forecasted values. A valid and finished TIM forecasting job ID is required to use this function.

forecasting_results_production_forecast = client.forecasting.results_production_forecast(
    id = job_id
)

results_model

This function allows the user to collect the model information of forecasting build jobs. A valid and finished TIM forecasting job ID is required to use this function.

forecasting_results_model = client.forecasting.results_model(
    id = job_id
)

results_accuracies

This function allows the user to collect the accuracy information of forecasting build jobs. A valid and finished TIM forecasting job ID is required to use this function.

forecasting_results_accuracies = client.forecasting.results_accuracies(
    id = job_id
)

results_rca

This function allows the user to collect the results table of forecasting root cause analysis jobs. A valid and finished TIM forecasting rca job ID is required to use this function. The user can filter for specific model indices and select a timestamp and a radius of number of samples around that sample to extract RCA information for.

forecasting_results_rca = client.forecasting.results_rca(
    id = forecasting_rca_id,
    index_of_model = 1,
    timestamp = None,
    radius = None
)

results_production_table

This function allows the user to collect the production table results for a sequence of forecasting jobs. A valid and finished TIM forecasting job ID is required to use this function. The user can filter for results from specific dataset versions, registration period, type and allow for overlapping or colocated jobs.

forecasting_results_production_table = client.forecasting.results_production_table(
    sequence_job_id = job_id,
    dataset_version_id = None,
    type = None,
    from_datetime = None,
    to_datetime = None,
    allow_overlapping = None,
    colocated_jobs = None,
)

results_production_accuracies

This function allows the user to collect the production accuracy results for a sequence of forecasting jobs. A valid and finished TIM forecasting job ID is required to use this function. The user can filter for results from specific dataset versions, registration period, type and allow for overlapping or colocated jobs.

forecasting_results_production_accuracies = client.forecasting.results_production_accuracies(
    sequence_job_id = forecasting_build_model_id,
    dataset_version_id = None,
    type = None,
    from_datetime = None,
    to_datetime = None,
    allow_overlapping = None,
    colocated_jobs = None,
)

Anomaly Detection

This chapter covers all the functions in the "detection" class which communicate directly to the endpoints in the Anomaly Detection section of the TIM API.

build_kpi_model

This function allows the user to register a new detection job of the type "build_model" with the KPI-driven approach. A JSON configuration with details about the new job is required to use this function.

detection_build_kpi_model = client.detection.build_kpi_model(
    configuration = detection_build_kpi_model_configuration
)

build_system_model

This function allows the user to register a new detection job of the type "build_model" with the system-driven approach. A JSON configuration with details about the new job is required to use this function.

detection_build_system_model = client.detection.build_system_model(
    configuration = detection_build_system_model_configuration
)

build_outlier_model

This function allows the user to register a new detection job of the type "outlier". A JSON configuration with details about the new job is required to use this function.

detection_build_outlier_model = client.detection.build_outlier_model(
    configuration = detection_build_outlier_model_configuration
)

build_drift_model_kolmogorov_smirnov

This function allows the user to register a new detection job of the type "build_drift_model" with the Kolmogorov-Smirnov approach. A JSON configuration with details about the new job is required to use this function.

detection_build_drift_model_kolmogorov_smirnov = client.detection.build_drift_model_kolmogorov_smirnov(
    configuration = detection_build_drift_model_kolmogorov_smirnov_configuration
)

build_drift_model_jensen_shannon

This function allows the user to register a new detection job of the type "build_drift_model" with the Jensen-Shannon approach. A JSON configuration with details about the new job is required to use this function.

detection_build_drift_model_jensen_shannon = client.detection.build_drift_model_jensen_shannon(
    configuration = detection_build_drift_model_jensen_shannon_configuration
)

upload_model

This function allows the user to register a new detection job by uploading a pre-built model. A JSON configuration with details about the new job and containing the pre-built model, is required to use this function.

detection_upload_model = client.detection.upload_model(
    configuration = detection_upload_model_configuration
)

rebuild_kpi_model

This function allows the user to register a new forecasting job of the type "rebuild_model" with the KPI-driven approach. A valid TIM ID from a previously built model and a JSON configuration with details about the new job are required to use this function.

detection_rebuild_kpi_model = client.detection.rebuild_kpi_model(
    parent_job_id = build_kpi_model_id,
    configuration = detection_rebuild_kpi_model_configuration
)

detect

This function allows the user to register a new detection job of the type "detect". A valid TIM ID from a previously built model and a JSON configuration with details about the new job are required to use this function.

detection_detect = client.detection.detect(
    parent_job_id = parent_job_id,
    configuration = detection_detect_configuration
)

rca

This function allows the user to register a new detection job of the type "rca". A valid TIM ID from an executed detection job is required to use this function.

detection_rca = client.detection.rca(
    parent_job_id = build_kpi_model_id
)

what_if

This function allows the user to register a new detection job of the type "what-if". A valid TIM ID from an executed detection job and a what-if configuration are required to use this function.

detection_what_if = client.detection.what_if(
    parent_job_id = detect_id,
    configuration = detection_what_if_configuration
)

job_list

This function allows the user to list all detection jobs to which the user has access. The user is able to sort the list, offset the start of the list of items and limit the number of items returned from the API. The user can also filter jobs from a specific experiment, use case and/or parent job. Next, the user can filter the jobs of a specific type, status and period of registration.

detection_job_list = client.detection.job_list(
    offset = None,
    limit = None,
    sort = None,
    experiment_id = None,
    use_case_id = None,
    type = None,
    status = None,
    parent_id = None,
    from_datetime = None,
    to_datetime = None,
)

delete_job_list

This function allows the user to delete multiple jobs within the TIM Platform. The user is able to sort the list of job, offset the start of the list of items and limit the number of jobs deleted within the API. The user can also filter jobs from a specific experiment, use case and/or parent job. Next, the user can filter the jobs of a specific type, status, approach and period of registration.

detection_delete_job_list = client.detection.delete_job_list(
    experiment_id = None,
    use_case_id = None,
    type = None,
    approach = None,
    status = None,
    parent_id = None,
    from_datetime = None,
    to_datetime = None,
)

job_details

This function allows the user to extract the details of an existing job within the TIM Platform. A valid TIM detection job ID is required to use this function.

detection_job_details = client.detection.job_details(
    id = job_id
)

delete_job

This function allows the user to delete an existing job within the TIM Platform. A valid TIM detection job ID is required to use this function.

detection_delete_job = client.detection.delete_job(
    id = job_id
)

copy_job

This function allows the user to copy an existing job within the TIM Platform and register a new one with the same configuration. A valid TIM detection job ID is required to use this function.

detection_copy_job = client.detection.copy_job(
    id = job_id
)

execute

This function allows the user to execute a registered detection job. A valid TIM detection job ID is required to use this function.

detection_execute = client.detection.execute(
    id = job_id
)

job_logs

This function allows the user to collect the logs of an executed detection job. A valid TIM detection job ID is required to use this function.

detection_job_logs = client.detection.job_logs(
    id = job_id
)

status

This function allows the user to check the status of an executed detection job. A valid TIM detection job ID is required to use this function.

detection_status = client.detection.status(
    id = job_id
)

status_collect

This function allows the user to collect the status history of an executed detection job. A valid TIM detection job ID is required to use this function.

detection_status_collect = client.detection.status_collect(
    id = job_id
)

results_table

This function allows the user to collect the results table of a detection job containing the calculated values and additional useful information. A valid TIM detection job ID is required to use this function.

detection_results_table = client.detection.results_table(
    id = job_id
)

results_model

This function allows the user to collect the model information of detection build jobs. A valid and finished TIM detection job ID is required to use this function.

detection_results_model = client.detection.results_model(
    id = job_id
)

results_accuracies

This function allows the user to collect the accuracy information of detection jobs. A valid and finished TIM detection job ID is required to use this function.

detection_results_accuracies = client.detection.results_accuracies(
    id = job_id
)

results_rca

This function allows the user to collect the results table of detection root cause analysis jobs. A valid and finished TIM detection rca job ID is required to use this function. The user can filter for specific model indices and select a timestamp and a radius of number of samples around that sample to extract RCA information for.

detection_results_rca = client.detection.results_rca(
    id = detection_rca_id,
    index_of_model = 1,
    timestamp = None,
    radius = None
)

results_production_table

This function allows the user to collect the production table results for a sequence of detection jobs. A valid and finished TIM detection job ID is required to use this function. The user can filter for results from specific dataset versions, registration period and allow for overlapping or colocated jobs.

detection_results_production_table = client.detection.results_production_table(
    sequence_job_id = detect_id,
    dataset_version_id = None,
    from_datetime = None,
    to_datetime = None,
    allow_overlapping = None,
    colocated_jobs = None,
)

results_production_accuracies

This function allows the user to collect the production accuracy results for a sequence of detection jobs. A valid and finished TIM detection job ID is required to use this function. The user can filter for results from specific dataset versions, registration period, type and allow for overlapping or colocated jobs and individual accuracies.

detection_results_production_accuracies = client.detection.results_production_accuracies(
    sequence_job_id = detect_id,
    dataset_version_id = None,
    from_datetime = None,
    to_datetime = None,
    allow_overlapping = None,
    colocated_jobs = None,
    individual_accuracies = None,
)

Licenses

This chapter covers all the functions in the "licenses" class which communicate directly to the endpoints in the Licenses section of the TIM API.

details_license

This function allows the user to collect information about his the details of his TIM license.

licenses_details_license = client.licenses.details_license()

storage_license

This function allows the user to collect information about his the amount of storage used on the TIM license.

licenses_storage_license = client.licenses.storage_license()

Users

This chapter covers all the functions in the "users" class which communicate directly to the endpoints in the Users section of the TIM API.

details_user

This function allows the user to collect information about his TIM user account.

user_details = client.users.details_user()

Telemetry

This chapter covers all the functions in the "telemetry" class which communicate directly to the endpoints in the Telemetry section of the TIM API.

dataset_calls

This function allows the user to see the amount of API calls that have been made on their license in dataset management. The user is able to sort the list, offset the start of the list of items and limit the number of items returned from the API. The user can also filter dataset calls from a specific experiment, dataset, user and/or endpoint. Lastly, the user can filter the jobs of a specific dataset_state and dataset_version_state.

telemetry_dataset_calls = client.telemetry.dataset_calls(
    offset = None,
    limit = None,
    dataset_id = None,
    experiment_id = None,
    user_id = None,
    dataset_state = None,
    dataset_version_state = None,
    endpoint_id = None,
    from_datetime = None,
    to_datetime = None,
    sort = None,
)

job_calls

This function allows the user to see the amount of API calls that have been made on their license in job management. The user is able to sort the list, offset the start of the list of items and limit the number of items returned from the API. The user can also filter jobs calls with a specific dataset, dataset version, experiment, user, job and/or parent job. Lastly, the user can filter the jobs of a specific job_state, endpoint and other elements.

telemetry_job_calls = client.telemetry.job_calls(
    offset = None,
    limit = None,
    dataset_id = None,
    dataset_version_id = None,
    experiment_id = None,
    job_id = None,
    parent_job_id = None,
    user_id = None,
    type = None,
    job_state = None,
    endpoint_id = None,
    from_datetime = None,
    to_datetime = None,
    from_calculation_time = None,
    to_calculation_time = None,
    sort = None,
)